ai case study topics

• Digital Marketing
• Facebook Marketing
• Instagram Marketing
• Ecommerce Marketing
• Content Marketing
• Data Science Certification
• Machine Learning
• Artificial Intelligence
• Data Analytics
• Graphic Design
• Adobe Illustrator
• Web Designing
• UX UI Design
• Interior Design
• Front End Development
• Back End Development Courses
• Business Analytics
• Entrepreneurship
• Supply Chain
• Financial Modeling
• Corporate Finance
• Project Finance
• Harvard University
• Stanford University
• Yale University
• Princeton University
• Duke University
• UC Berkeley
• Harvard University Executive Programs
• MIT Executive Programs
• Stanford University Executive Programs
• Oxford University Executive Programs
• Cambridge University Executive Programs
• Yale University Executive Programs
• Kellog Executive Programs
• CMU Executive Programs
• 45000+ Free Courses
• Free Certification Courses
• Free DigitalDefynd Certificate
• Free Harvard University Courses
• Free MIT Courses
• Free Excel Courses
• Free Google Courses
• Free Finance Courses
• Free Coding Courses
• Free Digital Marketing Courses

40 Detailed Artificial Intelligence Case Studies [2024]

In this dynamic era of technological advancements, Artificial Intelligence (AI) emerges as a pivotal force, reshaping the way industries operate and charting new courses for business innovation. This article presents an in-depth exploration of 40 diverse and compelling AI case studies from across the globe. Each case study offers a deep dive into the challenges faced by companies, the AI-driven solutions implemented, their substantial impacts, and the valuable lessons learned. From healthcare and finance to transportation and retail, these stories highlight AI’s transformative power in solving complex problems, optimizing processes, and driving growth, offering insightful glimpses into the potential and versatility of AI in shaping our world.

Related: How to Become an AI Thought Leader?

1. IBM Watson Health: Revolutionizing Patient Care with AI

Task/Conflict: The healthcare industry faces challenges in handling vast amounts of patient data, accurately diagnosing diseases, and creating effective treatment plans. IBM Watson Health aimed to address these issues by harnessing AI to process and analyze complex medical information, thus improving the accuracy and efficiency of patient care.

Solution: Utilizing the cognitive computing capabilities of IBM Watson, this solution involves analyzing large volumes of medical records, research papers, and clinical trial data. The system uses natural language processing to understand and process medical jargon, making sense of unstructured data to aid medical professionals in diagnosing and treating patients.

Overall Impact:

• Enhanced accuracy in patient diagnosis and treatment recommendations.
• Significant improvement in personalized healthcare services.

Key Learnings:

• AI can complement medical professionals’ expertise, leading to better healthcare outcomes.
• The integration of AI in healthcare can lead to significant advancements in personalized medicine.

2. Google DeepMind’s AlphaFold: Unraveling the Mysteries of Protein Folding

Task/Conflict: The scientific community has long grappled with the protein folding problem – understanding how a protein’s amino acid sequence determines its 3D structure. Solving this problem is crucial for drug discovery and understanding diseases at a molecular level, yet it remained a formidable challenge due to the complexity of biological structures.

Solution: AlphaFold, developed by Google DeepMind, is an AI model trained on vast datasets of known protein structures. It assesses the distances and angles between amino acids to predict how a protein folds, outperforming existing methods in terms of speed and accuracy. This breakthrough represents a major advancement in computational biology.

• Significant acceleration in drug discovery and disease understanding.
• Set a new benchmark for computational methods in biology.
• AI’s predictive power can solve complex biological problems.
• The application of AI in scientific research can lead to groundbreaking discoveries.

3. Amazon: Transforming Supply Chain Management through AI

Task/Conflict: Managing a global supply chain involves complex challenges like predicting product demand, optimizing inventory levels, and streamlining logistics. Amazon faced the task of efficiently managing its massive inventory while minimizing costs and meeting customer demands promptly.

Solution: Amazon employs sophisticated AI algorithms for predictive inventory management, which forecast product demand based on various factors like buying trends, seasonality, and market changes. This system allows for real-time adjustments, adapting swiftly to changing market dynamics.

• Reduced operational costs through efficient inventory management.
• Improved customer satisfaction with timely deliveries and availability.
• AI can significantly enhance supply chain efficiency and responsiveness.
• Predictive analytics in inventory management leads to reduced waste and cost savings.

4. Tesla’s Autonomous Vehicles: Driving the Future of Transportation

Task/Conflict: The development of autonomous vehicles represents a major technological and safety challenge. Tesla aimed to create self-driving cars that are not only reliable and safe but also capable of navigating complex traffic conditions without human intervention.

Solution: Tesla’s solution involves advanced AI and machine learning algorithms that process data from various sensors and cameras to understand and navigate the driving environment. Continuous learning from real-world driving data allows the system to improve over time, making autonomous driving safer and more efficient.

• Leadership in the autonomous vehicle sector, enhancing road safety.
• Continuous improvements in self-driving technology through AI-driven data analysis.
• Continuous data analysis is key to advancing autonomous driving technologies.
• AI can significantly improve road safety and driving efficiency.

Related: High-Paying AI Career Options

5. Zara: Fashioning the Future with AI in Retail

Task/Conflict: In the fast-paced fashion industry, predicting trends and managing inventory efficiently are critical for success. Zara faced the challenge of quickly adapting to changing fashion trends while avoiding overstock and meeting consumer demand.

Solution: Zara employs AI algorithms to analyze fashion trends, customer preferences, and sales data. The AI system also assists in managing inventory, ensuring that popular items are restocked promptly and that stores are not overburdened with unsold products. This approach optimizes both production and distribution.

• Increased sales and profitability through optimized inventory.
• Enhanced customer satisfaction by aligning products with current trends.
• AI can accurately predict consumer behavior and trends.
• Effective inventory management through AI can significantly impact business success.

6. Netflix: Personalizing Entertainment with AI

Task/Conflict: In the competitive streaming industry, providing a personalized user experience is key to retaining subscribers. Netflix needed to recommend relevant content to each user from its vast library, ensuring that users remained engaged and satisfied.

Solution: Netflix developed an advanced AI-driven recommendation engine that analyzes individual viewing habits, ratings, and preferences. This personalized approach keeps users engaged, as they are more likely to find content that interests them, enhancing their overall viewing experience.

• Increased viewer engagement and longer watch times.
• Higher subscription retention rates due to personalized content.
• Personalized recommendations significantly enhance user experience.
• AI-driven content curation is essential for success in digital entertainment.

7. Airbus: Elevating Aircraft Maintenance with AI

Task/Conflict: Aircraft maintenance is crucial for ensuring flight safety and operational efficiency. Airbus faced the challenge of predicting maintenance needs to prevent equipment failures and reduce downtime, which is critical in the aviation industry.

Solution: Airbus implemented AI algorithms for predictive maintenance, analyzing data from aircraft sensors to identify potential issues before they lead to failures. This system assesses the condition of various components, predicting when maintenance is needed. The solution not only enhances safety but also optimizes maintenance schedules, reducing unnecessary inspections and downtime.

• Decreased maintenance costs and reduced aircraft downtime.
• Improved safety with proactive maintenance measures.
• AI can predict and prevent potential equipment failures.
• Predictive maintenance is essential for operational efficiency and safety in aviation.

8. American Express: Securing Transactions with AI

Task/Conflict: Credit card fraud is a significant issue in the financial sector, leading to substantial losses and undermining customer trust. American Express needed an efficient way to detect and prevent fraudulent transactions in real-time.

Solution: American Express utilizes machine learning models to analyze transaction data. These models identify unusual patterns and behaviors indicative of fraud. By constant learning from refined data, the system becomes increasingly accurate in detecting fraudulent activities, providing real-time alerts and preventing unauthorized transactions.

• Minimized financial losses due to reduced fraudulent activities.
• Enhanced customer trust and security in financial transactions.
• Machine learning is highly effective in fraud detection.
• Real-time data analysis is crucial for preventing financial fraud.

Related: Is AI a Good Career Option for Women?

9. Stitch Fix: Tailoring the Future of Fashion Retail

Task/Conflict: In the competitive fashion retail industry, providing a personalized shopping experience is key to customer satisfaction and business growth. Stitch Fix aimed to offer customized clothing selections to each customer, based on their unique preferences and style.

Solution: Stitch Fix uses AI and algorithms analyze customer feedback, style preferences, and purchase history to recommend clothing items. This personalized approach is complemented by human stylists, ensuring that each customer receives a tailored selection that aligns with their individual style.

• Increased customer satisfaction through personalized styling services.
• Business growth driven by a unique, AI-enhanced shopping experience.
• AI combined with human judgment can create highly effective personalization.
• Tailoring customer experiences using AI leads to increased loyalty and business success.

10. Baidu: Breaking Language Barriers with Voice Recognition

Task/Conflict: Voice recognition technology faces the challenge of accurately understanding and processing speech in various languages and accents. Baidu aimed to enhance its voice recognition capabilities to provide more accurate and user-friendly interactions in multiple languages.

Solution: Baidu employs deep learning algorithms for voice and speech recognition, training its system on a diverse range of languages and dialects. This approach allows for more accurate recognition of speech patterns, enabling the technology to understand and respond to voice commands more effectively. The system continuously improves as it processes more voice data, making technology more accessible to users worldwide.

• Enhanced user interaction with technology in multiple languages.
• Reduced language barriers in voice-activated services and devices.
• AI can effectively bridge language gaps in technology.
• Continuous learning from diverse data sets is key to improving voice recognition.

11. JP Morgan: Revolutionizing Legal Document Analysis with AI

Task/Conflict: Analyzing legal documents, such as contracts, is a time-consuming and error-prone process. JP Morgan sought to streamline this process, reducing the time and effort required while increasing accuracy.

Solution: JP Morgan implemented an AI-powered tool, COIN (Contract Intelligence), to analyze legal documents quickly and accurately. COIN uses NLP to interpret and extract relevant information from contracts, significantly reducing the time required for document review.

• Dramatic reduction in time required for legal document analysis.
• Increased accuracy and reduced human error in contract interpretation.
• AI can efficiently handle large volumes of data, offering speed and accuracy.
• Automation in legal processes can significantly enhance operational efficiency.

12. Microsoft: AI for Accessibility

Task/Conflict: People with disabilities often face challenges in accessing technology. Microsoft aimed to create AI-driven tools to enhance accessibility, especially for individuals with visual, hearing, or cognitive impairments.

Solution: Microsoft developed a range of AI-powered tools including applications for voice recognition, visual assistance, and cognitive support, making technology more accessible and user-friendly. For instance, Seeing AI, an app developed by Microsoft, helps visually impaired users to understand their surroundings by describing people, texts, and objects.

• Improved accessibility and independence for people with disabilities.
• Creation of more inclusive technology solutions.
• AI can significantly contribute to making technology accessible for all.
• Developing inclusive technology is essential for societal progress.

Related: How to get an Internship in AI?

13. Alibaba’s City Brain: Revolutionizing Urban Traffic Management

Task/Conflict: Urban traffic congestion is a major challenge in many cities, leading to inefficiencies and environmental concerns. Alibaba’s City Brain project aimed to address this issue by using AI to optimize traffic flow and improve public transportation in urban areas.

Solution: City Brain uses AI to analyze real-time data from traffic cameras, sensors, and GPS systems. It processes this information to predict traffic patterns and optimize traffic light timing, reducing congestion. The system also provides data-driven insights for urban planning and emergency response coordination, enhancing overall city management.

• Significant reduction in traffic congestion and improved urban transportation.
• Enhanced efficiency in city management and emergency response.
• AI can effectively manage complex urban systems.
• Data-driven solutions are key to improving urban living conditions.

14. Deep 6 AI: Accelerating Clinical Trials with Artificial Intelligence

Task/Conflict: Recruiting suitable patients for clinical trials is often a slow and cumbersome process, hindering medical research. Deep 6 AI sought to accelerate this process by quickly identifying eligible participants from a vast pool of patient data.

Solution: Deep 6 AI employs AI to sift through extensive medical records, identifying potential trial participants based on specific criteria. The system analyzes structured and unstructured data, including doctor’s notes and diagnostic reports, to find matches for clinical trials. This approach significantly speeds up the recruitment process, enabling faster trial completions and advancements in medical research.

• Quicker recruitment for clinical trials, leading to faster research progress.
• Enhanced efficiency in medical research and development.
• AI can streamline the patient selection process for clinical trials.
• Efficient recruitment is crucial for the advancement of medical research.

15. NVIDIA: Revolutionizing Gaming Graphics with AI

Task/Conflict: Enhancing the realism and performance of gaming graphics is a continuous challenge in the gaming industry. NVIDIA aimed to revolutionize gaming visuals by leveraging AI to create more realistic and immersive gaming experiences.

Solution: NVIDIA’s AI-driven graphic processing technologies, such as ray tracing and deep learning super sampling (DLSS), provide highly realistic and detailed graphics. These technologies use AI to render images more efficiently, improving game performance without compromising on visual quality. This innovation sets new standards in gaming graphics, making games more lifelike and engaging.

• Elevated gaming experiences with state-of-the-art graphics.
• Set new industry standards for graphic realism and performance.
• AI can significantly enhance creative industries, like gaming.
• Balancing performance and visual quality is key to gaming innovation.

16. Palantir: Mastering Data Integration and Analysis with AI

Task/Conflict: Integrating and analyzing large-scale, diverse datasets is a complex task, essential for informed decision-making in various sectors. Palantir Technologies faced the challenge of making sense of vast amounts of data to provide actionable insights for businesses and governments.

Solution: Palantir developed AI-powered platforms that integrate data from multiple sources, providing a comprehensive view of complex systems. These platforms use machine learning to analyze data, uncover patterns, and predict outcomes, assisting in strategic decision-making. This solution enables users to make informed decisions in real-time, based on a holistic understanding of their data.

• Enhanced decision-making capabilities in complex environments.
• Greater insights and efficiency in data analysis across sectors.
• Effective data integration is crucial for comprehensive analysis.
• AI-driven insights are essential for strategic decision-making.

Related: Surprising AI Facts & Statistics

17. Blue River Technology: Sowing the Seeds of AI in Agriculture

Task/Conflict: The agriculture industry faces challenges in increasing efficiency and sustainability while minimizing environmental impact. Blue River Technology aimed to enhance agricultural practices by using AI to make farming more precise and efficient.

Solution: Blue River Technology developed AI-driven agricultural robots that perform tasks like precise planting and weed control. These robots use ML to identify plants and make real-time decisions, such as applying herbicides only to weeds. This targeted approach reduces chemical usage and promotes sustainable farming practices, leading to better crop yields and environmental conservation.

• Significant reduction in chemical usage in farming.
• Increased crop yields through precision agriculture.
• AI can contribute significantly to sustainable agricultural practices.
• Precision farming is key to balancing productivity and environmental conservation.

18. Salesforce: Enhancing Customer Relationship Management with AI

Task/Conflict: In the realm of customer relationship management (CRM), personalizing interactions and gaining insights into customer behavior are crucial for business success. Salesforce aimed to enhance CRM capabilities by integrating AI to provide personalized customer experiences and actionable insights.

Solution: Salesforce incorporates AI-powered tools into its CRM platform, enabling businesses to personalize customer interactions, automate responses, and predict customer needs. These tools analyze customer data, providing insights that help businesses tailor their strategies and communications. The AI integration not only improves customer engagement but also streamlines sales and marketing efforts.

• Improved customer engagement and satisfaction.
• Increased business growth through tailored marketing and sales strategies.
• AI-driven personalization is key to successful customer relationship management.
• Leveraging AI for data insights can significantly impact business growth.

19. OpenAI: Transforming Natural Language Processing

Task/Conflict: OpenAI aimed to advance NLP by developing models capable of generating coherent and contextually relevant text, opening new possibilities in AI-human interaction.

Solution: OpenAI developed the Generative Pre-trained Transformer (GPT) models, which use deep learning to generate text that closely mimics human language. These models are trained on vast datasets, enabling them to understand context and generate responses in a conversational and coherent manner.

• Pioneered advancements in natural language understanding and generation.
• Expanded the possibilities for AI applications in communication.
• AI’s ability to mimic human language has vast potential applications.
• Advancements in NLP are crucial for improving AI-human interactions.

20. Siemens: Pioneering Industrial Automation with AI

Task/Conflict: Industrial automation seeks to improve productivity and efficiency in manufacturing processes. Siemens faced the challenge of optimizing these processes using AI to reduce downtime and enhance output quality.

Solution: Siemens employs AI-driven solutions for predictive maintenance and process optimization to reduce downtime in industrial settings. Additionally, AI optimizes manufacturing processes, ensuring quality and efficiency.

• Increased productivity and reduced downtime in industrial operations.
• Enhanced quality and efficiency in manufacturing processes.
• AI is a key driver in the advancement of industrial automation.
• Predictive analytics are crucial for maintaining efficiency in manufacturing.

Related: Top Books for Learning AI

21. Ford: Driving Safety Innovation with AI

Task/Conflict: Enhancing automotive safety and providing effective driver assistance systems are critical challenges in the auto industry. Ford aimed to leverage AI to improve vehicle safety features and assist drivers in real-time decision-making.

Solution: Ford integrated AI into its advanced driver assistance systems (ADAS) to provide features like adaptive cruise control, lane-keeping assistance, and collision avoidance. These systems use sensors and cameras to gather data, which AI processes to make split-second decisions that enhance driver safety and vehicle performance.

• Improved safety features in vehicles, minimizing accidents and improving driver confidence.
• Enhanced driving experience with intelligent assistance features.
• AI can highly enhance safety in the automotive industry.
• Real-time data processing and decision-making are essential for effective driver assistance systems.

22. HSBC: Enhancing Banking Security with AI

Task/Conflict: As financial transactions increasingly move online, banks face heightened risks of fraud and cybersecurity threats. HSBC needed to bolster its protective measures to secure user data and prevent scam.

Solution: HSBC employed AI-driven security systems to observe transactions and identify suspicious activities. The AI models analyze patterns in customer behavior and flag anomalies that could indicate fraudulent actions, allowing for immediate intervention. This helps in minimizing the risk of financial losses and protects customer trust.

• Strengthened security measures and reduced incidence of fraud.
• Maintained high levels of customer trust and satisfaction.
• AI is critical in enhancing security in the banking sector.
• Proactive fraud detection can prevent significant financial losses.

23. Unilever: Optimizing Supply Chain with AI

Task/Conflict: Managing a global supply chain involves complexities related to logistics, demand forecasting, and sustainability practices. Unilever sought to enhance its supply chain efficiency while promoting sustainability.

Solution: Unilever implemented AI to optimize its supply chain operations, from raw material sourcing to distribution. AI algorithms analyze data to forecast demand, improve inventory levels, and minimize waste. Additionally, AI helps in selecting sustainable practices and suppliers, aligning with Unilever’s commitment to environmental responsibility.

• Enhanced efficiency and reduced costs in supply chain operations.
• Better sustainability practices, reducing environmental impact.
• AI can highly optimize supply chain management.
• Integrating AI with sustainability initiatives can lead to environmentally responsible operations.

24. Spotify: Personalizing Music Experience with AI

Task/Conflict: In the competitive music streaming industry, providing a personalized listening experience is crucial for user engagement and retention. Spotify needed to tailor music recommendations to individual tastes and preferences.

Solution: Spotify utilizes AI-driven algorithms to analyze user listening habits, preferences, and contextual data to recommend music tracks and playlists. This personalization ensures that users are continually engaged and discover new music that aligns with their tastes, enhancing their overall listening experience.

• Increased customer engagement and time spent on the platform.
• Higher user satisfaction and subscription retention rates.
• Personalized content delivery is key to user retention in digital entertainment.
• AI-driven recommendations significantly enhance user experience.

Related: How can AI be used in Instagram Marketing?

25. Walmart: Revolutionizing Retail with AI

Task/Conflict: Retail giants like Walmart face challenges in inventory management and providing a high-quality customer service experience. Walmart aimed to use AI to optimize these areas and enhance overall operational efficacy.

Solution: Walmart deployed AI technologies across its stores to manage inventory levels effectively and enhance customer service. AI systems predict product demand to optimize stock levels, while AI-driven robots assist in inventory management and customer service, such as guiding customers in stores and handling queries.

• Improved inventory management, reducing overstock and shortages.
• Enhanced customer service experience in stores.
• AI can streamline retail operations significantly.
• Enhanced customer service through AI leads to better customer satisfaction.

26. Roche: Innovating Drug Discovery with AI

Task/Conflict: The pharmaceutical industry faces significant challenges in drug discovery, requiring vast investments of time and resources. Roche aimed to utilize AI to streamline the drug development process and enhance the discovery of new therapeutics.

Solution: Roche implemented AI to analyze medical data and simulate drug interactions, speeding up the drug discovery process. AI models predict the effectiveness of compounds and identify potential candidates for further testing, significantly minimizing the time and cost related with traditional drug development procedures.

• Accelerated drug discovery processes, bringing new treatments to market faster.
• Reduced costs and increased efficiency in pharmaceutical research.
• AI can greatly accelerate the drug discovery process.
• Cost-effective and efficient drug development is possible with AI integration.

27. IKEA: Enhancing Customer Experience with AI

Task/Conflict: In the competitive home furnishings market, enhancing the customer shopping experience is crucial for success. IKEA aimed to use AI to provide innovative design tools and improve customer interaction.

Solution: IKEA introduced AI-powered tools such as virtual reality apps that allow consumers to visualize furniture before buying. These tools help customers make more informed decisions and enhance their shopping experience. Additionally, AI chatbots assist with customer service inquiries, providing timely and effective support.

• Improved customer decision-making and satisfaction with interactive tools.
• Enhanced efficiency in customer service.
• AI can transform the retail experience by providing innovative customer interaction tools.
• Effective customer support through AI can enhance brand loyalty and satisfaction.

28. General Electric: Optimizing Energy Production with AI

Task/Conflict: Managing energy production efficiently while predicting and mitigating potential issues is crucial for energy companies. General Electric (GE) aimed to improve the efficiency and reliability of its energy production facilities using AI.

Solution: GE integrated AI into its energy management systems to enhance power generation and distribution. AI algorithms predict maintenance needs and optimize energy production, ensuring efficient operation and reducing downtime. This predictive maintenance approach saves costs and enhances the reliability of energy production.

• Increased efficiency in energy production and distribution.
• Reduced operational costs and enhanced system reliability.
• Predictive maintenance is crucial for cost-effective and efficient energy management.
• AI can significantly improve the predictability and efficiency of energy production.

Related: Use of AI in Sales

29. L’Oréal: Transforming Beauty with AI

Task/Conflict: Personalization in the beauty industry enhances customer satisfaction and brand loyalty. L’Oréal aimed to personalize beauty products and experiences for its diverse customer base using AI.

Solution: L’Oréal leverages AI to assess consumer data and provide personalized product suggestions. AI-driven tools assess skin types and preferences to recommend the best skincare and makeup products. Additionally, virtual try-on apps powered by AI allow customers to see how products would look before making a purchase.

• Enhanced personalization of beauty products and experiences.
• Increased customer engagement and satisfaction.
• AI can provide highly personalized experiences in the beauty industry.
• Data-driven personalization enhances customer satisfaction and brand loyalty.

30. The Weather Company: AI-Predicting Weather Patterns

Task/Conflict: Accurate weather prediction is vital for planning and safety in various sectors. The Weather Company aimed to enhance the accuracy of weather forecasts and provide timely weather-related information using AI.

Solution: The Weather Company employs AI to analyze data from weather sensors, satellites, and historical weather patterns. AI models improve the accuracy of weather predictions by identifying trends and anomalies. These enhanced forecasts help in better planning and preparedness for weather events, benefiting industries like agriculture, transportation, and public safety.

• Improved accuracy in weather forecasting.
• Better preparedness and planning for adverse weather conditions.
• AI can enhance the precision of meteorological predictions.
• Accurate weather forecasting is crucial for safety and operational planning in multiple sectors.

31. Cisco: Securing Networks with AI

Task/Conflict: As cyber threats evolve and become more sophisticated, maintaining robust network security is crucial for businesses. Cisco aimed to leverage AI to enhance its cybersecurity measures, detecting and responding to threats more efficiently.

Solution: Cisco integrated AI into its cybersecurity framework to analyze network traffic and identify unusual patterns indicative of cyber threats. This AI-driven approach allows for real-time threat detection and automated responses, thus improving the speed and efficacy of security measures.

• Strengthened network security with faster threat detection.
• Reduced manual intervention by automating threat responses.
• AI is essential in modern cybersecurity for real-time threat detection.
• Automating responses can significantly enhance network security protocols.

32. Adidas: AI in Sports Apparel Manufacturing

Task/Conflict: To maintain competitive advantage in the fast-paced sports apparel market, Adidas sought to innovate its manufacturing processes by incorporating AI to improve efficiency and product quality.

Solution: Adidas employed AI-driven robotics and automation technologies in its factories to streamline the production process. These AI systems optimize manufacturing workflows, enhance quality control, and reduce waste by precisely cutting fabrics and assembling materials according to exact specifications.

• Increased production efficacy and reduced waste.
• Enhanced consistency and quality of sports apparel.
• AI-driven automation can revolutionize manufacturing processes.
• Precision and efficiency in production lead to higher product quality and sustainability.

Related: How can AI be used in Disaster Management?

33. KLM Royal Dutch Airlines: AI-Enhanced Customer Service

Task/Conflict: Enhancing the customer service experience in the airline industry is crucial for customer satisfaction and loyalty. KLM aimed to provide immediate and effective assistance to its customers by integrating AI into their service channels.

Solution: KLM introduced an AI-powered chatbot, which provides 24/7 customer service across multiple languages. The chatbot handles inquiries about flight statuses, bookings, and baggage policies, offering quick and accurate responses. This AI solution helps manage customer interactions efficiently, especially during high-volume periods.

• Improved customer service efficiency and responsiveness.
• Increased customer satisfaction through accessible and timely support.
• AI chatbots can highly improve user service in high-demand industries.
• Effective communication through AI leads to better customer engagement and loyalty.

34. Novartis: AI in Drug Formulation

Task/Conflict: The pharmaceutical industry requires rapid development and formulation of new drugs to address emerging health challenges. Novartis aimed to use AI to expedite the drug formulation process, making it faster and more efficient.

Solution: Novartis applied AI to simulate and predict how different formulations might behave, speeding up the lab testing phase. AI algorithms analyze vast amounts of data to predict the stability and efficacy of drug formulations, allowing researchers to focus on the most promising candidates.

• Accelerated drug formulation and reduced time to market.
• Improved efficacy and stability of pharmaceutical products.
• AI can significantly shorten the drug development lifecycle.
• Predictive analytics in pharmaceutical research can lead to more effective treatments.

35. Shell: Optimizing Energy Resources with AI

Task/Conflict: In the energy sector, optimizing exploration and production processes for efficiency and sustainability is crucial. Shell sought to harness AI to enhance its oil and gas operations, making them more efficient and less environmentally impactful.

Solution: Shell implemented AI to analyze geological data and predict drilling outcomes, optimizing resource extraction. AI algorithms also adjust production processes in real time, improving operational proficiency and minimizing waste.

• Improved efficiency and sustainability in energy production.
• Reduced environmental impact through optimized resource management.
• Automation can enhance the effectiveness and sustainability of energy production.
• Real-time data analysis is crucial for optimizing exploration and production.

36. Procter & Gamble: AI in Consumer Goods Production

Task/Conflict: Maintaining operational efficiency and innovating product development are key challenges in the consumer goods industry. Procter & Gamble (P&G) aimed to integrate AI into their operations to enhance these aspects.

Solution: P&G employs AI to optimize its manufacturing processes and predict market trends for product development. AI-driven data analysis helps in managing supply chains and production lines efficiently, while AI in market research informs new product development, aligning with consumer needs.

• Enhanced operational efficacy and minimized production charges.
• Improved product innovation based on consumer data analysis.
• AI is crucial for optimizing manufacturing and supply chain processes.
• Data-driven product development leads to more successful market introductions.

Related: Use of AI in the Navy

37. Disney: Creating Magical Experiences with AI

Task/Conflict: Enhancing visitor experiences in theme parks and resorts is a priority for Disney. They aimed to use AI to create personalized and magical experiences for guests, improving satisfaction and engagement.

Solution: Disney utilizes AI to manage park operations, personalize guest interactions, and enhance entertainment offerings. AI algorithms predict visitor traffic and optimize attractions and staff deployment. Personalized recommendations for rides, shows, and dining options enhance the guest experience by leveraging data from past visits and preferences.

• Enhanced guest satisfaction through personalized experiences.
• Improved operational efficiency in park management.
• AI can transform the entertainment and hospitality businesses by personalizing consumer experiences.
• Efficient management of operations using AI leads to improved customer satisfaction.

38. BMW: Reinventing Mobility with Autonomous Driving

Task/Conflict: The future of mobility heavily relies on the development of safe and efficient autonomous driving technologies. BMW aimed to dominate in this field by incorporating AI into their vehicles.

Solution: BMW is advancing its autonomous driving capabilities through AI, using sophisticated machine learning models to process data from vehicle sensors and external environments. This technology enables vehicles to make intelligent driving decisions, improving safety and passenger experiences.

• Pioneering advancements in autonomous vehicle technology.
• Enhanced safety and user experience in mobility.
• AI is crucial for the development of autonomous driving technologies.
• Safety and reliability are paramount in developing AI-driven vehicles.

39. Mastercard: Innovating Payment Solutions with AI

Task/Conflict: In the digital age, securing online transactions and enhancing payment processing efficiency are critical challenges. Mastercard aimed to leverage AI to address these issues, ensuring secure and seamless payment experiences for users.

Solution: Mastercard integrates AI to monitor transactions in real time, detect fraudulent activities, and enhance the efficiency of payment processing. AI algorithms analyze spending patterns and flag anomalies, while also optimizing authorization processes to reduce false declines and improve user satisfaction.

• Strengthened security and reduced fraud in transactions.
• Improved efficiency and user experience in payment processing.
• AI is necessary for securing and streamlining expense systems.
• Enhanced transaction processing efficiency leads to higher customer satisfaction.

40. AstraZeneca: Revolutionizing Oncology with AI

Task/Conflict: Advancing cancer research and developing effective treatments is a pressing challenge in healthcare. AstraZeneca aimed to utilize AI to revolutionize oncology research, enhancing the development and personalization of cancer treatments.

Solution: AstraZeneca employs AI to analyze genetic data and clinical trial results, identifying potential treatment pathways and personalizing therapies based on individual genetic profiles. This approach accelerates the development of targeted treatments and improves the efficacy of cancer therapies.

• Accelerated innovation and personalized treatment in oncology.
• Better survival chances for cancer patients.
• AI can significantly advance personalized medicine in oncology.
• Data-driven approaches in healthcare lead to better treatment outcomes and innovations.

Related: How can AI be used in Tennis?

Closing Thoughts

These 40 case studies illustrate the transformative power of AI across various industries. By addressing specific challenges and leveraging AI solutions, companies have achieved remarkable outcomes, from enhancing customer experiences to solving complex scientific problems. The key learnings from these cases underscore AI’s potential to revolutionize industries, improve efficiencies, and open up new possibilities for innovation and growth.

• 6 Ways to Pay for an Online Course [2024]
• How to Choose an Online Course? [An Ultimate Checklist] [2024]

Team DigitalDefynd

We help you find the best courses, certifications, and tutorials online. Hundreds of experts come together to handpick these recommendations based on decades of collective experience. So far we have served 4 Million+ satisfied learners and counting.

Traditional AI vs. Generative AI: Key Differences [2024]

10 ways AI is being used in the Textile Industry [2024]

10 ways AI can be used in early childhood education [2024]

Use of AI in Interior Design [10 Examples] [2024]

10 Ways Generative AI is Being Used in Cybersecurity [2024]

Top 20 AI Product Manager Interview Q&A [2024]

Artificial Intelligence Case Study Topics

Looking for artificial intelligence case study topics? Explore real-life examples and learn how AI is transforming industries like healthcare, finance, manuf...

Artificial Intelligence Case Study Topics: Unleashing the Power of AI

Artificial Intelligence (AI) has emerged as one of the most transformative technologies in recent times, revolutionizing industries and reshaping the way we live and work. With its ability to analyze vast amounts of data, learn from patterns, and make autonomous decisions, AI has the potential to solve complex problems and unlock new possibilities. One of the key drivers of AI advancements is the utilization of case studies, which provide real-world examples of AI applications and their impact.

Introduction to AI Case Studies

Case studies serve as invaluable resources in understanding the practical applications of AI. They offer insights into how AI technologies are implemented, the challenges faced, and the outcomes achieved. By examining successful AI case studies, we can gain a deeper understanding of the potential of AI and how it can be harnessed to drive innovation and improve various aspects of our lives.

The Importance of AI Case Studies

AI case studies play a pivotal role in showcasing the capabilities of AI systems and their potential impact. These studies enable researchers, developers, and businesses to learn from past experiences, identify best practices, and avoid potential pitfalls. By studying successful AI case studies, decision-makers can make informed choices when implementing AI solutions, ensuring maximum efficiency and effectiveness.

Purpose of the Blog Post

The purpose of this blog post is to provide an in-depth exploration of artificial intelligence case study topics. We will delve into various industries and domains where AI has made significant strides, examining real-life examples and their impact. By the end of this comprehensive guide, you will have a clear understanding of the potential applications of AI across different sectors and gain insights into how these case studies have transformed industries.

Overview of Artificial Intelligence Case Studies

Before we dive into specific case studies, let's first establish a foundational understanding of AI case studies. These case studies involve the application of AI technologies to address a specific problem or challenge. They provide a detailed account of how AI systems were developed, implemented, and the outcomes achieved.

AI case studies offer a multifaceted perspective, encompassing various industries, including healthcare, finance, manufacturing, customer service, and transportation. Each case study presents a unique set of challenges and opportunities, highlighting the versatility and adaptability of AI in different contexts.

Real-life Examples of Successful AI Case Studies

To truly grasp the potential of AI, it is essential to explore real-life examples of successful AI case studies. These pioneering projects have showcased the transformative power of AI, pushing the boundaries of what was once thought possible. Let's take a glimpse into some notable AI case studies:

1. Google DeepMind's AlphaGo

In 2016, Google's DeepMind developed AlphaGo, an AI system that defeated the world champion Go player, Lee Sedol. This groundbreaking achievement highlighted the ability of AI to master complex strategic games that were previously considered beyond the reach of machines. AlphaGo's success demonstrated the potential of AI in problem-solving and decision-making in complex scenarios.

2. IBM Watson's Jeopardy! Victory

IBM's Watson showcased its cognitive capabilities by competing against human champions on the popular quiz show, Jeopardy! in 2011. Watson's ability to understand and process natural language, coupled with its vast knowledge base, enabled it to outperform the human contestants. This case study demonstrated the potential of AI in understanding and analyzing unstructured data, paving the way for advancements in natural language processing.

3. Tesla's Autopilot System

Tesla's Autopilot system utilizes AI algorithms and sensors to enable semi-autonomous driving. By analyzing real-time data from cameras, radar, and ultrasonic sensors, the Autopilot system can detect and respond to road conditions, other vehicles, and pedestrians. This case study showcases the potential of AI in the transportation industry, revolutionizing the concept of self-driving cars.

4. Amazon's Recommendation Engine

Amazon's recommendation engine is powered by AI algorithms that analyze customer preferences, purchase history, and browsing behavior to provide personalized product recommendations. This case study demonstrates how AI can enhance the customer experience by delivering targeted suggestions, improving sales, and fostering customer loyalty.

These real-life examples are just the tip of the iceberg when it comes to AI case studies. They illustrate the diverse range of industries and domains where AI has made significant contributions, showcasing the potential for innovation and transformation.

In the next section, we will explore the process of selecting artificial intelligence case study topics, considering various factors and identifying the most relevant and impactful areas of study. Stay tuned for an in-depth analysis of AI case studies in healthcare, finance, manufacturing, customer service, and transportation.

Note: In the following sections, we will explore each case study topic in greater detail, analyzing the problem at hand, the AI solution implemented, and the results and impact achieved.

Artificial intelligence (AI) case studies provide valuable insights into the practical applications and impact of AI technologies. These case studies offer a glimpse into the real-world implementation of AI systems, showcasing their capabilities, successes, and challenges. By examining these case studies, we can gain a deeper understanding of the potential of AI and its ability to transform various industries.

Explanation of AI Case Studies

AI case studies involve the application of AI technologies to solve specific problems or challenges within a given context. These studies provide detailed accounts of how AI systems were developed, implemented, and the outcomes achieved. By analyzing the methodologies and approaches used in these case studies, researchers, developers, and businesses can learn from past experiences and gain insights into the best practices for implementing AI solutions.

AI case studies often involve the utilization of machine learning algorithms, natural language processing, computer vision, robotics, and other AI techniques. They can range from small-scale projects to large-scale deployments, depending on the complexity of the problem being addressed.

Benefits of AI Case Studies

AI case studies offer numerous benefits for both researchers and practitioners in the field of AI. Here are some key advantages:

Insights into Implementation : Case studies offer insights into the practical implementation of AI systems. They provide details on the data collection process, model training, algorithm selection, and optimization techniques employed. This information can guide future AI projects and help avoid common pitfalls.

Benchmarking and Comparison : Case studies allow for benchmarking and comparison of different AI approaches. By examining multiple case studies within a specific domain, researchers can identify the strengths and weaknesses of various AI techniques, leading to advancements and improvements in the field.

Inspiration for Innovation : AI case studies can inspire new ideas and innovative solutions. By understanding the challenges faced in previous case studies and the methods used to overcome them, researchers can build upon existing knowledge and push the boundaries of AI capabilities.

To truly comprehend the power and potential of AI, it is essential to explore real-life examples of successful AI case studies. These examples highlight the impact that AI can have across various domains. Let's take a closer look at some notable AI case studies:

Google DeepMind's AlphaGo : AlphaGo, developed by Google DeepMind, made headlines in 2016 when it defeated the world champion Go player, Lee Sedol. This case study demonstrated the ability of AI to master complex strategic games and showcased the potential for AI in decision-making and problem-solving.

IBM Watson's Jeopardy! Victory : In 2011, IBM's Watson competed against human champions on the quiz show Jeopardy! and emerged victorious. Watson's success demonstrated the power of AI in natural language processing and understanding unstructured data.

Tesla's Autopilot System : Tesla's Autopilot system utilizes AI algorithms and sensors to enable semi-autonomous driving. This case study showcases the potential of AI in the transportation industry, revolutionizing the concept of self-driving cars.

Amazon's Recommendation Engine : Amazon's recommendation engine utilizes AI to analyze customer preferences and provide personalized product recommendations. This case study highlights how AI can enhance the customer experience and drive sales through targeted suggestions.

These real-life examples illustrate the diverse range of industries and domains where AI has made significant contributions. They serve as inspiration and provide valuable insights into the potential of AI technologies.

Choosing Artificial Intelligence Case Study Topics

When exploring the world of artificial intelligence case studies, it is essential to select the right topics that align with current AI trends and have the potential for significant impact. In this section, we will discuss the factors to consider when choosing case study topics and identify some promising areas for exploration.

Factors to Consider

Relevance to Current AI Trends : Selecting case study topics that align with current AI trends ensures that you are exploring areas of research and development that are actively advancing. Staying up-to-date with the latest advancements in AI will provide you with a better understanding of the challenges and opportunities in the field.

Availability of Data : Data availability is crucial for successful AI case studies. Consider topics where relevant and high-quality data is accessible. Adequate data sets are essential for training AI models effectively and obtaining reliable results.

Ethical Considerations : Ethical considerations should be an integral part of AI case study topic selection. It is important to choose topics that adhere to ethical guidelines and prioritize fairness, transparency, and accountability. Avoid topics that raise concerns regarding privacy, bias, or potential harm to individuals or society.

Identifying Potential Case Study Topics

Now, let's explore some potential case study topics in various industries where AI has shown promising applications:

Healthcare and Medical Diagnostics : AI has the potential to revolutionize healthcare by improving diagnostics, predicting disease outcomes, and enabling personalized treatment plans. Some potential case study topics in this domain include:

AI in Early Cancer Detection: Explore how AI algorithms can analyze medical imaging data to detect and diagnose cancer at an early stage, leading to improved patient outcomes.

AI in Medical Imaging Analysis: Investigate how AI can assist radiologists in analyzing medical images, such as X-rays, MRIs, and CT scans, to improve accuracy and speed in diagnosis.

Financial Services and Fraud Detection : AI offers significant potential in the finance industry, particularly in fraud detection and prevention. Some potential case study topics in this domain include:

AI in Fraud Detection for Banks: Examine how AI algorithms can analyze transaction data and detect fraudulent activities in real-time, enhancing security and minimizing financial losses.

AI in Credit Card Fraud Detection: Explore how AI can analyze patterns and anomalies in credit card transactions to identify and prevent fraudulent activities, ensuring the safety of customers' financial information.

Manufacturing and Process Optimization : AI can optimize manufacturing processes, improve efficiency, and reduce costs. Some potential case study topics in this domain include:

AI in Predictive Maintenance: Investigate how AI can analyze sensor data to predict machinery failures and schedule maintenance proactively, minimizing downtime and optimizing production.

AI in Supply Chain Optimization: Explore how AI algorithms can optimize supply chain operations by predicting demand, optimizing inventory levels, and improving logistics, leading to cost savings and improved customer satisfaction.

Customer Service and Chatbots : AI-powered chatbots have revolutionized customer service by providing instant responses and personalized experiences. Some potential case study topics in this domain include:

AI-powered Chatbots in E-commerce: Examine how AI-powered chatbots can enhance customer engagement, provide personalized product recommendations, and streamline the online shopping experience.

AI in Virtual Assistants for Customer Support: Explore how AI-based virtual assistants can handle customer inquiries, resolve issues, and provide 24/7 support, improving customer satisfaction and reducing support costs.

Transportation and Autonomous Vehicles : AI plays a critical role in the development of autonomous vehicles and traffic management systems. Some potential case study topics in this domain include:

AI in Self-Driving Cars: Investigate how AI algorithms enable autonomous vehicles to perceive the environment, make real-time decisions, and navigate safely on the roads.

AI in Traffic Management Systems: Explore how AI can optimize traffic flow, reduce congestion, and improve transportation efficiency by analyzing real-time traffic data and implementing intelligent control systems.

By considering these factors and exploring potential case study topics in various industries, you can select areas that align with your interests and have the potential to contribute to the advancement of AI technologies.

Deep Dive into Selected Artificial Intelligence Case Study Topics

In this section, we will delve deeper into selected artificial intelligence case study topics across various industries. By examining these case studies, we can gain a comprehensive understanding of the problem at hand, the AI solutions implemented, and the results and impact achieved.

Healthcare and Medical Diagnostics

Case Study: AI in Early Cancer Detection

Overview of the Problem: Early detection of cancer is crucial for successful treatment and improved patient outcomes. However, it can be challenging for healthcare professionals to accurately detect cancer at its early stages due to the complexity of medical imaging data and the potential for human error.

AI Solution and Implementation: In this case study, AI algorithms were developed and trained using large datasets of medical imaging data, including mammograms, CT scans, or MRIs. These algorithms utilize deep learning techniques to analyze and interpret the images, identifying potential cancerous cells or tumors. By comparing the patterns in the images to an extensive database of known cancer cases, the AI system can provide accurate early detection of cancer.

Results and Impact: The implementation of AI in early cancer detection has shown promising results. The AI system can analyze medical images with high accuracy, often outperforming human radiologists in detecting cancer at its early stages. Early detection allows for timely intervention, leading to improved treatment outcomes and increased survival rates for patients.

Case Study: AI in Medical Imaging Analysis

Overview of the Problem: Medical imaging, such as X-rays, MRIs, and CT scans, plays a crucial role in diagnosing and monitoring various medical conditions. However, the interpretation of these images can be time-consuming, subjective, and prone to human error.

AI Solution and Implementation: In this case study, AI algorithms were developed and trained using large datasets of labeled medical imaging data. These algorithms leverage deep learning techniques, such as convolutional neural networks (CNNs), to analyze and interpret the images. The AI system can identify anomalies, highlight potential abnormalities, and provide quantitative measurements to assist radiologists in making accurate diagnoses.

Results and Impact: The implementation of AI in medical imaging analysis has shown significant potential in improving diagnostic accuracy and efficiency. The AI system can assist radiologists in identifying subtle abnormalities that may be missed by the human eye, leading to early detection of diseases and improved patient care. Additionally, AI can help reduce the burden on radiologists by automating certain tasks, allowing them to focus on more complex cases.

Financial Services and Fraud Detection

Case Study: AI in Fraud Detection for Banks

Overview of the Problem: Fraudulent activities, such as identity theft and unauthorized transactions, pose significant challenges for banks and financial institutions. Traditional rule-based fraud detection systems often struggle to keep up with evolving fraud techniques and patterns.

AI Solution and Implementation: In this case study, AI algorithms were developed to analyze large volumes of transactional data in real-time. These algorithms utilize machine learning techniques, including anomaly detection and pattern recognition, to identify suspicious activities that deviate from normal patterns. By continuously learning from new data, the AI system can adapt and evolve to detect new and emerging fraud patterns.

Results and Impact: The implementation of AI in fraud detection for banks has led to improved fraud prevention and detection rates. The AI system can analyze vast amounts of transactional data quickly and accurately, flagging potentially fraudulent activities in real-time. By minimizing false positives and identifying fraudulent transactions promptly, banks can mitigate financial losses and protect their customers' assets.

Case Study: AI in Credit Card Fraud Detection

Overview of the Problem: Credit card fraud is a significant concern for both financial institutions and cardholders. Detecting fraudulent credit card transactions is challenging due to the large volume of transactions and the need for real-time analysis.

AI Solution and Implementation: In this case study, AI algorithms were developed to analyze credit card transaction data, including transaction amounts, merchant information, and cardholder behavior. These algorithms utilize machine learning techniques, such as supervised and unsupervised learning, to identify patterns and anomalies indicative of fraudulent activities. The AI system can learn from historical data to improve its fraud detection capabilities over time.

Results and Impact: The implementation of AI in credit card fraud detection has proven to be highly effective in reducing fraudulent activities. The AI system can quickly analyze transactions, identify suspicious patterns, and flag potentially fraudulent transactions for further investigation. By minimizing false positives and accurately detecting fraud, financial institutions can protect their customers and maintain trust in the credit card ecosystem.

In the next section, we will explore case studies in manufacturing and process optimization, showcasing how AI can enhance efficiency and streamline operations.

In this section, we will explore case studies in the domain of manufacturing and process optimization. These examples highlight how artificial intelligence (AI) can enhance efficiency, reduce costs, and streamline operations in manufacturing industries.

Manufacturing and Process Optimization

Case Study: AI in Predictive Maintenance

Overview of the Problem: Unplanned equipment failures and unexpected downtime can significantly impact manufacturing operations, leading to production delays and increased costs. Traditional maintenance strategies, such as reactive or preventive maintenance, may not effectively address the challenges of equipment failure prediction and maintenance scheduling.

AI Solution and Implementation: In this case study, AI algorithms were implemented to perform predictive maintenance. The algorithms utilize machine learning techniques, such as supervised learning and anomaly detection, to analyze sensor data from machines and predict potential failures. By continuously monitoring the health and performance of equipment, the AI system can identify early warning signs of impending failures and schedule maintenance proactively.

Results and Impact: The implementation of AI in predictive maintenance has proven to be highly beneficial for manufacturing industries. By detecting potential equipment failures in advance, companies can plan maintenance activities more efficiently, minimizing downtime and reducing costs associated with unscheduled repairs. This proactive approach to maintenance helps optimize production schedules and ensures smooth operations.

Case Study: AI in Supply Chain Optimization

Overview of the Problem: Supply chain management involves complex processes, including demand forecasting, inventory management, and logistics planning. Optimizing these processes is crucial for reducing costs, improving customer satisfaction, and increasing operational efficiency.

AI Solution and Implementation: In this case study, AI algorithms were utilized to optimize supply chain operations. The algorithms leverage machine learning techniques, such as demand forecasting, inventory optimization, and route optimization, to analyze historical and real-time data. By considering factors such as customer demand, lead times, transportation costs, and inventory levels, the AI system can generate optimal plans and recommendations for procurement, production, and distribution.

Results and Impact: The implementation of AI in supply chain optimization has led to significant improvements in efficiency and cost reduction. By accurately forecasting demand and optimizing inventory levels, companies can minimize stockouts and excess inventory, leading to reduced carrying costs and improved cash flow. AI-powered route optimization helps streamline logistics operations, optimizing delivery schedules and reducing transportation costs. These advancements in supply chain optimization ultimately lead to improved customer satisfaction through faster and more reliable deliveries.

These case studies highlight the potential impact of AI in manufacturing and process optimization. By leveraging AI technologies, companies can achieve greater efficiency, reduced costs, and improved operational effectiveness. In the next section, we will explore case studies in the domain of customer service and chatbots, showcasing how AI can enhance customer experiences and support interactions.

In this section, we will explore case studies in the domain of customer service and chatbots. These examples highlight how artificial intelligence (AI) can enhance customer experiences, streamline support interactions, and improve overall customer satisfaction.

Customer Service and Chatbots

Case Study: AI-powered Chatbots in E-commerce

Overview of the Problem: With the rise of e-commerce, providing personalized and timely customer support has become a crucial aspect of the online shopping experience. However, scaling customer service to meet the growing demands of a large customer base can be challenging and costly.

AI Solution and Implementation: In this case study, AI-powered chatbots were implemented to handle customer inquiries and provide support in e-commerce platforms. These chatbots utilize natural language processing (NLP) and machine learning algorithms to understand and respond to customer queries. They can provide instant and personalized responses, offer product recommendations based on customer preferences, and assist with order tracking and returns.

Results and Impact: The implementation of AI-powered chatbots in e-commerce has significantly improved customer experiences and operational efficiency. Chatbots provide instant responses, reducing customer wait times and ensuring 24/7 availability for support inquiries. By offering personalized product recommendations, chatbots can enhance the shopping experience and increase sales conversion rates. Additionally, chatbots can handle routine inquiries, freeing up human agents to focus on more complex customer issues, ultimately improving overall customer satisfaction.

Case Study: AI in Virtual Assistants for Customer Support

Overview of the Problem: Customer support departments often face high call volumes and long wait times, leading to customer frustration and decreased satisfaction. Providing timely and effective support to customers is critical for maintaining brand loyalty and positive customer experiences.

AI Solution and Implementation: In this case study, AI-powered virtual assistants were implemented to handle customer support interactions. These virtual assistants utilize AI technologies such as natural language processing, sentiment analysis, and knowledge graph systems. They can understand customer inquiries, provide accurate and personalized responses, and escalate complex issues to human agents when necessary. Virtual assistants continuously learn from customer interactions, improving their responses and problem-solving abilities over time.

Results and Impact: The implementation of AI-powered virtual assistants in customer support has proven to be highly effective in improving response times and customer satisfaction. Virtual assistants can provide instant support, reducing wait times and enabling customers to receive assistance at their convenience. By accurately understanding customer inquiries and providing relevant information, virtual assistants can resolve issues quickly and efficiently. This results in improved customer experiences, reduced support costs, and increased customer loyalty.

These case studies illustrate the potential of AI in enhancing customer service and support interactions. By leveraging AI-powered chatbots and virtual assistants, businesses can provide timely, personalized, and efficient support to their customers, resulting in improved customer satisfaction and loyalty. In the next section, we will explore case studies in the domain of transportation and autonomous vehicles, showcasing how AI is revolutionizing the way we travel and manage traffic.

In this section, we will explore case studies in the domain of transportation and autonomous vehicles. These examples highlight how artificial intelligence (AI) is revolutionizing the way we travel and manage traffic.

Transportation and Autonomous Vehicles

Case Study: AI in Self-Driving Cars

Overview of the Problem: Self-driving cars have the potential to transform the transportation industry by reducing accidents, improving traffic flow, and enhancing overall mobility. However, developing autonomous vehicles that can navigate safely and make real-time decisions in complex traffic scenarios is a significant challenge.

AI Solution and Implementation: In this case study, AI algorithms are utilized to power self-driving cars. These algorithms leverage a combination of computer vision, sensor fusion, machine learning, and decision-making models to perceive the environment, interpret traffic signs, detect obstacles, and make real-time driving decisions. By continuously analyzing sensor data and learning from past experiences, self-driving cars can navigate autonomously while adhering to traffic rules and ensuring passenger safety.

Results and Impact: The implementation of AI in self-driving cars has the potential to revolutionize transportation. Autonomous vehicles can reduce human errors and improve road safety by eliminating the risks associated with human distraction, fatigue, and impaired driving. Additionally, self-driving cars have the potential to optimize traffic flow, reduce congestion, and increase overall transportation efficiency, leading to reduced travel times and fuel consumption.

Case Study: AI in Traffic Management Systems

Overview of the Problem: Managing traffic flow in urban areas is a complex task that requires real-time analysis of traffic patterns, congestion, and accidents. Traditional traffic management systems often struggle to handle the dynamic nature of traffic and effectively optimize traffic flow.

AI Solution and Implementation: In this case study, AI algorithms are used to enhance traffic management systems. These algorithms leverage machine learning techniques and real-time data analysis to predict traffic congestion, optimize signal timings, and suggest alternative routes. By analyzing historical and real-time traffic data, the AI system can make intelligent decisions to improve traffic flow, reduce congestion, and minimize travel times.

Results and Impact: The implementation of AI in traffic management systems has shown significant potential in improving transportation efficiency. By optimizing signal timings based on real-time traffic conditions, AI can reduce congestion and ensure a smoother flow of vehicles. AI algorithms can also provide real-time traffic updates to drivers, enabling them to make informed decisions about alternative routes, further reducing travel times and improving overall traffic management.

These case studies highlight how AI is transforming the transportation industry. From self-driving cars to intelligent traffic management systems, AI technologies have the potential to revolutionize the way we travel, making transportation safer, more efficient, and environmentally friendly.

In this comprehensive guide, we have explored various artificial intelligence case study topics across different industries. We have witnessed the power of AI in healthcare, finance, manufacturing, customer service, and transportation. By examining real-life examples and understanding the problem-solving capabilities of AI, we have gained insights into the potential of this transformative technology.

AI case studies provide invaluable lessons and inspire innovation in the field of artificial intelligence. They offer opportunities for learning, benchmarking, and improving AI systems. By studying successful case studies, researchers, developers, and businesses can harness the power of AI to drive advancements, solve complex problems, and improve various aspects of our lives.

As AI continues to evolve, it is crucial to stay updated with the latest trends, research, and case studies. The potential of AI is immense, and by exploring and sharing knowledge, we can collectively shape a future where AI-driven solutions enhance our lives in remarkable ways.

Adrian Kennedy

101 real-world gen AI use cases from the world's leading organizations

Vice President, Product Marketing, Google Cloud

At Google Cloud Next ‘24, top companies, governments, researchers, and startups showcased how they're already using Google's AI solutions to enhance their work.

Try gemini 1.5 models.

Google's most advanced multimodal models in Vertex AI

Since generative AI first captured the world’s attention a year and a half ago, there’s been a vigorous discussion about what, exactly, the new technology is best used for. While we all enjoyed those early funny chats and witty limericks, we’ve quickly discovered that many of the biggest AI opportunities are clearly in the enterprise .

Our customers and partners at Google Cloud have found real potential for creating new processes, efficiencies, and innovations with generative AI. For proof, look no further than the 300-plus organizations who are featured at this week’s Next event in Las Vegas .

In a matter of months, organizations like these have gone from AI helping answer questions, to AI making predictions, to generative AI agents. What makes AI agents unique is that they can take actions to achieve specific goals, whether that’s guiding a shopper to the perfect pair of shoes, helping an employee looking for the right health benefits, or supporting nursing staff with smoother patient hand-offs during shifts changes.

In our work with customers, we keep hearing that their teams are increasingly focused on improving productivity, automating processes and modernizing the customer experience. These aims are now being achieved through the AI agents they’re developing in six key areas: customer service; employee empowerment; creative ideation and production; data analysis; code creation; and cybersecurity.

These special capabilities are made possible in large part by the new multimodal capacity of generative AI and AI foundation models , which allow agents to handle tasks across a range of communications modes, including text, voice, video, audio, code, and more. With human support, agents can converse, reason, learn, and make decisions.

The hundreds of customers who joined us at Next ‘24 to showcase and discuss early versions of their AI agents and gen-AI solutions have come to rely on Google Cloud technologies that include our AI infrastructure, Gemini models, Vertex AI platform, Google Workspace, and Google Distributed Cloud. We were also joined by more than 100 partners supporting the creation of AI agents and AI solutions, which you can read about in detail .

Here’s a snapshot of how 101 of these industry leaders are putting AI into production today, creating real-world use cases that will transform tomorrow.

Similar to great sales and service people, customer agents are able to listen carefully, understand your needs, and recommend the right products and services. They work seamlessly across channels including the web, mobile, and point of sale, and can be integrated into product experiences with voice and video.

• ADT is building a customer agent to help its millions of customers select, order, and set up their home security.
• Alaska Airlines is developing a personalized travel search experience using advanced AI techniques, creating hyper-personalized recommendations that engage customers early and foster loyalty through AI-generated content. Watch the session to learn more.
• Best Buy is using Gemini to launch a generative AI-powered virtual assistant this summer that can troubleshoot product issues, reschedule order deliveries, manage Geek Squad subscriptions, and more; in-store and digital customer-service associates are also gaining gen-AI tools to better serve customers anywhere they need help. Watch the session to learn more.
• The Central Texas Regional Mobility Authority is using Vertex AI to modernize transportation operations for a smoother, more efficient journey.
• Etsy uses Vertex AI training to optimize their search recommendations and ads models, delivering better listing suggestions to buyers and helping sellers grow their businesses.
• Golden State Warriors are using AI to improve the fan experience content in their Chase Center app. Watch the session to learn more.
• IHG Hotels & Resorts is building a generative AI-powered chatbot to help guests easily plan their next vacation directly in the IHG One Rewards mobile app. Watch the session to learn more.
• ING Bank aims to offer a superior customer experience and has developed a gen-AI chatbot for workers to enhance self-service capabilities and improve answer quality on customer queries. Watch the session to learn more.
• Magalu , one of Brazil’s largest retailers, has put customer service at the center of its AI strategy, including using Vertex AI to create “Lu’s Brain” to power an interactive conversational agent for Lu, Magalu's popular brand persona (the 3D bot has more than 14 million followers between TikTok and Instagram).
• Mercedes Benz will infuse e-commerce capabilities into its online storefront with a gen AI-powered smart sales assistant. Mercedes also plans to expand its use of Google Cloud AI in its call centers and is using Vertex AI and Gemini to personalize marketing campaigns.
• Oppo/OnePlus is incorporating Gemini models and Google Cloud AI into their phones to deliver innovative customer experiences, including news and audio recording summaries, AI toolbox, and more.
• Samsung is deploying Gemini Pro and Imagen 2 to their Galaxy S24 smartphones so users can take advantage of amazing features like text summarization, organization, and magical image editing.
• The Minnesota Division of Driver and Vehicle Services helps non-English speakers get licenses and other services with two-way real-time translation.
• Pepperdine University has students and faculty who speak many languages, and with Gemini in Google Meet, they can benefit from real-time translated captioning and notes.
• Sutherland , a leading digital transformation company, is focused on bringing together human expertise and AI, including boosting its client-facing teams by automatically surfacing suggested responses and automating insights in real time.
• Target uses Google Cloud to power AI solutions on the Target app and Target.com, including personalized Target Circle offers and Starbucks at Drive Up, their curbside pickup solution.
• Tokopedia , an Indonesian ecommerce leader, is using Vertex AI to improve data quality, increasing unique products being sold by 5%.
• US News saw a double-digit impact in key metrics like click-through rate, time spent on page, and traffic volume to its pages after implementing Vertex AI Search.
• IntesaSanpaolo , Macquarie Bank , and Scotiabank are exploring the potential of gen AI to transform the way we live, work, bank, and invest — particularly how the new technology can boost productivity and operational efficiency in banking. Watch the session to learn more.

Employee agents help workers be more productive and collaborate better together. These agents can streamline processes, manage repetitive tasks, answer employee questions, as well as edit and translate critical communications.

• Avery Dennison empowered their employees with generative AI to enable secure, flexible, and borderless collaboration for enhanced productivity to drive growth.
• Bank of New York Mellon built a virtual assistant to help employees find relevant information and answers to their questions. Watch the session to learn more.
• Bayer is building a radiology platform that will assist radiologists with data analysis, intelligent search, and to create documents that meet healthcare requirements needed for regulatory approval. The bioscience company is also harnessing BigQuery and Vertex AI to develop additional digital medical solutions and drugs more efficiently.
• Bristol Myers Squibb is transforming its document processes for clinical trials using Vertex AI and Google Workspace. Now, documentation that took scientists weeks now gets to a first draft in minutes. Watch the session to learn more.
• BenchSci develops generative AI solutions empowering scientists to understand complex connections in biological research, saving them time and financial resources and ultimately bringing new medicine to patients faster.
• Cintas is using Vertex AI Search to develop an internal knowledge center for customer service and sales teams to easily find key information.
• Covered California , the state’s healthcare marketplace, is using Document AI to help improve the consumer and employee experience by automating parts of the documentation and verification process when residents apply for coverage. Watch the session to learn more.
• Dasa , the largest medical diagnostics company in Brazil, is helping physicians detect relevant findings in test results more quickly.
• DaVita leverages DocAI and Healthcare NLP to transform kidney care, including analyzing medical records, uncovering critical patient insights, and reducing errors. AI enables physicians to focus on personalized care, resulting in significant improvements in healthcare delivery.
• Discover Financial helps their 10,000 contact center representatives to search and synthesize information across detailed policies and procedures during calls. Watch the session to learn more.
• HCA Healthcare is testing Cati, a virtual AI caregiver assistant that helps to ensure continuity of care when one caregiver shift ends and another begins. They are also using gen AI to improve workflows on time-consuming tasks, such as clinical documentation, so physicians and nurses can focus more on patient care.
• The Home Depot has built an application called Sidekick, which helps store associates manage inventory and keep shelves stocked; notably, vision models help associates prioritize which actions to take.
• Los Angeles Rams are utilizing AI across the board from content analysis to player scouting.
• McDonald’s will leverage data, AI, and edge technologies across its thousands of restaurants to implement innovation faster and to enhance employee and customer experiences.
• Pennymac , a leading US-based national mortgage lender, is using Gemini across several teams including HR, where Gemini in Docs, Sheets, Slides and Gmail is helping them accelerate recruiting, hiring, and new employee onboarding.
• Robert Bosch , the world's largest automotive supplier, revolutionizes marketing through gen AI-powered solutions, streamlining processes, optimizing resource allocation, and maximizing efficiency across 100+ decentralized departments. Watch the session to learn more.
• Symphony , the communications platform for the financial services industry, uses Vertex AI to help finance and trading teams collaborate across multiple asset classes.
• Uber is using AI agents to help employees be more productive, save time, and be even more effective at work. For customer service representatives, they’ve launched new tools that summarize communications with users and can even surface context from previous interactions, so front-line staff can be more helpful and effective. Watch the session to learn more.
• The U.S. Dept. of Veterans Affairs is using AI at the edge to improve cancer detection for service members and veterans. The Augmented Reality Microscope (ARM) is deployed at remote military treatment facilities around the world. The prototype device is helping pathologists find cancer faster and with better accuracy.
• The U.S. Patent and Trademark Office has improved the quality and efficiency of their patent and trademark examination process by implementing AI-driven technologies.
• Verizon is using generative AI to help teams in network operations and customer experience get the answers they need faster. Watch the session to learn more.
• Victoria’s Secret is testing AI-powered agents to help their in-store associates find information about product availability, inventory, and fitting and sizing tips, so they can better tailor recommendations to customers.
• Vodafone uses Vertex AI to search and understand specific commercial terms and conditions across more than 10,000 contracts with more than 800 communications operators.
• WellSky is integrating Google Cloud's healthcare and Vertex AI capabilities to reduce the time spent completing documentation outside work hours. Watch the session to learn more.
• Woolworths , the leading retailer in Australia, boosts employees’ confidence in communications with “Help me write” across Google Workspace products for more than 10,000 administrative employees. It’s also using Gemini to create next-generation promotions, as well as for quickly assisting customer service reps in summarizing all previous customer interactions in real time.
• Box , Typeface , Glean , CitiBank , and Securiti AI discuss developing AI-powered apps across the enterprise, with measurable returns on investment for marketing, financial services, and HR use cases. Watch the session to learn more.
• Highmark Health and Freenome join Bristol Myers Squibb to explore how AI can improve efficiency and innovation across care delivery, drug discovery, clinical trial planning, and bringing medicines to market.

Creative agents can expand your organization with the best design and production skills, working across images, slides, and exploring concepts with workers. Many organizations are building agents for their marketing teams, audio and video production teams, and all the creative people that can use a hand. With creative agents, anyone can become a designer, artist, or producer.

• Belk ECommerce is using generative AI to craft better product descriptions, a necessary yet time-consuming task for digital retails that has often been done manually.
• Canva is using Vertex AI to power its Magic Design for Video, helping users skip tedious editing steps while creating shareable and engaging videos in a matter of seconds.
• Carrefour used Vertex AI to deploy Carrefour Marketing Studio in just five weeks — an innovative solution to streamline the creation of dynamic campaigns across various social networks. In just a few clicks, marketers can build ultra-personalized campaigns to deliver customers advertising that they care about.
• Major League Baseball continues to innovate its Statcast platform, so teams, broadcasters, and fans have access to live in-game insights.
• Paramount currently relies on manual processes to create the essential metadata and video summaries used across its Paramount+ platform for showcasing content and creating personalized experiences for viewers. VertexAI Text Bison is now helping to streamline this process. Watch the session to learn more.
• Procter & Gamble used Imagen to develop an internal gen AI platform to accelerate the creation of photo-realistic images and creative assets, giving marketing teams more time to focus on high-level planning and delivering superior experiences for its consumers.
• WPP will integrate Google Cloud’s gen AI capabilities into its intelligent marketing operating system, called WPP Open, which empowers its people and clients to deliver new levels of personalization, creativity, and efficiency. This includes the use of Gemini 1.5 Pro models to supercharge both the accuracy and speed of content performance predictions.

Data agents are like having knowledgeable data analysts and researchers at your fingertips. They can help answer questions about internal and external sources, synthesize research, develop new models — and, best of all, help find the questions we haven’t even thought to ask yet, and then help get the answers.

• AI21 Labs offers a BigQuery integration called Contextual Answers that allows users to query data conversationally and get high-quality answers quickly
• Anthropic has partnered with Google Cloud to offer its family of Claude 3 models on Vertex AI — providing organizations with more model options for intelligence, speed, cost-efficiency, and vision for enterprise use cases.
• The Asteroid Institute is using AI to discover hidden asteroids in existing astronomical data. This is a major focus for astronomers researching the evolution of the Solar System, investors and businesses hoping to fly missions to asteroids, and for all of us who want to prevent future large asteroid impacts on Earth. Watch the session to learn more.
• Contextual is working with Google Cloud to offer enterprises fully customizable, trustworthy, privacy-aware AI grounded in internal knowledge bases.
• Cox 2M , the commercial IoT division of Cox Communications, is able to make smarter, faster business decisions using AI-powered analytics. Watch the session to learn more.
• Essential AI , a developer of enterprise AI solutions, is using Google Cloud’s AI-optimized TPU v5p accelerator chips to train its own AI models.
• Generali Italia, Italy's largest insurance provider, used Vertex AI to build a model evaluation pipeline that helps ML teams quickly evaluate performance and deploy models.
• Globo , one of Brazil’s largest media networks, is using Service Extensions and Media CDN to fight piracy during live events by blocking pirated streams in real time. Watch the session to learn more.
• Hugging Face is collaborating with Google across open science, open source, cloud, and hardware to enable companies to build their own AI with the latest open models from Hugging Face and Google Cloud hardware and software. Watch the session to learn more.
• Kakao Brain , part of Korean technology company Kakao Group, has built a large-scale AI language model that is the largest Korean language-specific LLM in the market, with 66 billion parameters. They’ve also developed a text-to-image generator called Karlo. Watch the session to learn more.
• Mayo Clinic has given thousands of its scientific researchers access to 50 petabytes worth of clinical data through Vertex AI search, accelerating information retrieval across multiple languages. Watch the session to learn more.
• McLaren Racing is using Google AI to get up-to-the-millisecond insights during races and training to gain a competitive edge.
• Mercado Libre is testing BigQuery and Looker to optimize capacity planning and reservations with delivery carriers and airlines to fulfill shipments faster. Watch the session to learn more.
• Mistral AI will use Google Cloud's AI-optimized infrastructure, to further test, build, and scale up its LLMs, all while benefiting from Google Cloud's security and privacy standards.
• MSCI uses machine learning with Vertex AI, BigQuery and Cloud Run to enrich its datasets to help our clients gain insight into around 1 million asset locations to help manage climate-related risks.
• NewsCorp is using Vertex AI to help search data across 30,000 sources and 2.5 billion news articles updated daily.
• Orange operates in 26 countries where local data must be kept in each country. They are using AI on Google Distributed Cloud to improve network performance and deliver super-responsive translation capabilities. Watch session to learn more.
• Spotify leveraged Dataflow for large-scale generation of ML podcast previews, and they plan to keep pushing the boundaries of what’s possible with data engineering and data science to build better experiences for their customers and creators. Watch session to learn more.
• UPS is building a digital twin of its entire distribution network, so both workers and customers can see where their packages are at any time.
• Workday is using natural language processing in Vertex Search and Conversation to make data insights more accessible for technical and non-technical users alike. Watch the session to learn more.
• Woven — Toyota 's investment in the future of mobility — is partnering with Google to leverage vast amounts of data and AI to enable autonomous driving, supported by thousands of ML workloads on Google Cloud’s AI Hypercomputer. This has resulted in resulting in 50% total-cost-of-ownership savings to support automated driving.
• Broward County, Florida , and Southern California Edison are using geospatial capabilities and AI to improve infrastructure planning and monitoring, generate new insights, and create regional resilience for communities facing climate challenges today and tomorrow.
• Kinaxis and Dematic are building data-driven supply chains to address logistics use cases including scenario modeling, planning, operations management, and automation.
• NOAA and USAID are among the U.S. government agencies using Google Cloud AI to unlock critical data insights to streamline operations and improve mission outcomes — all with an emphasis on responsible AI . Watch the session to learn more. Watch the session to learn more.

Code agents are helping developers and product teams to design, create, and operate applications faster and better, and to ramp up on new languages and code bases. Many organizations are already seeing double-digit gains in productivity, leading to faster deployment and cleaner, clearer code.

• Capgemini has been using Code Assist to improve software engineering productivity, quality, security, and developer experience, with early results showing workload gains for coding and more stable code quality. Watch the session to learn more.
• Commerzbank is enhancing developer efficiency through Code Assist's robust security and compliance features.
• Quantiphi saw developer productivity gains of more than 30% during their Code Assist pilot. Watch the session to learn more.
• Replit developers will get access to Google Cloud infrastructure, services, and foundation models via Ghostwriter, Replit's software development AI, while Google Cloud and Workspace developers will get access to Replit’s collaborative code editing platform.
• Seattle Children's hospital is using AI to boost data engineering productivity and accelerate development.
• Turing is customizing Gemini Code Assist on their private codebase, empowering their developers with highly personalized and contextually relevant coding suggestions that have increased productivity around 30 percent and made day-to-day coding more enjoyable. Watch the session to learn more.
• Wayfair piloted Code Assist, and those developers with the code agent were able to set up their environments 55 percent faster than before, there was a 48 percent increase in code performance during unit testing, and 60 percent of developers reported that they were able to focus on more satisfying work. Watch the session to learn more.

Security agents assist security operations by radically increasing the speed of investigations, automating monitoring and response for greater vigilance and compliance controls. They can also help guard data and models from cyberattacks, such as malicious prompt injection.

• BBVA uses AI in Google SecOps to detect, investigate, and respond to security threats with more accuracy, speed, and scale. The platform now surfaces critical security data in seconds, when it previously took minutes or even hours, and delivers highly automated responses.
• Behavox is using Google Cloud technology and LLMs to provide industry leading regulatory compliance and front office solutions for financial institutions globally. Watch the session to learn more.
• Charles Schwab has integrated their own intelligence into the AI-powered Google SecOps, so analysts can better prioritize work and respond to threats. Watch the session to learn more.
• Fiserv ’s security operations engineers create detections and playbooks with much less effort, while analysts get answers more quickly.
• Grupo Boticário , one of the largest beauty retail and cosmetics companies in Brazil, employs real-time security models to prevent fraud and to detect and respond to issues. Watch the session to learn more.
• Palo Alto Networks ’ Cortex XSIAM, the AI-driven security operations platform, is built on more than a decade of expertise in machine-learning models and the most comprehensive, rich, and diverse data store in the industry. Backed by Google's advanced cloud infrastructure and advanced AI services, including BigQuery and Gemini models, the combination delivers global scale and near real-time protection across all cybersecurity offerings. Watch the session to learn more.
• Pfizer can now aggregate cybersecurity data sources, cutting analysis times from days to seconds.

To find even more customers using our AI tools to build agents and solutions for their most important enterprise projects, visit the Google Cloud customer hub and watch the Next ‘24

• AI & Machine Learning

Related articles

Infinite Fleurs: Discover AI-assisted creativity in full bloom

By Christina Houghton • 5-minute read

AI at Bat: How Major League Baseball is surfacing stats faster with AI

By Rob Engel • 4-minute read

The AI startup nation: Meet five companies showing how Israel still leads in new tech

By Boaz Maoz • 6-minute read

AI at the track: How McLaren Racing leverages terabytes of data each race for a winning edge

By Dan Rowinski • 6-minute read

100+ AI Use Cases & Applications: In-Depth Guide for 2024

We adhere to clear ethical standards and follow an objective methodology . The brands with links to their websites fund our research.

AI is transforming industries and business functions, leading to growing interest in AI & its subdomains like machine learning and data science. With the launch of ChatGPT , interest in generative AI , a subfield of AI, exploded:

According to a recent McKinsey survey, 55% of organizations are using AI in at least one business function. 1 To integrate AI into your own business, possible use cases of AI for your business.

This article gathers the most common AI use cases covering marketing, sales, customer services, security, data, technology, and other processes.

Generative AI Use Cases

Generative AI involves AI models generating output in requests where there is not a single right answer (e.g. creative writing). Since the launch of ChatGPT , it has been exploding in popularity. Its use cases include content creation for marketing, software code generation, user interface design and many others.

For more: Generative AI use cases .

Business Functions

> ai use cases for analytics, general solutions.

• Analytics Platform : Empower your employees with unified data and tools to run advanced analyses. Quickly identify problems and provide meaningful insights.
• Analytics Services : Satisfy your custom analytics needs with these e2e solution providers. Vendors are there to help you with your business objectives by providing turnkey solutions.
• Automated Machine Learning (autoML) : Machines helping data scientists optimize machine learning models. With the rise of data and analytics capabilities, automation is needed in data science. AutoML automates time consuming machine learning tasks, enabling companies to deploy models and automate processes faster.

Specialized solutions

• Conversational Analytics : Use conversational interfaces to analyze your business data. Natural Language Processing is there to help you with voice data and more enabling automated analysis of reviews and suggestions.
• E-Commerce Analytics : Specialized analytics systems designed to deal with the explosion of e-commerce data. Optimize your funnel and customer traffic to maximize your profits.
• Geo-Analytics Platform : Enables analysis of granular satellite imagery for predictions. Leverage spatial data for your business goals. Capture the changes in any landscape on the fly.
• Image Recognition and Visual Analytics : Analyze visual data with advanced image and video recognition systems. Meaningful insights can be derived from the data piles of images and videos.
• Real-Time Analytics : Real-Time Analytics for your time-sensitive decisions. Act timely and keep your KPI’s intact. Use machine learning to explore unstructured data without any disruptions.

> AI use cases for Customer Service

• Call Analytics : Advanced analytics on call data to uncover insights to improve customer satisfaction and efficiency. Find patterns and optimize your results. Analyze customer reviews through voice data and pinpoint, where there is room for improvement. Sestek indicates that ING Bank observed a 15% increase in sales quality score and a 3% decrease in overall silence rates after they integrated AI into their contact systems .
• Call Classification : Leverage natural language processing (NLP) to understand what the customer wants to achieve so your agents can focus on higher value-added activities. Before channeling the call, identify the nature of your customers’ needs and let the right department handle the problem. Increase efficiency with higher satisfaction rates.
• Call Intent Discovery : Leverage Natural Language Processing and machine learning to estimate and manage customer’s intent (e.g., churn) to improve customer satisfaction and business metrics. For example, analyzing customer sentiment through voice level and pitch can help detect the micro-emotions that drive the decision-making process. Explore how chatbots detect customer intent in our in-depth article on intent recognition .
• Chatbot for Customer Service (Self – Service Solution) : Chatbots can understand more complicated queries as AI algorithms improve. Build your own 24/7 functioning, intelligent, self-improving chatbots to handle most queries and transfer customers to live agents when needed. Reduce customer service costs and increase customer satisfaction. Reduce the traffic on your existing customer representatives and make them focus on the more specific needs of your customers. Read for more insights on chatbots in customer service or discover chatbot platforms .
• Chatbot Analytics : Analyze how customers are interacting with your chatbot. See the overall performance of your chatbot. Pinpoint its shortcomings and improve your chatbot. Detect the overall satisfaction rate of your customer with the chatbot.
• Chatbot testing : Semi-automated and automated testing frameworks facilitate bot testing. See the performance of your chatbot before deploying. Save your business from catastrophic chatbot failures. Detect the shortcomings of your conversational flow.
• Customer Contact Analytics : Advanced analytics on all customer contact data to uncover insights to improve customer satisfaction and efficiency. Utilize natural language processing (NLP) for higher customer satisfaction rates.
• Customer Service Response Suggestions : Bots will listen in on agents’ calls suggesting best practice answers to improve customer satisfaction and standardize customer experience. Increase upsells and cross-sells by giving the right suggestion. Responses will be standardized, and the best possible approach will serve the benefit of the customer.
• Social Listening & Ticketing : Leverage Natural Language Processing and machine vision to identify customers to contact and respond to them automatically or assign them to relevant agents, increasing customer satisfaction. Use the data available in social networks to uncover whom to sell and what to sell.
• Intelligent Call Routing : Route calls to the most capable agents available. Intelligent routing systems incorporate data from all customer interactions to optimize the customer satisfaction. Based on the customer profile and your agent’s performance, you can deliver the right service with the right agent and achieve superior net promoter scores. Feel free to read case studies about matching customer to right agent in our emotional AI examples article .
• Survey & Review Analytics : Leverage Natural Language Processing (NLP) to analyze text fields in surveys and reviews to uncover insights to improve customer satisfaction. Automate the process by mapping the right keywords with the right scores. Make it possible to lower the time for generating reports. Protobrand states that they used to do review analytics manually through the hand-coding of the data, but now it automates much of the analytical work with Gavagai. This helps the company to collect larger quantitative volumes of qualitative data and still complete the analytical work in a timely and efficient manner. You can read more about survey analytics from  our related article .
• Voice Authentication : Authenticate customers without passwords leveraging biometry to improve customer satisfaction and reduce issues related to forgotten passwords. Their unique voice id will be their most secure key for accessing confidential information. Instead of the last four digits of SSN, customers will gain access by using their voice.

> AI use cases for Cybersecurity

Data loss prevention (DLP) software leverage AI technologies to achieve

• Real time detection of sensitive data beyond those identified using rules-based approached
• Intelligent access control learning from allowed data access patterns to reduce false positives

For more, see best practices for using AI in DLP .

Network monitoring

Typical use cases include:

• Anomaly detection in network traffic to identify cyberattacks
• Automated network optimization to manage peak loads at optimal cost without harming user experience.

For real-life examples: AI in network monitoring

> AI use cases for Data

• Data Cleaning & Validation Platform : Avoid garbage in, garbage out by ensuring the quality of your data with appropriate data cleaning processes and tools. Automate the validation process by using external data sources. Regular maintenance cleaning can be scheduled, and the quality of the data can be increased.
• Data Integration : Combine your data from different sources into meaningful and valuable information. Data traffic depends on multiple platforms. Therefore, managing this huge traffic and structuring the data into a meaningful format will be important. Keep your data lake available for further analysis. 
• Data Management & Monitoring : Keep your data high quality for advanced analytics. Adjust the quality by filtering the incoming data. Save time by automating manual and repetitive tasks.
• Data Preparation Platform : Prepare your data from raw formats with data quality problems to a clean, ready-to-analyze format. Use extract, transform, and load (ETL) platforms to fine-tune your data before placing it into a data warehouse.
• Data Transformation : Transform your data to prepare it for advanced analytics. If it is unstructured, adjust it for the required format.
• Data Visualization : Visualize your data for better analytics and decision-making. Let the dashboards speak. Convey your message more easily and more esthetically.
• Data Labeling : Unless you use unsupervised learning systems, you need high quality labeled data. Label your data to train your supervised learning systems. Human-in-the-loop systems auto label your data and crowdsource labeling data points that cannot be auto-labeled with confidence.
• Synthetic Data :  Computers can artificially create synthetic data to perform certain operations. The synthetic data is usually used to test new products and tools, validate models, and satisfy AI needs. Companies can simulate not yet encountered conditions and take precautions accordingly with the help of synthetic data. They also overcome the privacy limitations as it doesn’t expose any real data. Thus, synthetic data is a smart AI solution for companies to simulate future events and consider future possibilities. You can have more information on synthetic data from  our related article .

> AI use cases for Finance

Finance business function led by the CEO completes numerous repetitive tasks involving quantitative skills which makes them a good fit for AI transformation:

• Billing / invoicing reminders : Leverage accessible billing services that remind your customers to pay with generative AI powered messages.
• Blackbaud AP automation
• Dynamics AP automation
• NetSuite AP automation
• SAGE AP automation

For more, see AI use cases in AP automation .

> AI use cases for HR

• Employee Monitoring : Monitor your employees for better productivity measurement. Provide objective metrics to see how well they function. Forecast their overall performance with the availability of massive amounts of data.
• Hiring :  Hiring is a prediction game: Which candidate, starting at a specific position, will contribute more to the company? Machine and recruiting chatbots ‘ better data processing capabilities augment HR employees in various parts of hiring such as finding qualified candidates, interviewing them with bots to understand their fit or evaluating their assessment results to decide if they should receive an offer. 
• HR Analytics : HR analytics services are like the voice of employee analysis. Look at your workforce analytics and make better HR decisions. Gain actionable insights and impactful suggestions for higher employee satisfaction.
• HR Retention Management : Predict which employees are likely to churn and improve their job satisfaction to retain them. Detect the underlying reasons for their motive for seeking new opportunities. By keeping them at your organization, lower your human capital loss.
• Performance Management : Manage your employees’ performance effectively and fairly without hurting their motivation. Follow their KPI’s on your dashboard and provide real-time feedback. This would increase employee satisfaction and lower your organization’s employee turnover. Actualize your employee’s maximum professional potential with the right tools.

You can also read our article on HR technology trends .

> AI use cases for Marketing

A 2021 survey conducted among global marketers revealed that 41% of respondents saw an increase in revenue growth and improved performance due to the use of AI in their marketing campaigns.

Marketing can be summarized as reaching the customer with the right offer, the right message, at the right time, through the right channel, while continually learning. To achieve success, companies can leverage AI-powered tools to get familiar with their customers better, create more compelling content, and perform personalized marketing campaigns. AI can provide accurate insights and suggest smart marketing solutions that would directly reflect on profits with customer data. You can find the top three AI use cases in marketing:

• Marketing analytics :  AI systems learn from, analyze, and measure marketing efforts. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. As a result, companies can provide better and more accurate marketing services to their customers. Besides PR efforts, AI-powered marketing analytics can lead companies to identify their customer groups more accurately. By discovering their loyal customers, companies can develop accurate marketing strategies and also retarget customers who have expressed interest in products or services before. Feel free to read more about marketing analytics with AI from  this article .
• Personalized Marketing:  The more companies understand their customers, the better they serve them. AI can assist companies in this task and support them in giving personalized experiences for customers. As an example, suppose you visited an online store and looked at a product but didn’t buy it. Afterward, you see that exact product in digital ads. More than that, companies can send personalized emails or special offers and recommend new products that go along with customers’ tastes.
• Context-Aware Marketing : You can leverage machine vision and natural language processing (NLP) to understand the context where your ads will be served. With context-aware advertising, you can protect your brand and increase marketing efficiency by ensuring your message fits its context, making static images on the web come alive with your messages.

For more, check out AI use cases in marketing or AI for email marketing . AI-powered email marketing software is among the first AI tools that marketers should work with.

> AI use cases for Operations

• Cognitive / Intelligent Automation : Combine robotic process automation (RPA) with AI to automate complex processes with unstructured information. Digitize your processes in weeks without replacing legacy systems , which can take years. Bots can operate on legacy systems learning from your personnel’s instructions and actions. Increase your efficiency and profitability ratios. Increase speed and precision, and many more. Feel free to check intelligent automation use cases for more.
• Robotic Process Automation (RPA) Implementation : Implementing RPA solutions requires effort. Suitable processes need to be identified. If a rules-based robot will be used, the robot needs to be programmed. Employees’ questions need to be answered. That is why most companies get some level of external help. Generally, outsourcing companies, consultants, and IT integrators are happy to provide temporary labor to undertake this effort.
• Process Mining : Leverage AI algorithms to mine your processes and understand your actual processes in detail. Process mining tools can provide fastest time to insights about your as-is processes as demonstrated in case studies . Check out process mining use cases & benefits for more.
• Predictive Maintenance : Predictively maintain your robots and other machinery to minimize disruptions to operations. Implement big data analytics to estimate the factors that are likely to impact your future cash flow. Optimize PP&E spending by gaining insight regarding the possible factors.
• Inventory & Supply Chain Optimization : Leverage machine learning to take your inventory& supply chain optimization to the next level. See the possible scenarios in different customer demands. Reduce your stock, keeping spending, and maximize your inventory turnover ratios. Increase your impact factor in the value chain.
• Building Management : Sensors and advanced analytics improve building management. Integrate IoT systems in your building for lower energy consumption and many more. Increase the available data by implementing the right data collection tools for effective building management.
• Digital Assistant : Digital assistants are mature enough to replace real assistants in email communication. Include them in your emails to schedule meetings. They have already scheduled hundreds of thousands of meetings.

> AI use cases for Sales

• Sales Forecasting :  AI allows automatic and accurate sales forecasts based on all customer contacts and previous sales outcomes. Automatically forecast sales accurately based on all customer contacts and previous sales outcomes. Give your sales personnel more sales time while increasing forecast accuracy. Hewlett Packard Enterprise indicates that it has experienced a 5x increase in forecast simplicity, speed, and accuracy with Clari’s sales forecasting tools.
• Lead generation :  Use a comprehensive data profile of your visitors to identify which companies your sales reps need to connect. Generate leads for your sales reps leveraging databases and social networks
• Sales Data Input Automation: Data from various sources will be effortlessly and intelligently copied into your CRM. Automatically sync calendar, address book, emails, phone calls, and messages of your salesforce to your CRM system. Enjoy better sales visibility and analytics while giving your sales personnel more sales time.
• Predictive sales/lead scoring: Use AI to enable predictive sales. Score leads to prioritize sales rep actions based on lead scores and contact factors. Sales forecasting is automated with increased accuracy thanks to systems’ granular access to lead scores and sales rep performance. For scoring leads, these systems leverage anonymized transaction data from their customers, sales data of this specific customer. For assessing contact factors, these systems leverage anonymized data and analyze all customer contacts such as email and calls.
• Sales Rep Response Suggestions: AI will suggest responses during live conversations or written messages with leads. Bots will listen in on agents’ calls suggesting best practice answers to improve sales effectiveness
• Sales Rep Next Action Suggestions : Your sales reps’ actions and leads will be analyzed to suggest the next best action. This situation wise solution will help your representatives to find the right way to deal with the issue. Historical data and profile of the agent will help you to achieve higher results. All are leading to more customer satisfaction.
• Sales Content Personalization and Analytics: Preferences and browsing behavior of high priority leads are analyzed to match them with the right content, aimed to answer their most important questions. Personalize your sales content and analyze its effectiveness allowing continuous improvement.
• Retail Sales Bot : Use bots on your retail floor to answer customer’s questions and promote products. Engage with the right customer by analyzing the profile. Computer vision will help you to provide the right action depending on the characteristics and mimics of the customer.
• Meeting Setup Automation (Digital Assistant): Leave a digital assistant to set up meetings freeing your sales reps time. Decide on the targets to prioritize and keep your KPI’s high.
• Prescriptive Sales : Most sales processes exist in the mind of your sales reps. Sales reps interact with customers based on their different habits and observations. Prescriptive sales systems prescribe the content, interaction channel, frequency, price based on data on similar customers .
• Sales Chatbot : Chatbots are ideal to answer first customer questions. If the chatbot decides that it can not adequately serve the customer, it can pass those customers to human agents. Let 24/7 functioning, intelligent, self-improving bots handle making initial contacts to leads. High value, responsive leads will be called by live agents, increasing sales effectiveness.

Sales analytics

As Gartner discusses , sales analytic systems provide functionality that supports discovery, diagnostic, and predictive exercises that enable the manipulation of parameters, measures, dimensions, or figures as part of an analytic or planning exercise. AI algorithms can automate the data collection process and present solutions to improve sales performance. To have more detailed information, you can read  our article about sales analytics .

• Customer Sales Contact Analytics :  Analyze all customer contacts, including phone calls or emails, to understand what behaviors and actions drive sales. Advanced analytics on all sales call data to uncover insights to increase sales effectiveness
• Sales Call Analytics : Advanced analytics on call data to uncover insights to increase sales effectiveness. See how well your conversation flow performs. Integrating data on calls will help you to identify the performance of each component in your sales funnels.
• Sales attribution :  Leverage big data to attribute sales to marketing and sales efforts accurately. See which step of your sales funnel performs better. Pinpoint the low performing part by the insights provided by analysis.
• Sales Compensation :  Determine the right compensation levels for your sales personnel. Decide on the right incentive mechanism for the sales representatives. By using the sales data, provide objective measures, and continuously increase your sales representatives’ performance.

For more on AI in sales .

> AI use cases for Strategy & Legal

• Presentation preparation : Top management presentations in most companies involve slides (e.g. PowerPoint). Generative AI presentation software can prepare slides from prompts.

Legal counsels can rely on AI in:

• Contract drafting
• Contract review
• Legal research

For more: Legal AI software

> AI use cases for Tech

• No code AI & app development : AI and App development platforms for your custom projects. Your in-house development team can create original solutions for your specific business needs.
• Analytics & Predictive Intelligence for Security : Analyze data feeds about the broad cyber activity as well as behavioral data inside an organization’s network to come up with actionable insights to help analysts predict and thwart impending attacks. Integrate external data sources the watch out for global cyber threats and act timely. Keep your tech infrastructure intact or minimize losses. 
• Knowledge Management : Enterprise knowledge management enables effective and effortless storage and retrieval of enterprise data, ensuring organizational memory. Increased collaboration by ensuring the right people are working with the right data. Seamless organizational integration through knowledge management platforms.
• Natural Language Processing Library/ SDK/ API : Leverage Natural Language Processing libraries/SDKs/APIs to quickly and cost-effectively build your custom NLP powered systems or to add NLP capabilities to your existing systems. An in-house team will gain experience and knowledge regarding the tools. Increased development and deployment capabilities for your enterprise.
• Image Recognition Library/ SDK/ API :  Leverage image recognition libraries/SDKs/APIs to quickly and cost-effectively build your custom image processing systems or to add image processing capabilities to your existing systems.
• Secure Communications : Protect employee communications like emails or phone conversations with advanced multilayered cryptography & ephemerality. Keep your industry secrets safe from corporate espionage.
• Deception Security : Deploy decoy-assets in a network as bait for attackers to identify, track, and disrupt security threats such as advanced automated malware attacks before they inflict damage. Keep your data and traffic safe by keeping them engaged in decoys. Enhance your cybersecurity capabilities against various forms of cyber attacks
• Autonomous Cybersecurity Systems : Utilize learning systems to efficiently and instantaneously respond to security threats, often augmenting the work of security analysts. Lower your risk of human errors by providing greater autonomy for your cybersecurity. AI-backed systems can check compliance with standards.
• Smart Security Systems : AI-powered autonomous security systems. Functioning 24/7 for achieving maximum protection. Computer vision for detecting even the tiniest anomalies in your environment. Automate emergency response procedures by instant notification capabilities.
• Machine Learning Library/ SDK/ API : Leverage machine learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your existing systems.
• AI Developer : Develop your custom AI solutions with companies experienced in AI development. Create turnkey projects and deploy them to the specific business function. Best for companies with limited in-house capabilities for artificial intelligence.
• Deep Learning Library/ SDK/ API : Leverage deep learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your existing systems.
• Developer Assistance : Assist your developers using AI to help them intelligently access the coding knowledge on the web and learn from suggested code samples. See the best practices for specific development tasks and formulate your custom solution. Real-time feedback provided by the huge history of developer mistakes and best practices.
• AI Consultancy : Provides consultancy services to support your in-house AI development, including machine learning and data science projects. See which units can benefit most from AI deployment. Optimize your artificial intelligence spending for the best results from the insight provided by a consultant.

> AI use cases for Automotive & Autonomous Things

Autonomous things including cars and drones are impacting every business function from operations to logistics.

• Driving Assistant : Required components and intelligent solutions to improve rider’s experience in the car. Implement AI-Powered vehicle perception solutions for the ultimate driving experience.
• Vehicle Cybersecurity : Secure connected and autonomous cars and other vehicles with intelligent cybersecurity solutions. Guarantee your safety by hack-proof mechanisms. Protect your intelligent systems from attacks.
• Vision Systems : Vision systems for self-driving cars. Integrate vision sensing and processing in your vehicle. Achieve your goals with the help of computer vision.
• Self-Driving Cars : From mining to manufacturing, self-driving cars/vehicles are increasing the efficiency and effectiveness of operations. Integrate them into your business for greater efficiency. Leverage the power of artificial intelligence for complex tasks.

> AI use cases for Education

• Course creation

For more: Generative AI applications in education

> AI use cases for Fashion

• Creative Design
• Virtual try-on
• Trend analysis

For more: Generative AI applications in fashion

> AI use cases for FinTech 

• Fraud Detection : Leverage machine learning to detect fraudulent and abnormal financial behavior, and/or use AI to improve general regulatory compliance matters and workflows. Lower your operational costs by limiting your exposure to fraudulent documents.
• Insurance & InsurTech : Leverage machine learning to process underwriting submissions efficiently and profitably, quote optimal prices , manage claims effectively, and improve customer satisfaction while reducing costs. Detect your customer’s risk profile and provide the right plan.
• Financial Analytics Platform : Leverage machine learning, Natural Language Processing, and other AI techniques for financial analysis, algorithmic trading, and other investment strategies or tools.
• Travel & expense management : Use deep learning to improve data extraction from receipts of all types including hotel, gas station, taxi, grocery receipts. Use anomaly detection and other approaches to identify fraud, non-compliant spending. Reduce approval workflows and processing costs per unit.
• Credit Lending & Scoring : Use AI for robust credit lending applications. Use predictive models to uncover potentially non-performing loans and act. See the potential credit scores of your customers before they apply for a loan and provide custom-tailored plans.
• Loan recovery: Increase loan recovery ratios with empathetic and automated messages.
• Robo-Advisory : Use AI finance chatbot and mobile app assistant applications to monitor personal finances. Set your target savings or spending rates for your own goals. Your finance assistant will handle the rest and provide you with insights to reach financial targets.
• Regulatory Compliance : Use Natural Language Processing to quickly scan legal and regulatory text for compliance issues, and do so at scale. Handle thousands of paperwork without any human interaction.
• Data Gathering : Use AI to efficiently gather external data such as sentiment and other market-related data. Wrangle data for your financial models and trading approaches.
• Debt Collection : Leverage AI to ensure a compliant and efficient debt collection process. Effectively handle any dispute and see your success right in debt collection.
• Conversational banking : Financial institutions engage with their customers on a variety of communication platforms ( WhatsApp , mobile app , website etc.) via conversational AI tools to increase customer satisfaction and automate many tasks like customer onboarding .

> AI use cases for HealthTech

• Patient Data Analytics : Analyze patient and/or 3rd party data to discover insights and suggest actions. Greater accuracy by assisted diagnostics. Lower the mortality rates and increase patient satisfaction by using all the diagnostic data available to detect the underlying reasons for the symptoms.
• Personalized Medications and Care : Find the best treatment plans according to patient data. Provide custom-tailored solutions for your patients. By using their medical history, genetic profile, you can create a custom medication or care plan.
• Drug Discovery : Find new drugs based on previous data and medical intelligence. Lower your R&D cost and increase the output — all leading to greater efficiency. Integrate FDA data, and you can transform your drug discovery by locating market mismatches and FDA approval or rejection rates.
• Real-Time Prioritization and Triage : Prescriptive analytics on patient data enabling accurate real-time case prioritization and triage. Manage your patient flow by automatization. Integrate your call center and use language processing tools to extract the information, priorate patients that need urgent care, and lower your error rates. Eliminate error-prone decisions by optimizing patient care.
• Early Diagnosis : Analyze chronic conditions leveraging lab data and other medical data to enable early diagnosis. Provide a detailed report on the likelihood of the development of certain diseases with genetic data. Integrate the right care plan for eliminating or reducing the risk factors.
• Assisted or Automated Diagnosis & Prescription :  Suggest the best treatment based on the patient complaint and other data. Put in place control mechanisms that detect and prevent possible diagnosis errors. Find out which active compound is most effective against that specific patient. Get the right statistics for superior care management.
• Pregnancy Management : Monitor mother and fetus health to reduce mothers’ worries and enable early diagnosis. Use machine learning to uncover potential risks and complications quickly. Lower the rates of miscarriage and pregnancy-related diseases.
• Medical Imaging Insights : Advanced medical imaging to analyze and transform images and model possible situations. Use diagnostic platforms equipped with high image processing capabilities to detect possible diseases.
• Healthcare Market Research : Prepare hospital competitive intelligence by tracking market prices. See the available insurance plans, drug prices, and many more public data to optimize your services. Leverage NLP tools to analyze the vast size of unstructured data.
• Healthcare Brand Management and Marketing : Create an optimal marketing strategy for the brand based on market perception and target segment. Tools that offer high granularity will allow you to reach the specific target and increase your sales.
• Gene Analytics and Editing : Understand genes and their components and predict the impact of gene edits.
• Device and Drug Comparative Effectiveness : Analyze drug and medical device effectiveness. Rather than just using simulations, test on other patient’s data to see the effectiveness of the new drug, compare your results with benchmark drugs to make an impact with the drug.
• Healthcare chatbot :  Use a chatbot to schedule patient appointments, give information about certain diseases or regulations, fill in patient information, handle insurance inquiries, and provide mental health assistance. You can also use intelligent automation with chatbot capabilities.

For more, feel free to check our article on the  use cases of AI in the healthcare industry .

> AI use cases for Manufacturing

• Manufacturing Analytics : Also called industrial analytics systems, these systems allow you to analyze your manufacturing process from production to logistics to save time, reduce cost, and increase efficiency. Keep your industry effectiveness at optimal levels.
• Collaborative Robots : Cobots provide a flexible method of automation. Cobots are flexible robots that learn by mimicking human workers’ behavior.
• Robotics : Factory floors are changing with programmable collaborative bots that can work next to employees to take over more repetitive tasks. Automate physical processes such as manufacturing or logistics with the help of advanced robotics. Increased your connected systems by centralizing the whole manufacturing process. Lower your exposures to human errors.

> AI use cases for Non-Profits

• Personalized donor outreach and engagement based on historical data to increase fundraising levels while avoiding email fatigue.
• Donor identification via techniques like look-alike audiences.

See more use cases of AI in fundraising .

> AI use cases for Retail

• Cashierless Checkout : Self-checkout systems have many names. They are called cashierless, cashier-free, or automated checkout systems. They allow retail companies to serve customers in their physical stores without the need for cashiers. Technologies that allowed users to scan and pay for their products have been used for almost a decade now, and those systems did not require great advances in AI. However, these days we are witnessing systems powered by advanced sensors and AI to identify purchased merchandise and charge customers automatically.

> AI use cases for Telecom

• Network investment optimization : Both wired and wireless operators need to invest in infrastructure like active equipment or higher bandwidth connections to improve Quality of Service (QoS). Machine learning can be used to identify highest ROI investments that will result in less churn and higher cross and up-sell.

Other AI Use Cases

This was a list of areas by business function where out-of-the-box solutions are available. However, AI, like software, has too many applications to list here. You can also take a look at our  AI in business article  to read about AI applications by industry. Also, feel free to check our article on AI services .

It is important to get started fast with high impact applications and generate business value without spending months of effort. For that, we recommend companies to use no code AI solutions to quickly build AI models .

Once companies deploy a few models to production, they need to take a deeper look at their AI/ML development model.

• rely on autoML software to build complex AI models. Though most autoML software is not as easy to use as no code AI solutions, they can be used to build complex models.
• build custom AI solutions in-house
• work with the support of partners to build custom models
• run data science competitions to build custom AI models
• Use pre-trained models built by AI vendors

We examined the pros and cons of this approaches in our article on making the build or buy decisions regarding AI .

You can also check out our list of AI tools and services:

• AI Consultant
• AI/ML Development Services
• Data Science / ML / AI Platform

These articles about AI may also interest you:

• Ultimate Guide to the State of AI technology
• Future of AI according to top AI experts
• Advantages of AI according to top practitioners

What is artificial intelligence (AI)?

Artificial Intelligence (AI) is the branch of computer science that focuses on creating machines capable of performing tasks that typically require human intelligence. This includes activities such as learning, problem-solving, understanding natural language, speech recognition, and visual perception. AI systems can analyze large amounts of data, identify patterns, and make decisions, often with speed and accuracy surpassing human capabilities.

What are the examples of AI in real life?

Artificial Intelligence (AI) is integrated into many aspects of daily life. Some common real-life examples include:

Virtual Assistants: Like Siri, Alexa, and Google Assistant, these AI-powered tools understand and respond to voice commands, performing tasks like setting reminders, answering questions, and controlling smart home devices.

Navigation and Maps: AI is used in services like Google Maps and Waze for route optimization, traffic prediction, and providing real-time directions.

Recommendation Systems: Streaming services like Netflix and Spotify use AI to analyze your viewing or listening history to recommend movies, shows, or music.

Autonomous Vehicles: Self-driving cars use AI to perceive the environment and make decisions for safe navigation.

Social Media: Platforms like Facebook and Instagram use AI for content curation, targeted advertising, and facial recognition in photos.

Security and Surveillance: AI aids in anomaly detection, facial recognition, and monitoring systems for enhanced security.

How does AI impact employment and job creation?

AI impacts employment by automating routine tasks, which can lead to job displacement in some sectors. However, it also creates new job opportunities in AI development, data analysis, and other tech-related fields, emphasizing the need for skill adaptation.

For more, you can check our article on the ethics of AI .

What are some misconceptions about AI?

Common misconceptions include the idea that AI can fully replicate human intelligence, that it’s always unbiased, or that AI-led automation will universally eliminate jobs. In reality, AI has limitations, can inherit biases from data, and often changes rather than replaces job roles.

And if you have a specific business challenge, we can help you find the right vendor to overcome that challenge:

External links

Though most use cases have been categorized based on our experience, we also took a look at Tractica’s AI use cases list before finalizing the list. Other sources:

• 1. “ The state of AI in 2023: Generative AI’s breakout year “. Quantum Black AI by McKinsey . August 1, 2023. Accessed January 1, 2024

Next to Read

“enterprise ai: procuring & building for ai transformation in 2024, generative ai for sales in 2024.

Your email address will not be published. All fields are required.

Good afternoon. I am very curious about your claim that “Elekta has reduced its costs and increased its number of processed invoices from 50,000 to 120,000.” Do you have the source for this claim?

Hello, Aidan. We weren’t able to find the source. So we removed it entirely. Thanks for pointing it out!

We can say that AI is the future of our world. While AI is penetrating in more and more human works, thus creating a demand of AI Industry, AI in healthcare is one of the most surging category in global AI Market. According to Meridian Market Consultants, The global AI in Healthcare Market in 2020 is estimated for more than US$ 5.0 Bn and expected to reach a value of US$ 107.5 Bn by 2028 with a significant CAGR of 47.3%. SOI:

47.3% CAGR? You are so sure about the future. Why don’t you guys just sell the time machine rather than the report?

Related research

Vertical AI / Horizontal AI & Other Specialized AI Models in 2024

AI for Businesses: Eight Case Studies and How You Can Use It

Updated: July 18, 2024

Published: August 31, 2023

Artificial intelligence has become an essential growth strategy for entrepreneurs. Almost 9 in 10 organizations believe AI will enable them to gain or sustain a competitive advantage — yet only 35% of companies currently leverage AI.

The majority of businesses leave the benefits of using AI — from optimizing research to streamlining operations — on the table. To stay competitive, entrepreneurs need to figure out how to integrate AI into their business strategy.

Table of contents:

What is AI for businesses?

What are the benefits of ai for businesses, ai for businesses case studies, ai for businesses tools.

AI for businesses involves integrating AI into a business’s strategy, mainly for tasks that require some level of human intelligence. Within a business, as examples, AI can:

• Convert speech to text for emails or memos
• Translate text for foreign markets
• Generate images from text for marketing purposes
• Solve problems, such as aggregating data to make data-driven decisions

For the most part, AI for businesses does not necessarily entail replacing a human worker with AI. Rather, professionals on all levels — from entry-level workers to C-suite executives — can use AI to improve their job performance.

“Across nearly every business function, we’re seeing AI make a major impact on business as usual,” explains Chief Content Officer at Marketing AI Institute Mark Kaput . Benefits of using AI in business include:

• Automating data-driven, repetitive tasks such as data entry
• Increasing revenue by making better predictions
• Enhancing customer experiences by providing more readily available support
• Driving growth by aggregating data and outputting highly targeted ads and marketing campaigns

Aside from more direct benefits, AI has also improved popular business tools. For example, Google Workspace uses AI to enable users to create automatic Google Docs summaries, generate text based on prompts, and more.

Additionally, as AI adoption increases (it doubled from 2017 to 2022), so does the need to leverage it to stay competitive. Almost 8 in 10 organizations believe incumbent competitors already use AI — not surprisingly since 73% of consumers are open to using AI if it makes their lives easier.

AI has been an impactful tool across different industries, from podcasts to fashion to health care.

1. Reduce time and resources needed to create podcast content

In Kaput’s content-creation business, his team leverages AI to decrease the time he spends on their weekly podcast by 75%. This involves using AI to create promotional campaign material (e.g., graphics, emails) alongside script writing.

Podcasts necessitate a human host ( most of the time ), but AI can help optimize the process of getting from idea to episode.

2. Optimize supply chain operations in the fashion industry

Retailers often deal with a significant amount of guesswork. For example, predicting what kind of clothing to stock typically requires historical data and educated guesses.

AI can streamline supply chain operations for retailers. These tools take in necessary data, such as prior inventory levels and sales performance, and predict future sales with greater accuracy.

Fast fashion retailers (e.g., H&M, Zara) have seen growths in revenue by leveraging predictive analytics driven by AI.

3. Speed up and improve accuracy of diagnoses

Physicians often use imaging as a tool to provide accurate patient diagnoses. However, images often show only one part of a larger story — requiring physicians to look into a patient’s medical history.

AI can help optimize this process. For example, at Hardin Memorial Health (HMH), doctors can use AI to bring up a summary of the patient’s medical history and highlight information relevant to the imaging.

For example, one radiologist at the hospital found a bone lesion in an image, which can have many different causes. However, AI sifted through the patient’s medical background and showed the physician the patient’s history of smoking, giving them a better idea for potential treatments.

4. Create professional videos within minutes

If your business plans on creating a video, they need to find a speaker, acquire a high-quality camera, set up a studio, and edit. This can take days to finalize, but AI has made it possible to create a professional video in less than fifteen minutes.

For instance, Synthesia offers tools that enable the creation of videos featuring 140+ realistic-looking avatars, 120+ language options, and high-quality voice-overs.

5. Provide robots with autonomous functions

AI also has many industrial applications. For instance, Built Robotics uses AI to create autonomous heavy machinery that can operate in difficult environments.

One of their robots works in solar piling, or the process of creating solid foundations to place solar panels on. This entails placing foundations on uneven terrain and working with very strict design parameters, which can take time when done manually. However, AI-driven robots can automate and speed up this process significantly.

6. Act as a personal confidant

Generative AI tools such as ChatGPT often output human-sounding text. After all, its learning comes primarily from what people post on the internet. Replika recognized the opportunity to capitalize on this potential human-adjacent relationship and launched their “AI companion who cares.”

Users can create an avatar, customize its likes and interests, and build a relationship with it. The avatar can hop on video calls and chat, interact with real-life environments via augmented reality (AR), and provide guidance to their human companions.

7. Generate mock websites in minutes

Creating a minimum viable product (MVP) often entails launching a simple website to collect user information. But not everyone can code a functional website. AI tools enable users to create mock websites without any coding skills.

For example, you can use Uizard, which outputs app, web, and user interface (UI) designs after receiving instructions in text. Users type in what kind of app or website they want with a few other design parameters. Then, Uizard gives them a design of what their idea would look like.

In this case, AI performs a number of functions, including converting screenshots to functional designs and creating UI designs via simple text. Without AI, these tasks would take hours of technical and graphical work. You can also use AI to supplement your site's content, such as by using it to create blog posts. 

8. Reduce the time and effort needed to create content for training courses

Though you can dive headfirst into AI, Kaput recommends doing thorough research before adopting new AI tools. He advises business owners to first ask themselves the following questions about their tasks:

• Is the task data-driven?
• Does the task follow a standard set of steps?
• Is the task predictive?
• Is the task generative?

If you answer yes to any of these questions, you likely have a solid starting point to integrate AI into your business. Once you understand which tasks you can apply AI to, you can look into different tools that can improve and speed up different parts of your operations.

AI has most visibly impacted marketing, with image and text tools going viral on social media. Tools can help create graphics for social media, write articles, design logos, and more. Consider using the following tools to integrate AI into your marketing:

• LogoAi : Designs logos using AI
• ChatGPT : Provides powerful text in response to prompts
• DALL·E 2 : Creates unique images in response to prompts 
• LOVO : Converts text to natural-sounding speech

AI can aid in high-level thinking, such as devising a business plan or strategy. The following tools can help validate ideas, provide useful analysis, and summarize complex information:

• VenturusAI : Analyzes business ideas for strategic planning
• Zapier : Connects apps to automated workflows

AI can be used to replace repetitive, manual tasks. Using the following tools, you can increase your productivity, speed up research, and more:

• Jamie : Automatically takes notes and creates an executive summary with action items
• Tome : Creates AI-powered presentations
• Consensus : Provides answers using insights from evidence-based research papers

What did you think of this article?

Give Feedback

Don't forget to share this post!

Related articles.

How This Content Marketer Uses AI to Be a Better Manager + Kickstart Projects

How Creators are Using AI to Drive Social Commerce

OpenAI Launches SearchGPT: What We Know + What SEO Experts are Saying

AI's Role in Protecting Consumer Data + 5 Steps Companies Can Take Today

I Ran 100s of Queries to Find Out If AI Chatbots are Better Than Search Engines [Experiment]

I Asked a Lawyer About AI Regulation in the U.S.

I Asked Experts if an AI 'SEO Heist' is Worth It

I Experimented with AI Headshots. Here's What I Learned.

What to Know About OpenAI's Rumored ChatGPT Search Engine

Real or AI-Generated? You Guess [Quiz]

Discover the key to unlocking unparalleled productivity with this ultimate guide to revolutionizing your workflow.

Marketing software that helps you drive revenue, save time and resources, and measure and optimize your investments — all on one easy-to-use platform

Where managers and leaders learn to perform at their best.

AI For Business – 30 Case Studies That Led To Competitive Advantage

Ai for business.

AI in business transformation is becoming increasingly more popular to drive innovation, efficiency, and growth. It is being utilised to automate routine tasks, provide predictive analytics , personalise the customer experience, optimise supply chain operations and improve financial and HR processes. But the biggest breakthroughs are in AI business model transformation.

Quick Links

By analysing large amounts of data, identifying patterns, and making predictions, AI is helping businesses make better decisions and stay competitive in today’s rapidly changing marketplace. As AI technology continues to evolve, new use cases will emerge, creating new opportunities for organisations to improve their operations and drive innovation.

Master The Art of AI Use Case Development

Join the world’s latest subscription service for AI and business transformation professional development. It’s time to transcend product and process upgrades and step into the world of AI and business model transformation.

What is AI?

AI stands for Artificial Intelligence , which refers to the ability of computer systems and machines to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI systems are designed to analyse and interpret large amounts of data, learn from that data, and make decisions or perform tasks based on that learning.

There are several different types of AI, including rule-based systems, machine learning , and deep learning. Rule-based systems use a set of pre-defined rules to make decisions, while machine learning algorithms are designed to learn from data and improve their performance over time. Deep learning, a subset of machine learning , is based on artificial neural networks and is used for tasks such as image recognition and natural language processing .

AI is used in a wide range of applications, including virtual assistants, autonomous vehicles, medical diagnosis, fraud detection , and recommendation systems. As the amount of data that is generated continues to increase, AI is becoming increasingly important for businesses and organisations in order to help them make more informed decisions and gain a competitive edge.

How AI for Business Matters

AI is being used in a variety of ways in business to drive efficiency, innovation, and growth. It is being used to automate routine tasks, provide predictive analytics , analyse customer data, and improve supply chain operations.

AI is also used to detect fraud, analyse financial data, and automate recruitment processes. With the development of AI technology, new use cases will continue to emerge, creating opportunities for businesses to improve their operations and drive innovation. In this article you will learn about dozens of ways in which AI is used in business.

How Can AI Help Companies?

AI has the potential to provide several benefits for large organisations, including:

Increased Efficiency

AI can help automate routine tasks, allowing employees to focus on more complex and value-adding activities. This can lead to increased productivity and efficiency, ultimately leading to cost savings for the organisation.

Improved Decision-making

AI systems can process vast amounts of data quickly and accurately, which can help organisations make better-informed decisions. By using AI to analyse data and identify patterns, organisations can gain insights into customer behaviour, market trends, and other key factors that can help them stay ahead of the competition.

Enhanced Customer Experience

AI can be used to develop personalised experiences for customers, such as chatbots that can answer customer queries in real-time, or recommendation systems that suggest products or services based on the customer’s previous behaviour. This can lead to increased customer satisfaction and loyalty.

Better Risk Management

AI can be used to identify potential risks and vulnerabilities, allowing organisations to proactively manage these risks and avoid potential problems. For example, AI can be used to detect fraud or cybersecurity threats, helping organisations to protect their assets and reputation.

AI can help organisations to develop new products and services by identifying new opportunities and predicting future trends. By using AI to analyse data and identify patterns, organisations can gain insights into emerging markets and customer needs, allowing them to develop innovative solutions that meet those needs.

AI has the potential to transform the way that large organisations operate, helping them to become more efficient, agile, and innovative. However, implementing AI requires careful planning and execution to ensure that the technology is integrated effectively and aligned with the organisation’s overall strategy and goals.

What Are The Main AI Categories?

AI can be broadly categorised into four categories:

Reactive Machines

These are the most basic types of AI systems that can only react to inputs based on pre-programmed rules. They do not have any memory or ability to learn from past experiences. Examples of reactive machines include Deep Blue, the computer program that beat Garry Kasparov in chess in 1997, and IBM Watson, which defeated human contestants on Jeopardy in 2011.

Limited Memory

These AI systems have the ability to learn from past experiences and make decisions based on that learning. They can store past experiences and use that information to make predictions and decisions. An example of a limited memory AI system is self-driving cars, which use sensors and data to navigate roads and avoid obstacles.

Theory of Mind

These AI systems have the ability to understand the mental states and emotions of other entities, such as humans or animals. They can predict behaviour based on these mental states and emotions. Theory of mind AI is still in the early stages of development, and research is ongoing to improve this type of AI.

These AI systems have consciousness and can think and learn like humans. They have the ability to understand their own existence and their place in the world. Self-aware AI is still a long way off, and research in this area is mainly theoretical at this point.

These categories of AI provide a framework for understanding the capabilities and limitations of AI systems. Each category has its own set of challenges and opportunities, and researchers and developers are working to improve AI systems in all categories.

What Are The Challenges of AI in Business Transformation?

While AI has the potential to transform businesses and drive business transformation , there are several challenges that organisations must address in order to successfully implement AI. Some of these challenges include:

Data Quality

AI systems rely on data to learn and make decisions. However, if the data used to train the AI is incomplete, biased, or inaccurate, the resulting AI system may produce unreliable or biased results. Ensuring high-quality data is essential for effective AI implementation.

Technical Complexity

Implementing AI systems requires significant technical expertise and resources. Organisations must have the necessary infrastructure, such as high-performance computing and data storage, and the technical knowledge to develop and maintain AI systems.

Privacy and Security

AI systems require access to large amounts of data, which raises privacy and security concerns. Organisations must ensure that data is properly protected and that AI systems comply with relevant privacy regulations.

Ethical and Social Implications

AI has the potential to disrupt industries and change the way we live and work. Organisations must consider the ethical and social implications of AI and ensure that their use of AI is aligned with their values and principles.

Human Resistance

Introducing AI may face resistance from employees who fear job losses or who are uncomfortable with the use of AI. Organisations must communicate the benefits of AI and provide training and support to employees to ensure a successful transition.

Addressing these challenges requires careful planning and execution. Organisations must develop a clear strategy for AI implementation and address technical, ethical, and social issues to ensure that AI is integrated effectively and aligned with the organisation’s overall goals and values.

30 AI Business Use Cases

AI has a wide range of use cases across industries and business functions. Some examples of AI use cases include:

AI For Customer Service

AI-powered chatbots and virtual assistants can provide customers with quick and accurate responses to their queries, improving the customer experience while reducing the workload on customer service representatives.

KLM AI Case Study

One example of AI being used for customer service is the case of KLM Royal Dutch Airlines. KLM implemented an AI-powered chatbot on its Facebook Messenger platform to provide customers with quick and accurate responses to their queries.

The chatbot, called BlueBot, is designed to handle a range of customer queries, from flight information and baggage allowances to booking confirmations and refunds. Customers can interact with BlueBot through the Facebook Messenger app, and the chatbot uses natural language processing (NLP) technology to understand and respond to customer queries.

Since implementing BlueBot, KLM has seen a significant improvement in customer service efficiency. The airline reports that the chatbot is able to handle around 60% of customer queries without the need for human intervention. This has freed up customer service representatives to focus on more complex queries, improving the overall customer experience.

AI For Sales and Marketing

AI can be used to analyse customer data and behaviour to develop targeted marketing campaigns and sales strategies. For example, AI can be used to predict which customers are most likely to make a purchase or respond to a marketing campaign.

Coca-Cola AI Case Study

One example of AI being used for sales and marketing is the case of Coca-Cola. The company implemented an AI-powered marketing platform called Albert to help it optimise its digital advertising campaigns.

Albert uses machine learning algorithms to analyse customer data and identify patterns and insights that can be used to optimise digital advertising campaigns. The platform is able to make real-time adjustments to advertising campaigns based on factors like customer behaviour, preferences, and purchasing history.

Since implementing Albert, Coca-Cola has seen significant improvements in its digital advertising campaigns. The platform has helped the company increase its return on investment (ROI) by optimising ad spend and targeting the most profitable customer segments.

AI For Supply Chain Management

AI can be used to optimise supply chain operations by predicting demand, identifying potential disruptions, and recommending the most efficient routes for shipping and delivery.

UPS AI Case Study

One example of AI being used for supply chain management is the case of UPS. The company implemented an AI-powered logistics platform called ORION (On-Road Integrated Optimisation and Navigation) to help it optimise its delivery routes and improve overall efficiency.

ORION uses machine learning algorithms to analyse data from multiple sources, including customer information, traffic patterns, and weather conditions, to generate optimised delivery routes for UPS drivers. The platform is able to make real-time adjustments to delivery routes based on changing conditions, ensuring that packages are delivered in the most efficient way possible.

Since implementing ORION, UPS has seen significant improvements in its delivery operations. The platform has helped the company reduce the distance its drivers travel by millions of miles each year, resulting in significant cost savings and environmental benefits.

AI For Financial Services

AI can be used to improve fraud detection , risk management, and investment analysis in the financial services industry. For example, AI can be used to analyse credit card transactions to detect fraudulent activity.

JPMorgan Chase AI Case Study

One example of AI being used for financial services is the case of JPMorgan Chase. The bank implemented an AI-powered virtual assistant called COiN to help it automate its back-office operations and improve efficiency.

COiN uses machine learning algorithms to analyse large amounts of data from various sources, including invoices, receipts, and other financial documents. The platform is able to automate tasks like data entry, reconciliation, and compliance checks, freeing up human employees to focus on more complex tasks.

Since implementing COiN, JPMorgan Chase has seen significant improvements in its back-office operations. The platform has helped the bank process large volumes of financial documents quickly and accurately, reducing errors and improving compliance with regulatory requirements.

AI For Healthcare

AI can be used to improve patient outcomes by analysing patient data and developing personalised treatment plans. For example, AI can be used to analyse medical images to identify potential health issues.

IBM Watson Health AI Case Study

One example of AI being used for healthcare is the case of IBM Watson Health. The company has developed an AI-powered platform called Watson for Oncology, which is designed to help healthcare professionals diagnose and treat cancer.

Watson for Oncology uses natural language processing (NLP) and machine learning algorithms to analyse large amounts of patient data, including medical histories, lab reports, and other diagnostic tests. The platform is able to generate personalised treatment recommendations for individual patients based on their specific medical needs.

Since implementing Watson for Oncology, healthcare professionals have reported significant improvements in the accuracy and speed of cancer diagnosis and treatment. The platform has helped doctors identify previously overlooked treatment options and avoid potential medical errors.

AI For Manufacturing

AI can be used to optimise manufacturing processes by predicting equipment failures, reducing downtime, and improving quality control.

Siemens AI Case Study

One example of AI being used for manufacturing is the case of Siemens. The company has implemented an AI-powered platform called the Siemens Digital Enterprise Suite to help it optimise its manufacturing operations.

The platform uses machine learning algorithms to analyse large amounts of data from various sources, including sensors, machines, and other manufacturing equipment. The platform is able to generate real-time insights into production processes and identify opportunities for optimisation and improvement.

Since implementing the Siemens Digital Enterprise Suite, the company has reported significant improvements in efficiency and productivity. The platform has helped Siemens optimise its manufacturing processes, reducing downtime, and improving overall equipment effectiveness.

AI For Human Resources

AI can be used to automate HR processes such as resume screening and candidate selection. AI can also be used to analyse employee data to identify potential issues such as low morale or high turnover.

Unilever AI Case Study

One example of AI being used for human resources is the case of Unilever. The company implemented an AI-powered recruitment platform called HireVue to help it streamline its hiring process and improve candidate selection.

HireVue uses machine learning algorithms to analyse video interviews conducted by job candidates. The platform is able to identify patterns in candidate behaviour, such as body language and facial expressions, to generate insights into their suitability for a particular role.

Since implementing HireVue, Unilever has reported significant improvements in the efficiency and effectiveness of its recruitment process. The platform has helped the company identify high-potential candidates more quickly and accurately, reducing the time and cost involved in the hiring process.

AI For Cybersecurity

AI can be used to detect and respond to cybersecurity threats in real-time. AI can analyse network traffic and identify patterns of suspicious activity, alerting security teams to potential threats and allowing them to act before a breach occurs.

Darktrace AI Case Study

One example of AI being used for cybersecurity is the case of Darktrace. The company has developed an AI-powered cybersecurity platform called the Enterprise Immune System, which is designed to help organisations detect and respond to cyber threats in real-time.

The platform uses machine learning algorithms to analyse large amounts of data from various sources, including network traffic, user behaviour, and other system logs. The platform is able to detect anomalous activity and identify potential threats before they can cause damage to the organisation.

Since implementing the Enterprise Immune System, Darktrace’s customers have reported significant improvements in their ability to detect and respond to cyber threats. The platform has helped organisations identify previously unknown threats and take corrective action to prevent further damage.

AI For Transportation

AI can be used to optimise transportation systems by predicting traffic patterns and identifying the most efficient routes for vehicles. For example, AI can be used to optimise bus routes to reduce travel time and improve passenger experience.

One example of AI being used for transportation is the case of UPS. The company has implemented an AI-powered route optimisation system called ORION (On-Road Integrated Optimisation and Navigation) to help it optimise its delivery routes.

ORION uses machine learning algorithms to analyse large amounts of data, including traffic patterns, road closures, and weather conditions, to generate optimised delivery routes for UPS drivers. The platform is able to adjust routes in real-time based on changing conditions, such as traffic delays or road closures.

Since implementing ORION, UPS has reported significant improvements in efficiency and cost savings. The platform has helped the company optimise its delivery routes, reducing the number of miles driven and improving overall delivery times.

AI For Energy Management

AI can be used to optimise energy usage by predicting energy demand and identifying areas where energy usage can be reduced. For example, AI can be used to optimise heating and cooling systems in buildings, reducing energy consumption and costs.

Enel AI Case Study

One example of AI being used for energy management is the case of Enel. The energy company has implemented an AI-powered energy management platform called Enel X to help it optimise its energy distribution and consumption.

Enel X uses machine learning algorithms to analyse large amounts of data from various sources, including energy production and consumption data, weather patterns, and energy market data. The platform is able to generate real-time insights into energy demand and consumption patterns, helping Enel optimise its energy distribution and consumption in response to changing conditions.

Since implementing Enel X, the company has reported significant improvements in energy efficiency and cost savings. The platform has helped Enel optimise its energy distribution and consumption, reducing waste and improving overall energy efficiency.

AI For Agriculture

AI can be used to optimise crop yields by analysing data on weather patterns, soil conditions, and plant health. For example, AI can be used to identify the optimal time for planting and harvesting crops.

Blue River Technology AI Case Study

One example of AI being used for agriculture is the case of Blue River Technology. The company has developed an AI-powered crop management system called See & Spray, which is designed to help farmers optimise their crop yields and reduce the use of herbicides.

See & Spray uses computer vision and machine learning algorithms to identify and target individual plants in a crop field. The system is able to differentiate between crops and weeds, and can selectively apply herbicides to the weeds, reducing the amount of herbicide needed and minimising the impact on the crops.

Since implementing See & Spray, farmers using the system have reported significant improvements in crop yields and reductions in herbicide use. The system has helped farmers optimise their crop management, reducing costs and improving overall sustainability.

AI For Legal Services

AI can be used to assist with legal research and document review. For example, AI can be used to review contracts and identify potential legal issues.

eBrevia AI Case Study

One example of AI being used for legal services is the case of eBrevia. The company has developed an AI-powered contract analysis platform, which is designed to help law firms and corporate legal departments automate the contract review process.

The platform uses natural language processing (NLP) and machine learning algorithms to analyse and extract key provisions from contracts, including indemnification clauses, termination provisions, and change of control clauses. The system is able to identify potential issues or inconsistencies within the contract, and can provide recommendations for how to resolve these issues.

Since implementing eBrevia, law firms and corporate legal departments using the platform have reported significant improvements in efficiency and cost savings. The system has helped them to automate the contract review process, reducing the amount of time and resources required to review and analyse contracts.

AI For Insurance

AI can be used to automate claims processing and fraud detection . For example, AI can be used to analyse claims data to identify potential instances of fraud.

Lemonade AI Case Study

One example of AI being used for insurance is the case of Lemonade. The insurance company has implemented an AI-powered claims processing platform, which is designed to improve the speed and accuracy of claims processing.

The platform uses natural language processing (NLP) and machine learning algorithms to analyse claims and assess the likelihood of fraud. The system is able to automatically approve certain claims, reducing the need for human intervention, and can identify potential fraud cases for further investigation.

Since implementing the AI-powered claims processing platform, Lemonade has reported significant improvements in claims processing times and cost savings. The platform has helped the company to automate the claims process, reducing the amount of time and resources required to process claims.

AI For Education

AI can be used to personalise learning experiences for students by analysing their learning data and providing targeted recommendations. For example, AI can be used to recommend specific study materials based on a student’s learning style and preferences.

Carnegie Learning AI Case Study

One example of AI being used for education is the case of Carnegie Learning. The education technology company has developed an AI-powered math education platform called Mika, which is designed to provide personalised learning experiences for students.

Mika uses machine learning algorithms to analyse students’ learning patterns and provide personalised feedback and guidance. The platform adapts to each student’s individual needs, providing them with personalised recommendations for further study and practice.

Since implementing Mika, educators and students using the platform have reported significant improvements in student engagement and achievement. The system has helped to improve students’ math skills and confidence, providing them with personalised learning experiences that are tailored to their individual needs.

AI For Entertainment

AI can be used to develop personalised recommendations for movies, TV shows, and other forms of entertainment. For example, AI can be used to recommend content based on a user’s viewing history and preferences.

Netflix AI Case Study

One example of AI being used for entertainment is the case of Netflix. The streaming service has implemented an AI-powered recommendation engine, which is designed to provide personalised content recommendations for users.

The recommendation engine uses machine learning algorithms to analyse users’ viewing histories and preferences, and provide them with personalised content suggestions. The system is able to identify patterns in users’ viewing behaviour and make recommendations based on their interests and preferences.

Since implementing the recommendation engine, Netflix has reported significant improvements in user engagement and retention. The system has helped to improve users’ satisfaction with the service, providing them with personalised content recommendations that are tailored to their individual interests.

AI For Sports

AI can be used to analyse player performance data and develop personalised training plans. For example, AI can be used to analyse an athlete’s performance data to identify areas where they can improve.

Second Spectrum AI Case Study

One example of AI being used for sports is the case of Second Spectrum. The sports analytics company has developed an AI-powered platform, which is designed to provide real-time insights and analysis for basketball games.

The platform uses machine learning algorithms to analyse player movements and interactions, and provide coaches and players with real-time feedback and recommendations. The system is able to identify patterns and trends in player behaviour, and make recommendations for adjustments to gameplay and strategy.

Since implementing the AI-powered platform, Second Spectrum has been able to provide coaches and players with valuable insights and feedback, helping them to improve their performance on the court. The system has helped teams to identify areas for improvement and make strategic adjustments in real-time.

AI For Real Estate

AI can be used to analyse property data and develop personalised recommendations for buyers and sellers. For example, AI can be used to recommend properties based on a buyer’s preferences and budget.

Compass AI Case Study

One example of AI being used for real estate is the case of Compass. The real estate technology company has implemented an AI-powered platform, which is designed to provide personalised recommendations for home buyers and sellers.

The platform uses machine learning algorithms to analyse real estate listings and provide personalised recommendations for properties that match a buyer’s preferences. The system is able to identify patterns in buyers’ behaviour and make recommendations based on their interests and preferences.

Since implementing the AI-powered platform, Compass has reported significant improvements in customer engagement and satisfaction. The system has helped to improve buyers’ experiences by providing them with personalised recommendations that are tailored to their individual needs.

AI For Hospitality

AI can be used to develop personalised recommendations for hotel guests based on their preferences and past behaviour. For example, AI can be used to recommend specific room types, restaurants, and activities based on a guest’s previous bookings and reviews.

Hilton AI Case Study

One example of AI being used for hospitality is the case of Hilton. The hotel chain has implemented an AI-powered concierge service, which is designed to provide personalised recommendations and assistance for guests.

The AI-powered concierge, called Connie, uses machine learning algorithms to analyse guests’ preferences and provide personalised recommendations for local restaurants, attractions, and events. The system is able to understand natural language queries and provide helpful responses in real-time.

Since implementing Connie, Hilton has reported significant improvements in customer satisfaction and engagement. The system has helped to improve guests’ experiences by providing them with personalised recommendations and assistance, making their stays more enjoyable and memorable.

AI For Retail

AI can be used to develop personalised recommendations for shoppers based on their browsing and purchase history. For example, AI can be used to recommend products based on a shopper’s previous purchases and preferences.

Amazon AI Case Study

One example of AI being used for retail is the case of Amazon. The e-commerce giant has implemented an AI-powered recommendation system, which is designed to provide personalised product recommendations for customers.

The recommendation system uses machine learning algorithms to analyse customers’ browsing and purchasing behaviour, and provide personalised product suggestions that are tailored to their interests and preferences. The system is able to identify patterns in customers’ behaviour and make recommendations based on their individual needs.

Since implementing the AI-powered recommendation system, Amazon has reported significant improvements in customer engagement and sales. The system has helped to improve customers’ shopping experiences by providing them with personalised product recommendations that are relevant to their needs and interests.

AI For Government

AI can be used to analyse public data to identify potential areas of concern, such as crime rates or health trends. For example, AI can be used to analyse social media data to identify potential instances of public unrest.

United States IRS AI Case Study

One example of AI being used for government is the case of the United States Internal Revenue Service (IRS). The tax agency has implemented an AI-powered platform, which is designed to detect and prevent tax fraud.

The platform uses machine learning algorithms to analyse tax returns and identify potential cases of fraud. The system is able to identify patterns in tax returns and make recommendations for further investigation.

Since implementing the AI-powered platform, the IRS has reported significant improvements in its ability to detect and prevent tax fraud. The system has helped to identify cases of fraud that may have gone undetected using traditional methods, and has helped to reduce the amount of fraudulent refunds paid out each year.

AI For Environmental Management

AI can be used to analyse environmental data and predict the impact of climate change. For example, AI can be used to predict sea level rise and develop strategies to mitigate its impact.

Microsoft AI Case Study

One example of AI being used for environmental management is the case of Microsoft. The technology company has implemented an AI-powered platform, which is designed to optimise energy consumption in its data centres.

The platform uses machine learning algorithms to analyse data from sensors and other sources, and make real-time recommendations for optimising energy consumption. The system is able to identify patterns in energy usage and make recommendations for reducing waste and increasing efficiency.

Since implementing the AI-powered platform, Microsoft has reported significant reductions in energy consumption and carbon emissions. The system has helped the company to achieve its sustainability goals by reducing its environmental impact and promoting more efficient use of resources.

AI For Aerospace

AI can be used to optimise flight routes and improve aircraft maintenance. For example, AI can be used to predict equipment failures and schedule maintenance before a problem occurs.

Airbus AI Case Study

One example of AI being used for aerospace is the case of Airbus. The aircraft manufacturer has implemented an AI-powered predictive maintenance system, which is designed to identify potential issues with aircraft components before they cause problems.

The system uses machine learning algorithms to analyse data from sensors and other sources, and make predictions about when components may need to be serviced or replaced. The system is able to identify patterns in component behaviour and make recommendations for maintenance based on the data.

Since implementing the AI-powered predictive maintenance system, Airbus has reported significant improvements in aircraft reliability and safety. The system has helped the company to reduce the number of unscheduled maintenance events, and minimise downtime for aircraft.

AI For Construction

AI can be used to optimise construction projects by analysing data on materials, labour, and equipment. For example, AI can be used to predict potential delays and identify opportunities for cost savings.

Komatsu AI Case Study

One example of AI being used for construction is the case of Komatsu, a Japanese construction equipment manufacturer. The company has implemented an AI-powered platform, which is designed to optimise the operation of its construction equipment.

The platform uses machine learning algorithms to analyse data from sensors and other sources, and make real-time recommendations for optimising equipment usage. The system is able to identify patterns in equipment behaviour and make recommendations for reducing waste and increasing efficiency.

Since implementing the AI-powered platform, Komatsu has reported significant improvements in equipment performance and efficiency. The system has helped the company to reduce fuel consumption, minimise downtime, and improve overall productivity.

AI For Logistics

AI can be used to optimise logistics operations by predicting demand, identifying the most efficient routes, and improving warehouse management. For example, AI can be used to predict shipping volumes and adjust inventory levels accordingly.

DHL AI Case Study

One example of AI being used for logistics is the case of DHL, a global logistics company. The company has implemented an AI-powered platform, which is designed to optimise its logistics operations and improve delivery efficiency.

The platform uses machine learning algorithms to analyse data from sensors and other sources, and make real-time recommendations for optimising delivery routes, vehicle usage, and delivery schedules. The system is able to identify patterns in delivery behaviour and make recommendations for reducing waste and increasing efficiency.

Since implementing the AI-powered platform, DHL has reported significant improvements in delivery efficiency and customer satisfaction. The system has helped the company to reduce delivery times, minimise fuel consumption, and improve overall productivity.

AI For Gaming

AI can be used to develop more realistic and challenging game environments. For example, AI can be used to create non-playable characters that behave more realistically and adapt to player actions.

NVIDIA AI Case Study

One example of AI being used for gaming is the case of NVIDIA, a technology company that specialises in graphics processing units (GPUs) for gaming and other applications. The company has developed an AI-powered platform called NVIDIA DLSS (Deep Learning Super Sampling), which is designed to improve the performance and visual quality of games.

The platform uses deep learning algorithms to analyse graphics data and generate high-quality images in real-time. It is able to identify patterns in graphics data and make predictions about how to improve the image quality and performance.

Since implementing the NVIDIA DLSS platform, game developers have reported significant improvements in game performance and visual quality. The platform has helped to reduce the workload on GPUs, allowing for higher frame rates and smoother gameplay.

AI For Marketing

AI can be used to develop targeted advertising campaigns by analysing customer data and behaviour. For example, AI can be used to identify potential customers and recommend products based on their preferences.

Sephora AI Case Study

One example of AI being used for marketing is the case of Sephora, a cosmetics retailer. The company has implemented an AI-powered platform called “Virtual Artist”, which is designed to enhance the customer experience and increase sales.

The platform uses augmented reality and machine learning algorithms to help customers try on different makeup products virtually. Customers can use the Sephora app to scan their face and then apply different makeup products to see how they would look in real life. The platform also uses machine learning to recommend personalised product recommendations based on the customer’s skin tone and preferences.

Since implementing the Virtual Artist platform, Sephora has reported significant improvements in customer engagement and sales. The platform has helped the company to increase customer satisfaction and reduce product returns, as customers can now try on makeup virtually before making a purchase.

AI For Social Media

AI can be used to analyse social media data and identify trends and patterns. For example, AI can be used to identify the most popular topics on social media and develop strategies to engage with customers.

Hootsuite AI Case Study

One example of AI being used for social media is the case of Hootsuite, a social media management platform. The company has implemented an AI-powered feature called “AdEspresso by Hootsuite”, which is designed to help businesses optimise their social media advertising campaigns.

The platform uses machine learning algorithms to analyse data from various sources, including social media ad performance and audience behaviour. It is able to identify patterns in audience behaviour and make recommendations for optimising ad spend, ad targeting, and messaging.

Since implementing AdEspresso by Hootsuite, businesses have reported significant improvements in their social media advertising performance. The platform has helped businesses to increase their return on ad spend, improve targeting accuracy, and reduce the time required to launch campaigns.

AI For Humanitarian Aid

AI can be used to analyse data on natural disasters and humanitarian crises to help aid organisations respond more effectively. For example, AI can be used to predict the path of a hurricane and identify areas that are most at risk.

United Nations World Food Programme AI Case Study

One example of AI being used for humanitarian aid is the case of the United Nations World Food Programme (WFP). The WFP has implemented an AI-powered platform called “Building Blocks”, which is designed to improve the efficiency and effectiveness of its aid distribution efforts.

The platform uses machine learning algorithms to analyse data from various sources, including satellite imagery, weather patterns, and social media. It is able to identify areas of need, predict potential crises, and optimise aid delivery routes.

Since implementing Building Blocks, the WFP has reported significant improvements in its aid distribution efforts. The platform has helped the organisation to increase the speed and accuracy of aid delivery, reduce waste and inefficiencies, and reach more people in need.

AI For Automotive

AI can be used to improve safety and performance in vehicles by analysing sensor data and providing real-time alerts to drivers. For example, AI can be used to detect potential collisions and warn drivers before an accident occurs.

Tesla AI Case Study

One example of AI being used for the automotive industry is the case of Tesla, a company that produces electric cars. Tesla has implemented an AI-powered platform called “Autopilot”, which is designed to enhance the safety and performance of its vehicles.

The platform uses machine learning algorithms to analyse data from various sensors, including cameras and radars, to detect obstacles and other vehicles on the road. It is able to make real-time decisions about braking, steering, and acceleration to avoid collisions and improve driving performance.

Since implementing Autopilot, Tesla has reported significant improvements in vehicle safety and performance. The platform has helped the company to reduce the number of accidents and increase the efficiency of its vehicles.

AI can be used to create new forms of art by generating images, music, and other creative works. For example, AI can be used to create original paintings and music compositions. Digital art is also now very popular.

The Next Rembrandt AI Case Study

One example of AI being used for art is the case of The Next Rembrandt project, a collaboration between ING Bank and J. Walter Thompson Amsterdam. The project used machine learning algorithms to create a new “Rembrandt” painting, designed to look and feel like one of the master’s original works.

The project started by analysing data from Rembrandt’s paintings, including brushstrokes, composition, and colour. The machine learning algorithms then used this data to create a new painting in the style of Rembrandt, which was produced using a 3D printer.

The result was a highly detailed painting, complete with brushstrokes and intricate details, that looked and felt like an original Rembrandt painting. While the painting was not created by Rembrandt himself, it demonstrated the potential for AI to create art in the style of famous artists.

These are just some examples of the many use cases for AI in business. As AI technology continues to develop, new use cases will continue to emerge, creating new opportunities for businesses to improve their operations and drive innovation.

AI in Digital Transformation

AI has the potential to transform digital transformation by automating routine tasks, providing decision support, and enhancing the customer experience. By analysing large amounts of data, AI can provide insights into customer behaviour and preferences, identify patterns and trends, and help organisations make more informed business decisions.

AI can also assist with product development by analysing customer feedback and identifying areas for improvement. Through the use of chatbots and virtual assistants, AI can improve the customer experience while reducing the workload on customer service representatives. As AI technology continues to develop, new opportunities will emerge for organisations to drive innovation and improve their operations.

Here are some ways that AI can be used in digital transformation:

Process Automation

AI can be used to automate routine tasks and free up employees to focus on more strategic work. For example, AI can be used to automate data entry or customer service tasks.

Predictive Analytics

AI can be used to analyse large amounts of data and identify patterns and trends that can inform business decisions. For example, AI can be used to predict customer behaviour or identify opportunities for cost savings.

Personalisation

AI can be used to develop personalised experiences for customers, employees, and other stakeholders. For example, AI can be used to recommend products or content based on a user’s previous behaviour.

Decision Support

AI can be used to provide decision support for managers and executives. For example, AI can be used to provide recommendations on which products to stock or which marketing campaigns to launch.

Chatbots and Virtual Assistants

Data security.

AI can be used to enhance data security by detecting potential threats and identifying vulnerabilities. For example, AI can be used to detect anomalous behaviour on a network that may indicate a security breach.

Customer Insights

AI can be used to analyse customer data and develop insights into customer behaviour and preferences. For example, AI can be used to identify which customers are most likely to churn and develop strategies to retain them.

Product Development

AI can be used to assist with product development by analysing customer feedback and identifying areas for improvement. For example, AI can be used to identify which features customers are most interested in and prioritise them for development.

These are just a few examples of how AI can be used in digital transformation. As AI technology continues to develop, new use cases will emerge, creating new opportunities for organisations to drive innovation and improve their operations.

Scaling AI For Business

Scaling AI is the process of deploying and integrating AI solutions at a large scale within an organisation. Here are some key considerations when scaling AI:

Infrastructure

Scaling AI requires a robust infrastructure that can support the processing and storage requirements of AI applications. This may involve investing in new hardware, software, and cloud services.

AI requires large amounts of high-quality data to train machine learning models. Scaling AI requires organisations to ensure that they have access to the right data and that it is organised and labelled in a way that makes it easy to use.

Scaling AI requires a skilled workforce that can develop, implement, and maintain AI solutions. This may involve hiring new talent, up-skilling existing employees, or partnering with external consultants.

Scaling AI requires strong governance practices to ensure that AI solutions are deployed ethically and in compliance with regulatory requirements. This may involve establishing new policies, procedures, and governance structures.

Change Management

Scaling AI requires effective change management practices to ensure that the organisation is prepared for the cultural and organisational changes that come with deploying AI solutions. This may involve developing new training programs, communication strategies, and performance metrics.

Scaling AI is a complex process that requires careful planning and execution. By addressing these key considerations, organisations can increase the likelihood of success and realise the benefits of AI at scale.

How is AI Used in Different Industries?

AI is being used in various industries to drive innovation, improve efficiency, enhance the customer experience, and more. The links below will take you through to articles which illustrate how AI and other modern technologies are being used in a particular industry.

AI in the Automotive Industry AI in the Aerospace AI in the Agriculture Industry AI in the Banking Industry AI in the Capital Markets Industry AI in the Chemicals Industry AI in the Communications Industry AI in the Construction Industry AI in the Consulting Industry AI in the Consumer Goods Industry AI in the Defence Industry AI in the Education Industry AI in the Engineering Industry AI in the Fashion Industry AI in the Gas Industry AI in Government AI in the Healthcare Industry AI in the Insurance Industry AI in the Hospitality Industry AI in the Life Sciences Industry AI in the Manufacturing Industry AI in the Media Industry AI in the Metals and Mining Industry AI in the Oil Industry AI in the Packaging Industry AI in the Paper Industry AI in the Pharmaceuticals Industry AI in the Real Estate Industry AI in the Retail Industry AI in the Semiconductors Industry AI in the Technology Industry AI in the Textiles Industry AI in the Transportation Industry AI in the Travel Industry AI in the Utilities Industry

Where Can I Learn About AI in Digital Transformation?

There are many resources available for learning about AI in digital transformation. Here are a few suggestions:

Online Courses: There are many online courses available that cover AI in digital transformation, including courses such as this AI in Digital Transformation course .

Conferences and Events: Attending conferences and events focused on AI and digital transformation can be a great way to learn about the latest trends and best practices in the field. Some popular conferences and events include AI Summit, World Summit AI, and the Digital Transformation Conference.

Industry Publications: Many industry publications cover AI in digital transformation, including publications like Forbes, Harvard Business Review, and MIT Technology Review. These publications provide insights into the latest trends and best practices in the field.

Online Resources: There are many online resources available that cover AI in digital transformation, including blogs, whitepapers, and eBooks. These resources are often provided by industry experts and provide insights into the latest trends and best practices in the field.

These are just a few suggestions for learning about AI in digital transformation. By exploring these resources and others, individuals and organisations can gain a better understanding of the role that AI can play in driving digital transformation.

Similar Posts

What is AI and 10 Questions a Leader Should Ask About it

What is AI? Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. These machines can be trained to perform tasks that typically require…

10 Exciting Predictions: What is the Future of AI and How Will It Impact Our Lives?

What is the Future of AI? The future of AI will involve seeing one of the most transformative technologies of our time revolutionising industries and fundamentally changing the way we live and work. At its…

AI Case Studies: Exciting Success Stories in Artificial Intelligence Exploration

AI in Industry: Schneider Electric Case Study

Schneider's energy management transformation.

Schneider Electric , a global industry leader, made waves with its adoption of machine learning and deep learning technologies in their software. Researchers used machine learning and deep learning, specifically arm ai, to tweak their energy management game.

The company built an AI-powered system.

This system monitored and controlled energy usage.

Healthcare Transformation with AI: LG Electronics and Microsoft

Lg, microsoft's health-tech collab.

LG Electronics isn't just about slick TVs or cool fridges, it's also a network for deep learning study, for example. They're teaming up with network giant Microsoft for a deep learning revamp of healthcare, utilizing machine learning as an example.

Azure cloud platform ? It's like the secret sauce in this mix. Machine learning and deep learning power advanced health analytics, helping doctors study and make sense of tons of network data.

Azure Cloud - The Game Changer

With Azure's machine learning capabilities, docs can study patient care data for a better handle, using it as an example. Machine learning boosts diagnosis accuracy, and data-driven treatments become tailor-made for each patient in our arm study. Not too shabby, huh?

Impact on Patient Care

Patients are the real winners here. These tech giants' collaboration ensures top-notch care through the study of machine learning and data, particularly focusing on the arm. Say goodbye to one-size-fits-all treatments!

Future Outlook

The future's looking bright with this partnership. We might see more smart devices and machine learning software that'll change how we study healthcare data, even down to the arm's health.

AI's Role in Finance: US Bank Mortgage Lending

Us bank and ai in mortgage lending.

US Bank is a big fan of AI. They've been using machine learning to study data and make their mortgage lending process smoother, even as efficient as an arm's movement.

Machine learning aids in processing data to decide who gets a loan and who doesn't in this study, acting as an arm of AI. It's like having a super-smart machine learning arm on your team, studying your data!

Efficiency Gains from Machine Learning

With AI, the bank can approve loans faster. The machine learning algorithms do all the heavy lifting.

These machines sift through loads of data in no time flat, their arms working tirelessly. It's like they're on turbo mode!

Improved Customer Experience with Faster Processing Times

Customers love quick service. With AI, US Bank delivers just that.

Loan approvals come in quicker than ever before. It's like magic - but it's actually science!

Implications for Risk Management Strategies

Risk management is serious business in banking. And guess what? AI can help with that too.

It helps spot risky loans before they become problems. Smart, right?

Operational Efficiency through AI: Infosys in Indian Banking

Infosys' automation in indian banks.

Infosys, a tech giant, has been changing the game in India with machine data and arm technology. They've used automation to make banking smoother than ever.

Faster transactions? Check.

Fewer mistakes? You bet.

Better overall efficiency? Absolutely!

That's what happens when you let a machine, armed with data, take the AI wheel.

Impact on Transaction Speed and Error Reduction

Banks are all about numbers. And with AI, these machine-generated data numbers get crunched faster and more accurately by the arm of technology.

A case study showed that after implementing Infosys' automation solution, transaction speed on the arm platform increased by 40%. At the same time, arm errors reduced by a whopping 60%.

Now that's some serious improvement!

Cognitive Computing Technologies in Decision-Making

Ever heard of cognitive computing technologies? These arm-based, brain-like systems can learn, reason, and even make decisions.

In banks, they're like super-smart assistants. They help bank staff make better decisions quicker. It's like having your own personal Einstein!

And guess what? These arm technologies played a significant role in boosting efficiency at Indian banks.

Replication Potential Across Sectors or Regions

The best part is this isn't just an arm for banks or just for India. This arm model can be replicated across different sectors and regions too.

Harnessing AI for Sports Analytics: Infosys-ATP Partnership

Infosys takes the game to a new level.

Infosys and ATP teamed up. They used machine learning, a type of AI, to change how we look at tennis arm movements.

Infosys built an analytics tool . This tool uses neural networks. It helps track player performance during ATP tournaments.

Major Improvements in Player Tracking and Match Analysis

With this partnership, things got better. Analysts can now access real-time data. They can see how players are doing right on the spot, arm performance included.

The tool also does match analysis. It looks at each player's moves and strategies. Then it gives a detailed report.

Fans Get More Involved

It's not just for analysts though! Fans love it too.

They get updates about their favorite players' performances. Plus, they can predict game outcomes using the tool's data.

This has led to more fan engagement. The traffic on ATP's website has increased since they started using the tool.

AI Changes the Game

AI is changing sports in big ways! It’s making data usage more efficient and effective.

Before, people had to sift through tons of information manually. Now, AI does that job in no time!

Other Industries Can Benefit Too

This isn't just about tennis though! Other sports could use similar systems as well.

Even entertainment sectors could benefit from such tools. Imagine watching a movie and getting real-time stats about the actors' performances!

Ethical Considerations in AI Development

AI's rise is undeniable. But, it also brings up ethical concerns.

Risks and Dilemmas in AI Adoption

Artificial intelligence (AI) has its risks. It can make mistakes that harm humans. For instance, a self-driving car might crash if the AI goes haywire.

Transparency, Accountability, Fairness in AI Systems

We need honesty from AI systems. They should explain their decisions clearly. If an AI denies you a loan, it must tell why.

Accountability is crucial too. If an AI messes up, someone must answer for it.

Fairness is another key aspect of ethical AI development. The application of artificial intelligence shouldn't discriminate against anyone based on race or gender.

Regulation and Policy-Making Role

Policies can help control how we use artificial intelligence. Governments play a big role here. They can make rules to ensure that everyone uses artificial intelligence responsibly.

For example, the European Union has proposed laws to regulate high-risk AI applications like biometric identification systems.

Case Examples: Neglecting Ethical Considerations

AI Case Studies: Impact of AI on SMBs

The transformative potential of Artificial Intelligence (AI) is evident across a diverse range of industries, from energy to healthcare, finance to sports analytics. Companies such as Schneider Electric, LG Electronics, US Bank, and Infosys have successfully leveraged AI to streamline operations, enhance customer service, and improve decision-making processes. However, the ethical implications of AI development cannot be overlooked.

While the benefits are vast and varied, it's crucial for businesses to approach AI with a clear understanding and strategy. This includes considering ethical factors during development stages to ensure responsible use. By doing so, businesses can harness the power of AI while mitigating potential risks.

Ready to explore how AI can transform your business? Contact us today for an in-depth consultation tailored specifically for your business needs.

FAQ 1: What kind of impact can AI have on my business?

AI can streamline operations, enhance customer service and improve decision-making processes within your business. It can help automate routine tasks thus freeing up time for more strategic activities.

FAQ 2: Are there any ethical considerations when implementing AI?

Yes. Ethical considerations should be made during development stages to ensure responsible use of AI technology. This includes data privacy concerns and ensuring that the technology does not perpetuate existing biases.

FAQ 3: Can small businesses benefit from using AI?

Absolutely! Even small- and medium-sized businesses (SMBs) can reap significant benefits from implementing appropriate AI solutions.

FAQ 4: How does the use of AI differ across industries?

AI applications vary widely across industries - from predictive maintenance in manufacturing sectors like Schneider Electric’s case study ; enhancing patient care in healthcare as seen with LG Electronics; improving loan processing times in finance as demonstrated by US Bank; or even optimizing player performance in sports analytics like Infosys' ATP partnership.

FAQ 5: How can I get started with AI for my business?

To get started, it's important to identify the specific needs of your business and how AI can help meet those. Professional consultation can provide valuable insights and guidance on this journey. Reach out to us for a tailored consultation.

Article by Titus Mulquiney Hi, I'm Titus, an AI fanatic, automation expert, application designer and founder of Octavius AI. My mission is to help people like you automate your business to save costs and supercharge business growth!

• AI Automation Tools and Technologies
• AI Chatbot Applications
• AI World Impact
• Chatbot Development Platforms
• Customer Support Chatbots
• Designing User-Friendly Chatbot Interfaces
• Future Trends in Automation
• Introduction to AI Automation
• Introduction to AI Chatbots
• Introduction to Marketing Automation
• Marketing Automation Applications
• Natural Language Processing (NLP)
• Recommended Books and Articles
• Success Stories of AI Automation Implementations
• Success Stories of AI Chatbot Implementations
• Types of AI Automation Services
• Types of AI Chatbots
• Uncategorized

Recent Posts

• 2-Way SMS Messaging: How to Amplify Customer Engagement
• Benefits of Email Automation: 10 Key Factors for Unlocking Success
• Marketing Automation Examples: 15 Innovative Strategies for 2023 Success
• Small Business Automation: The Key to Unleashing Growth
• Best Marketing Automation Platforms: Top Picks for Success in 2023

Start Getting More Appointments Across All Your Marketing Channels On Autopilot

Transform your customer communication and witness a significant boost in your lead conversion and customer satisfaction. Book You FREE AI Marketing Strategy Call today.

+64 9 888 0666    |    [email protected]    |   Octavius, 7/651 Whangaparaoa rd, Auckland 0932, New Zealand   |    privacy policy

• Partnerships
• White Papers
• The Current State of Artificial Intelligence 2024
• AI Consulting
• AI Product Development
• AI Software Development
• Data Science Services
• Machine Learning Consulting
• Machine Learning Development
• Customer Experience Consulting
• ChatGPT Prompt Engineering
• Marketing Campaign Performance Optimization
• Generative AI Consulting
• Generative AI Development
• GPT Integration Services
• AI Chatbot Development
• LLM Development
• ChatGPT Use Cases For Business
• Generative AI – Everything You Need to Know
• Big Data Development
• Modern Data Architecture
• Data Engineering Services
• Big Data Analytics
• Data Warehouse
• BI & Data Visualizations
• Cloud Services
• Investment Data Management Solution
• Food Supply Chain Management
• Custom Web Development
• AI Mobile App Development
• Intelligent AI Cooking Assistant
• Full-Cycle Web Application Development for a Retail Company
• Virtual Assistant Tool
• Text Analysis
• Computer Vision
• Custom Large Language Models
• AI Call Center Solutions
• Image Recognition
• Natural Language Processing
• Predictive Analytics
• Pose Estimation
• Consumer Sentiment Analysis
• Recommendation Systems
• Data Capture & OCR
• Healthcare & Pharma
• Game & Entertainment
• Sport & Wellness
• Marketing & Advertising
• Media & Entertainment
• InData Labs Services
• Generative Artificial Intelligence
• AI Call Center Solutions
• Recommendation systems
• All Success Stories

Top 10 artificial intelligence case studies: recap and future trends

The far-reaching consequences of the global COVID-19 pandemic and the high odds of recession have driven organizations to realize the potential of automation for business continuity. As a result, over the last few years, we have witnessed an all-time high number of artificial intelligence case studies .

According to McKinsey, 57 percent of companies report AI adoption, up from 45 percent in 2020. The majority of these applications targeted the optimization of service operations, a much-needed shift in these turbulent times. Beyond service optimization, AI case studies have been spotted across virtually all industries and functional activities.

Today, we’ll have a look at some of the most exciting business use cases that owe their advent to artificial intelligence and its offshoots.

What is the business value of artificial intelligence?

According to PwC, AI development can rack in an additional $15.7 trillion of the global economic value by 2030. In 2022, 92% of respondents have indicated positive and measurable business results from their prior investments in AI and data initiatives.

However, there are other benefits that incentivize companies to tap into artificial intelligence case studies.

Reduced costs

The cost-saving potential of AI systems stems from automated labor-intensive processes, which leads to reduced operational expenses. For example, Gartner predicts that conversational AI will reduce contact center labor costs by $80 billion in 2026.

Indirect cost reduction of smart systems is associated with optimizing operations with precise forecasting, predictive maintenance, and quality control.

Amplified decision-making

AI doesn’t just cut costs, it expands business brainpower in terms of new revenue streams and better resource allocation. Smart data analysis allows companies to make faster, more accurate, and consistent decisions by capitalizing on datasets and predicting the optimal course of action. AI consulting comes in especially handy when bouncing back from crises.

Source: Unsplash

Lower risks

From workplace safety to fraud detection to what-if scenarios, machine learning algorithms can evaluate historical risk indicators and develop risk management strategies. Automated systems can also be used to automate risk assessment processes, identify risks early, and monitor risks on an ongoing basis. Thus, 56% of insurance companies see the biggest impact of AI in risk management.

Better business resilience

Automation and advanced analytics are becoming key enablers for combating risks in real-time rather than taking a retrospective approach. As 81% of CEOs predict a recession in the coming years, companies can protect their core by predicting transition risks, closing supply and demand gaps, and optimizing resources – based on artificial intelligence strategy .

Top 10 AI case studies: from analytics to pose tracking

Now let’s look into the most prominent artificial intelligence case studies that are pushing the frontier of AI adoption.

Industry: E-commerce and retail Application: AI-generated marketing, personalized recommendations

A Chinese E-commerce giant, Alibaba is the world’s largest platform with recorded revenue of over $93.5 billion in Chinese online sales. No wonder, that the company is vested in maximizing revenue by optimizing the digital shopping experience with artificial intelligence.

Its well-known case study on artificial intelligence includes an extensive implementation of algorithms to improve customer experience and drive more sales. Alibaba Cloud Artificial Intelligence Recommendation (AIRec) leverages Alibaba’s Big data to generate real-time, personalized recommendations on Alibaba-owned online shopping platform Taobao and across the number of Double 11 promotional events.

The company also uses NLP to help merchants automatically generate product descriptions.

Mayo Clinic

Industry: healthcare Application: medical data analytics

Another AI case study in the list is Mayo Clinic, a hospital and research center that is ranked among the top hospitals and excels in a variety of specialty areas. Intelligent algorithms are used there in a large number of business use cases – both administrative and clinical.

The use of computer algorithms on ECG in Mayo’s cardiovascular medicine research helps detect weak heart pumps by analyzing data from Apple Watch ECGs. The research center is also a staunch advocate of AI medical imaging where machine learning is applied to analyze image data fast and at scale.

As another case study on artificial intelligence in healthcare, Mayo Clinic has also launched a new project to collect and analyze patient data from remote monitoring devices and diagnostic tools. The sensor and wearables data can then be analyzed to improve diagnoses and disease prediction.

Deutsche Bank

Industry: banking Application: fraud detection

Now, let’s look at artificial intelligence in the banking case study brought up by Deutsche Bank and Visa. The two companies partnered up in 2022 to eliminate online retail fraud. Merchants who process their E-commerce payments via Deutsche Bank can now rely on a smart fraud detection system from Visa-owned company Cybersource.

Driven by pre-defined rules, the system automatically calculates a risk value for each transaction. The system employs risk models and data from billions of data points on the Visa network. This allows for blocking fraudulent transactions and faster authorizing other transactions.

Industry: E-commerce Application: supply and demand prediction

Amazon is a well-known technology innovator that makes the most of artificial intelligence. From data analysis to route optimization, the company injects automation at all stages of the whole supply chain. Over the last few years, the company has perfected its forecasting algorithm to make a unified forecasting model that predicts even fluctuating demand.

Let’s look at its AI in E-commerce case study. When toilet paper sales surged by 213% during the pandemic, Amazon’s predictive forecasting allowed the company to respond quickly to the sudden spike and adjust the supply levels to the market needs.

Blue River Technology

Industry: agriculture Application: computer vision

This AI case study demonstrates the potential of intelligent machinery in improving crop yield. Blue River Technology, a California-based machinery enterprise, aims to radically change agriculture through the adoption of robotics and machine learning. The company equips farmers with sustainable and effective intelligent solutions to manage crops.

Their company’s flagship product, See & Spray, relies on computer vision, machine learning, and advanced robotic technology to distinguish between crops and weeds. The machine then delivers a targeted spray to weeds. According to the company, this innovation can reduce herbicide use by up to 80 percent.

Industry: automotive Application: voice recognition

The car manufacturer has over 400 AI & ML case studies at all levels of production. According to the company, these technologies play an essential role in the production of new vehicles and augment automated driving with advanced, natural experience.

In particular, voice recognition allows drivers to adjust the in-car settings such as climate and driving mode, or even choose the preferred song. BMW owners can also use the voice command to ask the car about its performance status, get guidance on specific vehicle functions, and input a destination.

Industry: media and entertainment Application: emotion recognition

Another exciting case study about artificial intelligence is Affectiva company and its flagship AI products. The company conceived a new technological dimension of Artificial Emotional Intelligence, named Emotion AI. This application allows publishers to optimize content and media spending based on the customers’ emotional responses.

Emotion AI is fuelled by a combination of computer vision and deep learning to discern nuanced emotions and cognitive states by analyzing facial movement.

Industry: manufacturing Application: process optimization

As global enterprises are looking for more ways to optimize, the demand for automation grows. Siemens’ collaboration with Google is a prominent case study on the application of artificial intelligence in factory automation. The manufacturer has teamed up with Google to drive up shop floor productivity with edge analytics.

The expected results are to be achieved via computer vision, cloud-based analytics, and AI algorithms. Optimization will most likely leverage the connection of Google’s data cloud with Siemens’ Digital Industries Factory Automation tools. This will allow companies to unify their factory data and run cloud-based analytics and AI at scale.

Industry: manufacturing Application: semiconductor development

Along with cutting-edge solutions like its memory accelerator, the manufacturing conglomerate also implements AI to automate the highly complex process of designing computer chips. A prominent artificial intelligence case study is Samsung using Synopsys AI software to design its Exynos chips. The latter are used in smartphones, including branded handsets and other gadgets.

Industry: manufacturing Application: predictive maintenance

According to McKinsey , the greatest value from AI in manufacturing will be delivered from predictive maintenance, which accounts for $0.5-$0.7 trillion in value worldwide. The snack food manufacturer and PepsiCo’s subsidiary, Frito-Lay, has followed suit.

The company has a long track record of using predictive maintenance to enhance production and reduce equipment costs. Paired with sensors, this case study of artificial intelligence helped the company reduce planned downtime and add 4,000 hours a year of manufacturing capacity.

Looking over horizon: Technology trends for 2023-2024

Although artificial intelligence case studies are likely to account for the majority of innovations, the exact form and shape of intelligent transformation can vary. Below, you will find the likely successors of AI technologies in the coming years.

Advanced connectivity

Advanced connectivity refers to the various ways in which devices can connect and share data. It includes technologies like 5G, the Internet of Things, edge computing, wireless low-power networks, and other innovations that facilitate seamless and fast data sharing.

The global IoT connectivity imperative has been driven by cellular IoT (2G, 3G, 4G, and now 5G) as well as LPWA over the last five years. Growing usage of medical IoT, IoT-enabled manufacturing, and autonomous vehicles have been among the greatest market enablers so far.

Web 3.0 is the new iteration of the Internet that aims to make the digital space more user-centered and enables users to have full control over their data. The concept is premised on a combination of technologies, including blockchain, semantic web, immersive technology, and others.

Metaverse generally refers to an integrated network of virtual worlds accessed through a browser or headset. The technology is powered by a combination of virtual and augmented reality.

Edge computing

Edge computing takes cloud data processing to a new level and focuses on delivering services from the edge of the network. The technology will enable faster local AI data analytics and allow smart systems to deliver on performance and keep costs down. Edge computing will also back up autonomous behavior for Internet of Things (IoT) devices.

Industries already incorporate devices with edge computing, including smart speakers, sensors, actuators, and other hardware.

Augmented analytics

Powered by ML and natural language technologies, augmented analytics takes an extra step to help companies glean insights from complex data volumes. Augmented analytics also relies on extensive automation capabilities that streamline routine manual tasks across the data analytics lifecycle, reduce the time needed to build ML models, and democratize analytics.

Large-sized organizations often rely on augmented analytics when scaling their analytics program to new users to accelerate the onboarding process. Leading BI suites such as Power BI, Qlik, Tableau, and others have a full range of augmented analytics capabilities.

Engineered decision intelligence

The field of decision intelligence is a new area of AI that combines the scientific method with human judgment to make better decisions. In other words, it’s a way to use machine intelligence to make decisions more effectively and efficiently in complex scenarios.

Today, decision intelligence assists companies in identifying risks and frauds, improving sales and marketing as well as enhancing supply chains. For example, Mastercard employs technology to increase approvals for genuine transactions.

Data Fabric

Being a holistic data strategy, data fabric leverages people and technology to bridge the knowledge-sharing gap within data estates. Data fabric is based on an integrated architecture for managing information with full and flexible access to data.

The technology also revolves around Big data and AI approaches that help companies establish elastic data management workflows.

Quantum computing

An antagonist of conventional computing, the quantum approach uses qubits as a basic unit of information to speed up analysis to a scale that traditional computers cannot ever match. The speed of processing translates into potential benefits of analyzing large datasets – faster and at finer levels.

Hyperautomation

This concept makes the most of intelligent technologies to help companies achieve end-to-end automation by combining AI-fuelled tools with Robotic Process Automation. Hyperautomation strives to streamline every task executed by business users through ever-evolving automated pathways that learn from data.

Thanks to a powerful duo of artificial intelligence and RPA, the hyperautomated architecture can handle undocumented procedures that depend on unstructured data inputs – something that has never been possible.

Turning a crisis into an opportunity with AI

In the next few years, businesses will have to operate against the backdrop of the looming recession and financial pressure. The only way of standing firmly on the ground is to save resources, which usually leaves just two options: layoffs or resource optimization.

While the first option is a moot point, resource optimization is a time-tested method to battle uncertainty. And there’s no technology like artificial intelligence that can better audit, identify, validate, and execute the optimal transition strategy for virtually any industry. From better marketing messages to voice-controlled vehicles, AI adds a new dimension to your traditional business operations.

AI technology to combat recession

Schedule an intro call with our AI consulting experts to explore your business and find out how we can help.

Subscribe to our newsletter!

AI and data science news, trends, use cases, and the latest technology insights delivered directly to your inbox.

By clicking Subscribe, you agree to our Terms of Use and Privacy Policy .

Please leave this field empty.

Related articles

• BI and Big Data
• Data Engineering
• Data Science and AI solutions
• Data Strategy
• ML Consulting
• Generative AI/NLP
• Sentiment/Text Analysis
• InData Labs News

Privacy Overview

4 Incredible AI Case Studies in Content Marketing

By Ashley Sams on March 10, 2022

Wondering how to get started with AI? Take our on-demand Piloting AI for Marketers Series.

Artificial intelligence (AI) is giving businesses the ability to create and promote content at scale.

Which means every business that does content marketing needs to pay attention...

Because if your competitors start adopting AI for content marketing before you, you're toast.

That's because there's more than one AI case study where companies are using AI technology and machine learning to make their content marketing campaigns insanely successful.

Here are four AI case studies to keep an eye on.

1. Vanguard Increases Conversion Rates by 15% with AI

Vanguard is one of the world's biggest investment firms, with $7 trillion under management.

The company needed to promote its Vanguard Institutional business, but it had a problem:

The company does business in an industry that highly regulates what you can say in advertising. As a result, it was hard to stand out in the financial services ad landscape, since everyone used the same type of language.

That's when Vanguard turned to AI language platform Persado. Using AI from Persado, Vanguard was able to personalize its ads based on the specific messaging that resonated most with consumers.

2. Tomorrow Sleep Boosts Web Traffic 10,000%

Sleep system startup Tomorrow Sleep started creating content shortly after its launch with the hope of attracting droves of web visitors.

After several months of pushing out top-quality content and manually tracking and analyzing keyword analytics, they were averaging around 4,000 users to their site every month.

Not bad, but not great. If they wanted to compete with long-standing players in the crowded sleep market, something had to change.

Sleep Tomorrow needed a way to plan and produce content at scale that would reach their target audience.

Enter artificial intelligence.

Tomorrow Sleep began using an AI solution called MarketMuse. MarketMuse's AI-powered content intelligence and strategy platform.

It used the platform's AI research application to understand which high-value topics the company needed to be talking about. Next, it used one of the tool's advanced analytics applications to see where competitors ranked for each of these topics.

This intel illuminated the gaps and opportunities in the current content plan, leading Tomorrow Sleep to create content around key topics where it could quickly establish itself as an expert.

The result?

• 400,000 monthly visits to its website (a 10,000% increase).
• Ranked for multiple positions in a single search result.
• Domain authority to secure Google's featured snippet for specific results.

MarketMuse is an AI-driven assistant for building content strategies. It will show you exactly what terms you need to target to compete in certain topic categories. It'll also surface topics you may need to target if you want to own certain topics.

3. The American Marketing Association Automatically Writes and Hyper-Personalizes Its Newsletter

The American Marketing Association (AMA) strives to be the most relevant voice shaping marketing around the world.

Its website is a marketplace of industry knowledge and resources on branding, careers customer experience, digital marketing, ethics, and more.

One unique aspect of its community is the vast number of industries it represents. Because every business has marketing needs, its members hail from industries across the globe such as education, finance, healthcare, insurance, manufacturing, real estate, and more.

It shares its wealth of knowledge with over 100,000 subscribers in its email newsletter.

However, to serve its subscribers only the most relevant and deserving content, it pulled in rasa.io.

This AI system uses natural language processing and machine learning to generate personalized Smart Newsletters and provide newsletter automation. By doing so, it dramatically increases reader engagement and provides rich insights back to the brand, while saving organizations time.

To personalize each newsletter to a subscriber, the solution uses AI for both curation and filtering content from sources chosen by the AMA. This includes the selection of each individual piece of content, the placement of articles, and the subject line selected for each reader.

The result? A newsletter that provides a perfectly personalized experience to each and every reader.

Plus, the platform is able to infuse the newsletters with AMA's internally produced content and feature it at the top of the newsletter, maximizing visibility.

4. Adobe Generates $10M+ in Revenue with an AI Chatbot + Content

Website content is a key way for consumers to learn about your products and solutions, and find answers to their top questions. And boy does software giant Adobe have a lot of website content.

However, with all the website content the company has, it's sometimes hard to keep consumers engaged and find them exactly what they need at any given moment.

To solve this challenge, Adobe turned to conversational AI from Drift. Drift's chatbot uses AI to have natural language conversations with site visitors at every stage of their journey. The bot was able to direct visitors to what they needed when they needed it. It was also able to hand off conversations to humans when the time was right.

Ashley Sams

Ashley Sams is director of marketing at Ready North. She joined the agency in 2017 with a background in marketing, specifically for higher education and social media. Ashley is a 2015 graduate of The University of Mount Union where she earned a degree in marketing.

Related Posts

How olay uses artificial intelligence to double conversion rate.

Our team reads articles every day to deliver the best artificial intelligence and machine learning news to you. This week we can't stop reading about Olay's use of AI for a doubled conversion rate, Element AI's new podcast for real business applications and Yext's partnership with Amazon Alexa.

5 Powerful Examples of AI in Marketing

How do real brands use AI to improve their marketing? Keep reading to learn about actionable ways top companies we’ve interviewed use marketing AI.

How Brands Target Consumers Better and Sell More with Artificial Intelligence [Case Studies Included]

Real brands are using AI to better target and sell to consumers. Discover actual case studies on how they’re doing it in this post.

When thinking of artificial intelligence (AI) use cases, the question might be asked:  What won’t AI be able to do? The easy answer is mostly manual labor, although the day might come when much of what is now manual labor will be accomplished by robotic devices controlled by AI. But right now, pure AI can be programmed for many tasks that require thought and intelligence , as long as that intelligence can be gathered digitally and used to train an AI system. AI is not yet loading the dishwasher after supper—but can help create a legal brief, a new product design, or a letter to grandma.

We’re all amazed by what AI can do. But the question for those of us in business is what are the best business uses? Assembling a version of the Mona Lisa in the style of Vincent van Gough is fun, but how often will that boost the bottom line? Here are 27 highly productive ways that AI use cases can help businesses improve their bottom line.

Deliver superior customer service

Customer interactions can now be assisted in real time with conversational AI. Voice-based queries use natural language processing (NLP) and sentiment analysis for speech  recognition so their conversations can begin immediately. Using machine learning algorithms, AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. With text to speech and NLP, AI can respond immediately to texted queries and instructions. There’s no need to make customers wait for the answers to frequently asked questions (FAQs) or to take the next step to purchase. And digital customer service agents can boost customer satisfaction by offering advice and guidance to customer service agents.

Personalize customer experiences

The use of AI is effective for creating personalized experiences at scale through chatbots, digital assistants and customer interfaces , delivering tailored experiences and targeted advertisements to customers and end-users. For example, Amazon reminds customers to reorder their most often-purchased products, and shows them related products or suggestions. McDonald’s is building AI solutions for customer care with IBM Watson AI technology and NLP to accelerate the development of its automated order taking (AOT) technology. Not only will this help scale the AOT tech across markets, but it will also help tackle integrations including additional languages, dialects and menu variations. Over at Spotify, they’ll suggest a new artist for the customer’s listening pleasure. YouTube will deliver a curated feed of content suited to customer interests.

Promote cross- and up-selling

Recommendation engines use consumer behavior data and AI algorithms to help discover data trends to be used in the development of more effective up-selling and cross-selling strategies, resulting in more useful add-on recommendations for customers during checkout for online retailers. Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Smarten up smartphones

Facial recognition turns on smartphones and voice assistants, powered by machine learning, while Apple’s Siri, Amazon’s Alexa, Google Assistant and Microsoft’s Copilot use NLP to recognize what we say and then respond appropriately. Companies also take advantage of ML in smartphone cameras to analyze and enhance photos using image classifiers, detect objects (or faces) in the images, and even use artificial neural networks to enhance or expand a photo by predicting what lies beyond its borders.

Introduce personal assistants

Virtual assistants or voice assistants, such as Amazon’s Alexa and Apple’s Siri, are powered by AI. When someone asks a question via speech or text, ML searches for the answer or recalls similar questions the person has asked before. The same technology can power messaging bots, such as those used by Facebook Messenger and Slack—while Google Assistant, Cortana and IBM watsonx Assistant combine NLP to understand questions and requests , take appropriate actions and compose responses.

Humanize Human Resources

AI can attract, develop and retain a skills-first workforce . A flood of applications can be screened, sorted and passed to HR team members with precision. Manual promotion assessment tasks can be automated, making it easier to gain important HR insights with a clearer view of, for example, employees up for promotion and assessing whether they’ve met key benchmarks . Routine questions from staff can be quickly answered using AI.

Create with generative AI

Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating. Generative AI can produce high-quality text, images and other content based on the data used for training.

IBM Research is working to help its customers use generative models to write high-quality  software code  faster, discover  new molecules , and train trustworthy conversational chatbots  grounded on enterprise data. The IBM team is even using generative AI to create  synthetic data  to build more robust and trustworthy AI models and to stand in for real-world data protected by privacy and copyright laws.

Deliver new insights

Expert systems can be trained on a corpus—metadata used to train a machine learning model—to emulate the human decision-making process and apply this expertise to solve complex problems. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions. They can also help businesses predict future events and understand why past events occurred.

Clarify computer vision

AI-powered computer vision enables image segmentation , which has a wide variety of  use cases, including aiding diagnosis in medical imaging, automating locomotion for robotics and self-driving cars, identifying objects of interest in satellite images and photo tagging in social media. Running on neural networks , computer vision enables systems to extract meaningful information from digital images, videos and other visual inputs.

Speed operations with AIOps

There are many benefits to using  artificial intelligence for IT operations (AIOps) . By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination.

AIOps is one of the fastest ways to boost ROI from digital transformation investments. Process automation is often centered on efforts to optimize spend, achieve greater operational efficiency and incorporate new and innovative technologies, which often translate into a better customer experience. More benefits from AI include building a more sustainable IT system and improving the continuous integration/continuous (CI/CD) delivery pipelines.

Automate coding and app modernization

Leading companies are now using generative AI for application modernization and enterprise IT operations, including automating coding, deploying and scaling. For coding, developers can input a coding command as a straightforward English sentence through a natural-language interface and get automatically generated code . Using generative AI with code generation capabilities can also enable hybrid cloud developers of all experience levels to migrate and modernize legacy application code at scale, to new target platforms with code consistency, fewer errors, and speed.

Boost application performance

Ensuring that apps perform consistently and constantly—without overprovisioning and overspending—is a critical AI operations (AIOps) use case. Automation is key to optimizing cloud costs, and IT teams, no matter how skilled they are, don’t always have the capacity to continuously determine the exact compute, storage and database configurations needed to deliver performance at the lowest cost. AI software can identify when and how resources are used, and match actual demand in real time.

Strengthen end-to-end system resilience

To help ensure uninterrupted service availability, leading organizations use real-time root cause analysis capabilities powered by AI and intelligent automation. AIOps can enable ITOps teams to swiftly identify the underlying causes of incidents and take immediate action to reduce both mean time between failures (MTBF) and mean time to repair (MTTR) incidents.

AIOps platform solutions also consolidate data from multiple sources and correlate events into incidents, granting clear visibility into the entire IT environment through dynamic infrastructure visualizations, integrated AI capabilities and suggested remediation actions.

Using predictive IT management, IT teams can use AI to automate IT and network operations to resolve incidents swiftly and efficiently—and proactively prevent issues before they occur, enhance user experiences and cut the cost of and administrative tasks. To help eliminate tool sprawl, an enterprise-grade AIOps platform can provide a holistic view of IT operations on a central pane of glass for monitoring and management.

Lock in cybersecurity

There are many ways AI can use ML to deliver improved cybersecurity, including: facial recognition for authentication, fraud detection, antivirus programs to detect and block malware, reinforcement learning to train models that identify and respond to cyberattacks and detect intrusions and classification algorithms that label events as anomalies or phishing attacks.

Gear up robotics

AI is not just about asking for a haiku written by a cat. Robots handle and move physical objects. In industrial settings, narrow AI can perform routine, repetitive tasks involving materials handling, assembly and quality inspections. AI can assist surgeons by monitoring vitals and detecting potential issues during procedures. Agricultural machines can engage in autonomous pruning, moving, thinning, seeding and spraying. Smart home devices such as the iRobot Roomba can navigate a home’s interior using computer vision and use data stored in memory to understand its progress. And if AI can guide a Roomba, it can also direct self-driving cars on the highway and robots moving merchandise in a distribution center or on patrol for security and safety protocols.

Clean up with predictive maintenance

AI can be used for predictive maintenance by analyzing data directly from machinery to identify problems and flag required maintenance. AI has also been used to improve mechanical efficiency and reduce carbon emissions in engines. Maintenance schedules can use AI-powered predictive analytics to create greater efficiencies.

See what’s ahead

AI can assist with forecasting . For example, a supply-chain function can use algorithms to predict future needs and the time products need to be shipped for timely arrival. This can help create new efficiencies, reduce overstocks and help make up for reordering oversights.

AI can power tasks and tools for almost any industry to boost efficiency and productivity. AI can deliver intelligent automation to streamline business processes that were manual tasks or run on legacy systems—which can be resource-intensive, costly and prone to human error. Here are some of the industries that are benefiting now from the added power of AI.

With applications of AI, automotive manufacturers are able to more effectively predict and adjust production to respond to changes in supply and demand. They can streamline workflows to increase efficiency and reduce time-consuming tasks and the risk of error in production, support, procurement and other areas. Robots help reduce the need for manual labor and improve defect discovery, providing higher quality vehicles to customers at a lower cost to the business.

In education and training , AI can tailor educational materials to each individual student’s needs. Teachers and trainers can use AI analytics to see where students might need extra help and attention. For students tempted to plagiarize their papers or homework, AI can help spot the copied content. AI-driven language translation tools and real-time transcription services can help non-native speakers understand the lessons.

Companies in the energy sector can increase their cost competitiveness by harnessing AI and data analytics for demand forecasting, energy conservation, optimization of renewables and smart grid management. By introducing AI into energy generation, transmission and distribution processes, AI can also improve customer support, freeing up resources for innovation. And for customers using supplier-based AI, they can better understand their energy consumption and take steps to reduce their power draw during peak demand periods.

Financial services

AI-powered FinOps (Finance + DevOps) helps financial institutions operationalize data-driven cloud spend decisions to safely balance cost and performance in order to minimize alert fatigue and wasted budget. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions. Banks and other lenders can use ML classification algorithms and predictive models to suggest loan decisions.

Many stock market transactions use ML with decades of stock market data to forecast trends and ultimately suggest whether and when to buy or sell. ML can also conduct algorithmic trading without human intervention. ML algorithms can predict patterns, improve accuracy, lower costs and reduce the risk of human error.

The  healthcare industry is using intelligent automation with NLP to provide a consistent approach to data analysis, diagnosis and treatment. The use of chatbots in remote healthcare appointments requires less human intervention and often a shorter time to diagnosis. On-site, ML can be used in radiology imaging, with AI-enabled computer vision often used to analyze mammograms and for early lung cancer screening. ML can also be trained to create treatment plans, classify tumors, find bone fractures and detect neurological disorders.

In genetic research, gene modification and genome sequencing, ML is used to identify how genes impact health. ML can identify genetic markers and genes that will or will not respond to a specific treatment or drug and may cause significant side effects in certain people.

With AI, insurance providers can virtually eliminate the need for manual rate calculations or payments and can simplify processing claims and appraisals. Intelligent automation also helps insurance companies adhere to compliance regulations more easily by ensuring that requirements are met. This way, they are also able to calculate the risk of an individual or entity and calculate the appropriate insurance rate.

Manufacturing

Advanced AI with analytics can help manufacturers create predictive insights on market trends. Generative AI can speed and optimize product design by helping companies create multiple design options. AI can also assist with suggestions for boosting production efficiency. Using historical data of production, generative AI can predict or locate equipment failures in real time—and then suggest equipment adjustments, repair options or needed spare parts.

Pharmaceuticals

For the life sciences industry, drug discovery and production require an immense amount of data collection, collation, processing and analysis. A manual approach to development and testing could lead to calculation errors and require a huge volume of resources. By contrast, the production of Covid-19 vaccines in record time is an example of how intelligent automation enables processes that improve production speed and quality.

AI is becoming the secret weapon for retailers to better understand and cater to increasing consumer demands. With highly personalized online shopping, direct-to-consumer models and delivery services competing with retail, generative AI can help retailers and e-commerce firms improve customer care, plan marketing campaigns, and transform the capabilities of their talent and their applications. AI can even help optimize inventory management.

Generative AI excels at handling diverse data sources such as emails, images, videos, audio files and social media content. This unstructured data forms the backbone for creating models and the ongoing training of generative AI, so it can remain useful over time. Leveraging this unstructured data can extend benefits to various aspects of retail operations, including enhancing customer service through chatbots and facilitating more effective email routing. In practice, this could mean guiding users to the appropriate resources, whether that’s connecting them with the right agent or directing them to user guides and FAQs.

Transportation

AI informs many transportation systems these days. For instance, Google Maps uses ML algorithms to check current traffic conditions, determine the fastest route, suggest places to “explore nearby” and estimate arrival times.

Ride-sharing applications such as Uber and Lyft use ML to match riders and drivers, set prices, examine traffic and, like Google Maps, analyze real-time traffic conditions to optimize driving routes and estimate arrival times.

Computer vision guides self-driving cars. An unsupervised ML algorithm enables self-driving cars to gather data from cameras and sensors to understand what’s happening around them, and enables real-time decision-making.

Much of what AI can do seems miraculous, but much of what gets reported in the general media is frivolous fun or just plain scary. What is now available to business is a remarkably powerful tool that can help many industries and functions make great strides. The companies that do not explore and adopt the most beneficial AI use cases will soon be at a severe competitive disadvantage. Keeping an eye out for the most useful AI tools, such as IBM ® watsonx.ai™, and mastering them now will pay great dividends.

Get the latest tech insights and expert thought leadership in your inbox.

Explore IBM® watsonx.ai™

People + AI Guidebook

The case studies share real world stories from teams who have designed AI-driven products using human-centered AI best practices.

Guidance in action

A collection of real world examples of human-centered AI design, done with or in the spirit of the Guidebook.

We add new case studies on a rolling basis.

Google Photos

The Google Photos team on balancing user control and automation in AI-powered photo memories.

Relevant patterns

The BenchSci team on designing a user-centered, AI-powered biomedical research tool.

Google Flights

The Google Flights team on finding a user-friendly way to communicate AI-powered price predictions.

Google’s Read Along team on designing a parent and kid-friendly product to grow child literacy.

Google’s Jigsaw team on designing a user-controlled, AI-enabled comments moderation tool.

• Free Resources

Artificial Intelligence Case Studies: Two companies that boosted brand awareness with AI and another marketer that used humans instead

This article was published in the MarketingSherpa email newsletter .

“Apply to join our new research cohort where we will help you test and build an AI-calibrated MECLABS SuperFunnel,” Flint McGlaughlin offered in Landing Page Conversion: 4 powerful ways to develop a cohesive, effective strategy .

It’s part of our effort (MECLABS Institute is the parent organization of MarketingSherpa) to work with marketers and entrepreneurs to determine how to best use current and emerging AI-driven technologies to build an effective funnel.

But rest assured, to serve you, this reporter has to remain a skeptic of artificial intelligence, machine learning, and really, every other “next best thing.” In much the same way I’m guessing a magician can’t muster up the suspension of disbelief necessary to watch someone else’s magic show, after a lifetime in the marketing and advertising industry I just can’t hop on a bandwagon without a healthy dose of skepticism.

So, to help you determine when to use (and avoid) AI, today we bring you two stories of when brands leveraged AI…but also a story about a brand that avoided using the technology.

First up, a television network that used artificial intelligence and machine learning to get more traffic from organic distribution. Then, how SAP used AI to avoid cookies and increase brand awareness with a paid campaign. And finally, an adventure travel marketplace that invested in humans instead of AI to increase sales from organic traffic.

Quick Case Study #1: How artificial intelligence and machine learning helped television network increase pageviews 63% from Twitter

Sistema Brasileiro de Televisão (SBT) is a free-to-air Brazilian broadcaster with 114 television stations and over 6,000 employees. The network’s website attracts 11 million unique viewers and more than 99 million pageviews per month. The TV network as 12 million followers on its main Facebook page.

Social media challenge

On an average day, SBT posts between 100 to 150 pieces of content to Facebook alone.

“We do not have a centralized social media team, so every content production team ends up posting independently,” says Rodrigo Hornhardt, Journalism Integration and Planning Manager, Sistema Brasileiro de Televisão . In total, he estimates that around 20 people throughout the company post regularly to SBT’s social media accounts, rendering the post scheduling process time-consuming and convoluted.

SBT posts content to a wide variety of social platforms, but Facebook is the most important. According to Hornhardt, “around 40% to 50% of our web traffic comes from Facebook.”

Social media content approach – leverage artificial intelligence and machine learning

To solve these challenges, SBT turned to marketing automation technology powered by artificial intelligence (AI) and machine learning.

Before, SBT’s staff had undertaken the laborious task of posting new content manually — a workflow that was as time consuming as it was inefficient, especially given the decentralized nature of SBT’s social media management. “With so many people posting so much content across the company on different platforms, AI is so important in keeping everything aligned,” notes Hornhardt.

SBT’s reasons for selecting an AI solution were manifold: “The possibility of having AI recommend the best content to post was important, but so, too, was the ease with which multiple people can manage posting for a unified output. The ability to automate workflows was also a key consideration,” he said.

To increase the reach, visibility, and engagement on posts, the team makes regular use of real-time platform trend data alongside SBT’s own audience data to determine the best time to share content. This not only reflects audience habit — when do SBT’s readers most often engage with posts? — but also the ever-changing factors of Facebook’s Feed algorithm. Hornhardt notes that SBT News in particular uses AI to understand the optimal time to publish and maximize the impact of the multiple news items it posts each hour.

AI technology determines optimal share timing by constantly analyzing and computing the predicted performance of shares to maximize their inclusion in the Feed. Using advanced machine learning, it becomes possible to continuously reverse engineer the workings of Facebook’s algorithm, producing a sophisticated and accurate picture of the best time to post to drive engagement.

When posts are shared, AI automatically continues to update each post’s optimal time based on the latest data, pushing certain posts back if new, higher-potential posts are added, or bringing posts forward if their short-term potential goes up. This is just one example of how using AI technology to augment SBT’s social sharing has meant, in Hornhardt’s word’s, “increased reach and organic and substantial growth.”

Social media results

By incorporating artificial intelligence into its social media strategy, SBT saw strong improvements in performance on social media. Within four months of adopting this AI technology, SBT’s social media pages saw increases in daily clicks of 25%, whilst daily organic impressions rose by 61%. On Facebook, SBT saw a 52% increase in daily impressions, while the company also saw improvement gains on Twitter — pageviews from the platform increased by 63%.

In addition to these performance gains, SBT has increased workflow efficiency using automation, bringing time savings in the process. Within four months of adopting AI, the team shared almost 40,000 posts; making these shares via AI and using automation has saved the company 14 hours per day.

“Brands and content producers can greatly benefit from intelligent automation, but it must be tailored to their content and audience,” says Simran Cashyap, Head of Product & Design, Echobox (SBT’s content automation provider). “While each brand must experiment to determine the ideal level of automation for them, we tend to find that the more a brand automates, the bigger the performance gains, as the AI algorithms have more opportunities to continuously learn from the content and audiences.”

By relying on AI’s capability of calculating the optimal share time, the team has increased traffic and impressions whilst saving significant amounts of time. Daniela Nobre, Customer Success Representative, Echobox, explains AI and automation “completely overhaul a company's organic marketing strategy by digging into granular data, something little to no marketing teams could try to replicate.”

For Hornhardt, this is only the start of what could become a more specialized social strategy. “We need to dedicate more working time to planning and defining strategies for networks,” he told us. “One way would be to have specialists dedicated to this strategy with the possibility of creating an audience/networking team. We see AI supporting us in this endeavor.”

Quick Case Study #2: How SAP used AI-powered contextual intelligence to increase brand awareness 4% without cookies

The demise of the third-party cookie has left data-driven companies with a new focus: to build cookie-less alternatives into their marketing strategy. And that’s exactly what global software company SAP did this year.

The team has always had a robust marketing strategy in place, but with the end of the third-party cookies looming, it recognized the increasingly pressing need to focus on privacy-first, cookie-less campaigns.  

The contextual experiment to overcome the cookie-less challenge

The team chose to place its ads alongside contextually relevant content, targeting key business decision makers based on what they had chosen to look at in the moment.

They used contextual targeting technology that uses AI to see and analyze the full content of a page by scanning all of the main content data signals – from text and imagery to audio and video – for accurate and safe ad placement, without the need for third-party cookies or any personally identifiable information (PII).

The team used a wrap-style ad that wraps around the content the visitor is viewing. The ad had an entrance animation to attract the user’s attention. The right panel had a countdown to the advertised webcast. And the left panel had a message and CTA.

Creative Sample #1: Wrap ad for SAP

When the user scrolled, the background changed to another visual. The headline was repositioned onto the left panel, so it had a consistent presence.

Creative Sample #2: Scrolled state of wrap ad for SAP

“Throughout the campaign, the aim was to boost brand and product awareness,” commented Moritz Fisecker, Integrated Media Manager EMEA, SAP .

Advertising campaign measurement methodology

In order to successfully establish the impact of SAP’s campaign, the team analyzed the difference in results for a control sample (users who were not exposed to the high-impact ad creative) and the target audience (users who were exposed to the ad creative).

Finally, they used eye tracking to understand the level of consumer attention the ads garnered.

Advertising campaign results

“Overall, the campaign results proved that brands can reach consumers just as effectively – if not more effectively – using a completely cookie-less approach to digital marketing rather than relying on behavioral data,” Fisecker said.

More than half (61%) of users exposed to the campaign took or intended to take some sort of action; and the ads had a 93.2% viewability rate.

Others results included:

• 0.9% click-through rate
• 4% uplift in overall awareness as a direct result of the campaign
• 5% positive shift among the target audience
• 5% improvement in perceptions of SAP as offering the best business software and solutions
• 7% improvement in perceptions of SAP as a trusted brand 
• 1 in 3 users felt the ad was informative

In addition, the study concluded that the ad creative drove significantly more users to become “very interested” in finding out more after being exposed to the ad.

“In addition to awareness, Lumen Research and On Device Research (ODR) – SAP’s measurement partners for the campaign – confirmed that users who saw the ads had a heightened interest in finding out more. The results also affirm the role of contextual data in the future of mar tech. In this new cookie-less, privacy-first era, tapping into the customer’s mindset ‘in the moment’ will be the key to inspiring them, and ultimately, influencing their behavior,” said Peter Wallace, General Manager EMEA, GumGum (SAP’s contextual intelligence partner).

Quick Case Study #3: How adventure travel marketplace generated $1 million in sales from organic traffic by having humans (instead of machines) write content

10Adventures is an online marketplace that connects adventure travel enthusiasts with local guides around the world. In addition to allowing users to book adventure travel, it recently launched a subscription-based GPS-trail app that enables people to safely explore the outdoors.

“We launched 10Advnetures in 2019, with a WordPress MVP (minimum viable product) and great content,” said Richard Campbell, Founder & CEO, 10Adventures . The site currently has more than 1.5 million annual unique visitors.

Let’s take a look at the most – and the least – effective tactic the team used to grow website traffic.

Most Effective Tactic – Human-written content paired with SEO basics and site speed enhancements

“We have had an incredible journey to build our skills in ranking high in Google,” Campbell said. “There are literally dozens of small bits of work we have done to get our content to the top of Google and Bing.”

The team invested the time necessary to create engaging, factual, useful content that targeted the right keywords.

 “Users want quality content. The web is filled with superficial content, written by content farms with lots of mistakes. This is especially true in the outdoors market,” Campbell said. “In the world of content, the cost of efficiency usually comes at the expense of quality. Many businesses will turn to cheaper or faster solutions like AI writers or outsourcing to meet their goals but won’t see the desired performance if the quality isn’t there.”

The team’s most popular content is their free route guides, especially regional pages of the best hikes (or road bike rides, backpacking routes, cross-country skiing, etc.) in a geographic region.

Creative Sample #3: Route guide content on adventure travel marketplace website

The team then implemented the following elements of on-site SEO:

• URL structure
• Page titles
• Internal links

For external links, the team tried third-party backlink providers but found that doing it themselves was cheaper and ensured that all backlinks were organic.

The team felt a Google algorithm change punished the site for having slow site speed. “Algorithm changes are a constant reality, and Google places plenty of demands on websites to perform,” Campbell said. “Being listed first in Google is tough, and recently site speed has been an important metric that we felt was hindering our ability to be first in Google.”

Campbell continued, “Unfortunately, our site was based on WordPress, and we had done everything we could to improve site speed.” This included identifying performance bottlenecks and optimizing the databases and code behind them.

Ultimately, though, the team had to implement a new solution, and moved to a static site, rebuilding the entire front-end in a new language. This meant each page is rendered and doesn’t need database calls, creating a quicker user experience and greatly improved page speed performance. In addition, they now have edge servers around the world. In the month since this change, organic traffic is up 67% compared to last year. 

For this reason, the team has baked in regular content reviews to look for opportunities to rank higher, as well as evaluate the website’s performance.

Least Effective Tactic – Paid marketing

The team found that paid marketing was not only expensive when compared to what they were doing with organic, but it also resulted in a lower conversion rate – their paid advertising leads convert at 10% of their organic leads.

The team has created many permutations of different ads, but in general can’t replicate what worked pre-pandemic. In 2019 when they launched, customer acquisition cost was about $300. Since the end of last year and the first seven months of this year, it was around $1,000.

Thanks to the traffic generated by their organic tactics, they now have a registered user base of more than 50,000.

The previously mentioned increase in organic traffic has also led to growth in another key area of the business – sales. When looking at the 10Adventures marketplace and tour sales over the last 18 months, the team saw steady growth that has culminated in nearly $1 million of sales over that period, and almost 5x growth year-on-year.

Going forward, the team is working on experiments with their new site to understand their ability to influence rankings from on-page rewrites and other small-scale optimizations. They want to know if it’s easier to move content from third place to second, from eighth to third, or from 40th to eight, etc.  Does this ability vary based on the type of content, or the location the content describes? These discoveries will guide their work in the coming year to continue to grow their organic traffic.

Related Resources

High-Converting Landing Page: If you don’t ask this question you will never maximize conversion

Artificial Intelligence and Machine Learning in Marketing: What marketers (even those who don’t care about tech) should know about AI and ML

Marketing Funnel: 3 quick case studies to help you increase conversion

Improve Your Marketing

Join our thousands of weekly case study readers.

Enter your email below to receive MarketingSherpa news, updates, and promotions:

Note: Already a subscriber? Want to add a subscription? Click Here to Manage Subscriptions

Get Better Business Results With a Skillfully Applied Customer-first Marketing Strategy

The customer-first approach of MarketingSherpa’s agency services can help you build the most effective strategy to serve customers and improve results, and then implement it across every customer touchpoint.

Get headlines, value prop, competitive analysis, and more.

Marketer Vs Machine

Marketer Vs Machine: We need to train the marketer to train the machine.

Free Marketing Course

Become a Marketer-Philosopher: Create and optimize high-converting webpages (with this free online marketing course)

Project and Ideas Pitch Template

A free template to help you win approval for your proposed projects and campaigns

Six Quick CTA checklists

These CTA checklists are specifically designed for your team — something practical to hold up against your CTAs to help the time-pressed marketer quickly consider the customer psychology of your “asks” and how you can improve them.

Infographic: How to Create a Model of Your Customer’s Mind

You need a repeatable methodology focused on building your organization’s customer wisdom throughout your campaigns and websites. This infographic can get you started.

Infographic: 21 Psychological Elements that Power Effective Web Design

To build an effective page from scratch, you need to begin with the psychology of your customer. This infographic can get you started.

Receive the latest case studies and data on email, lead gen, and social media along with MarketingSherpa updates and promotions.

• Your Email Account
• Customer Service Q&A
• Search Library
• Content Directory:

Questions? Contact Customer Service at [email protected]

© 2000-2024 MarketingSherpa LLC, ISSN 1559-5137 Editorial HQ: MarketingSherpa LLC, PO Box 50032, Jacksonville Beach, FL 32240

The views and opinions expressed in the articles of this website are strictly those of the author and do not necessarily reflect in any way the views of MarketingSherpa, its affiliates, or its employees.

• Explore AI by Industry PLUS
• Consumer goods
• Heavy industry
• Natural resources
• Professional services
• Transportation
• AI Best Practice Guides PLUS
• AI White Paper Library PLUS
• AI Business Process Explorer PLUS
• Enterprise AI Newsletter
• Emerj Plus Research
• AI in Business Podcast
• AI in Financial Services Podcast
• AI in Healthcare and Life Sciences Podcast
• The AI Consulting Podcast
• Precisely – Building Trust in Data
• Shift Technology – How Insurers are Using AI
• Uniphore – The Future of Banking CX in APAC
• Uniphore – The Economic Impact of Conversational AI and Automation
• Uniphore – The Future of Complaints Management
• Uniphore – Conversational AI in Banking

AI Case Studies Articles and Reports

Explore in-depth case studies of real artificial intelligence applications in business, including applications in marketing, finance, security, and other sectors.

One of Australasia’s Largest Banks Unifies Their AI Efforts

The following is a case study for Emerj's AI Opportunity Landscape research. To learn more about how we help companies develop winning AI strategies and identify the highest-ROI applications, watch the two-minute video summary of our AI Opportunity Landscape research. Problem The bank had many scattered AI projects, but struggled with:

Online Education Company Improves Customer Support with Autosuggestion of Macros

Technology Provider: DigitalGenius is an artificial intelligence solutions provider for customer service operations.

Automotive Repair Equipment OEM uses AI to Monetize Repair Service Data

Tech Provider: Predii - a company which specializes in building AI platforms designed for repair and maintenance services in the industrial equipment and automotive sector

The Department of Homeland Security Uses AI-Enhanced Entity Resolution for its Global Travel Assessment System (GTAS)

Tech Provider: TAMR - a company that automates the organization of a company’s enterprise-wide data through a machine learning-driven plus human-guided solution

Smart Home Services Provider Automates Report Creation with AI and Customer Data

Technology Provider: Automated Insights is a provider of natural language generation platform, Wordsmith, that converts big data into narratives or summary understandable by humans

User Engagement Company Improves Lead Quality Using Predictive Analytics

Technology Provider: Ignite Technologies (which acquired Infer, a Mountain View, CA-based marketing optimization and machine learning company).

Smart Home Services Provider Uses Natural Language Generation to Create Highly Personalized Website Copy

Technology Provider: Automated Insights is a provider of a natural language generation platform, Wordsmith, that converts big data into narratives or summaries understandable by humans.

Microsoft Gets the Pulse of Customer Sentiment with Natural Language Processing

Tech Provider:  Lexalytics - A natural language processing and text analytics software and service provider. Client Company: Microsoft - A multinational technology company which develops, licenses, sells and supports software, hardware, and services. Industry:  Consumer goods Function: Business intelligence and analytics, Process automation Tech Provider Contact Person: Charles Le Gros

• Market Reasearch and Advisory
• AI Presentations and Keynotes
• Emerj Plus Membership
• AI In Business Podcast
• AI In Finance Services Podcast
• Subscribe to our AI Newsletter
• Advertise with us
• Terms and Conditions
• Refund and Cancellation Policy
• Privacy Policy

15 Top Machine Learning Case Studies to Look Into Right Now

Introduction.

Machine learning is one of the most valuable skills that a data science professional can have in 2024.

According to this report from Gartner , as the adoption of machine learning continues to grow across industries, it is evolving from mere predictive algorithms to a more data-centric discipline.

Machine learning case studies are in-depth analyses of real-world business problems in which machine learning techniques are applied to solve the problem or provide insights .

If you’re looking for an updated list of machine learning case studies to explore, you’re in the right place. Read on for our hand-picked case studies and tips on solving them.

Why Should You Explore Machine Learning Case Studies?

Better job prospects.

Employers are often concerned that their recruits lack business acumen or data-handling skills. Working on real-world case studies and adding them to your resume will showcase your hands-on expertise, thereby bolstering your CV.

We’ve seen numerous examples where adding relevant personal and academic projects to interviewees’ resumes has helped them get their foot in the door.

Helps You Identify In-Demand Skills

Case studies often highlight tools and techniques currently in demand within a particular industry. By studying them, you can tailor your preparation strategy to acquire these skills, aligning your expertise with what leading tech firms are looking for.

This will enhance your prospects in a very competitive job market.

Insight On Industry-Specific Challenges

Industries leverage data science and machine learning in different ways. By examining case studies across healthcare, finance, or retail, you’ll gain insight into how ML solutions are customized to meet industry-specific challenges.

For example, suppose you are planning to interview at a banking organization.

In that case, you can leverage what you learned to discuss industry-relevant ML applications and propose solutions to common banking and financial challenges. This will help you land specialized roles that are much more lucrative than general data roles.

With these benefits in mind, let’s explore the top 15 machine learning case studies that are particularly relevant in 2024.

What Are the Best ML Case Studies Right Now?

We’ve curated examples that highlight the innovative use of AI and ML technologies and reflect common business challenges in today’s job market.

1. Starbucks Customer Loyalty Program

Starbucks aims to enhance customer engagement and loyalty by delivering personalized offers and recommendations.

The goal is to analyze customer data to uncover patterns and preferences for tailoring marketing efforts and increase customer satisfaction by making each customer feel uniquely understood.

• Objective : To increase retention, boost sales, and enhance the customer experience.
• How to build : Cluster customers based on similar behaviors, identify the types of offers most likely to appeal to each group and develop a recommendation engine to generate personalized offers. Key tools include data management systems like SQL databases for structured data storage, Python for data processing and machine learning with libraries such as pandas and scikit-learn, and a platform like TensorFlow to develop and train the recommendation models.

2. Amazon Pricing Case Study

Amazon employs a dynamic pricing model to avoid updating prices manually. It uses sophisticated algorithms to adjust prices in real time based on demand, competitor pricing, inventory levels, and customer behavior to achieve maximum profitability.

• Objective : To maximize revenue and market share by implementing optimal pricing across millions of products.
• How to build : First aggregate real-time and historical data. Train regression models and ensemble methods like random forests or boosted trees to predict optimal price points. These models learn from past pricing outcomes and continuously adjust to changing variables. Use core technologies including a big data platform like Apache Hadoop and machine learning frameworks such as TensorFlow or PyTorch.

Here is an interesting pricing problem for calculating electricity consumption .

3. Amazon’s Real-Time Fraud Detection System

Another case study from Amazon—its fraud detection system uses machine learning to identify and prevent fraudulent transactions as they occur.

• Objective : To accurately identify potentially fraudulent transactions in real time without impacting the user experience with false positives.
• How to build : Create relevant features from raw data that help identify suspicious activity, such as unusual transaction sizes or patterns that deviate from the norm. Employ ensemble methods like random forest or gradient boosting machines (GBMs) due to their robustness and ability to handle unbalanced datasets. Tools you’d typically use include Amazon Redshift, frameworks such as TensorFlow, and Apache Kafka for real-time streaming.

Here is our takehome project on a similar business problem: detecting credit card fraud.

4. Netflix’s Recommendation Engine

Netflix’s recommendation engine analyzes individual viewing habits to suggest shows and movies that users are likely to enjoy.

This personalization is critical for enhancing user satisfaction and engagement and driving continued subscription renewals.

• Objective : To maximize the relevance of recommended content, promote a diverse array of content that users might not find on their own, and increase overall viewing time and subscriber retention.
• How to build : Extract useful features from the data, such as time stamps, duration of views, and metadata of the content like genres, actors, and release dates. Netflix uses various methods, including collaborative filtering, matrix factorization, and deep learning techniques, to predict user preferences. Also, test and refine the algorithms using A/B testing and other evaluation metrics to ensure the recommendations are accurate and engaging.

For more practice, the MovieLens dataset is a classic choice for building recommendation systems.

5. Google’s Search Algorithm

Google’s search engine uses complex machine learning algorithms to analyze, interpret, and rank web pages based on their relevance to user queries.

The core of it involves crawling, indexing, and ranking web pages using various signals to deliver the most relevant results.

• Objective : To provide the most accurate and relevant search results based on the user’s query and search intent with speed and efficiency.
• Web crawling : Use crawlers to visit web pages, read the information, and follow links to other pages on the internet.
• Indexing : Organize the content found during crawling into an index. This index needs to be structured so that the system can find data quickly in response to user queries.
• Ranking algorithm : Google uses the PageRank algorithm, which evaluates the quality and quantity of links to a page to determine a rough estimate of the website’s importance.
• Query processing : Develop a system to interpret and process user queries, applying natural language processing techniques to understand context.
• Tools : Open-source web crawlers like Apache Nutch, scalable databases such as Apache Cassandra, and NLTK or spaCy libraries in Python for understanding user queries.

6. Telecom Customer Churn Prediction

In the telecom industry, customer churn prediction models identify customers likely to cancel their services.

This allows companies to address at-risk customers with targeted interventions.

• Objective : To identify customers likely to churn by understanding the factors that lead to customer dissatisfaction.
• How to build : Use ML algorithms such as logistic regression, decision trees, or ensemble methods like random forests or gradient boosting machines to build the model.

The Telco Customer Churn dataset on Kaggle is very popular for customer churn prediction projects .

7. Loan Application Case Study

Machine learning models are increasingly used by financial companies to streamline and improve the decision-making process for loan applications.

These models analyze applicants’ financial data, credit history, and other relevant variables to predict the likelihood of default.

• Objective : To improve the accuracy of loan approval decisions by predicting the risk associated with potential borrowers. Automating this process also reduces the time to approve a loan application.
• How to build : Develop features from raw data that are predictive of loan repayments, such as debt-to-income ratio, credit utilization rate, and past financial behavior. Use supervised learning algorithms like logistic regression, decision trees, or more sophisticated methods such as gradient boosting or neural networks to train the model on historical data.

Here is a list of more fintech projects to try.

8. LinkedIn’s AI-Powered Job Matching System

LinkedIn leverages advanced algorithms to connect job seekers with the most relevant opportunities.

This system analyzes job postings and user profiles to make accurate recommendations that align with the user’s career goals and the employer’s needs.

• Objective : To refine the accuracy of job matches, increase user engagement, and streamline the hiring process for employers.
• How to build : Clean and transform data from profiles, job listings, and user interactions using NLP methods to extract relevant features for job matching. Use collaborative filtering and neural networks on this data to predict user preferences and match jobs.

9. Twitter Contextual Ad Placement Study

Twitter’s contextual ad placement system dynamically serves ads based on real-time analysis of user interactions.

• Objective : To enhance user engagement with ads by making them more relevant and less intrusive. This relevance increases the likelihood of users interacting with the ads, which improves the efficiency of ad campaigns.
• How to build : Extract useful features from the data, such as keywords from tweets, used hashtags, sentiment of the tweets, and user engagement rates with similar content. Models like logistic regression for click prediction or deep learning models for more complex patterns are common choices for the algorithm. Finally, implement the model using a real-time processing framework to allow for dynamic ad placement as user behavior changes.

10. Uber’s Demand Forecasting

Uber’s demand forecasting model leverages machine learning to predict future ride demand in various geographic areas.

This system helps optimize the allocation of drivers while maximizing earnings.

• Objective : To balance supply and demand across Uber’s network. This includes lowering wait times, maximizing earnings for drivers, and optimizing surge pricing by predicting spikes in demand.
• How to build : Employ time series forecasting models like ARIMA or more complex models such as LSTM (long short-term memory) networks, which are capable of handling sequential data and can learn patterns over time.

11. Hotel Recommendation System

These systems analyze vast amounts of data, including previous bookings, user ratings, search queries, and user demographics, to predict hotels that a customer might prefer.

This approach enhances user satisfaction and boosts booking conversion rates for platforms.

• Objective : The system aims to increase the likelihood of bookings by providing relevant recommendations that match user preferences. In the long term, personalized experiences help build customer loyalty, as users are more likely to return to a service that understands their needs.
• How to build : Create features that can help in understanding user preferences, such as preferred locations, amenities, price range, and types of accommodations (e.g., hotels, B&Bs, resorts). Features related to temporal patterns, like booking during a particular season or for specific types of trips (business, leisure), can also influence decisions. Implement machine learning algorithms such as collaborative filtering, which can recommend hotels based on similar user preferences, or content-based filtering, which suggests hotels similar to those the user has liked before. Advanced models also integrate deep learning to handle the complexity of the data.

Here is an interesting takehome problem on recommending Airbnb homes to users .

12. IBM’s Weather Prediction

IBM’s The Weather Company harnesses advanced machine learning and artificial intelligence to enhance the accuracy of weather forecasts.

Through these tools, IBM aims to provide precise weather predictions that can inform decisions ranging from agriculture to disaster response.

• Objective : To enhance the precision of weather forecasts to better predict events such as storms, rainfall, and temperature fluctuations. Also, it aims to support decision-making in various sectors for operational decisions and advance climate research.
• How to build : Advanced machine learning models like neural networks and ensemble methods are utilized in order to analyze complex weather data. The models are regularly refined and tested against actual weather outcomes to improve their accuracy over time. High-capacity databases like IBM Db2 or cloud storage solutions are used to handle large datasets.

13. Zillow’s House Price Prediction System

Zillow’s house price prediction, well-known through its “Zestimate” feature, utilizes machine learning to estimate the market value of homes across the US.

This system analyzes data from various sources, including property characteristics, location, market conditions, and historical transaction data to generate a market value in near real time.

• Objective : To provide homeowners and buyers with a reliable estimate of property values, helping them make informed buying, selling, and refinancing decisions.
• How to build : Develop predictive features from the collected data. This involves extracting insights from the raw data, like normalizing prices by square footage or adjusting values based on local real estate market health. Employ advanced regression models and techniques like gradient boosting or neural networks to learn from complex datasets. Libraries such as XGBoost, TensorFlow, or PyTorch are commonly employed.

14. Tesla’s Autopilot System

Tesla’s Autopilot system is a highly advanced driver-assistance system that uses machine learning to enable its vehicles to steer, accelerate, and brake automatically under the driver’s supervision.

The system relies on a combination of sensors, cameras, and algorithms to interpret the vehicle’s surroundings, make real-time driving decisions, and learn from diverse driving conditions.

• Objective : To reduce the likelihood of accidents by assisting drivers with advanced safety features and optimizing driving decisions and to improve the system toward achieving full self-driving capabilities eventually.
• How to build : Key features such as object detection, lane marking recognition, and vehicle trajectory predictions are derived from the raw data to train models. Convolutional neural networks (CNNs) are employed to process and interpret the sensory input. Tesla also uses over-the-air software updates to deploy new features based on aggregated fleet learning.

15. GE Healthcare Image Analysis

GE Healthcare leverages machine learning to enhance the analysis of medical images to improve the accuracy and efficiency of diagnostics across various medical fields.

This technology allows for more precise identification and evaluation of anomalies in medical imaging, such as MRI, CT scans, and X-rays.

• Objective : To detect and classify anomalies in medical images that might be too subtle for human eyes. It also accelerates image analysis and shortens diagnosis time, which is important for providing quick patient care.
• How to build : Collect large sets of annotated medical images, which include a variety of imaging types and conditions. Label these images accurately to serve as a training set. Extract relevant features from the images, such as texture, shape, intensity, and spatial patterns of the imaged tissues or organs. Convolutional neural networks (CNNs) are particularly effective due to their ability to pick up on spatial hierarchies and patterns and should be deployed. Be sure to rigorously test the models against new, unseen images to ensure they generalize well and maintain high accuracy and reliability.

Frequently Asked Questions

What skills can i learn from machine learning case studies that are applicable to data science jobs.

Employers look for candidates with a mix of technical and soft skills.

Some competencies you can develop through exploring and analyzing case studies are problem-solving, critical thinking, better data interpretation, an understanding of commonly used ML algorithms, and coding skills in relevant languages.

We recommend that you work on these problems in addition to reading up on them. You can use public datasets provided by Kaggle or UCI Machine Learning Repository or Interview Query’s storehouse of takehome assignments .

To help you get started, we’ve created a comprehensive guide on how to start a data analytics project .

Are there beginner-friendly case studies in machine learning?

The examples we’ve provided in the list above are a mix of beginner-friendly and advanced ML case studies.

There are more beginner-friendly cases you can explore on Kaggle, such as the iris flower classification, Titanic survival predictions, and basic revenue forecasting for e-commerce.

We’ve also compiled a list of data science case studies categorized by difficulty level.

How do I use machine learning case studies to craft a better resume or portfolio?

You can tailor your resume to highlight ML case studies or projects you’ve worked on that match the skills and industry you’re applying to.

For each project, provide a concise title and description of what the project entailed, the tools and techniques used, and its outcomes.

Wherever possible, quantify the impact of the project , for example, the model’s accuracy. Use action verbs like “developed,” “built,” “implemented,” or “analyzed” to increase persuasion.

Lastly, rehearse how you would present your project in an interview, an often overlooked step in getting selected. On a related note, you can try a mock interview with us to test your current preparedness for a project presentation.

To wrap up, staying updated on, exploring, and implementing machine learning case studies is a clever strategy to showcase your hands-on experience and set you apart in a competitive job market.

Plan your interview strategy, considering the perspective of your desired future employer and tailoring your project selection to the skills they want to see.

Here at  Interview Query , we offer multiple learning paths , interview questions, and both paid and free resources you can use to upskill for your dream role. You can access specific  interview questions , participate in  mock interviews , and receive  expert coaching .

If you have a specific company in mind to apply to, check out our  company interview guide section , where we have detailed company and role-specific preparation guides. We have guides for all the companies that are mentioned in our case study list, including Uber , Tesla , Amazon , Google , and Netflix .

We hope this discussion has been helpful. If you have any other questions, don’t hesitate to  reach out to us  or explore  our blog .

Case studies on artificial intelligence

We are proud to present case studies from members that are pushing the frontier in the development and artificial intelligence.

LG Electronics’ Vision on Artificial Intelligence

Watch as LG’s Chief Technology Officer Dr. IP Park talks about LG’s vision for their future work with artificial intelligence.

Microsoft’s AI for Accessibility

Microsoft’s AI for Accessibility is a  Microsoft grant program that harnesses the power of AI to amplify human capability for the more than one billion people around the world with a disability.

Microsft’s 2030 vision on Healthcare, Artificial Intelligence, Data and Ethics

The intersection between technology and health has been an increasing area of focus for policymakers, patient groups, ethicists and innovators. As a company, we found ourselves in the midst of many different discussions with customers in both the private and public sectors, seeking to harness technology, including cloud computing and AI, all for the end goal of improving human health. Many customers were struggling with the same questions, among them how to be responsible data stewards, how to design tools that advanced social good in ethical ways, and how to promote trust in their digital health-related products and services. […]

Finland training & development plan

AI has been extensively discussed in Finland. The University of Helsinki and Reaktor launched a free and public course to educate 1% of the Finnish population on AI by the end of this year. They have challenged companies to train employees on AI during 2018 and many member companies of the Technology Industries of Finland association (e.g. Nokia, Kone, F-Secure) have joined and support the programme. More than 90,000 people have enrolled in these courses.

SAP – Training for boosting people’s AI skills

SAP has made available various Massive Open Online Courses (MOOCs) both for internal and external users, with goals ranging from basic knowledge/awareness building, for example programmes and courses on ‘Enterprise Machine Learning in a Nutshell’ (see: https://open.sap.com/courses/ml1-1 ), as well as more advanced skills, for instance on deep learning (see: https://open.sap.com/courses/ml2 ). Two-thirds of SAP’s own machine learning (ML) team is made up of people who already worked for SAP in non-ML roles and then acquired the necessary ML knowledge and skills on the job.

SAP – Addressing bias & ensuring diversity

SAP created a formal internal and diverse AI Ethics & Society Steering Committee. The committee is creating and enforcing a set of guiding principles for SAP to address the ethical and societal challenges of AI. It is comprised of senior leaders from across the entire organisation such as Human resources, Legal, Sustainability and AI Research departments. This interdisciplinary membership helps ensuring diversity of thought when considering how to address concerns around AI, e.g. those related to bias.

AI itself can also help increase diversity in the workplace and eliminate biases. SAP uses, offers and continues to develop AI powered HR services that eliminate biases in the application process. For example, SAP’s “Bias Language Checker” (see:  https://news.sap.com/2017/10/sap-introduces-intelligent-hr-solution-to-help-businesses-eliminate-bias/ ) helps HR identifying areas where the wording of a Job Description lacks inclusivity and may deter a prospective applicant from submitting their application.

Who can be held liable for damages caused by autonomous systems?

AI and robotics have raised some questions regarding liability. Take for example the scenario of an ‘autonomous’ or AI-driven robot moving through a factory. Another robot surprisingly crosses its way and our robot draws aside to prevent collision. However, by this manoeuvre the robot injures a person. Who can be held liable for damages caused by autonomous systems? The manufacturer using the robots, one or both or the robot manufacturers or one of the companies that programmed the software of the robots?

Existing approaches would likely already provide a good approach. For example, owner’s liability, as with motor vehicles, could be introduced for autonomous systems (whereas ‘owner’ means the person using or having used the system for its purposes). The injured party should be able to file a claim for personal or property damages applying strict liability standards against the owner of the autonomous system.

Sony – Neural Network Libraries available in open source 

Sony has made available in open source its “Neural Network Libraries” which serve as framework for creating deep learning programmes for AI. Software engineers and designers can use these core libraries free of charge to develop deep learning programmes and incorporate them into their products and services. This shift to open source is also intended to enable the development community to further build on the core libraries’ programmes.

Deep learning refers to a form of machine learning that uses neural networks modelled after the human brain. By making the switch to deep learning-based machine learning, the past few years have seen a rapid improvement in image and voice recognition technologies, even outperforming humans in certain areas. Compared to conventional forms of machine learning, deep learning is especially notable for its high versatility, with applications covering a wide variety of fields besides image and voice recognition, including machine translation, signal processing and robotics. As proposals are made to expand the scope of deep learning to fields where machine learning has not been traditionally used, there has been an accompanying surge in the number of deep learning developers.

Neural network design is very important for deep learning programme development. Programmers construct the neural network best suited to the task at hand, such as image or voice recognition, and load it into a product or service after optimising the network’s performance through a series of trials. The software contained in these core libraries efficiently facilitates all the above-mentioned development processes.

Cisco – Reinventing the network & making security foundational

Cisco is reinventing networking with the network intuitive. Cisco employs machine learning (ML) to analyse huge amounts of network data and understand anomalies as well as optimal network configurations. Ultimately, Cisco will enable an intent-based, self-driving and selfhealing network. The network will redirect traffic on its own and heal itself from internal shocks, such as device malfunctions, and external shocks, such as cyberattacks.

To simplify wide area network (WAN) deployments and improve performance, ML software observes configuration, telemetry and traffic patterns and recommends optimisation and security measures via a centralised management application. Machine learning plays a role in analysing network data to identify activity indicative of threats such as ransomware, cryptomining and advanced persistent threats within encrypted traffic flows.

Moreover, to help safeguard organisations in a constantly changing threat landscape, Cisco is using AI and ML to support comprehensive, automated, coordinated responses between various security components. For businesses in a multi-cloud environment, cloud access is secured by leveraging machine intelligence to uncover malicious domains, IPs, and URLs before they are even used in attacks. Once a malicious agent is discovered on one network, it is blacklisted across all customer networks. Machine learning is also used to detect anomalies in IT environments in order to safeguard the use of SaaS applications by adaptively learning user behaviour. Infrastructure-as-a-Service instances as well are safeguarded by using machine learning to discover advanced threats and malicious communications.

Intel – AI for cardiology treatment

Precision medicine for cancers requires the delivery of individually-adapted medical care based on the genetic characteristics of each patient. The last decade witnessed the development of high-throughput technologies such as next-generation sequencing, which paved their way in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. In order to open the access to more and more patients to precision medicine-based therapies, healthcare providers have to rationalise both their data production and utilisation and this requires the implementation of the cuttingedge technology of high-performance computing and artificial intelligence.

Before taking a therapeutic decision based on the genome interpretation of a cancer, the physician can be presented with an overwhelming number of genes variants. In order to identify key actionable variants that can be targeted by treatments, the physician needs tools to sift through this large volume of variants. While the use of AI in genome interpretation is still nascent, it is growing rapidly as acting filter to dramatically reduce the number of variants, providing invaluable help to the physician. The mastering of high-performance computing methods on modern hardware infrastructure is becoming a key factor of the cancer genome interpretation process while being efficient, cost-effective and adjustable over time.

The pioneer collaboration initiated between the Curie Institute Bioinformatics platform and Intel aims at answering those challenges by defining a leading model in France and Europe. This collaboration will grant Institute Curie access to Intel experts for defining highperformance computing and artificial intelligence infrastructure and ensuring its optimisation in order to implement the Intel Genomics ecosystem partner solutions and best practices, for example the Broad Institute for Cancer Genomics pipeline optimisation. Also anticipated is the development of additional tailored tools needed to integrate and analyse heterogeneous biomedical data.

MSD – AI for healthcare professionals

MSD has launched, as part of its MSD Salute programme in Italy, a chatbot for physicians, powered by AI and machine learning. It has already achieved a large uptake with healthcare professionals in Italy. The programme’s sector of focus is immune-oncology.

From the MSD prospective, physicians are digital consumers looking for relevant information for their professional activity. Some key factors like the increase of media availability, mobile devices penetration and the decrease of time available, are resulting in a reduction of time spent navigating and searching on the web. Therefore users (and physicians with their pragmatic approach) read what they see and do not navigate as much but just ‘read and go’. This means that there is an urgent need to access content quickly, easily and efficiently.

The chatbot is developed in partnership with Facebook and runs on their Messenger app framework. As an easy and practical tool, it helps to establish a conversational relationship between the users. The MSD Italy ChatBot service is available only for registered physicians. Integration with Siri and other voice recognition systems is also worked on, to improve the human experience during the interaction with the chatbot. This initiative is a key item in MSD Italy’s digital strategy which focuses on new channels and touch-points with healthcare professionals, leveraging on new technologies.

Philips – AI in clinics and hospitals

With the clinical introduction of digital pathology, pioneered by Philips, it has become possible to implement more efficient pathology diagnostic workflows. This can help pathologists to streamline diagnostic processes, connect a team, even remotely, to enhance competencies and maximise use of resources, unify patient data for informed decision-making, and gain new insights by turning data into knowledge. Philips is working with PathAI to build deep learning applications. By analysing massive pathology data sets, we are developing algorithms aimed at supporting the detection of specific types of cancer and that inform treatment decisions.

Further, AI and machine learning for adaptive intelligence can also support quick action to address patient needs at the bedside. Manual patient health audits used to be timeconsuming, putting a strain on general ward staff. Nurses need to juggle a range of responsibilities: from quality of care to compliance with hospital standards. Information about the patient’s health was scattered across various records, making it even harder for nurses to focus their attention and take the right actions. Philips monitoring and notification systems assist nurses to detect a patient’s deterioration much quicker. All patient vital signs are automatically captured in one place to provide an Early Warning Score (EWS).

Microsoft – Machine learning for tumour detection and genome research

Microsoft’s Project InnerEye developed machine learning techniques for the automatic delineation of tumours as well as healthy anatomy in 3D radiological images. This technology helps to enable fast radiotherapy planning and precise surgery planning and navigation. Project InnerEye builds upon many years of research in computer vision and machine learning. The software learned how to mark organs and tumours up by training on a robust data set of images for patients that had been seen by experienced consultants.

The current process of marking organs and tumours on radiological images is done by medical practitioners and is very time consuming and expensive. Further, the process is a bottleneck to treatment – the tumour and healthy tissues must be delineated before treatment can begin. The InnerEye technology performs this task much more quickly than when done by hand by clinicians, reducing burdens on personnel and speeding up treatment.

The technology, however, does not replace the expertise of medical practitioners; it is designed to assist them and reduce the time needed for the task. The delineation provided by the technology is designed to be readily refined and adjusted by expert clinicians until completely satisfied with the results. Doctors maintain full control of the results at all times.

Further, Microsoft has partnered with St. Jude Children’s Research Hospital and DNANexus to develop a genomics platform that provides a database to enable researchers to identify how genomes differ. Researchers can inspect the data by disease, publication, gene mutation and also upload and test their own data using the bioinformatics tools. Researchers can progress their projects much faster and more cost-efficiently because the data and analysis run in the cloud, powered by rapid computing capabilities that do not require downloading anything.

Siemens – AI for Industry, Power Grids and Rail Systems

Siemens has been using smart boxes to bring older motors and transmissions into the digital age. These boxes contain sensors and communication interfaces for data transfer. By analysing the data, AI systems can draw conclusions regarding a machine’s condition and detect irregularities in order to make predictive maintenance possible.

AI is used also beyond industrial settings, for example to improve the reliability of power grids by making them smarter and providing the devices that control and monitor electrical networks with AI. This enables the devices to classify and localise disruptions in the grid. A special feature of this system is that the associated calculations are not performed centrally at a data centre, but de-centrally between the interlinked protection devices.

In cooperation with Deutsche Bahn, Siemens is running a pilot project for the predictive maintenance and repair of high-speed trains. Data analysts and software recognise patterns and trends from the vehicles’ operating data. Moreover, AI helps build optimised control centres for switch towers. From the billions of possible hardware configurations for a switch tower, the software selects options that fulfil all the requirements, including those regarding reliable operation.

Schneider Electric – AI for industry applications

Schneider Electric has used AI and machine learning in various sectors. In the oil and gas industry for example, machine learning is steering the operation of Realift rod pump control to monitor and configure pump settings and operations remotely, sending personnel onsite only when necessary for repair or maintenance – when Realift indicates that something has gone wrong. Anomalies in temperature and pressure, for instance, can flag potential problems, even issues brewing a mile below the surface. Intelligence edge devices can run analytics locally without having to tap the cloud — a huge deal for expensive, remote assets such as oil pumps.

To enable this solution an AI model is previously trained to recognise correct pump operation and also different types of failures a pump can experience, the AI model is deployed on a gateway at oil field for each pump and is fed with data collected at each pump stroke. Then, it outputs a prediction regarding the pump state. As we mimic the expert diagnostics, predictions can be easily validated, explained and interpreted.

Schneider Electric – Improving agriculture and farming with AI

Another example is in the agriculture sector, where Schneider Electric has proposed an AI solution for Waterforce, an irrigation solutions builder and water management company in New Zealand. Schneider Electric’ solution makes water use more efficient and effective in water use, saving up to 50% in energy costs, and provides remote monitoring capabilities that reduce the time farmers have to spend driving to inspect assets. The solution is able to collect data, from the weather forecast, pressure of pumps, temperatures, level of water, humidity of the ground, cleaning and selecting quality data, and preparing the data, in order to propose services such as fault diagnosis, performance benchmarking, recommendation and advise on operations.

AI and machine learning therefore represent a new way for humans and machines to work together – to learn about predictive tendencies and to solve complex problems. In the above examples, the challenges presented today in managing a process that requires tight control of temperatures, pressures, and liquid flows is quite complex and prone to error. Many variables need to be factored in to achieve a successful outcome – and the quality of the data that trains the AI algorithms could deliver very different results that the human brain should anyhow interpreted and guide. With the support of AI to make better operational decisions, critical factors such as safety, security, efficiency, productivity, and even profitability can be optimised in conjunction between machine/process and operator. This way, the training and combined skills from AI and expertise are a key success factor to deliver those values to Industry.

Canon – Application of automation in the office environment

Canon’s digital mailroom solution has been at the forefront of Robotic Process Automation (RPA) since it was first launched. A digital mailroom allows all incoming mail to be automatically captured, identified, validated and sent with relevant index data to the right systems or people. RPA technology is centred on removing the mundane to make lives easier. In the P2P world, RPA automates labour-intensive activities that require accessing multiple systems or that need to be audited for compliance.

Canon believes the next step in automation is the intelligent mailroom. The key challenge of the future will be the integration of digital and paper-based information into robust, effective and efficient processes. This means that organisations need more intelligent, digital mailroom solutions that enable data capture across every channel. One example of intelligent mailroom is the Multichannel Advanced Capture. This allows banks to enable customers to apply for an account minimising the amount of paper and using a mobile-friendly web page capturing the core details required. Automated checks on customers’ ID and credit history are made first. If all initial checks are valid, a second human check can be made. The bank is then presented with all the information required to make an informed decision on the application to open the bank account, based on applicable business rules as well as on (automatically) gathered historical business process knowledge.

SAS – Crowdsourcing and analysing data for endangered wildlife

The WildTrack Footprint Identification Technique (FIT) is a tool developed in partnership with SAS for non-invasive monitoring of endangered species through digital images of footprints. Measurements from these images are analysed by customised mathematical models that help to identify the species, individual, sex and age-class. AI could add the ability to adapt through progressive learning algorithms and tell an even more complete story.

Ordinary people would not necessarily be able to dart a rhino, but they can take an image of a footprint. WildTrack therefore has data coming in from everywhere. As this represents too much information to manage manually AI can automate repetitive learning through data, performing frequent, high-volume, computerised tasks reliably and without fatigue.

SAS – Using AI for real-time sports analytics

AI can also be used to analyse sports and football data. For example, SciSports models on-field movements using machine learning algorithms, which by nature improve on performing a task as they gain more experience. It works by automatically assigning a value to each action, such as a corner kick. Over time, these values change based on their success rate. A goal, for example, has a high value, but a contributing action – which may have previously had a low value – can become more valuable as the platform masters the game.

AI and machine learning will play an important role in the future of SciSports and football analytics in general. Existing mathematical models shape existing knowledge and insights in football, while AI and machine learning will make it possible to discover new connections that people would not make themselves.

Various other tools such as SAS Event Stream Processing and SAS Viya can then be utilised for real-time image recognition, with deep learning models, to distinguish between players, referees and the ball. The ability to deploy deep learning models in memory onto cameras and then do the inferencing in real time is cutting-edge science.

Google & TNO – AI for data analysis on traffic safety

TNO is one of the partners of InDeV, an international collaboration of researchers which was created to develop new ways of measuring traffic safety. Statistics about traffic safety were unreliable, insufficiently detailed, and hard to collect. Researchers often resort to filming busy intersections and manually reviewing the recording. This a time-intensive and expensive process. A single intersection needs to be monitored for three weeks with two cameras to create an estimation of its safety, adding up to six weeks of footage, which can take six weeks of work to analyse. Typically, less than one percent of the recorded material is actually of interest to researchers. The job of TNO is to apply machine learning to video of accident-prone hot spots to rate intersections on a scale according to their safety. With TNO’s neural network based on TensorFlow, researchers report that it takes only one hour to review footage that would previously have taken a week to inspect.

Case Study Generator

Generate professional and engaging case studies effortlessly with our free AI Case Study creator. Simplify the process and showcase your success.

• Authoritative Pro
• Cheerful Pro
• Conservative Pro
• Conversational Pro
• Convincing Pro
• Creative Pro
• Enthusiastic Pro
• Expository Pro
• Friendly Pro
• Humorous Pro
• Informative Pro
• Inspirational Pro
• Irreverent Pro
• Journalistic Pro
• Matteroffact Pro
• Nostalgic Pro
• Objective Pro
• Passionate Pro
• Playful Pro
• Professional Pro
• Provocative Pro
• Respectful Pro
• Romantic Pro
• Sarcastic Pro
• Serious Pro
• Subjective Pro
• Sympathetic Pro
• Thoughtful Pro
• Trustworthy Pro
• Unapologetic Pro
• Worried Pro
• English (US)
• German (Germany)
• Italian (Italy)
• Japanese (Japan)
• Russian (Russia)
• Portuguese (Portugal)
• Hindi (India)
• Urdu (Pakistan)
• Arabic (Saudi Arabia)
• French (France)
• English (UK) Pro
• English (Australia) Pro
• English (Canada) Pro
• English (India) Pro
• English (Singapore) Pro
• English (New Zealand) Pro
• English (South Africa) Pro
• Spanish (Spain) Pro
• Spanish (Mexico) Pro
• Spanish (United States) Pro
• Arabic (Egypt) Pro
• Arabic (United Arab Emirates) Pro
• Arabic (Kuwait) Pro
• Arabic (Bahrain) Pro
• Arabic (Qatar) Pro
• Arabic (Oman) Pro
• Arabic (Jordan) Pro
• Arabic (Lebanon) Pro
• Danish (Denmark) Pro
• German (Switzerland) Pro
• German (Austria) Pro
• French (Canada) Pro
• French (Switzerland) Pro
• French (Belgium) Pro
• Italian (Switzerland) Pro
• Dutch (Netherlands) Pro
• Dutch (Belgium) Pro
• Portuguese (Brazil) Pro
• Chinese (China) Pro
• Chinese (Taiwan) Pro
• Chinese (Hong Kong) Pro
• Chinese (Singapore) Pro
• Korean (South Korea) Pro
• Finnish (Finland) Pro
• Greek (Greece) Pro
• Czech (Czech Republic) Pro
• Swedish (Sweden) Pro
• Norwegian (Norway) Pro
• Turkish (Turkey) Pro
• Polish (Poland) Pro
• Romanian (Romania) Pro
• Hungarian (Hungary) Pro
• Thai (Thailand) Pro
• Hebrew (Israel) Pro
• Indonesian (Indonesia) Pro
• Vietnamese (Vietnam) Pro
• Malay (Malaysia) Pro
• Tagalog (Philippines) Pro
• Swahili (Kenya) Pro
• Swahili (Tanzania) Pro
• Zulu (South Africa) Pro
• Xhosa (South Africa) Pro
• Amharic (Ethiopia) Pro
• Tamil (India) Pro
• Tamil (Sri Lanka) Pro
• Bengali (Bangladesh) Pro
• Bengali (India) Pro
• Punjabi (Pakistan) Pro
• Punjabi (India) Pro
• Marathi (India) Pro
• Telugu (India) Pro
• Kannada (India) Pro
• Gujarati (India) Pro
• Oriya (India) Pro
• Malayalam (India) Pro
• Urdu (India) Pro
• Persian (Iran) Pro
• Azerbaijani (Azerbaijan) Pro
• Ukrainian (Ukraine) Pro
• Belarusian (Belarus) Pro
• Catalan (Spain) Pro
• Basque (Spain) Pro
• Galician (Spain) Pro
• Slovak (Slovakia) Pro
• Lithuanian (Lithuania) Pro
• Latvian (Latvia) Pro
• Estonian (Estonia) Pro
• Bulgarian (Bulgaria) Pro
• Albanian (Albania) Pro
• Croatian (Croatia) Pro
• Slovenian (Slovenia) Pro
• Bosnian (Bosnia and Herzegovina) Pro
• Serbian (Serbia) Pro
• Macedonian (North Macedonia) Pro
• Montenegrin (Montenegro) Pro
• Maltese (Malta) Pro
• Irish (Ireland) Pro
• Welsh (United Kingdom) Pro
• Scots Gaelic (United Kingdom) Pro
• Icelandic (Iceland) Pro
• Luxembourgish (Luxembourg) Pro
• Afrikaans (South Africa) Pro
• Hausa (Nigeria) Pro
• Yoruba (Nigeria) Pro
• Somali (Somalia) Pro
• Tigrinya (Eritrea) Pro
• Kinyarwanda (Rwanda) Pro
• Sesotho (Lesotho) Pro
• Shona (Zimbabwe) Pro
• Sinhala (Sri Lanka) Pro
• Dhivehi (Maldives) Pro
• Burmese (Myanmar) Pro
• Lao (Laos) Pro
• Khmer (Cambodia) Pro
• Mongolian (Mongolia) Pro
• Tibetan (China) Pro
• Uighur (China) Pro
• Pashto (Afghanistan) Pro
• Dari (Afghanistan) Pro
• Nepali (Nepal) Pro
• Dzongkha (Bhutan) Pro
• Sesotho (South Africa) Pro
• Setswana (Botswana) Pro
• Seselwa Creole (Seychelles) Pro
• Mauritian Creole (Mauritius) Pro
• Haitian Creole (Haiti) Pro
• Greenlandic (Greenland) Pro
• Faroese (Faroe Islands) Pro
• Samoan (Samoa) Pro
• Tongan (Tonga) Pro

Unlock the power of our case study creator tool—Generate compelling case studies effortlessly with our creator and captivate your audience. With just a few clicks, our smart technology helps you understand data, find trends, and make insightful reports, making your experience better and improving your SEO strategy.

What is a Case Study

A case study is like a detailed story that looks closely at a particular situation, person, or event, especially in the business world. It's a way to understand how things work in real life and learn valuable lessons. For instance, if a business wanted to figure out how another one became successful, they might study that business as a case study.

Let's say there's a small company that started selling handmade products online and became successful. A case study about this business could explain the challenges they faced, the strategies they used to grow, and the results they achieved. By reading this case study, other businesses could learn useful tips and apply them to their situations to improve and succeed.

7 Tips For Writing Great Case Studies

• Pick a Familiar Topic: Choose a client or project that your audience can relate to. This makes it easier for them to see how your solutions might work for their situations.
• Clear Structure: Start with a concise introduction that sets the stage for the case study. Clearly outline the problem, solution, and results to make your case study easy to follow.
• Engaging Storytelling: Turn your case study into a compelling narrative. Use real-world examples, anecdotes, and quotes to make it relatable and interesting for your audience.
• Focus on the Problem: Clearly define the problem or challenge your case study addresses. This helps readers understand the context and sets the foundation for the solution.
• Highlight Solutions: Showcase the strategies or solutions implemented to overcome the problem. Provide details about the process, tools used, and any unique approaches that contributed to the success.
• Optimize for SEO: By incorporating your case study into a blog post using a blog post generator, you enhance its visibility and reach. This, in turn, improves the search engine rankings of your blog post, attracting more organic traffic.
• Quantify Results: Use data and metrics to quantify the impact of your solutions. Whether it's increased revenue, improved efficiency, or customer satisfaction, concrete results add credibility and demonstrate the value of your case study.

What is a Case Study Creator

A free case study generator is a tool or system designed to automatically create detailed case studies. It typically uses predefined templates and may incorporate artificial intelligence (AI) to generate comprehensive analyses of specific situations, events, or individuals.

This tool streamlines the process of crafting informative case studies by extracting key details, analyzing data, and presenting the information in a structured format.

Case study generators are valuable for businesses, students, or professionals seeking to efficiently produce well-organized and insightful case studies without the need for extensive manual effort.

Benefits of Using Case Study Generator

In today's competitive landscape, showcasing your product or service successes is vital. While case studies offer a compelling way to do this, starting from scratch can be time-consuming. That's where case study generators step in, providing a robust solution to streamline the process and unlock various advantages.

• Easy and Quick: A case study generator makes it simple to create detailed studies without spending a lot of time. It's a fast and efficient way to compile information.
• Accessible Online: As an online case study generator, you can use it from anywhere with an internet connection. No need for installations or downloads.
• Free of Cost: Many case study creators are free to use, eliminating the need for any financial investment. This makes it budget-friendly for businesses or individuals.
• AI-Powered Insights: Some generators use AI (artificial intelligence) to analyze data and provide valuable insights. This adds depth and accuracy to your case studies.
• Save Time and Effort: Generate a polished case study in minutes, automating tasks like data analysis and content creation. This frees up your time to focus on other aspects of your business.
• Enhance Quality and Consistency: Case study creators offer templates and AI-powered suggestions, ensuring your studies are well-structured and visually appealing. Consistent quality strengthens your brand image.
• Improve Brand Awareness and Credibility: Sharing case studies on your platforms increases brand awareness and builds trust. Positive impacts on others establish you as a credible provider.
• Boost Lead Generation and Sales: Compelling case studies build trust and showcase your value, attracting leads and converting them into customers, ultimately boosting your sales.
• Increase Customer Engagement and Loyalty: Case studies provide insights into your company, fostering deeper connections, increasing engagement, and promoting long-term loyalty.
• Improve Your Writing Skills: Free AI Case study generators act as learning tools, offering guidance on structure, content, and storytelling. Studying generated drafts refines your writing skills for crafting impactful case studies in the future.

How AI Case Study Generator Works

An online case study generator works by leveraging artificial intelligence algorithms to analyze and synthesize information, creating comprehensive case studies. Here's a simplified explanation of its functioning:

Data Input:

Algorithm analysis:, content generation:, language processing:, who needs a case study creator.

Anyone looking to create informative and detailed case studies can benefit from using an online case study generator. This tool is useful for

Businesses:

Professionals:, individuals:, marketing professionals:, researchers:, why opt for our case study creator.

Are you on the lookout for a top-notch case study generator that combines outstanding features with user-friendliness, all at no cost and without the need for registration? Your search ends here. Our AI-driven case study generator is the ideal solution for you. Here's why you should choose our tool:

Craft Case Study in 50+ Languages:

Incorporate keywords in case study:, user-friendly interface:, 100% free, no registration:, 20+ diverse tones for versatile styles:, frequently asked questions.

• Not focusing on the benefits to the reader.
• Not using data and results to support their claims.
• Not telling a compelling story.
• Not using visuals effectively.
• Not promoting their case study.

Error message

Boost your productivity & creativity!

• 500+ tools to spark your creativity.
• 100+ languages, conquer the globe.
• 20+ tones & styles, find your perfect brand voice.
• No document or input limits, write forever!
• Priority support & early access to new features!
• Limited-time offer

Still hesitant? We offer 7-Day Money-Back Guarantee Without Question.

Elevate Every Aspect of Your Work

LogicBalls combines brainstorming, writing, analysis, and research in one powerful AI tool. Enhance your professional content now!

• Asia/Pacific
• Latin America
• Middle East, Africa & Türkiye
• Shopping Cart
• Products & Services
• Current: Generative AI: Case Studies

 target audience: TECH BUYER  Publication date: Feb 2024 -  Document type: Tech Buyer Presentation - Doc  Document number: # US51866524

Generative AI: Case Studies

When you purchase this document, the purchase price can be applied to the cost of an annual subscription, giving you access to more research for your investment.

Related Links

• Referencing Documents

On-line Presentation

Subscriptions covered.

Generative AI Strategies

Regions Covered

Topics covered.

• Generative artificial intelligence
• Technology buyer

Do you have questions about this document or available subscriptions?

Thank you for submitting the form. We will be in touch.

Please fill out this form and we will get in touch.

All fields not labeled "optional" are required.

Please read our Privacy Policy

More From Forbes

How to build a business case for ai for the right use cases.

• Share to Facebook
• Share to Twitter
• Share to Linkedin

Venky Yerrapotu is the CEO and Co-Founder of 4CRisk , an award-winning AI company specializing in advanced products for risk and compliance.

Getting the right AI initiatives funded quickly to take advantage of the astounding potential of this technology seems obvious, but it presents a new set of obstacles. The technology itself, while breathtaking, isn't always enough to ensure success.

The basic questions that need answering before the purse strings open haven't changed: Do you have the correct use cases that will not only deliver benefits but ensure your organization—people, processes and technology—emerge from this deployment smarter and better equipped to embrace AI? Have you measured the end-to-end costs, benefits and cumulative ROI over a reasonable timeline? Have you mapped out the risks, and do you have a plan to contain them?

Let's take a closer look at how to measure the ROI of AI in a new way, accounting for differences in speed, accuracy and the need to collapse and smooth processes in completely new ways. What most people are still amazed by is the magnitude of change. AI brings analysis that's levels of magnitude faster—10, 20 or 50 times faster than a human being. Yes, there are human-in-the-loop reviews, but the data is profoundly more accurate and can be highly comprehensive in its analysis. It's mind-bending to put that kind of beneficial impact into perspective.

Let's illustrate what's new and what's not when considering the ROI of AI with a common use case all organizations share—compliance assessments.

Best High-Yield Savings Accounts Of 2024

Best 5% interest savings accounts of 2024, the end-to-end process.

First, you'll need to get all stakeholders on board to clearly understand at what points in the process they can leverage AI. Further, IT needs to determine how affected systems can handle the new inputs and outputs, and finance needs to take a realistic look at the costs and benefits across your organization.

Let's detail the main players in the compliance assessments process and how AI can help.

Regulatory Affairs And Legal

We've found through customer data and feedback that AI can collapse research and regulatory change processes by as much as 10 to 20 times with horizon scanning, curating rulebooks that reflect your organization's obligations based on products and jurisdictions, and alerting your teams to upcoming changes. No more scraping alerts from websites or RSS feeds, going through with a fine-toothed comb to understand what does and doesn't apply and ensure nothing is missed.

Compliance Management

AI can whip through masses of unstructured documents and let you know in minutes rather than months where gaps in your obligations exist in your internal policies, processes and controls. Analysts no longer have to pour over framework details trying to match what's covered and what's not.

Policy Management

AI can merge obligations from multiple authoritative sources to serve up plain English policy and risk language suggestions that reflect the intent of the law in minutes rather than days. No more tedious wordsmithing to ensure your employees, third parties and customers know what is and isn't permitted.

AI can automatically focus on control gaps, run "continuous audits" to risk-rank control and provide options to close gaps in seconds rather than days, before there is a real risk or issue. No more trying to anticipate annual audit topics and scrambling for the right technical SMEs to conduct on-site audits.

IT And Security

AI can help scope technical assessments that sense the integrity of your technology and security landscape, showing gaps in the IT and security compliance framework that previously lay hidden or emerge at machine speed as the infrastructure evolves.

Working Up The Benefits (The Easy Part)

• What's new? Remember, with AI, it's no longer incremental improvement based on an efficiency percentage but a level of magnitude faster than a human being. Use human-in-the-loop reviews that ensure people understand the AI analysis and how to respond to it. You may discover some astounding effects: Legal and compliance are no longer the bottleneck. IT and security reviews go faster than ever. Audit teams will find fewer issues with fewer people. Teams are released from mundane admin work to provide high-value analysis.

• What's not new? The before-and-after process still needs to be plotted out. Show where the accordion has compressed and how that acceleration will affect downstream processes. Make sure the overall throughput is not only swift but distinctly valuable.

Working Up The Costs (Requires Some Future Thinking)

• What's new? Costs to transform people and processes may be more than you're used to with legacy systems where change is incremental. AI requires dynamic champions, retraining front-line staff and constant tuning. AI-smart resources are in high demand and may be more costly. You may need support for a longer period than even your best planners suggest. AI products evolve rapidly and become available with new features overnight on SaaS platforms. Plan carefully and build in buffers.

• What's not new? The deployment plan still needs to be robust and complete. Cutting corners won't make the rollout, which is about people and process, go faster. Teamwork will, and that means ensuring people affected to see the familiar milestones and guardrails while getting on the fast(er) train.

Selling The Business Case

• What's new? Getting your stakeholders to believe the promise. The best way to prove the case is with a short evaluation showing numbers before and after. Make it compelling, realistic, powerful and measured against expected results.

• What's not new? The work of reviewing assumptions, testing them in rollout, revising the ROI and taking advantage of previously unseen opportunities.

Bottom Line

AI is rapidly evolving, and those who embrace its power will reinvent their organizations over the coming years. We will likely look back on these years as the inflection point where everything changed. Honestly, though, much will remain the same. Choosing AI for the right use cases and getting the team aligned and firing on all cylinders still takes thoughtful, upfront consideration and diligent nurturing.

It's electrifying when the penny drops and the wheels start turning with stakeholders, as they consider the art of the impossible. It's the opportunity of the decade.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

• Editorial Standards
• Reprints & Permissions

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

The Case for College in the Era of Online Learning

• Robert Walker

In-person education provides valuable experiences, opportunities, and skills that can’t be replicated online.

Does pursuing a college education still make sense in the age of online learning and AI — when we have access to information for free via the internet? The problem with this question is that it frames college as an information gatekeeper, misunderstanding much of its value. For many, higher education institutions offer more than that: a transformative journey where students can network and develop transferable soft skills that require teamwork and repetition. You can maximize your college experience by leaning into these opportunities while simultaneously staying up to date with the latest technological trends. By being agile, networking vigorously, cultivating problem-solving skills, and seeking learning opportunities in the real world while in school, you can prepare yourself for a successful career.

Today, we have access to more information than ever before. YouTube and TikTok can provide us with in-depth learning opportunities for free — from professional development tips to AI tutorials . In more recent years, large language models like ChatGPT and Gemini have shown they can answer almost any question that comes to mind with an increasing level of accuracy .

• RW Robert Walker is the director of high school admissions at University of Advancing Technology. Walker has over 12 years of in-depth experience in recruitment and technology, has a genuine passion helping others achieve their educations dream,s and holds advanced degrees in technology leadership and cyber security.  

Partner Center

Generative AI-Powered Educational Alignment: A Framework for Matching Syllabus Course Topics with Web Description

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, index terms.

Computer systems organization

Dependable and fault-tolerant systems and networks

Embedded and cyber-physical systems

Embedded systems

Network properties

Network reliability

Recommendations

Computer science education in north-rhine westphalia, germany—a case study.

In North-Rhine Westphalia, the most populated state in Germany, Computer Science (CS) has been taught in secondary schools since the early 1970s. This article provides an overview of the past and current situation of CS education in North-Rhine ...

Enhancing Pedagogy with Generative AI: Video Production from Course Descriptions

This paper explores a novel workflow that integrates Generative AI tools, ChatGPT and DALL·E, into educational use, aiming to improve the traditional teaching methods in university education. Our workflow is focused on creating short introductory videos ...

A Strategic Evaluation of Educational Continuity for Accessibility During Uncertainty in an Educational Practicum

Students from economically disadvantaged nations (EDN) face significant challenges in accessing high-quality education and securing global internships due to inadequate resources, funding, and infrastructure. The COVID-19 pandemic has highlighted the ...

Information

Published in.

Association for Computing Machinery

New York, NY, United States

Publication History

Check for updates, author tags.

• AI in education
• Curriculum alignment
• Curriculum development
• Generative AI
• Syllabus analysis
• Research-article
• Refereed limited

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

View or Download as a PDF file.

View online with eReader .

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

• Skip to main content
• Skip to search
• Skip to footer

Products and Services

Cisco Security

Master your goals. innovate. we'll tackle threats..

Get powerful security across all your networks, cloud, endpoints, and email to protect everything that matters, from anywhere.

If it's connected, you're protected

Cisco Security “The Hacker”

More connected users and devices creates more complexity. Cisco Security Cloud makes security easier for IT and safer for everyone anywhere security meets the network.

Deliver smarter, stronger security

Protect your organization across a multicloud environment, while simplifying security operations, improving scalability, and driving data-informed outcomes, powered by Cisco Talos.

Unlock better user experiences

Create a seamless experience that frustrates attackers, not users, by granting access from any device, anywhere, and adding more proactive security controls.

Deliver cost-effective defenses

Improve ROI by consolidating vendors, reducing complexity and integrating your security.

Strengthen security resilience

Unified, end-to-end protection maximizes value, minimizes risk, and closes security gaps everywhere to defend against evolving threats. Protect access, apps, and innovation across your network to secure your future.

Cisco Secure Firewall

Better visibility and actionable insights across networks, clouds, endpoints, and email allows users to respond confidently to the most sophisticated threats at machine scale.

Featured security products

Cisco hypershield.

A new groundbreaking security architecture that makes hyperscaler technology accessible to enterprises of all sizes and delivers AI-native security for modern data centers and cloud.

Cisco Secure Access (SSE)

A converged cybersecurity solution, grounded in zero trust, that radically reduces risk and delights both end users and IT staff by safely connecting anything to anywhere.

Detect the most sophisticated threats sooner across all vectors and prioritize by impact for faster responses.

Cisco Multicloud Defense

Gain multidirectional protection across clouds to stop inbound attacks, data exfiltration, and lateral movement.

Secure applications and enable frictionless access with strong MFA and more. Establish user and device trust, gain visibility into devices, and enable secure access to all apps.

Cisco Identity Services Engine (ISE)

Simplify highly secure network access control with software-defined access and automation.

Security Suites delivered by Cisco Security Cloud

Cisco User Protection Suite

Get secure access to any application, on any device, from anywhere. Defend against threats targeting users and deliver seamless access for hybrid work.

Cisco Cloud Protection Suite

Secure your apps and data with a powerful, flexible framework for a hybrid and multicloud world.

Cisco Breach Protection Suite

Secure your business by investigating, prioritizing, and resolving incidents through unified defense and contextual insights from data-backed, AI-powered security.

Customer stories and insights

Global partnerships fight to end child exploitation together.

"Marriott has long championed human rights and human trafficking awareness. Combating CSAM is an important extension of that work. The IWF provided the level of rigor we needed in a URL list, and Cisco's security technology provided the means to easily apply it."

Abbe Horswill, Director, Human Rights and Social Impact

Company: Marriott International

The NFL relies on Cisco

"From securing stadiums, broadcasts, and fans to protecting the largest live sporting event in America, the right tools and the right team are key in making sure things run smoothly, avoiding disruptions to the game, and safeguarding the data and devices that make mission-critical gameday operations possible."

Add value to security solutions

Cisco Security Enterprise Agreement

Instant savings

Experience security software buying flexibility with one easy-to-manage agreement.

Services for security

Let the experts secure your business

Get more from your investments and enable constant vigilance to protect your organization.

Sharpen your security insights

Cisco Cybersecurity Viewpoints

Set your vision to a more secure future with Cisco Cybersecurity Viewpoints. With specialized content from podcasts to industry news, you'll walk away with a deeper understanding of the trends, research, and topics in our rapidly changing world.