digital marketing related research papers

Digital transformation and marketing: a systematic and thematic literature review

• Review Article
• Open access
• Published: 15 March 2023
• Volume 2023 , pages 207–288, ( 2023 )

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• Marco Cioppi 1 ,
• Ilaria Curina   ORCID: orcid.org/0000-0001-7702-7664 1 ,
• Barbara Francioni 1 &
• Elisabetta Savelli 2  

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This article provides a systematic review of the extensive and fragmented literature focused on Digital Transformation (DT) and marketing by identifying the main themes and perspectives (i.e., employees, customers, and business processes) studied by previous research. By mapping the DT literature in the area of marketing, 117 articles, published between 2014 and 2020, have been identified. Through the adoption of a content analysis process, a multi-dimensional framework synthesizing the DT and marketing binomial has been provided. Results identify two thematical patterns: the macro-themes, related to the main digital technologies adopted within the marketing function, and the micro-themes, related to the effect/impact of these technologies on marketing processes and activities. Concerning the micro-themes, findings show how they have mainly studied from the customer and business processes’ perspectives, thus identifying an interesting research gap related to the analysis of the DT-marketing phenomenon from the employees’ standpoint. Based on these results, the paper derives a research agenda by also providing theoretical and managerial implications. Theoretically, it is the first systematic and thematic review focused on DT and marketing. In particular, it analyses this binomial from a broad and comprehensive perspective, thus offering a synergistic framework of the existing literature, which allows an inclusive vision and understanding about the phenomenon. At the managerial level, the paper could help organizations to enhance their awareness about marketing areas and processes that could better benefit from digitalization, thus driving the overall transition of firms towards DT.

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1 Introduction and background

Over the last decades, digital transformation (DT) has received growing attention in the business literature since it represents a prominent feature for organizations to be leaders of change and competitive in their domain (Kraus et al., 2022 ). At once, in light of the COVID-19 pandemic, the DT phenomenon has experienced an abrupt acceleration (Priyono et al., 2020 ), as firms and organizations are forced to redesign their strategies and operating models through a massive adoption of technologies in order to respond to the crisis-caused changes (Hai et al., 2021 ; Hanelt et al., 2021 ). Therefore, the necessity of analysing the DT topic has become ever more crucial in the last few years.

Conceptually, DT refers to all changes that digital technologies can bring in a firm’s business model, concerning products, processes, and organizational structures (Hess et al., 2016 ). Starting from this definition, it appears clear the pervasiveness of this phenomenon, which represents a real transition toward a new reality made of risks and challenges (Horvat and Szabo, 2019 ; Kraus et al., 2022 ; Vial, 2019 ). DT, indeed, can change every aspect of business, especially the marketing one (Caliskan et al., 2020 ).

Notably, the connection between DT and marketing has become ever more decisive in the last two years. The critical changes related to the COVID-19 crisis have particularly altered the firm and consumer relations, forcing companies to modify their marketing strategies through the massive exploitation of the digital technologies. In particular, marketing currently represents one of the main functions requiring to be adapted to the DT in order to protect firms’ competitiveness (Caliskan et al., 2020 ). By following this research stream, some authors have tried to synthetize the main impacts of DT on marketing practices (Shkurupskaya and Litovchenko, 2016 ; Sunday and Vera, 2018 ), including (i) The increasing spread of information and communication technology (ICT) in the marketing communication channels; (ii) The opportunity to adopt real-time communication with customers; (iii) The development of new relationships between producers and consumers; (iv) The increasing effectiveness of the marketing activities through the monitoring of real-time data. Meanwhile, other authors have specifically focused their attention on the main digital technologies able to offer significant benefits to the marketing function (Ardito et al., 2019 ; Cluley et al., 2019 ; Giannakis et al., 2019 ; Ungerman et al., 2018 ) by also categorizing them on the basis of the marketing mix (Caliskan et al., 2020 ).

Despite the DT-marketing topic has received growing attention, to date, no systematic review exists concerning the analysis of the DT phenomenon with specific application to the marketing processes and activities. Notably, several studies have tried to review the DT literature from very restricted research areas (Hanelt et al., 2021 ) different with respect to the broader one of marketing, such as B2B relationships (Hofacker et al., 2020 ), business model innovation (Favoretto et al., 2022 ; Li, 2020 ), accounting (Knudsen, 2020 ), multinational enterprises (George and Schillebeeckx, 2022 ), leadership (Carvalho et al., 2022 ; Henderikx and Stoffers, 2022 ), quality management (Dias et al., 2021 ; Thekkoote, 2022 ), production applications (D’Almeida et al., 2022 ), business management adaptability (Zhang et al., 2021 ), stakeholder management (Prebanić and Vukomanović, 2021 ), and sustainability (Gomez-Trujillo and Gonzalez-Perez, 2021 ). Faced with this context, some authors have tried to analyse and systematize the previous DT literature within broader research areas such as the business and management (Kraus et al., 2022 ) and the organizational change (Hanelt et al., 2021 ). However, despite these contributions, until now, no study has focused on reviewing the literature dedicated to the binomial DT-marketing.

Starting from these assumptions, the present study aims to provide a comprehensive review of the extant literature focused on DT in the marketing area by identifying the main themes and perspectives of analysis. More in detail, the paper addresses the following research questions: (i) What themes have been studied by previous research on DT in the field of marketing? (ii) What are the main perspectives adopted by the research on DT in the field of marketing?

To answer these research questions, the study has been organized in two phases: while in the first one the DT literature has been mapped by focusing on all studies addressing the digital transformation and marketing topics during the period 2014–2020, in the second phase a synergistic framework with the main macro and micro themes characterizing DT in the marketing area (concerning the digital technologies use and effects, respectively), along with the related analysed perspectives, has been provided.

By doing so, this study informs the academicians about the recent evolution of DT literature on marketing-related topics. Additionally, by proposing a synergistic framework of results, the paper provides a solid support for discussing and delineating future research directions. Finally, the main results of this review could help organizations to increase their awareness about marketing areas and processes that could better benefit from digitalization, thus driving the overall transition of firms towards DT.

The remainder of the paper is structured as follows. Section  2 presents the methodology and Sect.  3 outlines the descriptive and thematic results of the study. Section  4 provides theoretical and managerial implications and proposes future research directions based on the main gaps in existing literature. Finally, Sect.  5 concludes the study by also discussing the main limitations.

2 Methodology

This study adopts the systematic review method (Tranfield et al., 2003 ) to detect, classify, and interpret “all the available research relevant to a particular research question, or topic area or phenomenon of interest” (Kitchenham, 2004 ; p. 1). Structurally, the review process has been divided into three phases: (i) Data collection; (ii) Paper selection; (iii) Content analysis.

The identification of specific keywords and terms represents the first systematic review step (Tranfield et al., 2003 ). In our research, the following string has been adopted: [“Digital transformation” AND “marketing”], with the final aim of identifying all the contributions simultaneously focused on these two topics, regardless of the subject area (e.g., business, management, etc.) and research approach (e.g., qualitative vs . quantitative). The Scopus database has been employed as it represents the broader abstract and citation database of peer-review literature, and it also contains most of the publications from other databases (Guerrero et al., 2015 ).

All the proposed document typologies have been included in the analysis (i.e., articles, conference papers, conference reviews, literature reviews) by applying the above string on their title, abstract, and keywords (Table 1 ). As for the time frame, contributions published between 2014 and 2020 have been considered following the study of Vaska and Colleagues ( 2021 ), which reveals a growth in interest toward DT field, particularly from 2014.

A total number of 134 publications have been identified and further selected by considering only those studies effectively focused on the investigated topics. At the end of this process, 117 documents have been retained and subjected to content analysis to identify the main DT themes and perspectives in the marketing field (Fig.  1 ).

Main steps of the literature analysis

Notably, the content analysis allows the “systematic and theory-guided reduction of a large amount of text data from any type of communication down to its essence by classifying the material into unifying categories” (Hanelt et al., 2021 ; p. 1163). It is distinguished from other qualitative procedures, such as the thematic one, since it permits to build category systems in line with the research questions, thus providing both qualitative and quantitative insights (Mikelsone et al., 2019 ).

3 Results and discussion

In the following sub-paragraphs, the descriptive and thematic results of the literature review will be presented.

3.1 Descriptive results

Concerning the yearly research trend (Fig.  2 ), a growing interest in the digital transformation-marketing topic emerged during the time-period under review. Particularly, we went from only one contribution published in 2014 to three in 2017; starting from 2018, the attention increased with 13 published articles, while the most significant peaks have been reached between 2019 and 2020, characterized by the higher production of contributions (45 in 2019 and 50 in 2020).

Year distribution of contributions

Table 2 ranks the sources with the highest number of published contributions focused on the investigated topic. Ninety-three sources have published the 117 reviewed papers with the more relevant contribution from the Advances in Intelligent Systems and Computing (3,4%), followed by Industrial Marketing Management (3,4%), and IOP Conferences series: Materials Science and Engineering (3,4%), Communications in Computer and Information Science (2,6%), and Journal of Physics (2,6%).

Additional sources with only one published contribution are shown in Table 3 . Notably, fifty-seven sources are Journals, eighteen are conference proceedings, and two sources are book series. Concerning the Journals, those from a domain especially related to the business management, society, technology innovation, economics, and engineering have shown interest toward this specific issue. With respect to the conference proceedings, the main fields of study concern the smart trends, technology innovation management, computer science, and information systems. Finally, regarding the book series, they are specifically focused on the information and communication and tourism research streams.

The source’s distribution is informant about the main future publication opportunities in the area of DT and marketing. Equally relevant is the result related to the contributions’ ranking per citation since it allows to figure out the widespread and dissemination of the analysed research stream. Table 4 shows the top-ten contributions in terms of citations. Notably, the more cited contributions are very recent (published between 2019 and 2020) and mainly focused on the following topics: technological innovations as enablers for firms’ digitalization strategies (Ballestar et al., 2019 ; Gil-Gomez et al., 2020 ; Hausberg et al., 2019 ; Peter et al., 2020 ; Sestino et al., 2020 ; Ulas, 2019 ; Yigitcanlar et al., 2020 ) and business sustainability (Sivarajah et al., 2020 ), and the impact of the COVID-19 crisis on consumers’ (Kim, 2020 ) and firms’ digital behaviours (Almeida et al., 2020 ).

Finally, concerning the adopted methodologies, 93 (79,5%) contributions are based on qualitative methods, while the remaining 24 (20,5%) are quantitative in nature.

3.2 Thematic results

By employing the content analysis, it has been possible to extract the main DT themes and perspectives in the marketing fields. As for the DT themes, two main clusters have been identified:

Macro-themes related to the use of digital technologies within the marketing function;

Micro-themes related to the effects emerging from the use of digital technologies on marketing processes and activities.

3.2.1 Macro-themes related to the use of digital technologies

The identification of the most investigated digital technologies analysed in the marketing domain by the reviewed contributions represents the first result deriving from the content analysis. Appendix 1 displays the list of technologies along with their main conceptualizations. As shown in Table 5 , the majority of contributions (67,1%) have focused their attention on the analysis of specific digital tools. In particular, the social media channels (social media marketing) represent the most examined technology (being investigated by 9,4% of the selected studies), followed by Big Data (8,7%), mobile marketing (i.e., mobile technology and smart apps) (8,1%), Internet of Things (6,7%), Artificial Intelligence (6,7%), and Industry 4.0 (6,7%). The remaining technologies (i.e., Machine learning; Online collaborative/support platforms/systems; Virtual/Augmented Reality; Websites/SEO; Cloud infrastructures; Chatbots; Drones/Smart robots; Security Protection systems; 3D print) have experienced a reduced interest by the extant literature (less than 6% of the identified contributions). Finally, a not negligible percentage of studies (32,9%) has analysed the topic of digitalization without investigating specific technologies. Rather, they broadly referred to the “digitalization phenomenon” as an overall macro-theme investing the marketing area.

The sum of the identified macro-themes ( n  = 149) exceeds the number of papers analysed during the review process ( n  = 117) since some papers have simultaneously examined more than one macro-theme.

3.2.2 Micro-themes related to the effects emerging from the use of digital technologies

The second result achieved by the content analysis concerns the main effects (i.e., micro-themes) deriving from the adoption and exploitation of the already identified digital technologies (Par. 3.2.1 ) on the marketing function. The most examined effects fall within the following areas: customer relationship management, customer connectivity, and customer centricity (12,3%), human resources (10,3%), digital metrics (8,8%), customer experience/journey (8,3%), business process efficiency (8,3%), MarTech (7,8%), market knowledge (7,4%), communication policy (5,9%), and customer behaviour (5,4%). The remaining effects (i.e., product policy, sales processes; production; buying/consumption processes; value co-creation; supply chain; branding; customer service; etc.) received less attention, being investigated by less than 5% of the identified contributions (Table 6 ).

The sum of the identified micro-themes ( n  = 204) exceeds the number of papers analysed during the review process ( n  = 117) since some papers have simultaneously examined more than one micro-theme.

The content analysis allowed as to go deep into the study of each micro-theme by revealing both a detailed list of specific sub-themes (Table 7 ) and the main perspectives of analysis adopted in the reviewed manuscripts (Table 8 ).

Specifically, three main perspectives emerged from our study, namely employees, customers, and business. While the employee perspective focuses on the human resources and their coexistence with new technologies, the customer one is mainly related to the digital opportunities offered on the consumer side, especially concerning the overall shopping journey. Finally, the process-focused perspective is primarily concerned with the influence of digital technologies on the different business practices and procedures.

3.2.3 Macro-themes, micro-themes, and analysed perspectives: a combined overview

In this section, the macro-themes, micro-themes, and analysed perspectives will be combined with the final aim of building a comprehensive overview (Table 9 ).

By focusing on the first macro-theme (i.e., social media channels), no studies have specifically examined it from the employee perspective, thus identifying an interesting research gap. Conversely, research widely underlined the key-role of these tools from the business processes and customer perspectives. Concerning the first one, different contributions highlighted how social media support a multitude of business processes (e.g., segmentation, brand positioning, promotion, advertising, buying, after-sales), thus improving firms and marketing performance (Al-Azani and El-Alfy, 2020 ; Kazaishvili and Khmiadashvili, 2020 ; Lestari et al., 2019 ; Melović et al., 2020 ; Rebelli, 2019 ; Safiullin et al., 2020 ; Sivarajah et al., 2020 ; Ulas, 2019 ; Van Osch et al., 2019 ). At once, an equally relevant number of studies has also examined the social media impact from the customers’ viewpoint (Hahn, 2019 ; Kumar-Singh and Thirumoorthi, 2019 ; Rebelli, 2019 ; Yusmarni et al., 2020 ) by identifying the main advantages for them, such as their involvement and engagement in the value creation process and the access to personalized assistance services (Kazaishvili and Khmiadashvili, 2020 ; Sivarajah et al., 2020 ).

Big Data represent the second macro-theme extracted from the thematic literature review. These have been especially analysed from the business processes perspective, recognizing them as one of the most significant challenges and innovations of recent years within the DT framework. Almaslamani et al. ( 2020 ), for instance, explained how the Big Data adoption can lead firms to use intelligent market basket analysis, thus enhancing the relationship with customers. Similarly, the study of Miklosik and Evans ( 2020 ) analysed the impact of Big Data on the digital transformation of the marketing industry by examining the main challenges it faces from a data and information management viewpoint. At once, Sestino et al. ( 2020 ) provided interesting implications for marketers by underlining how the DT, enabled by Big Data, can positively influence many facets of business (e.g., collection of large-scale data allowing to identify emerging trends on consumer behaviour; creation of promotion campaigns with real-time data; creation of stronger bonds with consumers). By specifically focusing on the B2B market, the study of Sivarajah et al. ( 2020 ) demonstrated the Big Data capability to allow B2B firms to become profitable and remain sustainable through strategic operations and marketing-related business activities. Overall, the research offers interesting implications for all the stakeholders interested in understanding and exploiting the use of Big Data with the final aim of achieving business sustainability.

As for mobile marketing (mobile technology and smart apps), research has mainly examined it by focusing on the customer perspective. Indeed, mobile devices have deeply influenced customers’ behaviours and preferences toward online shopping (Sundaram et al., 2020 ) by also transforming them into an integral part of the value creation process. Meanwhile, mobile technology and smart apps have also been studied from the business processes viewpoint since they have become an excellent opportunity to analyse consumers in more meaningful manners, thus supporting the development of appropriate marketing strategies (Sundaram et al., 2020 ). Additionally, mobility, along with other digital technologies, is creating relevant opportunities for firms to transform themselves by impacting on their purchasing processes (Ulas, 2019 ) as well as on their distribution activities, since mobile apps represent omni-channel retail platforms allowing consumers to obtain products from different channels, such as e-commerce, modern markets, and traditional ones. In this way, the shopping experience streamlines and integrates itself across channels (Cahyadi, 2020 ). Conversely, even if the employee perspective has been less investigated, it represents an interesting field of study since the mobile technology is impacting, on a massive scale, the workplace (Attaran and Attaran, 2020 ). More in detail, it can raise employee engagement; increase productivity through the scheduling/automation of daily activities; enable real-time communications through different tools, such as group chats or one-to-one messaging. Moreover, the 5G advent could revolutionize the way employees work “in much the same way the Internet did in the 1980s” (Attaran and Attaran, 2020 ; p. 66). Notably, it can allow employees to (i) Fast download and upload files and documents; (ii) Quicker move data; (iii) Carry the office anywhere; (iv) Exploit resources such as real-time video interaction and smart conference/meetings rooms, thus maximizing the workplace productivity and efficiency, reducing travel time, and saving operational costs for remote employees; (v) Increase office collaboration; (vi) Synchronize and access to large amounts of data storage.

Another macro-theme widely analysed by the literature focused on the DT and marketing is Internet of Things, which represents one of the main megatrends related to the technological revolution (Hamidi et al., 2020 ). Extant research (e.g., Almeida et al., 2020 ; Chehri and Jeon, 2019 ) has particularly examined the main improvements provided by this technology in terms of business processes. Notably, Sestino et al. ( 2020 ) underlined how IoT can contribute to: (i) Design products/services based on consumers’ consumption experiences; (ii) Collect consumption data useful, for marketing managers, to identify new gaps, trends, or variables in understanding consumer behaviour; (iii) Identify consumers’ attitudes and choices on a large scale. At once, different studies (e.g., Almeida et al., 2020 ; Sestino et al., 2020 ) have also investigated the impact of IoT from the customer perspective by focusing on their ability to provide new types of services and high-quality products; as well as to improve the customer journey through more targeted promotions, announcements, and email marketing. Finally, even if the employee perspective represents the least investigated one, some authors (e.g., Almeida et al., 2020 ; Peter et al., 2020 ) identified several IoT advantages from this viewpoint, including the possibility of adopting mobile, flexible, team-oriented, and non-routine working methods, which allow the creation of digital workplaces; activating collaborative practices between all the staff’s levels; and communicating and disseminating corporate strategies, thus creating innovative workplaces.

Concerning the Artificial Intelligence (AI), it has been analysed from all the perspectives, especially the customer and business processes ones. Different studies investigated the advantages of the AI-based digital humans for customers, including the possibility to obtain better knowledge of their preferences and needs (Kumar-Singh and Thirumoorthi, 2019 ), to build an innovative and real-time relationship with the firms (Cherviakova and Cherviakova, 2018 ), to experience a completely new and interactive journey, and to receive personalized offers (Ianenko et al., 2019 ). From the processes perspective, AI significantly influences marketing processes and activities (Almeida et al., 2020 ; Ianenko et al., 2019 ; Sargut, 2019 ) through the analysis of the customers’ behaviours and the realization of more specific targeted profiles (Ianenko et al., 2019 ). AI also influences the distribution activities and, in particular, the automation of the ordering process of products and services (Cherviakova and Cherviakova, 2018 ). Moreover, by considering unexpected events, AI allows to recalculate new routes and to maintain constant contacts with clients and the logistics service providers. Literature (Cherviakova and Cherviakova, 2018 ) underlined the AI role in allowing the automatic placement of advertisements across channels, while Kumar-Singh and Thirumoorthi ( 2019 ) analysed the AI relevance also with respect to the buying/consumption process. Finally, it has been recognized the importance of AI with respect to both sales (Almeida et al., 2020 ) and after-sales processes, as it permits to better examine the customers’ opinions about products/services, and to identify their satisfaction level as well as the possible enhancements that could be applied to the firm’s offering. Concerning the employee perspective, AI–by representing a disruptive technology–has significantly influenced the labour relations model and, in particular, the knowledge sharing among employees (Almeida et al., 2020 ; Subramani, 2019 ; Ulas, 2019 ). Therefore, it becomes fundamental to enhance the employee training toward this digital tool, which is becoming more and more integrated into the workplace (Yigitcanlar et al., 2020 ).

By representing a multifaceted term, the Industry 4.0 has emerged as an additional macro-theme related to the DT-marketing binomial. Notably, research (e.g., Chehri and Jeong, 2019 , Del Giorgio and Mon, 2019 ; Hamidi et al., 2020 ) has mainly investigated this topic from the customer and business processes perspectives, especially by focusing on the main principles behind it, namely 5c (i.e., Cooperation, Conversation, Co-creation, Cognitivity, Connectivity). This technology has created the basis of the digital ecosystem, thus offering the key ability, for firms and customers, to exchange data in real-time (Nosalska and Mazurek, 2019 ). By specifically focusing on the business processes perspective, an interesting point of view has been provided by Naglič et al. ( 2020 ), who analysed the Industry 4.0 macro-theme in combination with the export market orientation/export performance micro-theme. The authors offered a framework on how companies can enhance their export performance through the knowledge related to the Industry 4.0. Overall, their study detected how firms that invest in digital technologies, by effectively embracing DT, are better prepared to compete internationally, thus achieving better export performance.

Also the Machine Learning (ML) macro-theme has been mainly analysed from the business processes perspective. In particular, some studies have tried to identify the main ML implications on DT in marketing (Miklosik and Evans, 2020 ) by investigating the advantages this technology can bring from this perspective (Kazaishvili and Khmiadashvili, 2020 ; Miklosik and Evans, 2020 ; Polyakov and Gordeeva, 2020 ; Sargut, 2019 ). Literature focused its attention on the social media analysis (e.g., sentiment analysis on social media); packaging; product and purchasing decision-making; and advertising (e.g., interactive ad placement and targeting ads). Given that ML is a subset of AI, the literature focused on ML usually underlined, from the employee and customer perspectives, advantages very similar to the AI-related ones. More in detail, from the customers’ perspective, ML can offer personalized shopping experiences thanks to its ability to deeply know their preferences and interests. Conversely, from the employees’ viewpoint, literature mainly highlighted the key impact of ML on knowledge building and sharing (Subramani, 2019 ).

Concerning the online collaborative/support platforms/systems macro-theme, it emerges how it has been equally analysed from the employee and business processes perspectives. From the employee perspective, Azeredo et al. ( 2020 ) provided a proposal for the realization of an online business consulting plan through the adoption of an online collaborative platform called LexDoBusiness. More in detail, the research aimed to analyse the acceptability of this platform, which offers several benefits, especially for what concerns the levels of cohesion and cooperation between the actors involved in the business plan. In their study, Bhatnagar and Grosse ( 2019 ) underlined the relevance of a digitalized agile workplace since it allows to make employees more productive and satisfied. Similarly, Minculete and Minculete ( 2019 ) emphasized the key role of education and training actions aimed at providing staff members with the required skills for the new technologies and systems adoption. By specifically focusing on the business processes perspective, Bruskin et al. ( 2017 ) examined the development of support systems for decision-making in terms of marketing by specifically focusing on the analysis of the business effects from the adoption of similar systems.

As regards the virtual and augmented reality, literature has mainly examined it from the customer and business processes perspectives. For what concerns the first viewpoint, the majority of studies have investigated the consumers’ propensity to interact with this tool (Voronkova, 2018 ). Additional researches have focused their attention on the new opportunities deriving from adopting virtual and augmented reality for personalized online shopping experiences (Kim, 2020 ). From the business processes perspective, the virtual/augmented reality has been particularly examined with respect to the communication and advertising procedures. Notably, extant research underlined how firms can adopt the virtual reality technology to promote products and services in innovative and visual ways (Voronkova, 2018 ).

For what concerns the last identified macro-themes (i.e., websites/SEO; cloud infrastructure; chatbots; drones/smart robots; security protection systems; 3D print), results have already revealed a minor attention dedicated to them by the extant research (Table 5 ). By focusing on the websites/SEO topic, the customer and business processes perspectives represent the most investigated viewpoints. Existing studies have particularly analysed the websites topic with respect to the customer relationship management/customer connectivity/centricity (Ballestar et al., 2019 ) and customer experience/journey (García et al., 2019 ) micro-themes. With regard to the business processes perspective, the reviewed contributions have especially deepened the micro-themes of branding, communication policy, and business process efficiency. Specifically, Natorina ( 2020 ) underlined the need to implement effective marketing strategies within the DT scenario by specifically focusing on the search engine optimization (SEO). Overall, the author highlighted how the SEO represents an integral component of a successful marketing strategy since it increases the organic traffic and conversion by also enhancing the firms’ attractiveness in the sight of the Internet users.

Concerning the cloud infrastructure, it has been especially analysed from the customer perspective (Ulas, 2019 ) by investigating its impact on consumers’ preferences and behaviours. At the same time, the cloud infrastructure has also increased the human resources capabilities (Ulas, 2019 ) and improved the business processes. Notably, Kumar-Singh and Thirumoorthi ( 2019 ) shown that cloud-based digital infrastructures allow firms to increase agility, maximize resources, and improve services by also reducing operational costs. The authors also underlined the importance to analyse the impact of this technology from the demand side in order to examine how it can impact on customer preferences and behaviours.

As for the chatbots, these have been analysed from the business processes perspective and, to a lesser extent, from the employee one. Hence, an interesting research gap emerges with respect to the customer viewpoint. In particular, concerning the business processes perspective, Damnjanovic ( 2019 ) proposed a case study analysing the international positioning and go-to-market strategy of a chatbot solution, namely Weaver, which can be defined as an AI-based firm platform allowing to facilitate and simplify the sales processes. In the same year, the study of Sargut ( 2019 ) offered an insight related to the SMEs awareness, readiness, and capability in facing the DT challenge. Almost all the interviewed SMEs have confirmed to be interested in the DT subject and ready to implement chatbots and/or voice-operated machines in their business activities and processes.

Even if results underlined scarce attention of the recent literature on the robotics macro-theme (with the few identified contributions focused on the employee and business processes perspective), with the advent of the COVID-19 and the consequent reduction of human contacts, this topic will probably obtain, in the future, greater emphasis. Notably, robots will be increasingly adopted not only in order to substitute human resources but also to interact with customers. Indeed, robots “are expected to be progressively more autonomous, flexible, and cooperative” (Almeida et al., 2020 , p. 102).

As for the last identified macro-themes (i.e., security protection systems and 3D print), while Li et al. ( 2020 ) emphasized the need to establish a new generation of security protection systems to increase the business processes efficiency, Ulas ( 2019 ) especially highlighted the key relevance of 3D printers in the process of new products development and design.

By considering the residual (but not irrelevant number of) contributions referring to the digitalization phenomenon as a broader macro-theme of analysis (i.e., digitalization phenomenon), it emerged an overall preference towards the adoption of a business processes and customer perspective. With regard to the former, two of the most investigated effects are the so-called “digital metrics” and “business process efficacy”. Indeed, the digitalization phenomenon has profoundly affected the analysis of the firms’ performance. Hence, the adoption of digital tools allows firms to precisely monitor and measure their social ROI (Return on Investment) in a totally new and disruptive way compared to the past. In particular, by measuring online reactions (e.g., customers’ views, likes, comments, shares), the digital metrics can contribute significantly to evaluating an ad campaign in real-time, thus permitting to modify it accordingly (e.g., Bughin et al., 2019 ). Moreover, a number of contributions focused on the business processes perspective has specifically analysed the role played by the digital tools in increasing the quality of the firms’ processes, thus elevating their levels of operational and organizational excellence (e.g., Kuimov et al., 2019 ). On the other hand, from the customer perspective, literature has mainly investigated the impact of the digitalization phenomenon on the customer journey (e.g., Taylor et al., 2020 ) and on the relationship management between firms and customers (e.g., Barann, 2018 ).

After the content analysis process has been concluded, Appendix 2 has been created, displaying the classification of the articles based on the following categorizations: (i) Author/s; (ii) Title; (iii) Source; (iv) Year of publication; (v) Analysed macro-theme; (vi) Analysed micro-theme with (vii) The respective analysis perspective (i.e., EP, CP, BPP).

4 Implications and future research agenda

4.1 general discussion.

Both the descriptive and thematic results of this study provide interesting insights into the analysis of the DT-marketing topic, while crafting new propositions for future research agenda.

Descriptive data highlight the growing focus of the literature on the digital transformation-marketing topic over the last few years, with the majority of contributions published between 2019 and 2020. Notably, only nine publications have been found in the four-year period 2014–2017, while thirteen publications were reviewed in 2018, forty-five in 2019, and fifty in 2020. The publication sources are highly fragmented, given that ninety-three sources have published the 117 reviewed papers. The more cited contributions—besides being published between 2019 and 2020—have especially focused on the impact of the digitalization phenomenon on (i) Customer relationship management (Ballestar et al., 2019 ; Gil-Gomez et al., 2020 ; Hausberg et al., 2019 ; Peter et al., 2020 ; Sivarajah et al., 2020 ), (ii) Its coexistence with the human resources (Almeida et al., 2020 ; Gil-Gomez et al., 2020 ; Ulas, 2019 ; Yigitcanlar et al., 2020 ), and (iii) The improvement of the business processes’ performance (Sestino et al., 2020 ) by specifically focusing on market knowledge (Hausberg et al., 2019 ), communication (Ballestar et al., 2019 ), product development (Ulas, 2019 ), and sales activities (Almeida et al., 2020 ). Moreover, the majority of contributions here analysed has employed qualitative methods. Overall, these data, while suggesting an increasing interest by the scientific community towards the DT-marketing phenomenon, depict the absence of sources systematically and continuously dealing with this field of study, a dominant focus on certain issues, and the need to improve the adoption of quantitative methods in future research, both to validate previous research findings and to make them more generalizable.

Concerning the research questions guiding this study and, in particular the analysed themes (RQ1), these can be grouped on a twofold level concerning (i) The study of digital technologies employed in the field of marketing ( macro-themes) , and (ii) The impact of such technologies on specific marketing activities ( micro-themes ). Overall, the literature analysis suggests an increasing pervasiveness of digital technologies in the marketing field. The use of such technologies, in fact, affects the consumer behaviour, as well as the way marketers work and marketing activities are managed and organized. In particular, it is worthy to note that DT involves the most operational marketing activities (e.g., Caliskan et al., 2020 ), such as sales (e.g., Almeida et al., 2020 ) and communication policies (e.g., Alassani and Göretz, 2019 ; Dasser, 2019 ), allowing a general increase in these processes’ quality. Meanwhile, DT also affects the analytic and strategic areas of marketing, improving the opportunities to reach new groups of consumers through the systematic use of digital technologies (such as Big Data) that allow a deeper segmentation of the market (e.g., Almaslamani et al., 2020 ). It supports the development of new branding strategies and the increasing visibility of brands, thanks to the use of online and social channels (e.g., Kazaishvili and Khmiadashvili, 2020 ; Melović et al., 2020 ). Moreover, DT impacts on companies’ innovativeness, helping the implementation of more effective and efficient innovative processes (Calle et al., 2020 ), and changes the overall relationships between firms and consumers by encouraging a customer-centric organizational culture (Cherviakova and Cherviakova, 2018 , Graf et al., 2019 ) and the customer participation in the value creation process (Hughes and Vafeas, 2019 ). According to Dasser ( 2019 ), DT also implies a deeper change of marketing by elevating its strategic role as a catalytic accelerator in the digital business transformation journey.

These studies are driven by different perspectives of analysis (RQ2). The majority of research considered in this review employed a business process perspective by examining how digital technologies impact on specific marketing processes, such as sales and communication management. Nevertheless, by focusing on the main investigated topics, findings reveal that the existing research has been principally guided by a customer perspective, i.e. the way in which digital technologies are transforming customers’ behaviour, experience, and relationship with companies, followed by the business processes perspective concerning the investigation of potential improvements occurring in the area of marketing analysis and control. The employees’ perspective emerges as the less relevant among the others, despite it includes a critical part of the literature focused on the relationship between DT and human resources management. More in detail, as it emerged from our dataset, the employees’ perspective mainly characterized the first publications, investigating how digital technologies are enhancing (and requiring) the development of new marketing and business skills dealing with DT (Kwon and Park, 2017 ; Van Belleghem, 2015 ). Over the time, the scientific attention has been moved increasingly towards the customer and business processes’ perspectives. Most of the contributions published in 2020, indeed, dealt with the analysis of the DT phenomenon from the consumer viewpoint, specifically investigating the management of the customer-firm relationship (e.g., Gil-Gomez et al., 2020 ; Sivarajah et al., 2020 ), and from the business processes’ viewpoint, especially analysing the key relevance of the digital tools in measuring the firms’ performance in the social sphere (e.g., Al-Azani and El-Alfy, 2020 ; Lin et al., 2020 ). Probably, this growing interest of the research derives from the advent and unleashing, during 2020, of the COVID-19 health crisis that has led companies to almost completely digitize the relationship with customers due to the limitations imposed by the anti-COVID-19 decrees.

All these findings provide several contributions both theoretically and practically.

4.2 Theoretical implications and research gaps

From a theoretical standpoint, this is the first study that offers a systematic and thematic review of the existing literature on DT and Marketing, while previous reviews, in the marketing field, have been very narrow in perspective. Hofacker et al. ( 2020 ), for example, examined the relevant literature on digital marketing and B2B relationships, while Miklosik and Evans ( 2020 ) focused on the impact of big data and machine learning on marketing activities. Our review, instead, addresses the DT-Marketing binomial from a wider and more comprehensive perspective, including all prior research dealing with DT in the marketing area. By doing so, this study outruns the scope of prior reviews that have been often limited to certain domains, and provides a comprehensive framework that offers a synergistic view of the existing literature, which allows a more inclusive vision and understanding about the phenomenon.

By doing so, this review also permits to highlight some relevant research gaps on which future studies might focus on.

From the combined overview between macro- and micro-themes, the main research gaps relate to the necessity of deepening the analysis of the impact of specific macro-themes from the employee (i.e., social media channels, big data, mobile marketing, Artificial Intelligence, Industry 4.0, Cloud infrastructure, Virtual/augmented reality, and websites), customer (i.e., Social media channels, Big Data, Industry 4.0; Internet of Things; Machine Learning; Websites; Chatbots), and business processes perspective (i.e., Mobile technology; Artificial Intelligence; Virtual/Augmented reality; Cloud infrastructure; Drones/Smart robots).

Besides that, the variety of analysed studies, while manifesting the pervasive use of digital technologies in the marketing field, reveals that the extant literature is quite fragmented and even sparse with regard to specific micro-themes. Some topics, like customer service, smart factories, consumer behaviour, have been investigated by few contributions, thus highlighting potential opportunities for further studies. In this respect, our review can be viewed as a solid basis for additional discussion and research within each perspective emerged from the analysis (see Fig.  3 ).

Areas of future research on DT and Marketing

More in detail, the findings reveal that the employees’ perspective is worthy of further attention, as it is the less investigated one. Although several contributions (n. 21) focused on DT and human resources by highlighting the need for enhanced skills in using technology (e.g., Dethine et al., 2020 ; Ulas, 2019 ), the development of new prominent job positions for the future (e.g. digital marketing manager; social media manager; big data/data analyst) (e.g., Di Gregorio et al., 2019 , Hafezieh and Pollock, 2018), and the critical role of training and educational actions enhancing the appropriate use of digital technologies in the marketing context (Yigitcanlar et al., 2020 ), other themes have been under-investigated. In particular, only two papers dealt with the subject of smart technologies by investigating how they can help cities to face the increasing urbanization (Visan and Ciurea, 2020 ), and their importance for establishing a predictive maintenance of production systems, which can increase the process quality (Chehri and Jeon, 2019 ). The application of smart technologies can also redefine the way people conduct business, bringing benefits in terms of productivity and employee well-being (Papagiannidis and Marikyan, 2020 ). Thus, there is scope for considering, in future research, how smart technologies are used to conduct marketing activities and how they are changing the way marketers work and organize their processes.

Under the customer perspective, several topics might deserve attention in future research. Most of the analysed contributions addressed the impact of DT on firms/customers relationships, highlighting the need for new forms of interaction and collaborations with customers due to changes in behaviour. Several scholars recognized the advantage of DT as it allows to establish innovative and real-time relationships with the market (e.g. Almaslamani et al., 2020 ), to engage customers in the value creation process (e.g. Saravanabhavan et al., 2020 ; Taylor et al., 2020 ), and to provide customers with more interactive and personalized experiences (e.g. Taylor et al., 2020 ; Venermo et al., 2020 ). However, our findings suggest that other topics, although relevant, are still at the begin of their investigation. Only three contributions focused on customer service (Lieberman, 2019 ; Lin et al., 2020 ; Safiullin et al., 2020 ), especially revealing the role of digital tools in the online customer service and the importance of electronic services for improving customer satisfaction (Lin et al., 2020 ). A recent study (Galvani and Bocconcelli, 2021 ) revealed that a new business model is emerging in the BtoB context characterized by an overall revolution towards the digital servitization strategy, which replaces the traditional product-centric paradigm. Hence, future research could investigate whether and how the digital servitization strategy is currently implemented in the BtoC context, which opportunities and benefits can offer—especially concerning the firm-customers’ relationship, and how marketing managers can act to face the imperative complexity linked to its adoption. Another theme receiving increasing—but still few—attention concerns the buying/consumption processes. Few scholars analysed the impact of digital tools on customers buying processes (Kim, 2020 ), the increasing use of e-commerce (Cahyadi, 2020 ), and structural changes occurring in consumption during COVID-19 pandemic (Kim, 2020 ). However, the identification of consumption patterns and trends has been always a central topic in the marketing literature, as proved by the wide number of literature reviews, even focused on specific areas such as electronic word of mouth (Huete-Alcocer, 2017 ), online consumption (Hwang and Jeong, 2016 ), or COVID-19 crisis (Cruz-Cárdenas et al., 2021 ). Therefore, continuing the research on DT and consumption/buying behaviour is desirable to properly adapt the marketing management with the aim of satisfying specific market needs and expectations, as well as realizing a stronger engagement of customers in the value creation process, which is getting more and more attention within the recent marketing and management literature (Fan and Luo, 2020 ). Besides, future studies on DT and consumption/buying behaviour might also employ modern research methods, such as neuromarketing. We found only one contribution based on the analysis of the use of advanced methods in the field of artificial neural networks (Polyakov and Gordeeva, 2020 ). However, neuromarketing could contribute to overcome several limitations associated with traditional data collection method (i.e. self-report data), while allowing to capture unconscious brain processes that relate to consumer decision-making (Sung et al., 2021 ).

Finally, an additional space for future research emerged from our review of publications is related to the business processes perspective. This area shows the greatest potential for exploration, given the richness of themes it includes. In this perspective, in fact, except for some activities related to marketing analysis and control, and operational policies—especially product and communication ones—the rest of the literature appears very fragmented and scarce. Notably, specific attention might be devoted to DT and export process management, as Naglič et al. ( 2020 ) found that firms which invest in DT are better prepared to compete internationally and achieve better export performance; branding strategies, as they have been recognized as critical for marketing competitiveness (Kazaishvili and Khmiadashvili, 2020 ), drivers/barriers and risks associated to DT implementation in the marketing areas; and sustainable/social opportunities and treats that digital technologies can bring with them, as they can differently affect the success of human-centric marketing programs in the digital environment (Agafonova et al., 2020 ). All these topics have been very little investigated by previous research, while deserving increasing attention given their relation with companies’ success and long-term competitiveness.

4.3 Practical implications

Regarding the practical contributions, our review offers a number of suggestions to marketing managers as it analyses the DT-Marketing binomial both internally (i.e. on the firm level) and externally (i.e. on the inter-firm level). This approach results from the recognition of different perspectives of analysis adopted by prior research, which combines contributions focused on the management of internal processes and marketing activities with studies investigating the DT phenomenon from a customer-based viewpoint. Consistent with our twofold approach of analysis, the practical implications deserving particular attention can be summarized into two main groups concerning (i) The changing role of marketing in the company resulting from the increasing use of digital technologies, and (ii) The changing relationships between firms (and marketing) and external stakeholders (especially consumers).

Literature suggests that DT could improve the strategic role of marketing within the firm, as it enhances the marketing capability to analyse the market scenario and to develop a more comprehensive understanding of the demand (Papagiannopoulos and Lopez, 2018 ), which, in turn, can support new products development that are better aligned with customers’ expectations (Kuimov et al., 2019 ). Overall, digital technologies can help companies to become data-driven subjects, where marketing covers a central position given its informative and intra-firm coordinating role. However, the full exploitation of such opportunities means change, at both cultural and structural levels. Our review, in particular, reveals that DT requires a cultural upgrading, to cope with DT and its effects on the business (e.g., Álvarez-Flores et al., 2018 ; Dethine et al., 2020 ), the enhancement of internal competences in the field of technology (Ulas, 2019 ), the development of new job positions (Di Gregorio et al., 2019 ), and the gradual adoption of new working habits and patterns (Minculete and Minculete, 2019 ). Of course, educational and training activities become prominent to support such changes, passing through the acquisition of new skills from the market labour, as well as through the enhancement and conversion of internal resources. Besides training programs, organized both internally and externally in collaboration with private and public institutions such as high schools and universities, companies could also provide ad hoc rewards to encourage the commitment and interest of marketing employees in digital innovation.

The second group of advices concerns the changing relationships between firms (and marketing) and external stakeholders (especially consumers). DT affects the customer behaviour and changes his ability to communicate with the company (e.g., Caliskan et al., 2020 ), to be engaged in the value creation process (e.g., Taylor et al., 2020 ), and to live personalized consumption experiences (e.g., Fokina and Barinov, 2019 ). All this implies a general re-thinking about the firm-customer relationship management. Consumers are becoming empowered subjects that no longer accept the role of passive receivers of marketing initiatives (Acar and Puntoni, 2016 ) and companies need to open to their customers, accepting their participation in the marketing decision-processes. Undoubtedly, the use of social-media platforms can be decisive to create engaging content and connect with customers, improving the interaction and the dialog with them, for example by responding to a specific comment or complaint (Acar and Puntoni, 2016 ). However, digital technologies can be also used to create more advanced tools that are able to strengthen the connection between brands and customers, such as crowdsourcing, co-creation, and/or brand communities. These platforms can be used successfully by firms to improve the dialog with customers and their involvement in several marketing processes, such as the selection of an advertising campaign and/or the creation of new product ideas.

Acknowledgements

This publication includes, among the authors, a researcher awarded with a fixed-term type A research contract on innovation topics as per art. 24, para. 3, of Italian Law no. 240 of 30 December 2010, co-financed by the European Union—NOP Research and Innovation 2014-2020 resources as per Italian MD no. 1062 of 10 August 2021.

Open access funding provided by Università degli Studi di Urbino Carlo Bo within the CRUI-CARE Agreement.

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Cioppi, M., Curina, I., Francioni, B. et al. Digital transformation and marketing: a systematic and thematic literature review. Ital. J. Mark. 2023 , 207–288 (2023). https://doi.org/10.1007/s43039-023-00067-2

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Artificial intelligence in digital marketing: insights from a comprehensive review.

1. Introduction

2. materials and methods, 3.1. descriptive and scientometric analysis of records, 3.1.1. most frequent sources, 3.1.2. bradford’s law, 3.1.3. lotka’s law, 3.1.4. the most relevant countries by corresponding authors, 3.2. literature clustering, cluster-based literature table.

• a is the mean distance between a sample and all other points in the same class;
• b is the mean distance between a sample and all other points in the next nearest cluster.
• B k is the between-group dispersion matrix and T r B k its trace;
• W k is the within-cluster dispersion matrix and T r W k its trace;
• N is the number of points, and k is the number of clusters.
• k is the number of clusters;
• σ i   is the average distance of all points in cluster i to the centroic c i   of cluster i ;
• d   c i + c j   is the distance between centroids c i   and c j   .

4. Discussion

4.1. ai/ml algorithms cluster.

• Examination of the long-term impacts of AI and machine learning on consumer trust across diverse cultural contexts.
• Advanced machine learning techniques for real-time hyper-personalization in both online and physical retail environments.
• Comparative studies on the effectiveness of different AI algorithms in predictive analytics for various marketing domains.

4.2. Social Media Cluster

• Examination of the evolving role of AI in managing and interpreting complex social media data for personalized marketing.
• Analysis of the effectiveness of AI-driven advertisements on different social media platforms and their impact on consumer behavior.
• Ethical implications and privacy concerns of AI in social media marketing, with a focus on user personality prediction and behavior analysis.

4.3. Consumer Behavior Cluster

• The integration of virtual agents in retail and service industries and their impact on consumer relationship building.
• The effectiveness of decision trees and genetic algorithms in predicting consumer behavior across digital and physical shopping platforms.
• Analysis of the role of AI in influencing consumer perceptions and decision-making in e-commerce settings.

4.4. E-Commerce Cluster

• Development of sophisticated AI-driven chatbots for enhancing customer experience in e-commerce.
• Impact of conversational AI on customer service and sales in industries like banking and hospitality.
• Challenges and opportunities in implementing AI technologies in e-commerce, particularly in privacy and security aspects.

4.5. Digital Advertising Cluster

• Examination of the effectiveness of AI in creating and delivering personalized advertisements through emerging channels like smart speakers.
• Ethical considerations and consumer attitudes toward AI in advertising, particularly in voice and data mining.
• The role of AI in combating challenges such as click fraud in online advertising.

4.6. Optimization and Budget Control

• Development of AI algorithms for more efficient real-time bidding and ad allocation in digital advertising.
• Potential of AI in predictive budget allocation and its impact on marketing campaign performance.
• Integration of AI in optimizing marketing strategies across various digital platforms.

4.7. Competitive Strategies Cluster

• The role of AI in innovative e-commerce marketing models and market segmentation strategies.
• The impact of AI on the development of marketing strategies in specific sectors like retail.
• The challenges and opportunities in adopting AI for strategic marketing decisions, particularly in the B2B context.

5. Conclusions

Author contributions, data availability statement, conflicts of interest.

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Share and Cite

Ziakis, C.; Vlachopoulou, M. Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review. Information 2023 , 14 , 664. https://doi.org/10.3390/info14120664

Ziakis C, Vlachopoulou M. Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review. Information . 2023; 14(12):664. https://doi.org/10.3390/info14120664

Ziakis, Christos, and Maro Vlachopoulou. 2023. "Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review" Information 14, no. 12: 664. https://doi.org/10.3390/info14120664

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International Journal of Innovation Science

ISSN : 1757-2223

Article publication date: 5 April 2021

Issue publication date: 5 September 2022

The purpose of this paper is to review the literature on the topic “impact of digital marketing” over the span of past eight years, published in various prominent research journals in the past eight years. The purpose of this paper is to act as a starting point for several further researches in this area of study, and also to get the overview of the research that has happened and understand the relevant research gaps that exist in the area of digital marketing.

Design/methodology/approach

Scopus database is used to search the research publications on the selected topic. The papers selected for this paper have been published in the past eight years (2012–2020).

It has been concluded by many of the research papers reviewed that “Digital Marketing Efforts” influence the purchase intention of the customer. It can be also inferred that the distinction between the “marketing” and “digital marketing” is soon fading as every type of marketing effort will have an element of “digital marketing” in it.

Research limitations/implications

The approach to the review is theoretical and no primary data have been collected. This bibliometric review is expected to provide overview of the research that has happened over the span of past eight years in the area of digital marketing.

Practical implications

Many of the papers have expressed the limitations and opportunities for the future research. Few of the prominent and relevant research gaps are listed in this paper. This paper is expected to lay a foundation for several further studies in this area of study.

Originality/value

The paper is original in terms of reviewing the literature published on the topic, “impact of digital marketing”, between years 2012 to 2020. As the world has been forced to go digital due to COVID-19 outbreak, it has become all the more significant to take an account of developments in the field of “digital marketing”.

• Purchase intention
• Online marketing
• Digital marketing
• Touchpoints

Dunakhe, K. and Panse, C. (2022), "Impact of digital marketing – a bibliometric review", International Journal of Innovation Science , Vol. 14 No. 3/4, pp. 506-518. https://doi.org/10.1108/IJIS-11-2020-0263

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Call for Papers | Journal of International Marketing: Digital Platforms and Ecosystems in International Marketing

The business world has witnessed the emergence of new digital technologies and business models in the past two decades. For example, the rise of digital platform business models in retail and IT services has disrupted traditional models, making firms reconsider their business strategies and creating new opportunities for marketers to create value for buyers and other stakeholders (Perren and Kozinets 2018). Digital platforms intermediate between sellers, buyers, and other stakeholders via digital architecture often manifested as mobile and web applications (e.g., sharing economy platforms; Kozlenkova et al. 2021). The platform firm decides on the extent of governance accountability it will take in the entire value-creation process. Ecosystems, of which platforms can be a part, are networks of independent but interdependent actors participating in an industry’s or economic sector’s value chain (Nambisan, Zahra, and Luo 2019). Both platforms and ecosystems create value via direct and indirect network externalities (Kumar, Nim, and Agarwal 2021; Sridhar, Mantrala, Naik, Thorson 2011; ), with value cocreation at the core of actors’ business models and strategies.

In today’s Internet Age, digital platforms have no geographic borders. Platform business models are becoming a go-to strategy for international firms across the globe. For example, retailers are increasingly considering the platformization of their brands to add more value to their core offerings (Wichmann, Wiegand, and Reinartz 2022). With the help of digital technologies, it has become easier to expand into multiple markets simultaneously without diluting the supply chain advantages and brand positioning. Consider the low-cost e-commerce firm Temu from China, operating in more than 50 global markets and developing a strong ecosystem after launching in 2022. Temu consumers get access to various global sellers, making the domestic and international markets more competitive (Deighton 2023).

At the same time, marketers with new ways to create and capture value get access to an expanded target market. For example, as an entertainment platform, Netflix has launched different product and subscription pricing strategies in markets like India to compete with Disney, Amazon, and Reliance (Sull and Turconi 2021). Crowdfunding platforms like Kickstarter connect project creators and backers across the globe. Social media platforms have further amplified the reach of such retail and commerce platforms across both business and consumer markets (Gao et al. 2018). Thus, digital platforms and ecosystems can be considered a contemporary approach to internationalization and thereby are of great interest to marketing managers, policy makers, and regulators in both developed and emerging markets (Hewett et al. 2022).

However, research and knowledge of the dynamics of digital platforms and ecosystems in international marketing is still rather limited. A deeper understanding of how digital platforms can be utilized in the global marketing efforts of businesses is needed. Understanding digital platforms utilized across markets for sustainability, water conservation, health care, and other pressing issues and from the perspectives of NGOs and governments is urgently needed (Falcke, Zobel, and Comello 2024).

The purpose of this special issue, therefore, is to significantly advance research investigating the role of platforms and ecosystem business models across various facets of international marketing. Of special interest are papers focusing on the evolution and formation of digital platform-based global marketing strategies and business models, providing concepts, frameworks, theories, and empirical insights helpful for customers, firms, regulators, policy makers, and governments.

Research bearing on (but not limited to) the following questions is welcome: 

• Different modes of platforms and ecosystems as internationalization approaches: a. How orchestrator firms build digital and nondigital architecture across different markets while entering or growing in a market. b. How these modes differ due to within and across market heterogeneity leading to different marketing strategies. c. How the consumer culture of a market complements the different modes.
• Impact of platform and ecosystem approach on the international marketing mix: a. How stakeholders drive or impact product development and innovation processes. b. How integrating private label brands by platforms and using seller data impacts platform outcomes. c. How pricing strategies evolve and change over time with platforms and ecosystem approaches. d. How subscription and non-subscription pricing strategies evolve across various markets. e. How the supply chain and distribution network strengthens or weakens as the industry moves toward platform and ecosystem approaches. f. How the orchestrator firm develops or chooses partners for various marketing activities across and within developed and developing markets. g. How the promotion mix of the platform and ecosystem-based offering differ from traditional business models within and across industries (B2B vs. B2C) and markets. h. How culture interacts with platform and ecosystem strategies, and how this impacts firms and other stakeholders.
• Impact on market structure, competition, and consumer and stakeholder welfare: a. Do platforms and ecosystems command higher market power, impacting the welfare of consumers and other stakeholders? b. Do platform and ecosystems approaches vary across industries (e.g., retail, energy, transportation)? How does these approaches impact the marketing mix in a market? c. Does higher platform power lead to consumer and stakeholder welfare erosion? d. How can marketers navigate the competition and coopetition to make a platform successful across various markets? e. What lessons can be learned from technology platforms like Google and Apple to navigate the tricky technological and regulatory landscape? f. What is the role of government and regulatory bodies in supporting or deterring the platform’s growth to ensure the welfare of stakeholders? g. Do dark patterns affect customer welfare? (For example, subscription pricing charges and policies that are not visible to consumers and the role of regulators.) h. Is there a loss of local livelihood that affects sellers as platforms integrate private label brands? i. What is the impact of the rise of circular platforms on sustainable value chains and stakeholders across developed and developing markets?
• Customer attitudes and actions within and across platforms and ecosystems: a. Conceptual similarity for customer-based outcomes for platform firms and other stakeholders. b. Measurement of customer experience, satisfaction, and engagement with digital platforms and ecosystems. c. Management of failures and customer recovery in multisided platforms and ecosystems. d. Customer journey management across various digital and nondigital touchpoints in platforms and ecosystems. e. Interdependence of consumers’ relationships with and perceptions of brands or partners operating across platforms and ecosystems. f. How marketers can explore circular platforms and ecosystems to help firms be sustainable value chains and positive customer attitudes. g. Platform exploitation by customers and disintermediation.

This list of topics and questions is reflective but not exhaustive of the current state of industry and academic literature. We call for more interdisciplinary and foundational research to expand the horizons of platforms and ecosystems literature in International Marketing. We invite all types of research—qualitative, behavioral, and empirical—and encourage researchers to identify multiple sources of data and motivation for this special issue.

Submission Process

All manuscripts will be reviewed as a cohort for this special issue of the Journal of International Marketing. Manuscripts must be submitted between March 1, 2025 and May 30, 2025. All submissions will go through Journal of International Marketing ’s double-anonymized review and follow standard norms and processes. Submissions must be made via the journal’s ScholarOne site , with author guidelines available here . For any queries, feel free to reach out to the special issue editors.

Special Issue Editors

Nandini Nim, Assistant Professor of Marketing, University of Texas at El Paso (email: [email protected] )

Murali Krishna Mantrala, Ned Fleming Professor of Marketing, University of Kansas (email: [email protected] )

Ayşegül Özsomer, Professor, Koç University, and Editor in Chief, Journal of International Marketing (email: [email protected] )

Adner, Ron (2022), “Sharing Value for Ecosystem Success,”  MIT Sloan Management Review , 63 (2), 85–90.

Deighton, John (2023), “How SHEIN and Temu Conquered Fast Fashion—and Forged a New Business Model,” Harvard Business School (April 25), https://hbswk.hbs.edu/item/how-shein-and-temu-conquered-fast-fashion-and-forged-a-new-business-model .

Falcke, Lukas, Ann-Kristin Zobel, and Stephen D. Comello (2024), “How Firms Realign to Tackle the Grand Challenge of Climate Change: An Innovation Ecosystems Perspective,”  Journal of Product Innovation Management , 41 (2), 403–27.

Gao, Hongzhi, Mary Tate, Hongxia Zhang, Shijiao Chen, and Bing Liang (2018). “Social Media Ties Strategy in International Branding: An Application of Resource-Based Theory.  Journal of International Marketing , 26 (3), 45–69.

Hewett, Kelly, G. Tomas M. Hult, Murali K. Mantrala, Nandini Nim, and Kiran Pedada (2022), “Cross-Border Marketing Ecosystem Orchestration: A Conceptualization of Its Determinants and Boundary Conditions,”  International Journal of Research in Marketing , 39 (2), 619–38.

Kozlenkova, Irina V., Ju-Yeon Lee, Diandian Xiang, and Robert W. Palmatier (2021), “Sharing Economy: International Marketing Strategies,”  Journal of International Business Studies , 52, 1445–73.

Kumar, V., Nandini Nim, and Amit Agarwal (2021), “Platform-Based Mobile Payments Adoption in Emerging and Developed Countries: Role of Country-Level Heterogeneity and Network Effects,”  Journal of International Business Studies , 52, 1529–58.

Nambisan, Satish, Shaker A. Zahra, and Yadong Luo (2019), “Global Platforms and Ecosystems: Implications for International Business Theories,”  Journal of International Business Studies , 50, 1464–86.

Perren, Rebeca and Robert V. Kozinets (2018), “Lateral Exchange Markets: How Social Platforms Operate in a Networked Economy,”  Journal of Marketing , 82 (1), 20–36.

Sridhar, Shrihari, Murali K. Mantrala, Prasad A. Naik, and Esther Thorson. “Dynamic marketing budgeting for platform firms: Theory, evidence, and application.”  Journal of Marketing Research  48, no. 6 (2011): 929-943.

Sull, Donald and Stefano Turconi (2021), “Netflix Goes to Bollywood,” Teacher Resources Library, MIT Sloan School of Management (February 22), https://mitsloan.mit.edu/teaching-resources-library/netflix-goes-to-bollywood.

Wichmann, Julian R.K., Nico Wiegand, and Werner J. Reinartz (2022), “The Platformization of Brands,”  Journal of Marketing , 86 (1), 109–31.

Other Resources

Adner, Ron (2017). Ecosystem as Structure: An Actionable Construct for Strategy,”  Journal of Management , 43 (1), 39–58.

Akaka, Melissa A., Stephen L. Vargo, and Robert F. Lusch (2013), “The Complexity Of Context: A Service Ecosystems Approach for International Marketing,”  Journal of International Marketing , 21 (4), 1–20.

Gawer, Annabelle and Michael A. Cusumano (2014). “Industry Platforms and Ecosystem Innovation,”  Journal of Product Innovation Management , 31 (3), 417–33.

Glavas, Charmaine, Shane Mathews, and Rebekah Russell-Bennett (2019), “Knowledge Acquisition via Internet-Enabled Platforms: Examining Incrementally and Non-Incrementally Internationalizing SMEs,”  International Marketing Review , 36 (1), 74–107.

Kanuri, Vamsi K., Murali K. Mantrala, and Esther Thorson (2017), “Optimizing a Menu of Multiformat Subscription Plans for Ad-Supported Media Platforms,”  Journal of Marketing , 81 (2), 45–63.

Zhou, Qiang (Kris), B.J. Allen, Richard T. Gretz, and Mark B. Houston (2022), “Platform Exploitation: When Service Agents Defect with Customers from Online Service Platforms,”  Journal of Marketing , 86 (2), 105–25.

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B2B Content Marketing Benchmarks, Budgets, and Trends: Outlook for 2024 [Research]

• by Stephanie Stahl
• | Published: October 18, 2023
• | Trends and Research

Creating standards, guidelines, processes, and workflows for content marketing is not the sexiest job.

But setting standards is the only way to know if you can improve anything (with AI or anything else).

Here’s the good news: All that non-sexy work frees time and resources (human and tech) you can apply to bring your brand’s strategies and plans to life.  

But in many organizations, content still isn’t treated as a coordinated business function. That’s one of the big takeaways from our latest research, B2B Content Marketing Benchmarks, Budgets, and Trends: Outlook for 2024, conducted with MarketingProfs and sponsored by Brightspot .

A few symptoms of that reality showed up in the research:

• Marketers cite a lack of resources as a top situational challenge, the same as they did the previous year.
• Nearly three-quarters (72%) say they use generative AI, but 61% say their organization lacks guidelines for its use.
• The most frequently cited challenges include creating the right content, creating content consistently, and differentiating content.

I’ll walk you through the findings and share some advice from CMI Chief Strategy Advisor Robert Rose and other industry voices to shed light on what it all means for B2B marketers. There’s a lot to work through, so feel free to use the table of contents to navigate to the sections that most interest you.

Note: These numbers come from a July 2023 survey of marketers around the globe. We received 1,080 responses. This article focuses on answers from the 894 B2B respondents.

Table of contents

• Team structure
• Content marketing challenges

Content types, distribution channels, and paid channels

• Social media

Content management and operations

• Measurement and goals
• Overall success
• Budgets and spending
• Top content-related priorities for 2024
• Content marketing trends for 2024

Action steps

Methodology, ai: 3 out of 4 b2b marketers use generative tools.

Of course, we asked respondents how they use generative AI in content and marketing. As it turns out, most experiment with it: 72% of respondents say they use generative AI tools.

But a lack of standards can get in the way.

“Generative AI is the new, disruptive capability entering the realm of content marketing in 2024,” Robert says. “It’s just another way to make our content process more efficient and effective. But it can’t do either until you establish a standard to define its value. Until then, it’s yet just another technology that may or may not make you better at what you do.”

So, how do content marketers use the tools today? About half (51%) use generative AI to brainstorm new topics. Many use the tools to research headlines and keywords (45%) and write drafts (45%). Fewer say they use AI to outline assignments (23%), proofread (20%), generate graphics (11%), and create audio (5%) and video (5%).

Some marketers say they use AI to do things like generate email headlines and email copy, extract social media posts from long-form content, condense long-form copy into short form, etc.

Only 28% say they don’t use generative AI tools.

Most don’t pay for generative AI tools (yet)

Among those who use generative AI tools, 91% use free tools (e.g., ChatGPT ). Thirty-eight percent use tools embedded in their content creation/management systems, and 27% pay for tools such as Writer and Jasper.

AI in content remains mostly ungoverned

Asked if their organizations have guidelines for using generative AI tools, 31% say yes, 61% say no, and 8% are unsure.

We asked Ann Handley , chief content officer of MarketingProfs, for her perspective. “It feels crazy … 61% have no guidelines? But is it actually shocking and crazy? No. It is not. Most of us are just getting going with generative AI. That means there is a clear and rich opportunity to lead from where you sit,” she says.

“Ignite the conversation internally. Press upon your colleagues and your leadership that this isn’t a technology opportunity. It’s also a people and operational challenge in need of thoughtful and intelligent response. You can be the AI leader your organization needs,” Ann says.

Why some marketers don’t use generative AI tools

While a lack of guidelines may deter some B2B marketers from using generative AI tools, other reasons include accuracy concerns (36%), lack of training (27%), and lack of understanding (27%). Twenty-two percent cite copyright concerns, and 19% have corporate mandates not to use them.

How AI is changing SEO

We also wondered how AI’s integration in search engines shifts content marketers’ SEO strategy. Here’s what we found:

• 31% are sharpening their focus on user intent/answering questions.
• 27% are creating more thought leadership content.
• 22% are creating more conversational content.

Over one-fourth (28%) say they’re not doing any of those things, while 26% say they’re unsure.

AI may heighten the need to rethink your SEO strategy. But it’s not the only reason to do so, as Orbit Media Studios co-founder and chief marketing officer Andy Crestodina points out: “Featured snippets and people-also-ask boxes have chipped away at click-through rates for years,” he says. “AI will make that even worse … but only for information intent queries . Searchers who want quick answers really don’t want to visit websites.

“Focus your SEO efforts on those big questions with big answers – and on the commercial intent queries,” Andy continues. “Those phrases still have ‘visit website intent’ … and will for years to come.”

Will the AI obsession ever end?

Many B2B marketers surveyed predict AI will dominate the discussions of content marketing trends in 2024. As one respondent says: “AI will continue to be the shiny thing through 2024 until marketers realize the dedication required to develop prompts, go through the iterative process, and fact-check output . AI can help you sharpen your skills, but it isn’t a replacement solution for B2B marketing.”

Back to table of contents

Team structure: How does the work get done?

Generative AI isn’t the only issue affecting content marketing these days. We also asked marketers about how they organize their teams .

Among larger companies (100-plus employees), half say content requests go through a centralized content team. Others say each department/brand produces its own content (23%), and the departments/brand/products share responsibility (21%).

Content strategies integrate with marketing, comms, and sales

Seventy percent say their organizations integrate content strategy into the overall marketing sales/communication/strategy, and 2% say it’s integrated into another strategy. Eleven percent say content is a stand-alone strategy for content used for marketing, and 6% say it’s a stand-alone strategy for all content produced by the company. Only 9% say they don’t have a content strategy. The remaining 2% say other or are unsure.

Employee churn means new teammates; content teams experience enlightened leadership

Twenty-eight percent of B2B marketers say team members resigned in the last year, 20% say team members were laid off, and about half (49%) say they had new team members acclimating to their ways of working.

While team members come and go, the understanding of content doesn’t. Over half (54%) strongly agree, and 30% somewhat agree the leader to whom their content team reports understands the work they do. Only 11% disagree. The remaining 5% neither agree nor disagree.

And remote work seems well-tolerated: Only 20% say collaboration was challenging due to remote or hybrid work.

Content marketing challenges: Focus shifts to creating the right content

We asked B2B marketers about both content creation and non-creation challenges.

Content creation

Most marketers (57%) cite creating the right content for their audience as a challenge. This is a change from many years when “creating enough content” was the most frequently cited challenge.

One respondent points out why understanding what audiences want is more important than ever: “As the internet gets noisier and AI makes it incredibly easy to create listicles and content that copy each other, there will be a need for companies to stand out. At the same time, as … millennials and Gen Z [grow in the workforce], we’ll begin to see B2B become more entertaining and less boring. We were never only competing with other B2B content. We’ve always been competing for attention.”

Other content creation challenges include creating it consistently (54%) and differentiating it (54%). Close to half (45%) cite optimizing for search and creating quality content (44%). About a third (34%) cite creating enough content to keep up with internal demand, 30% say creating enough content to keep up with external demand, and 30% say creating content that requires technical skills.

Other hurdles

The most frequently cited non-creation challenge, by far, is a lack of resources (58%), followed by aligning content with the buyer’s journey (48%) and aligning content efforts across sales and marketing (45%). Forty-one percent say they have issues with workflow/content approval, and 39% say they have difficulty accessing subject matter experts. Thirty-four percent say it is difficult to keep up with new technologies/tools (e.g., AI). Only 25% cite a lack of strategy as a challenge, 19% say keeping up with privacy rules, and 15% point to tech integration issues.

We asked content marketers about the types of content they produce, their distribution channels , and paid content promotion. We also asked which formats and channels produce the best results.

Popular content types and formats

As in the previous year, the three most popular content types/formats are short articles/posts (94%, up from 89% last year), videos (84%, up from 75% last year), and case studies/customer stories (78%, up from 67% last year). Almost three-quarters (71%) use long articles, 60% produce visual content, and 59% craft thought leadership e-books or white papers. Less than half of marketers use brochures (49%), product or technical data sheets (45%), research reports (36%), interactive content (33%), audio (29%), and livestreaming (25%).

Effective content types and formats

Which formats are most effective? Fifty-three percent say case studies/customer stories and videos deliver some of their best results. Almost as many (51%) names thought leadership e-books or white papers, 47% short articles, and 43% research reports.

Popular content distribution channels

Regarding the channels used to distribute content, 90% use social media platforms (organic), followed by blogs (79%), email newsletters (73%), email (66%), in-person events (56%), and webinars (56%).

Channels used by the minority of those surveyed include:

• Digital events (44%)
• Podcasts (30%)
• Microsites (29%)
• Digital magazines (21%)
• Branded online communities (19%)
• Hybrid events (18%)
• Print magazines (16%)
• Online learning platforms (15%)
• Mobile apps (8%)
• Separate content brands (5%)

Effective content distribution channels

Which channels perform the best? Most marketers in the survey point to in-person events (56%) and webinars (51%) as producing better results. Email (44%), organic social media platforms (44%), blogs (40%) and email newsletters (39%) round out the list.

Popular paid content channels

When marketers pay to promote content , which channels do they invest in? Eighty-six percent use paid content distribution channels.

Of those, 78% use social media advertising/promoted posts, 65% use sponsorships, 64% use search engine marketing (SEM)/pay-per-click, and 59% use digital display advertising. Far fewer invest in native advertising (35%), partner emails (29%), and print display ads (21%).

Effective paid content channels

SEM/pay-per-click produces good results, according to 62% of those surveyed. Half of those who use paid channels say social media advertising/promoted posts produce good results, followed by sponsorships (49%), partner emails (36%), and digital display advertising (34%).

Social media use: One platform rises way above

When asked which organic social media platforms deliver the best value for their organization, B2B marketers picked LinkedIn by far (84%). Only 29% cite Facebook as a top performer, 22% say YouTube, and 21% say Instagram. Twitter and TikTok see 8% and 3%, respectively.

So it makes sense that 72% say they increased their use of LinkedIn over the last 12 months, while only 32% boosted their YouTube presence, 31% increased Instagram use, 22% grew their Facebook presence, and 10% increased X and TikTok use.

Which platforms are marketers giving up? Did you guess X? You’re right – 32% of marketers say they decreased their X use last year. Twenty percent decreased their use of Facebook, with 10% decreasing on Instagram, 9% pulling back on YouTube, and only 2% decreasing their use of LinkedIn.

Interestingly, we saw a significant rise in B2B marketers who use TikTok: 19% say they use the platform – more than double from last year.

To explore how teams manage content, we asked marketers about their technology use and investments and the challenges they face when scaling their content .

Content management technology

When asked which technologies they use to manage content, marketers point to:

• Analytics tools (81%)
• Social media publishing/analytics (72%)
• Email marketing software (69%)
• Content creation/calendaring/collaboration/workflow (64%)
• Content management system (50%)
• Customer relationship management system (48%)

But having technology doesn’t mean it’s the right technology (or that its capabilities are used). So, we asked if they felt their organization had the right technology to manage content across the organization.

Only 31% say yes. Thirty percent say they have the technology but aren’t using its potential, and 29% say they haven’t acquired the right technology. Ten percent are unsure.

Content tech spending will likely rise

Even so, investment in content management technology seems likely in 2024: 45% say their organization is likely to invest in new technology, whereas 32% say their organization is unlikely to do so. Twenty-three percent say their organization is neither likely nor unlikely to invest.

Scaling content production

We introduced a new question this year to understand what challenges B2B marketers face while scaling content production .

Almost half (48%) say it’s “not enough content repurposing.” Lack of communication across organizational silos is a problem for 40%. Thirty-one percent say they have no structured content production process, and 29% say they lack an editorial calendar with clear deadlines. Ten percent say scaling is not a current focus.

Among the other hurdles – difficulty locating digital content assets (16%), technology issues (15%), translation/localization issues (12%), and no style guide (11%).

For those struggling with content repurposing, content standardization is critical. “Content reuse is the only way to deliver content at scale. There’s just no other way,” says Regina Lynn Preciado , senior director of content strategy solutions at Content Rules Inc.

“Even if you’re not trying to provide the most personalized experience ever or dominate the metaverse with your omnichannel presence, you absolutely must reuse content if you are going to deliver content effectively,” she says.

“How to achieve content reuse ? You’ve probably heard that you need to move to modular, structured content. However, just chunking your content into smaller components doesn’t go far enough. For content to flow together seamlessly wherever you reuse it, you’ve got to standardize your content. That’s the personalization paradox right there. To personalize, you must standardize.

“Once you have your content standards in place and everyone is creating content in alignment with those standards, there is no limit to what you can do with the content,” Regina explains.

Why do content marketers – who are skilled communicators – struggle with cross-silo communication? Standards and alignment come into play.

“I think in the rush to all the things, we run out of time to address scalable processes that will fix those painful silos, including taking time to align on goals, roles and responsibilities, workflows, and measurement,” says Ali Orlando Wert , senior director of content strategy at Appfire. “It takes time, but the payoffs are worth it. You have to learn how to crawl before you can walk – and walk before you can run.”

Measurement and goals: Generating sales and revenue rises

Almost half (46%) of B2B marketers agree their organization measures content performance effectively. Thirty-six percent disagree, and 15% neither agree nor disagree. Only 3% say they don’t measure content performance.

The five most frequently used metrics to assess content performance are conversions (73%), email engagement (71%), website traffic (71%), website engagement (69%), and social media analytics (65%).

About half (52%) mention the quality of leads, 45% say they rely on search rankings, 41% use quantity of leads, 32% track email subscribers, and 29% track the cost to acquire a lead, subscriber, or customer.

The most common challenge B2B marketers have while measuring content performance is integrating/correlating data across multiple platforms (84%), followed by extracting insights from data (77%), tying performance data to goals (76%), organizational goal setting (70%), and lack of training (66%).

Regarding goals, 84% of B2B marketers say content marketing helped create brand awareness in the last 12 months. Seventy-six percent say it helped generate demand/leads; 63% say it helped nurture subscribers/audiences/leads, and 58% say it helped generate sales/revenue (up from 42% the previous year).

Success factors: Know your audience

To separate top performers from the pack, we asked the B2B marketers to assess the success of their content marketing approach.

Twenty-eight percent rate the success of their organization’s content marketing approach as extremely or very successful. Another 57% report moderate success and 15% feel minimally or not at all successful.

The most popular factor for successful marketers is knowing their audience (79%).

This makes sense, considering that “creating the right content for our audience” is the top challenge. The logic? Top-performing content marketers prioritize knowing their audiences to create the right content for those audiences.

Top performers also set goals that align with their organization’s objectives (68%), effectively measure and demonstrate content performance (61%), and show thought leadership (60%). Collaboration with other teams (55%) and a documented strategy (53%) also help top performers reach high levels of content marketing success.

We looked at several other dimensions to identify how top performers differ from their peers. Of note, top performers:

• Are backed by leaders who understand the work they do.
• Are more likely to have the right content management technologies.
• Have better communication across organizational silos.
• Do a better job of measuring content effectiveness.
• Are more likely to use content marketing successfully to generate demand/leads, nurture subscribers/audiences/leads, generate sales/revenue, and grow a subscribed audience.

Little difference exists between top performers and their less successful peers when it comes to the adoption of generative AI tools and related guidelines. It will be interesting to see if and how that changes next year.

Budgets and spending: Holding steady

To explore budget plans for 2024, we asked respondents if they have knowledge of their organization’s budget/budgeting process for content marketing. Then, we asked follow-up questions to the 55% who say they do have budget knowledge.

Content marketing as a percentage of total marketing spend

Here’s what they say about the total marketing budget (excluding salaries):

• About a quarter (24%) say content marketing takes up one-fourth or more of the total marketing budget.
• Nearly one in three (29%) indicate that 10% to 24% of the marketing budget goes to content marketing.
• Just under half (48%) say less than 10% of the marketing budget goes to content marketing.

Content marketing budget outlook for 2024

Next, we asked about their 2024 content marketing budget. Forty-five percent think their content marketing budget will increase compared with 2023, whereas 42% think it will stay the same. Only 6% think it will decrease.

Where will the budget go?

We also asked where respondents plan to increase their spending.

Sixty-nine percent of B2B marketers say they would increase their investment in video, followed by thought leadership content (53%), in-person events (47%), paid advertising (43%), online community building (33%), webinars (33%), audio content (25%), digital events (21%), and hybrid events (11%).

The increased investment in video isn’t surprising. The focus on thought leadership content might surprise, but it shouldn’t, says Stephanie Losee , director of executive and ABM content at Autodesk.

“As measurement becomes more sophisticated, companies are finding they’re better able to quantify the return from upper-funnel activities like thought leadership content ,” she says. “At the same time, companies recognize the impact of shifting their status from vendor to true partner with their customers’ businesses.

“Autodesk recently launched its first global, longitudinal State of Design & Make report (registration required), and we’re finding that its insights are of such value to our customers that it’s enabling conversations we’ve never been able to have before. These conversations are worth gold to both sides, and I would imagine other B2B companies are finding the same thing,” Stephanie says.

Top content-related priorities for 2024: Leading with thought leadership

We asked an open-ended question about marketers’ top three content-related priorities for 2024. The responses indicate marketers place an emphasis on thought leadership and becoming a trusted resource.

Other frequently mentioned priorities include:

• Better understanding of the audience
• Discovering the best ways to use AI
• Increasing brand awareness
• Lead generation
• Using more video
• Better use of analytics
• Conversions
• Repurposing existing content

Content marketing predictions for 2024: AI is top of mind

In another open-ended question, we asked B2B marketers, “What content marketing trends do you predict for 2024?” You probably guessed the most popular trend: AI.

Here are some of the marketers’ comments about how AI will affect content marketing next year:

• “We’ll see generative AI everywhere, all the time.”
• “There will be struggles to determine the best use of generative AI in content marketing.”
• “AI will likely result in a flood of poor-quality, machine-written content. Winners will use AI for automating the processes that support content creation while continuing to create high-quality human-generated content.”
• “AI has made creating content so easy that there are and will be too many long articles on similar subjects; most will never be read or viewed. A sea of too many words. I predict short-form content will have to be the driver for eyeballs.”

Other trends include:

• Greater demand for high-quality content as consumers grow weary of AI-generated content
• Importance of video content
• Increasing use of short video and audio content
• Impact of AI on SEO

Among the related comments:

• “Event marketing (webinars and video thought leadership) will become more necessary as teams rely on AI-generated written content.”
• “AI will be an industry sea change and strongly impact the meaning of SEO. Marketers need to be ready to ride the wave or get left behind.”
• “Excitement around AI-generated content will rise before flattening out when people realize it’s hard to differentiate, validate, verify, attribute, and authenticate. New tools, processes, and roles will emerge to tackle this challenge.”
• “Long-form reports could start to see a decline. If that is the case, we will need a replacement. Logically, that could be a webinar or video series that digs deeper into the takeaways.”

What does this year’s research suggest B2B content marketers do to move forward?

I asked CMI’s Robert Rose for some insights. He says the steps are clear: Develop standards, guidelines, and playbooks for how to operate – just like every other function in business does.

“Imagine if everyone in your organization had a different idea of how to define ‘revenue’ or ‘profit margin,’” Robert says. “Imagine if each salesperson had their own version of your company’s customer agreements and tried to figure out how to write them for every new deal. The legal team would be apoplectic. You’d start to hear from sales how they were frustrated that they couldn’t figure out how to make the ‘right agreement,’ or how to create agreements ‘consistently,’ or that there was a complete ‘lack of resources’ for creating agreements.”

Just remember: Standards can change along with your team, audiences, and business priorities. “Setting standards doesn’t mean casting policies and templates in stone,” Robert says. “Standards only exist so that we can always question the standard and make sure that there’s improvement available to use in setting new standards.”

He offers these five steps to take to solidify your content marketing strategy and execution:

• Direct. Create an initiative that will define the scope of the most important standards for your content marketing. Prioritize the areas that hurt the most. Work with leadership to decide where to start. Maybe it’s persona development. Maybe you need a new standardized content process. Maybe you need a solid taxonomy. Build the list and make it a real initiative.
• Define . Create a common understanding of all the things associated with the standards. Don’t assume that everybody knows. They don’t. What is a white paper? What is an e-book? What is a campaign vs. an initiative? What is a blog post vs. an article? Getting to a common language is one of the most powerful things you can do to coordinate better.
• Develop . You need both policies and playbooks. Policies are the formal documentation of your definitions and standards. Playbooks are how you communicate combinations of policies so that different people can not just understand them but are ready, willing, and able to follow them.
• Distribute . If no one follows the standards, they’re not standards. So, you need to develop a plan for how your new playbooks fit into the larger, cross-functional approach to the content strategy. You need to deepen the integration into each department – even if that is just four other people in your company.
• Distill . Evolve your standards. Make them living documents. Deploy technology to enforce and scale the standards. Test. If a standard isn’t working, change it. Sometimes, more organic processes are OK. Sometimes, it’s OK to acknowledge two definitions for something. The key is acknowledging a change to an existing standard so you know whether it improves things.

For their 14 th annual content marketing survey, CMI and MarketingProfs surveyed 1,080 recipients around the globe – representing a range of industries, functional areas, and company sizes — in July 2023. The online survey was emailed to a sample of marketers using lists from CMI and MarketingProfs.

This article presents the findings from the 894 respondents, mostly from North America, who indicated their organization is primarily B2B and that they are either content marketers or work in marketing, communications, or other roles involving content.

Thanks to the survey participants, who made this research possible, and to everyone who helps disseminate these findings throughout the content marketing industry.

Cover image by Joseph Kalinowski/Content Marketing Institute

About Content Marketing Institute

Content Marketing Institute (CMI) exists to do one thing: advance the practice of content marketing through online education and in-person and digital events. We create and curate content experiences that teach marketers and creators from enterprise brands, small businesses, and agencies how to attract and retain customers through compelling, multichannel storytelling. Global brands turn to CMI for strategic consultation, training, and research. Organizations from around the world send teams to Content Marketing World, the largest content marketing-focused event, the Marketing Analytics & Data Science (MADS) conference, and CMI virtual events, including ContentTECH Summit. Our community of 215,000+ content marketers shares camaraderie and conversation. CMI is organized by Informa Connect. To learn more, visit www.contentmarketinginstitute.com .

About MarketingProfs

Marketingprofs is your quickest path to b2b marketing mastery.

More than 600,000 marketing professionals worldwide rely on MarketingProfs for B2B Marketing training and education backed by data science, psychology, and real-world experience. Access free B2B marketing publications, virtual conferences, podcasts, daily newsletters (and more), and check out the MarketingProfs B2B Forum–the flagship in-person event for B2B Marketing training and education at MarketingProfs.com.

About Brightspot

Brightspot , the content management system to boost your business.

Why Brightspot? Align your technology approach and content strategy with Brightspot, the leading Content Management System for delivering exceptional digital experiences. Brightspot helps global organizations meet the business needs of today and scale to capitalize on the opportunities of tomorrow. Our Enterprise CMS and world-class team solves your unique business challenges at scale. Fast, flexible, and fully customizable, Brightspot perfectly harmonizes your technology approach with your content strategy and grows with you as your business evolves. Our customer-obsessed teams walk with you every step of the way with an unwavering commitment to your long-term success. To learn more, visit www.brightspot.com .

Stephanie Stahl

• DOI: 10.22616/esrd.2023.57.019
• Corpus ID: 267650612

The factors influencing legal and ethical digital marketing communication

• Santa Bormane , Marta Urbāne
• Published in Economic Science for Rural… 10 May 2023
• Law, Business, Environmental Science

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Influencer Marketing

What is influencer marketing: An influencer strategy guide for 2024

Learn about the evolving world of influencer marketing in this guide from understanding the types of influencers to crafting effective strategies.

Reading time  13 minutes

Published on  June 6, 2024

Table of Contents

• Influencer marketing has evolved significantly over the past decade. It has expanded beyond celebrities to include social media influencers who foster authentic connections and influence purchase decisions and brand reputation.
• Understanding the types of influencers, from mega to nano, is crucial for brands to choose the right partners. The types of influencers you choose to partner with will determine the reach, engagement and ROI you get.
• Common influencer marketing mistakes to avoid include failing to define clear goals and expectations, prioritizing follower-count over engagement, neglecting influencer research and sharing unclear briefs.

Influencers are here to stay. According to a Q3 2023 Sprout Pulse Survey, more than 80% of marketers agree that influencers are essential to their overall social media strategy.

But the world of influencer marketing is constantly evolving.

A decade ago, the influencer marketing arena was limited only to celebrities and a few dedicated bloggers, whereas now, social media influencers are abound across all social networks. Their followings may vary in size, but these influencers pack a punch. Their tight-knit communities foster authentic connections and influence purchase behaviors, leading to higher brand engagement and ultimately, sales.

However, working with digital creators and influencers needs a well-planned and strategic approach. And this guide aims to help you navigate it. Read on for tips on how to build an effective influencer marketing strategy, what mistakes to avoid and how to find the right influencers for your brand.

Download the 2024 Influencer Marketing Benchmarks Report

What is influencer marketing?

Influencer marketing is a social media marketing approach that uses endorsements and product mentions from influencers. These individuals have a dedicated social following and are viewed as experts within their niche.

Influencer marketing works because of the high trust social influencers have built with their following over time. Recommendations from these influencers serve as a form of social proof to your brand’s potential customers.

Types of influencers: By size and reach

Partnering with an influencer with millions of followers might sound like a dream come true but they may not be the best fit for your brand.

Some social media influencers have large, broad audiences spanning across several demographics. Others boast smaller but more targeted and engaged communities.

Knowing what each type of influencer can offer you in terms of reach, range, cost and engagement is key to choosing the right ones for your brand.

Let’s take a deeper look at the various types of influencers out there:

Mega or celebrity influencers

These influencers have a massive following of over 1 million and often include famous actors, musicians, athletes and other public figures. Their celebrity status allows them to captivate a diverse audience, making them ideal for large-scale brand awareness campaigns. Think: Cristiano Ronaldo .

Mega influencers can give your brand unparalleled exposure, but partnering with them can be incredibly expensive. Plus, since their audience is often broad, their engagement rates may not be as high as influencers with smaller, more niche followings.

Here are some businesses that might benefit from working with mega influencers:

• Large enterprise corporations that have the budget and resources
• Brands targeting a broad audience with varying characteristics
• Luxury or high-end brands that want to create a sense of exclusivity

Macro-influencers

With a following that typically ranges from 100,000 to 1 million, macro-influencers are established personalities within their respective niches.

These influencers have earned their reputation through consistent content creation and engagement over time, and are now thought leaders in their niche .

Macro-influencers offer a more targeted approach compared to celebrities, as their followers usually share common interests. Collaborating with macro-influencers can provide your brand with substantial reach, but it may still be relatively costly depending on your budget.

Here are some examples of brands that might work with macro-influencers:

• Startups seeking rapid exposure, growth and credibility (e.g., Canva )
• Nonprofit organizations looking to raise funds and awareness
• Hotels and airlines targeting a specific but large audience

Micro-influencers

With 10,000 to 100,000 highly engaged followers, micro-influencers are the rising stars of influencer marketing. These influencers typically have a strong presence on specific platforms, like Instagram, YouTube and TikTok.

Marketers love working with micro-influencers as they captivate a niche, passionate audience with their creative content, relatable recommendations and genuine interactions. They’re also more affordable than larger influencers.

Nano-influencers

Nano-influencers have between 1,000 to 10,000 followers. These influencers often have a strong connection with their audience, thanks to the close-knit community they’ve built and their personable content.

While they offer a smaller reach, nano-influencers can be excellent partners for businesses who want to target specific communities and demographics without breaking the bank. In fact, per the latest Influencer Marketing Hub data, 44% of brands prefer to partner with nano influencers in 2024, compared to 39% in 2023.

There are many reasons for this, namely, since nano-influencers work on a small scale, they dedicate more time and effort to individual partnerships. This means more tailored content for your brand and personal relationships within niche communities. They are perfect for businesses such as:

• Local businesses targeting specific communities, cities or regions
• Small businesses with limited budgets that want to run cost-effective campaigns
• Artisan, home-based or speciality food businesses reaching a niche audience interested in their one-of-a-kind products

Why use influencer marketing?

Influencer marketing can be an incredibly powerful marketing tool for you, and brands are already using it to their advantage, as these influencer marketing examples show.

According to The 2024 Influencer Marketing Report , almost half of all consumers (49%) make purchases at least once a month because of influencer posts; and almost all consumers (86%) make a purchase inspired by an influencer at least once a year. Not surprisingly, the influencer marketing industry is expected to grow to $24 billion in 2024 as indicated by the Influencer Marketing Hub's latest research.

Here are more reasons why you should add influencers to your marketing mix.

Increased brand awareness

Collaborating with an influencer draws a wider audience to your brand. By featuring your brand in their content, influencers introduce it to new audiences who may not have been familiar with it. Also, the trust influencers hold with their followers enhances your brand's reputation and credibility and boosts market awareness.

Precise audience targeting

Influencer marketing helps brands with precise audience targeting by connecting brands to influencers whose followers align with the brand's target demographic. This ensures their message reaches the right audience and is delivered from a perspective that resonates with the audience, making the campaign more effective.

Higher conversions

Social proof is a powerful factor in the likelihood of making a purchase after seeing an influencer campaign, because it helps sway undecided consumers in your favor. Similarly, influencers often share interactive content such as giveaways, challenges and live sessions that drive engagement and prompt actions like making purchases or signing up for deals. They also share exclusive discount codes or special offers making it easy to track conversions directly linked to their promotion.

Building trust and authenticity with your audience

Influencers build trust with their followers by sharing personal experiences and opinions that resonate with their audience, making them more relatable. They further increase their credibility by being open about brand partnerships and only promoting products they genuinely support.

Plus, their seemingly unscripted content contrasts with traditional advertisements, making it sound authentic and spontaneous, which fosters personal connection with followers. This trust is further reinforced by how they respond to comments, making their audiences feel valued and heard, thus strengthening the bond. All this combined, helps influencers drive higher conversion rates for brands.

How to create an influencer marketing strategy in 5 steps

While Instagram influencer marketing is a well-known strategy, other platforms like TikTok , YouTube and Snapchat resonate increasingly well with different demographics.

Like any marketing tactic, an influencer program takes thoughtful planning. Here are key factors to consider while developing your influencer marketing strategy.

1. Find influencers and understand their payment structure

Finding the right influencer for your brand is the first step to building a successful influencer marketing strategy. You must invest time in market research to understand your audience’s preferences and choose the right platform to engage them with an influencer collaboration.

This is especially important because each network caters to a specific audience. For example, beauty and fashion brands shine on Instagram and YouTube, while the video game industry dominates Twitch.

Here are some factors to consider when searching for influencers:

• Does the influencer already post about similar topics related to your service?
• Are they legit? Scroll through their feed and click through on posts. A poor engagement ratio to follower count and spam-like comments are signs of a fake account or fake followers.
• Have they worked with similar brands before? Depending on what type of influencer you’re looking for, a seasoned one will be able to show you a press kit that contains a portfolio of their work.

Social listening can assist you in identifying where people are discussing your industry and brand, and to find influential voices within your industry on each platform.

40 Unique Ways To Use Social Listening To Make An Impact On Your Business

Similarly, focus on the type of influencers you want, and plan for their pricing . Whether it’s celebrities with massive followings or micro-influencer s with less than 2,000 followers, do your due diligence because that will determine your budget.

Compensation varies based on platform, influencer types and types of influencer collaborations, so think about the expected ROI of your social influencer marketing campaign. How will you measure the impact of influencer posts on your overall marketing goals? For instance, compare how you would budget for a video production firm creating an ad versus an influencer creating a video. Resources like this Instagram influencer rate map can help you estimate influencer costs based on audience size and industry.

2. Set a budget and management strategy

The next step is to create an influencer marketing budget.

Use an influencer marketing budgeting template to manage your expenses and strategically allocate resources to high-value partnerships. Be sure to account for the time needed to plan, execute and review your influencer program because, unlike automated ad strategies, influencers often juggle multiple partnerships, requiring a more hands-on approach from you.

If your budget allows, consider establishing an ambassador program to diversify and enrich your content. Similar to Fujifilm , which uses ambassadors for new product launches and to highlight new product features.

Whether you engage an influencer marketing agency or not, investing in an all-in-one influencer marketing software is a good idea, helping you sift through suitable influencers, manage pricing negotiations and review and approve content.

3. Decide on campaign goals and messaging

To ensure your influencer strategy succeeds, focus on your campaign's goals and needs. Determine whether you want to reach a new demographic, introduce a new product or highlight your brand values through influencers. Also, explore influencer trends to see what’s resonating in your industry.

Your message is as crucial as your goal. And since influencers target specific audiences, refining your campaign messaging is important for effective content.

Influencer content is typically more conversational and personal, which helps differentiate it from brand-driven or sales-oriented posts. But while it’s important to preserve an influencer’s creativity and uniqueness, ensure their content aligns with your brand values.

4. Establish influencer outreach: How to contact influencers

Your outreach will depend on the type of influencer you’ve chosen. For example, celebrities and macro influencers often work through agencies, so you might have to connect with an agency to reach them. Some may also list their contact information for business inquiries in their bio and have a website that denotes brand partnerships.

For example, Summer Rayne Oakes has a multi-channel presence, which is a perk for her brand partners.

For micro-influencers, you could reach out directly in a private message via their social platform.

5. Review and refine your strategy

It’s important to refine and review your strategy so you’ll be more successful with each campaign going forward. Having predetermined milestones where you’ll measure progress can prove very helpful in this.

While these tips serve as a guide to help you craft a well-planned strategy, it's crucial to be aware of common mistakes to avoid in influencer marketing. We’ll dive into those, next.

Influencer marketing mistakes to avoid

Influencer marketing can be highly rewarding — if done right. Sidestep these potential pitfalls to ensure smooth influencer collaborations and successful campaign outcomes.

Failing to define clear goals and KPIs

First things first, know why you’re doing this in the first place. Partnering with an influencer is a big deal — you need to be clear about the purpose and goals of your campaign.

Here are a few ways setting goals in advance can help you:

• Choose the right influencers: Defining goals helps you identify the specific characteristics and qualities you need in an influencer to achieve those outcomes. For example, if your goal is to increase brand awareness, you can find influencers who have a strong presence and reach within your niche.
• Define and measure success: What does success mean to you? Is it the number of impressions, post engagement or the amount of traffic coming to your website? Define which KPIs and metrics to track both during and after the campaign to assess how well your influencer campaign is performing.
• Keep everyone on track: Setting clear goals ensures that both the brand and the influencer are working towards a common purpose. This facilitates effective communication and constructive feedback, saving everyone’s valuable time.
• Hold influencers accountable: Establishing clear expectations and performance benchmarks makes influencers feel responsible. They know the results they need to provide and will focus on creating content that aligns with those goals.

Prioritizing follower count over engagement

A large following doesn't always mean high engagement. It’s entirely possible an influencer has millions of passive followers but very low engagement.

Instead, partner with influencers with an engaged and loyal audience. A handful of people who trust the influencer are more valuable to your brand than thousands of indifferent followers unlikely to convert.

Look at the influencer’s engagement metrics, comments and interactions, as well as past results for other brands to gauge their level of influence and likeability.

Neglecting to research the influencer

Choosing the wrong influencers, including fake influencers , can cost your business valuable time and money. But this hasn’t deterred marketers, with 60% of marketers intending to increase their influencer marketing budget over 2024, the Influencer Marketing Hub states.

An easy fix is to properly research the influencer of your choice, before signing a partnership. Vet influencers and evaluate their influencer media kit to ensure they share your vision and complement your brand’s personality.

Here are some key areas to look into when researching influencers for your brand:

• Audience demographics: Study the influencer's followers to ensure your campaign reaches the right audience. Analyze factors like age, gender, location and interests (e.g., Millennials who identify as women) to determine if they are likely to become your customers.
• Interactions, voice and content: Look at the influencer’s engagement rate, the tone of voice they use and the type of content they create. For example, if your brand has a playful, casual image, partnering with an influencer known for their formal, business-oriented content might not be the best fit.
• Authenticity and influence: Forced partnerships can appear insincere and hurt both your campaign and brand image. Collaborate with influencers who genuinely love your brand and products. Their followers trust them for a reason and you don't want your brand to get in the way of their (and your) credibility.
• Experience with branded content: Has the influencer worked with other brands in the past? Have they ever worked with your competitors? Carefully scrutinize their content to spot any red flags and gauge the value they can provide.

Writing poorly constructed briefs

Crafting well-structured briefs is key to maximizing your influencer marketing campaigns. A good social media campaign brief equips influencers with the details and resources they need to represent your brand effectively, without being overly restrictive.

Here’s a quick rundown on what to include in your brief:

• What is the main goal of your campaign? What are you hoping to achieve?
• What is your company’s background? What is your brand and what product/s are you selling?
• What are your product’s key benefits, features and differentiators?
• Who is your target audience? Include an audience persona if you have one.
• What does your budget look like for this campaign?
• Do you have a timeline in mind?
• Do you want the influencer to use your brand assets? Provide them with your logo, colors and fonts if necessary.

Don’t forget to inform influencers of any words or ideas to avoid in their content. For example, if you’re an eco-friendly brand, let the influencer know that sustainability is a core value and they should avoid using plastic and other such products in their content.

Restricting the influencer's creative freedom

While comprehensive briefs are important, there’s such a thing as too much information.

Avoid going overboard with your guidelines. You don’t need to dictate the influencer’s exact words or actions. Doing so can stifle the influencer's creative freedom, resulting in content that looks scripted and inauthentic.

Some brands also make the mistake of micro-managing every aspect of the content creation process. For example, you don’t need to vet multiple drafts just before they go live.

Remember, influencers are the experts in creating content their audience loves and trusts. Your brand just needs to support them with resources they need to create great content for effective influencer marketing.

Not setting expectations upfront

Establishing clear expectations beforehand enables a smooth, productive collaboration. The result? A successful campaign aligned with your goals.

Make sure you agree on the following items in advance:

• Timeline and deliverables: Clearly outline the campaign timeline, including start and end dates, as well as any deadlines for content submission and publication. Also, specify the required deliverables, such as the number of posts, stories or videos the influencer needs to create.
• Payment and terms: Agree on the payment structure, whether it's a one-time fee, ongoing retainer or performance-based compensation. Discuss the payment schedule and any additional terms, such as bonuses for exceptional performance or penalties for missed deadlines.

Focusing on the wrong metrics

Influencer marketing can offer more benefits to your business than merely boosting sales. Fixating only on conversions and revenue data can mislead brands into thinking their campaigns are not working.

Here are some other metrics to consider when measuring the impact of your campaigns:

• Engagement metrics: Evaluate likes, comments, and shares to understand content resonance and audience interaction.
• Brand awareness metrics: Measure views, clicks and website traffic to gauge campaign reach and audience interest.
• Follower growth: Track new followers to determine influencer impact on brand visibility and audience expansion.
• Inbound leads: Track the number of inquiries and messages your brand gets to analyze the campaign’s impact on inbound lead generation.

How to track influencer marketing campaigns

There are a few ways of measuring the success of your campaign.

If you want to keep a pulse on the content your influencers are creating and interacting with, you can create a specific branded hashtag, like #SproutPartner. The Sprout Social Smart Inbox makes it easy to see what’s being talked about with specific hashtags, or to watch for mentions of specific keywords .

Use Sprout’s reporting feature to tag and track campaign-related posts. You can also compare how each post is performing and view metrics such as post engagements, clicks and impressions.

Similarly, if you’re aiming for more sales, giving out affiliate codes or tracking links will help you measure the revenue generated from influencers.

Create a successful influencer marketing strategy for your brand

It’s clear—influencers are the new wave in marketing. However, the influencer marketing world is constantly evolving, and in five years may be drastically different from today.

While working with influencers has unique considerations, setting up a campaign is the same as most marketing campaigns: research, set a budget, determine goals, find your influencers, and review and revise. Once you’ve got a hold of the rhythm, creating different influencer marketing campaigns to meet your brand’s various needs will become second nature.

If you need more resources for your team on running influencer campaigns, check out our ultimate guide for running successful social media campaigns .

Influencer Marketing FAQs

The three R’s in influencer marketing strategy are relevance, reach and resonance.

• Reach alludes to the number of followers the influencer has and the people they can potentially reach with their content.
• Relevance is how well the influencer’s content aligns with your brand voice, target audience and marketing goals.
• Resonance is the level of meaningful interactions and connections the influencer’s content creates with their followers, which translates to greater audience loyalty.

An influencer marketing strategy needs meticulous planning. Here are five things you need to keep in mind to ace it.

• Find influencers and understand their payment structure
• Set a budget and management strategy
• Decide on campaign goals and messaging
• Establish influencer outreach process
• Review and refine your strategy

Social media influencers collaborate with brands to promote products or services to their followers through sponsored posts, product reviews and endorsements. In doing so, they help shape consumer opinions and purchasing decisions.

Additional resources for Influencer Marketing

• Influencers
• Social Media Strategy

Influencer pricing: how much influencers really cost

• Social Media Content

UK Food influencers redefining taste for your brand

Influencer marketing trends across industries

Spotlight on UK beauty influencers for your brand

An Influencer Marketing Toolkit to Kickstart Your Strategy

Influencer relationships (with consumers & brands) are evolving—what does that mean for marketers?

11 best influencer marketing tools you need in 2024

UK fashion influencers redefining style

• Future of Marketing

The rise of virtual influencers: are they here to stay?

Elevate Your Next Campaign With This Influencer Marketing Brief Template

Powerful UK fitness influencers for your next campaign

Find UK TikTok Influencers for your brand

24 Top YouTube influencers to check out in 2024

• Social Media Analytics

8 top influencer analytics tools to boost your campaign ROI in 2024

The benefits of influencer marketing (+ what the C-Suite cares about)

Rethinking the influencer-brand relationship

10 metrics to track influencer marketing success in 2024

How to find influencers for your brand’s marketing campaign

How B2B influencer marketing will grow your brand

16 influencer marketing platforms to boost your campaigns in 2024

Mastering Instagram influencer marketing: strategies for success

The complete guide to TikTok influencer marketing

A 6-point framework for maximizing influencer marketing ROI

• Social Media Trends

22 influencer marketing statistics to guide your brand’s strategy in 2024

7 examples of influencer marketing campaigns

• Branding & Creative

The ultimate guide to evaluating influencer media kits

Micro-influencer marketing guide: Facts and uses

YouTube influencer marketing guide to boosting brand awareness

The rise of senior influencers: Age is just a number but follower count isn’t

Today’s top TikTok influencers: Inspiration for your brand’s evolving social strategy

Digital creators vs. influencers: What’s the difference?

Why LinkedIn influencer marketing matters for brands

• Social Media Engagement

What are fake influencers and how can you spot them?

• Community Management
• Consumer Goods

Nanoinfluencer marketing 101: How I got 1000 engaged followers in 30 days

Influencer marketing

10 Lessons From Instagram Influencers

Influencer Marketing Ain’t Easy: 5 Client Questions to Answer Before They Ask

Using Sprout Social for Influencer Marketing Campaigns

Build and grow stronger relationships on social

Sprout Social helps you understand and reach your audience, engage your community and measure performance with the only all-in-one social media management platform built for connection.

The economic potential of generative AI: The next productivity frontier

AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. As a result, its progress has been almost imperceptible. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness.

Generative AI applications such as ChatGPT, GitHub Copilot, Stable Diffusion, and others have captured the imagination of people around the world in a way AlphaGo did not, thanks to their broad utility—almost anyone can use them to communicate and create—and preternatural ability to have a conversation with a user. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it.

About the authors

This article is a collaborative effort by Michael Chui , Eric Hazan , Roger Roberts , Alex Singla , Kate Smaje , Alex Sukharevsky , Lareina Yee , and Rodney Zemmel , representing views from QuantumBlack, AI by McKinsey; McKinsey Digital; the McKinsey Technology Council; the McKinsey Global Institute; and McKinsey’s Growth, Marketing & Sales Practice.

The speed at which generative AI technology is developing isn’t making this task any easier. ChatGPT was released in November 2022. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities. 1 “Introducing ChatGPT,” OpenAI, November 30, 2022; “GPT-4 is OpenAI’s most advanced system, producing safer and more useful responses,” OpenAI, accessed June 1, 2023. Similarly, by May 2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000 words in a minute—the length of the average novel—compared with roughly 9,000 tokens when it was introduced in March 2023. 2 “Introducing Claude,” Anthropic PBC, March 14, 2023; “Introducing 100K Context Windows,” Anthropic PBC, May 11, 2023. And in May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that will power its Bard chatbot, among other Google products. 3 Emma Roth, “The nine biggest announcements from Google I/O 2023,” The Verge , May 10, 2023.

To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. For the purposes of this report, we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task.

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks.

All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. The following sections share our initial findings.

For the full version of this report, download the PDF .

Key insights

Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. This would increase the impact of all artificial intelligence by 15 to 40 percent. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.

About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D. Across 16 business functions, we examined 63 use cases in which the technology can address specific business challenges in ways that produce one or more measurable outcomes. Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales, and draft computer code based on natural-language prompts, among many other tasks.

Generative AI will have a significant impact across all industry sectors. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI. Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year.

Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities. Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today. In contrast, we previously estimated that technology has the potential to automate half of the time employees spend working. 4 “ Harnessing automation for a future that works ,” McKinsey Global Institute, January 12, 2017. The acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time. Thus, generative AI has more impact on knowledge work associated with occupations that have higher wages and educational requirements than on other types of work.

The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. Our updated adoption scenarios, including technology development, economic feasibility, and diffusion timelines, lead to estimates that half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates.

Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities. Combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world.

The era of generative AI is just beginning. Excitement over this technology is palpable, and early pilots are compelling. But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills.

Where business value lies

Generative AI is a step change in the evolution of artificial intelligence. As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions. We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be (Exhibit 1).

The first lens scans use cases for generative AI that organizations could adopt. We define a “use case” as a targeted application of generative AI to a specific business challenge, resulting in one or more measurable outcomes. For example, a use case in marketing is the application of generative AI to generate creative content such as personalized emails, the measurable outcomes of which potentially include reductions in the cost of generating such content and increases in revenue from the enhanced effectiveness of higher-quality content at scale. We identified 63 generative AI use cases spanning 16 business functions that could deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefits annually when applied across industries.

That would add 15 to 40 percent to the $11 trillion to $17.7 trillion of economic value that we now estimate nongenerative artificial intelligence and analytics could unlock. (Our previous estimate from 2017 was that AI could deliver $9.5 trillion to $15.4 trillion in economic value.)

Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. We modeled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities”—such as “communicating with others about operational plans or activities”—that make up those occupations across the world economy. This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce.

Some of this impact will overlap with cost reductions in the use case analysis described above, which we assume are the result of improved labor productivity. Netting out this overlap, the total economic benefits of generative AI —including the major use cases we explored and the myriad increases in productivity that are likely to materialize when the technology is applied across knowledge workers’ activities—amounts to $6.1 trillion to $7.9 trillion annually (Exhibit 2).

How we estimated the value potential of generative AI use cases

To assess the potential value of generative AI, we updated a proprietary McKinsey database of potential AI use cases and drew on the experience of more than 100 experts in industries and their business functions. 1 ” Notes from the AI frontier: Applications and value of deep learning ,” McKinsey Global Institute, April 17, 2018.

Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies.

We analyzed only use cases for which generative AI could deliver a significant improvement in the outputs that drive key value. In particular, our estimates of the primary value the technology could unlock do not include use cases for which the sole benefit would be its ability to use natural language. For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis.

We then estimated the potential annual value of these generative AI use cases if they were adopted across the entire economy. For use cases aimed at increasing revenue, such as some of those in sales and marketing, we estimated the economy-wide value generative AI could deliver by increasing the productivity of sales and marketing expenditures.

Our estimates are based on the structure of the global economy in 2022 and do not consider the value generative AI could create if it produced entirely new product or service categories.

While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”).

In this section, we highlight the value potential of generative AI across business functions.

Generative AI could have an impact on most business functions; however, a few stand out when measured by the technology’s impact as a share of functional cost (Exhibit 3). Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases.

Notably, the potential value of using generative AI for several functions that were prominent in our previous sizing of AI use cases, including manufacturing and supply chain functions, is now much lower. 5 Pitchbook. This is largely explained by the nature of generative AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI.

In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue. This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies.

In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information. If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. Such virtual expertise could rapidly “read” vast libraries of corporate information stored in natural language and quickly scan source material in dialogue with a human who helps fine-tune and tailor its research, a more scalable solution than hiring a team of human experts for the task.

In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks.

Following are four examples of how generative AI could produce operational benefits in a handful of use cases across the business functions that could deliver a majority of the potential value we identified in our analysis of 63 generative AI use cases. In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator.

Customer operations: Improving customer and agent experiences

Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. Research found that at one company with 5,000 customer service agents, the application of generative AI increased issue resolution by 14 percent an hour and reduced the time spent handling an issue by 9 percent. 1 Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond, Generative AI at work , National Bureau of Economic Research working paper number 31161, April 2023. It also reduced agent attrition and requests to speak to a manager by 25 percent. Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents. This is because AI assistance helped less-experienced agents communicate using techniques similar to those of their higher-skilled counterparts.

The following are examples of the operational improvements generative AI can have for specific use cases:

• Customer self-service. Generative AI–fueled chatbots can give immediate and personalized responses to complex customer inquiries regardless of the language or location of the customer. By improving the quality and effectiveness of interactions via automated channels, generative AI could automate responses to a higher percentage of customer inquiries, enabling customer care teams to take on inquiries that can only be resolved by a human agent. Our research found that roughly half of customer contacts made by banking, telecommunications, and utilities companies in North America are already handled by machines, including but not exclusively AI. We estimate that generative AI could further reduce the volume of human-serviced contacts by up to 50 percent, depending on a company’s existing level of automation.
• Resolution during initial contact. Generative AI can instantly retrieve data a company has on a specific customer, which can help a human customer service representative more successfully answer questions and resolve issues during an initial interaction.
• Reduced response time. Generative AI can cut the time a human sales representative spends responding to a customer by providing assistance in real time and recommending next steps.
• Increased sales. Because of its ability to rapidly process data on customers and their browsing histories, the technology can identify product suggestions and deals tailored to customer preferences. Additionally, generative AI can enhance quality assurance and coaching by gathering insights from customer conversations, determining what could be done better, and coaching agents.

We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs.

Our analysis captures only the direct impact generative AI might have on the productivity of customer operations. It does not account for potential knock-on effects the technology may have on customer satisfaction and retention arising from an improved experience, including better understanding of the customer’s context that can assist human agents in providing more personalized help and recommendations.

Marketing and sales: Boosting personalization, content creation, and sales productivity

Generative AI has taken hold rapidly in marketing and sales functions, in which text-based communications and personalization at scale are driving forces. The technology can create personalized messages tailored to individual customer interests, preferences, and behaviors, as well as do tasks such as producing first drafts of brand advertising, headlines, slogans, social media posts, and product descriptions.

Introducing generative AI to marketing functions requires careful consideration. For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data. Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs.

Potential operational benefits from using generative AI for marketing include the following:

• Efficient and effective content creation. Generative AI could significantly reduce the time required for ideation and content drafting, saving valuable time and effort. It can also facilitate consistency across different pieces of content, ensuring a uniform brand voice, writing style, and format. Team members can collaborate via generative AI, which can integrate their ideas into a single cohesive piece. This would allow teams to significantly enhance personalization of marketing messages aimed at different customer segments, geographies, and demographics. Mass email campaigns can be instantly translated into as many languages as needed, with different imagery and messaging depending on the audience. Generative AI’s ability to produce content with varying specifications could increase customer value, attraction, conversion, and retention over a lifetime and at a scale beyond what is currently possible through traditional techniques.
• Enhanced use of data. Generative AI could help marketing functions overcome the challenges of unstructured, inconsistent, and disconnected data—for example, from different databases—by interpreting abstract data sources such as text, image, and varying structures. It can help marketers better use data such as territory performance, synthesized customer feedback, and customer behavior to generate data-informed marketing strategies such as targeted customer profiles and channel recommendations. Such tools could identify and synthesize trends, key drivers, and market and product opportunities from unstructured data such as social media, news, academic research, and customer feedback.
• SEO optimization. Generative AI can help marketers achieve higher conversion and lower cost through search engine optimization (SEO) for marketing and sales technical components such as page titles, image tags, and URLs. It can synthesize key SEO tokens, support specialists in SEO digital content creation, and distribute targeted content to customers.
• Product discovery and search personalization. With generative AI, product discovery and search can be personalized with multimodal inputs from text, images, and speech, and a deep understanding of customer profiles. For example, technology can leverage individual user preferences, behavior, and purchase history to help customers discover the most relevant products and generate personalized product descriptions. This would allow CPG, travel, and retail companies to improve their e-commerce sales by achieving higher website conversion rates.

We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending.

Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments. Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies.

Generative AI could also change the way both B2B and B2C companies approach sales. The following are two use cases for sales:

• Increase probability of sale. Generative AI could identify and prioritize sales leads by creating comprehensive consumer profiles from structured and unstructured data and suggesting actions to staff to improve client engagement at every point of contact. For example, generative AI could provide better information about client preferences, potentially improving close rates.
• Improve lead development. Generative AI could help sales representatives nurture leads by synthesizing relevant product sales information and customer profiles and creating discussion scripts to facilitate customer conversation, including up- and cross-selling talking points. It could also automate sales follow-ups and passively nurture leads until clients are ready for direct interaction with a human sales agent.

Our analysis suggests that implementing generative AI could increase sales productivity by approximately 3 to 5 percent of current global sales expenditures.

This analysis may not fully account for additional revenue that generative AI could bring to sales functions. For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover new leads and facilitate more effective outreach that would bring in additional revenue. Also, the time saved by sales representatives due to generative AI’s capabilities could be invested in higher-quality customer interactions, resulting in increased sales success.

Software engineering: Speeding developer work as a coding assistant

Treating computer languages as just another language opens new possibilities for software engineering. Software engineers can use generative AI in pair programming and to do augmented coding and train LLMs to develop applications that generate code when given a natural-language prompt describing what that code should do.

Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity.

According to our analysis, the direct impact of AI on the productivity of software engineering could range from 20 to 45 percent of current annual spending on the function. This value would arise primarily from reducing time spent on certain activities, such as generating initial code drafts, code correction and refactoring, root-cause analysis, and generating new system designs. By accelerating the coding process, generative AI could push the skill sets and capabilities needed in software engineering toward code and architecture design. One study found that software developers using Microsoft’s GitHub Copilot completed tasks 56 percent faster than those not using the tool. 1 Peter Cihon et al., The impact of AI on developer productivity: Evidence from GitHub Copilot , Cornell University arXiv software engineering working paper, arXiv:2302.06590, February 13, 2023. An internal McKinsey empirical study of software engineering teams found those who were trained to use generative AI tools rapidly reduced the time needed to generate and refactor code—and engineers also reported a better work experience, citing improvements in happiness, flow, and fulfillment.

Our analysis did not account for the increase in application quality and the resulting boost in productivity that generative AI could bring by improving code or enhancing IT architecture—which can improve productivity across the IT value chain. However, the quality of IT architecture still largely depends on software architects, rather than on initial drafts that generative AI’s current capabilities allow it to produce.

Large technology companies are already selling generative AI for software engineering, including GitHub Copilot, which is now integrated with OpenAI’s GPT-4, and Replit, used by more than 20 million coders. 2 Michael Nuñez, “Google and Replit join forces to challenge Microsoft in coding tools,” VentureBeat, March 28, 2023.

Product R&D: Reducing research and design time, improving simulation and testing

Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs.

For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design. Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others.

While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit their application. Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, have much broader areas of application than models optimized for a single task. They can therefore accelerate time to market and broaden the types of products to which generative design can be applied. For now, however, foundation models lack the capabilities to help design products across all industries.

In addition to the productivity gains that result from being able to quickly produce candidate designs, generative design can also enable improvements in the designs themselves, as in the following examples of the operational improvements generative AI could bring:

• Enhanced design. Generative AI can help product designers reduce costs by selecting and using materials more efficiently. It can also optimize designs for manufacturing, which can lead to cost reductions in logistics and production.
• Improved product testing and quality. Using generative AI in generative design can produce a higher-quality product, resulting in increased attractiveness and market appeal. Generative AI can help to reduce testing time of complex systems and accelerate trial phases involving customer testing through its ability to draft scenarios and profile testing candidates.

We also identified a new R&D use case for nongenerative AI: deep learning surrogates, the use of which has grown since our earlier research, can be paired with generative AI to produce even greater benefits. To be sure, integration will require the development of specific solutions, but the value could be significant because deep learning surrogates have the potential to accelerate the testing of designs proposed by generative AI.

While we have estimated the potential direct impacts of generative AI on the R&D function, we did not attempt to estimate the technology’s potential to create entirely novel product categories. These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall.

Industry impacts

Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on a variety of factors, such as the mix and importance of different functions, as well as the scale of an industry’s revenue (Exhibit 4).

For example, our analysis estimates generative AI could contribute roughly $310 billion in additional value for the retail industry (including auto dealerships) by boosting performance in functions such as marketing and customer interactions. By comparison, the bulk of potential value in high tech comes from generative AI’s ability to increase the speed and efficiency of software development (Exhibit 5).

In the banking industry, generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data. In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development.

We share our detailed analysis of these industries below.

Generative AI supports key value drivers in retail and consumer packaged goods

The technology could generate value for the retail and consumer packaged goods (CPG) industry by increasing productivity by 1.2 to 2.0 percent of annual revenues, or an additional $400 billion to $660 billion. 1 Vehicular retail is included as part of our overall retail analysis. To streamline processes, generative AI could automate key functions such as customer service, marketing and sales, and inventory and supply chain management. Technology has played an essential role in the retail and CPG industries for decades. Traditional AI and advanced analytics solutions have helped companies manage vast pools of data across large numbers of SKUs, expansive supply chain and warehousing networks, and complex product categories such as consumables. In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence. For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise.

Generative AI at work in retail and CPG

Reinvention of the customer interaction pattern.

Consumers increasingly seek customization in everything from clothing and cosmetics to curated shopping experiences, personalized outreach, and food—and generative AI can improve that experience. Generative AI can aggregate market data to test concepts, ideas, and models. Stitch Fix, which uses algorithms to suggest style choices to its customers, has experimented with DALL·E to visualize products based on customer preferences regarding color, fabric, and style. Using text-to-image generation, the company’s stylists can visualize an article of clothing based on a consumer’s preferences and then identify a similar article among Stitch Fix’s inventory.

Retailers can create applications that give shoppers a next-generation experience, creating a significant competitive advantage in an era when customers expect to have a single natural-language interface help them select products. For example, generative AI can improve the process of choosing and ordering ingredients for a meal or preparing food—imagine a chatbot that could pull up the most popular tips from the comments attached to a recipe. There is also a big opportunity to enhance customer value management by delivering personalized marketing campaigns through a chatbot. Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI.

Accelerating the creation of value in key areas

Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation. The potential improvement in writing and visuals can increase awareness and improve sales conversion rates.

Rapid resolution and enhanced insights in customer care

The growth of e-commerce also elevates the importance of effective consumer interactions. Retailers can combine existing AI tools with generative AI to enhance the capabilities of chatbots, enabling them to better mimic the interaction style of human agents—for example, by responding directly to a customer’s query, tracking or canceling an order, offering discounts, and upselling. Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information.

Disruptive and creative innovation

Generative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly. A designer can generate packaging designs from scratch or generate variations on an existing design. This technology is developing rapidly and has the potential to add text-to-video generation.

Factors for retail and CPG organizations to consider

As retail and CPG executives explore how to integrate generative AI in their operations, they should keep in mind several factors that could affect their ability to capture value from the technology:

• External inference. Generative AI has increased the need to understand whether generated content is based on fact or inference, requiring a new level of quality control.
• Adversarial attacks. Foundation models are a prime target for attack by hackers and other bad actors, increasing the variety of potential security vulnerabilities and privacy risks.

To address these concerns, retail and CPG companies will need to strategically keep humans in the loop and ensure security and privacy are top considerations for any implementation. Companies will need to institute new quality checks for processes previously handled by humans, such as emails written by customer reps, and perform more-detailed quality checks on AI-assisted processes such as product design.

Why banks could realize significant value

Generative AI could have a significant impact on the banking industry , generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion. On top of that impact, the use of generative AI tools could also enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk.

Banking, a knowledge and technology-enabled industry, has already benefited significantly from previously existing applications of artificial intelligence in areas such as marketing and customer operations. 1 “ Building the AI bank of the future ,” McKinsey, May 2021. Generative AI applications could deliver additional benefits, especially because text modalities are prevalent in areas such as regulations and programming language, and the industry is customer facing, with many B2C and small-business customers. 2 McKinsey’s Global Banking Annual Review , December 1, 2022.

Several characteristics position the industry for the integration of generative AI applications:

• Sustained digitization efforts along with legacy IT systems. Banks have been investing in technology for decades, accumulating a significant amount of technical debt along with a siloed and complex IT architecture. 3 Akhil Babbar, Raghavan Janardhanan, Remy Paternoster, and Henning Soller, “ Why most digital banking transformations fail—and how to flip the odds ,” McKinsey, April 11, 2023.
• Large customer-facing workforces. Banking relies on a large number of service representatives such as call-center agents and wealth management financial advisers.
• A stringent regulatory environment. As a heavily regulated industry, banking has a substantial number of risk, compliance, and legal needs.
• White-collar industry. Generative AI’s impact could span the organization, assisting all employees in writing emails, creating business presentations, and other tasks.

Generative AI at work in banking

Banks have started to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications. Three uses demonstrate its value potential to the industry.

A virtual expert to augment employee performance

A generative AI bot trained on proprietary knowledge such as policies, research, and customer interaction could provide always-on, deep technical support. Today, frontline spending is dedicated mostly to validating offers and interacting with clients, but giving frontline workers access to data as well could improve the customer experience. The technology could also monitor industries and clients and send alerts on semantic queries from public sources. For example, Morgan Stanley is building an AI assistant using GPT-4, with the aim of helping tens of thousands of wealth managers quickly find and synthesize answers from a massive internal knowledge base. 4 Hugh Son, “Morgan Stanley is testing an OpenAI-powered chatbot for its 16,000 financial advisors,” CNBC, March 14, 2023. The model combines search and content creation so wealth managers can find and tailor information for any client at any moment.

One European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert by synthesizing and extracting from long documents with unstructured information. The model answers complex questions based on a prompt, identifying the source of each answer and extracting information from pictures and tables.

Generative AI could reduce the significant costs associated with back-office operations. Such customer-facing chatbots could assess user requests and select the best service expert to address them based on characteristics such as topic, level of difficulty, and type of customer. Through generative AI assistants, service professionals could rapidly access all relevant information such as product guides and policies to instantaneously address customer requests.

Code acceleration to reduce tech debt and deliver software faster

Generative AI tools are useful for software development in four broad categories. First, they can draft code based on context via input code or natural language, helping developers code more quickly and with reduced friction while enabling automatic translations and no- and low-code tools. Second, such tools can automatically generate, prioritize, run, and review different code tests, accelerating testing and increasing coverage and effectiveness. Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. Last, the tools can review code to identify defects and inefficiencies in computing. The result is more robust, effective code.

Production of tailored content at scale

Generative AI tools can draw on existing documents and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing. In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts.

Factors for banks to consider

When exploring how to integrate generative AI into operations, banks can be mindful of a number of factors:

• The level of regulation for different processes. These vary from unregulated processes such as customer service to heavily regulated processes such as credit risk scoring.
• Type of end user. End users vary widely in their expectations and familiarity with generative AI—for example, employees compared with high-net-worth clients.
• Intended level of work automation. AI agents integrated through APIs could act nearly autonomously or as copilots, giving real-time suggestions to agents during customer interactions.
• Data constraints. While public data such as annual reports could be made widely available, there would need to be limits on identifiable details for customers and other internal data.

Pharmaceuticals and medical products could see benefits across the entire value chain

Our analysis finds that generative AI could have a significant impact on the pharmaceutical and medical-product industries—from 2.6 to 4.5 percent of annual revenues across the pharmaceutical and medical-product industries, or $60 billion to $110 billion annually. This big potential reflects the resource-intensive process of discovering new drug compounds. Pharma companies typically spend approximately 20 percent of revenues on R&D, 1 Research and development in the pharmaceutical industry , Congressional Budget Office, April 2021. and the development of a new drug takes an average of ten to 15 years. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value. For example, lead identification—a step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drug—can take several months even with “traditional” deep learning techniques. Foundation models and generative AI can enable organizations to complete this step in a matter of weeks.

Generative AI at work in pharmaceuticals and medical products

Drug discovery involves narrowing the universe of possible compounds to those that could effectively treat specific conditions. Generative AI’s ability to process massive amounts of data and model options can accelerate output across several use cases:

Improve automation of preliminary screening

In the lead identification stage of drug development, scientists can use foundation models to automate the preliminary screening of chemicals in the search for those that will produce specific effects on drug targets. To start, thousands of cell cultures are tested and paired with images of the corresponding experiment. Using an off-the-shelf foundation model, researchers can cluster similar images more precisely than they can with traditional models, enabling them to select the most promising chemicals for further analysis during lead optimization.

Enhance indication finding

An important phase of drug discovery involves the identification and prioritization of new indications—that is, diseases, symptoms, or circumstances that justify the use of a specific medication or other treatment, such as a test, procedure, or surgery. Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications.

Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data. Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications. The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups.

Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug. This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process.

Factors for pharmaceuticals and medical products organizations to consider

Before integrating generative AI into operations, pharma executives should be aware of some factors that could limit their ability to capture its benefits:

• The need for a human in the loop. Companies may need to implement new quality checks on processes that shift from humans to generative AI, such as representative-generated emails, or more detailed quality checks on AI-assisted processes, such as drug discovery. The increasing need to verify whether generated content is based on fact or inference elevates the need for a new level of quality control.
• Explainability. A lack of transparency into the origins of generated content and traceability of root data could make it difficult to update models and scan them for potential risks; for instance, a generative AI solution for synthesizing scientific literature may not be able to point to the specific articles or quotes that led it to infer that a new treatment is very popular among physicians. The technology can also “hallucinate,” or generate responses that are obviously incorrect or inappropriate for the context. Systems need to be designed to point to specific articles or data sources, and then do human-in-the-loop checking.
• Privacy considerations. Generative AI’s use of clinical images and medical records could increase the risk that protected health information will leak, potentially violating regulations that require pharma companies to protect patient privacy.

Work and productivity implications

Technology has been changing the anatomy of work for decades. Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies. More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually.

These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves. At a conceptual level, the application of generative AI may follow the same pattern in the modern workplace, although as we show later in this chapter, the types of activities that generative AI could affect, and the types of occupations with activities that could change, will likely be different as a result of this technology than for older technologies.

The McKinsey Global Institute began analyzing the impact of technological automation of work activities and modeling scenarios of adoption in 2017. At that time, we estimated that workers spent half of their time on activities that had the potential to be automated by adapting technology that existed at that time, or what we call technical automation potential. We also modeled a range of potential scenarios for the pace at which these technologies could be adopted and affect work activities throughout the global economy.

Technology adoption at scale does not occur overnight. The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific work activity—developing such solutions takes time. Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy. Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time.

About the research

This analysis builds on the methodology we established in 2017. We began by examining the US Bureau of Labor Statistics O*Net breakdown of about 850 occupations into roughly 2,100 detailed work activities. For each of these activities, we scored the level of capability necessary to successfully perform the activity against a set of 18 capabilities that have the potential for automation.

We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time. Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts.

Based on these assessments of the technical automation potential of each detailed work activity at each point in time, we modeled potential scenarios for the adoption of work automation around the world. First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development. Second, we estimated a range of potential costs for this technology when it is first introduced, and then declining over time, based on historical precedents. We modeled the beginning of adoption for a specific detailed work activity in a particular occupation in a country (for 47 countries, accounting for more than 80 percent of the global workforce) when the cost of the automation technology reaches parity with the cost of human labor in that occupation.

Based on a historical analysis of various technologies, we modeled a range of adoption timelines from eight to 27 years between the beginning of adoption and its plateau, using sigmoidal curves (S-curves). This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms.

The modeled scenarios create a time range for the potential pace of automating current work activities. The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction. The reality is likely to fall somewhere between the two.

The analyses in this paper incorporate the potential impact of generative AI on today’s work activities. The new capabilities of generative AI, combined with previous technologies and integrated into corporate operations around the world, could accelerate the potential for technical automation of individual activities and the adoption of technologies that augment the capabilities of the workforce. They could also have an impact on knowledge workers whose activities were not expected to shift as a result of these technologies until later in the future (see sidebar “About the research”).

Automation potential has accelerated, but adoption to lag

Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6). For example, MGI previously identified 2027 as the earliest year when median human performance for natural-language understanding might be achieved in technology, but in this new analysis, the corresponding point is 2023.

As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities.

Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7).

Our analysis of adoption scenarios accounts for the time required to integrate technological capabilities into solutions that can automate individual work activities; the cost of these technologies compared with that of human labor in different occupations and countries around the world; and the time it has taken for technologies to diffuse across the economy. With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8).

As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work. With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance.

Our previously modeled adoption scenarios suggested that 50 percent of time spent on 2016 work activities would be automated sometime between 2035 and 2070, with a midpoint scenario around 2053. Our updated adoption scenarios, which account for developments in generative AI, models the time spent on 2023 work activities reaching 50 percent automation between 2030 and 2060, with a midpoint of 2045—an acceleration of roughly a decade compared with the previous estimate. 6 The comparison is not exact because the composition of work activities between 2016 and 2023 has changed; for example, some automation has occurred during that time period.

Adoption is also likely to be faster in developed countries, where wages are higher and thus the economic feasibility of adopting automation occurs earlier. Even if the potential for technology to automate a particular work activity is high, the costs required to do so have to be compared with the cost of human wages. In countries such as China, India, and Mexico, where wage rates are lower, automation adoption is modeled to arrive more slowly than in higher-wage countries (Exhibit 9).

Generative AI’s potential impact on knowledge work

Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks.

As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10). Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023.

Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply. Some 40 percent of the activities that workers perform in the economy require at least a median level of human understanding of natural language.

As a result, many of the work activities that involve communication, supervision, documentation, and interacting with people in general have the potential to be automated by generative AI, accelerating the transformation of work in occupations such as education and technology, for which automation potential was previously expected to emerge later (Exhibit 11).

Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased. We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12).

Another way to interpret this result is that generative AI will challenge the attainment of multiyear degree credentials as an indicator of skills, and others have advocated for taking a more skills-based approach to workforce development in order to create more equitable, efficient workforce training and matching systems. 7 A more skills-based approach to workforce development predates the emergence of generative AI. Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do.

Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor. Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles.

However, generative AI’s impact is likely to most transform the work of higher-wage knowledge workers because of advances in the technical automation potential of their activities, which were previously considered to be relatively immune from automation (Exhibit 13).

Generative AI could propel higher productivity growth

Global economic growth was slower from 2012 to 2022 than in the two preceding decades. 8 Global economic prospects , World Bank, January 2023. Although the COVID-19 pandemic was a significant factor, long-term structural challenges—including declining birth rates and aging populations—are ongoing obstacles to growth.

Declining employment is among those obstacles. Compound annual growth in the total number of workers worldwide slowed from 2.5 percent in 1972–82 to just 0.8 percent in 2012–22, largely because of aging. In many large countries, the size of the workforce is already declining. 9 Yaron Shamir, “Three factors contributing to fewer people in the workforce,” Forbes , April 7, 2022. Productivity, which measures output relative to input, or the value of goods and services produced divided by the amount of labor, capital, and other resources required to produce them, was the main engine of economic growth in the three decades from 1992 to 2022 (Exhibit 14). However, since then, productivity growth has slowed in tandem with slowing employment growth, confounding economists and policy makers. 10 “The U.S. productivity slowdown: an economy-wide and industry-level analysis,” Monthly Labor Review, US Bureau of Labor Statistics, April 2021; Kweilin Ellingrud, “ Turning around the productivity slowdown ,” McKinsey Global Institute, September 13, 2022.

The deployment of generative AI and other technologies could help accelerate productivity growth, partially compensating for declining employment growth and enabling overall economic growth. Based on our estimates, the automation of individual work activities enabled by these technologies could provide the global economy with an annual productivity boost of 0.5 to 3.4 percent from 2023 to 2040, depending on the rate of automation adoption—with generative AI contributing 0.1 to 0.6 percentage points of that growth—but only if individuals affected by the technology were to shift to other work activities that at least match their 2022 productivity levels (Exhibit 15). In some cases, workers will stay in the same occupations, but their mix of activities will shift; in others, workers will need to shift occupations.

Considerations for business and society

History has shown that new technologies have the potential to reshape societies. Artificial intelligence has already changed the way we live and work—for example, it can help our phones (mostly) understand what we say, or draft emails. Mostly, however, AI has remained behind the scenes, optimizing business processes or making recommendations about the next product to buy. The rapid development of generative AI is likely to significantly augment the impact of AI overall, generating trillions of dollars of additional value each year and transforming the nature of work.

But the technology could also deliver new and significant challenges. Stakeholders must act—and quickly, given the pace at which generative AI could be adopted—to prepare to address both the opportunities and the risks. Risks have already surfaced, including concerns about the content that generative AI systems produce: Will they infringe upon intellectual property due to “plagiarism” in the training data used to create foundation models? Will the answers that LLMs produce when questioned be accurate, and can they be explained? Will the content generative AI creates be fair or biased in ways that users do not want by, say, producing content that reflects harmful stereotypes?

Using generative AI responsibly

Generative AI poses a variety of risks. Stakeholders will want to address these risks from the start.

Fairness: Models may generate algorithmic bias due to imperfect training data or decisions made by the engineers developing the models.

Intellectual property (IP): Training data and model outputs can generate significant IP risks, including infringing on copyrighted, trademarked, patented, or otherwise legally protected materials. Even when using a provider’s generative AI tool, organizations will need to understand what data went into training and how it’s used in tool outputs.

Privacy: Privacy concerns could arise if users input information that later ends up in model outputs in a form that makes individuals identifiable. Generative AI could also be used to create and disseminate malicious content such as disinformation, deepfakes, and hate speech.

Security: Generative AI may be used by bad actors to accelerate the sophistication and speed of cyberattacks. It also can be manipulated to provide malicious outputs. For example, through a technique called prompt injection, a third party gives a model new instructions that trick the model into delivering an output unintended by the model producer and end user.

Explainability: Generative AI relies on neural networks with billions of parameters, challenging our ability to explain how any given answer is produced.

Reliability: Models can produce different answers to the same prompts, impeding the user’s ability to assess the accuracy and reliability of outputs.

Organizational impact: Generative AI may significantly affect the workforce, and the impact on specific groups and local communities could be disproportionately negative.

Social and environmental impact: The development and training of foundation models may lead to detrimental social and environmental consequences, including an increase in carbon emissions (for example, training one large language model can emit about 315 tons of carbon dioxide). 1 Ananya Ganesh, Andrew McCallum, and Emma Strubell, “Energy and policy considerations for deep learning in NLP,” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , June 5, 2019.

There are economic challenges too: the scale and the scope of the workforce transitions described in this report are considerable. In the midpoint adoption scenario, about a quarter to a third of work activities could change in the coming decade. The task before us is to manage the potential positives and negatives of the technology simultaneously (see sidebar “Using generative AI responsibly”). Here are some of the critical questions we will need to address while balancing our enthusiasm for the potential benefits of the technology with the new challenges it can introduce.

Companies and business leaders

How can companies move quickly to capture the potential value at stake highlighted in this report, while managing the risks that generative AI presents?

How will the mix of occupations and skills needed across a company’s workforce be transformed by generative AI and other artificial intelligence over the coming years? How will a company enable these transitions in its hiring plans, retraining programs, and other aspects of human resources?

Do companies have a role to play in ensuring the technology is not deployed in “negative use cases” that could harm society?

How can businesses transparently share their experiences with scaling the use of generative AI within and across industries—and also with governments and society?

Policy makers

What will the future of work look like at the level of an economy in terms of occupations and skills? What does this mean for workforce planning?

How can workers be supported as their activities shift over time? What retraining programs can be put in place? What incentives are needed to support private companies as they invest in human capital? Are there earn-while-you-learn programs such as apprenticeships that could enable people to retrain while continuing to support themselves and their families?

What steps can policy makers take to prevent generative AI from being used in ways that harm society or vulnerable populations?

Can new policies be developed and existing policies amended to ensure human-centric AI development and deployment that includes human oversight and diverse perspectives and accounts for societal values?

Individuals as workers, consumers, and citizens

How concerned should individuals be about the advent of generative AI? While companies can assess how the technology will affect their bottom lines, where can citizens turn for accurate, unbiased information about how it will affect their lives and livelihoods?

How can individuals as workers and consumers balance the conveniences generative AI delivers with its impact in their workplaces?

Can citizens have a voice in the decisions that will shape the deployment and integration of generative AI into the fabric of their lives?

Technological innovation can inspire equal parts awe and concern. When that innovation seems to materialize fully formed and becomes widespread seemingly overnight, both responses can be amplified. The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it.

All of us are at the beginning of a journey to understand this technology’s power, reach, and capabilities. If the past eight months are any guide, the next several years will take us on a roller-coaster ride featuring fast-paced innovation and technological breakthroughs that force us to recalibrate our understanding of AI’s impact on our work and our lives. It is important to properly understand this phenomenon and anticipate its impact. Given the speed of generative AI’s deployment so far, the need to accelerate digital transformation and reskill labor forces is great.

These tools have the potential to create enormous value for the global economy at a time when it is pondering the huge costs of adapting and mitigating climate change. At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence. They are capable of that most human of abilities, language, which is a fundamental requirement of most work activities linked to expertise and knowledge as well as a skill that can be used to hurt feelings, create misunderstandings, obscure truth, and incite violence and even wars.

We hope this research has contributed to a better understanding of generative AI’s capacity to add value to company operations and fuel economic growth and prosperity as well as its potential to dramatically transform how we work and our purpose in society. Companies, policy makers, consumers, and citizens can work together to ensure that generative AI delivers on its promise to create significant value while limiting its potential to upset lives and livelihoods. The time to act is now. 11 The research, analysis, and writing in this report was entirely done by humans.

Michael Chui is a partner in McKinsey’s Bay Area office, where Roger Roberts is a partner and Lareina Yee is a senior partner; Eric Hazan is a senior partner in McKinsey’s Paris office; Alex Singla is a senior partner in the Chicago office; Kate Smaje and Alex Sukharevsky are senior partners in the London office; and Rodney Zemmel is a senior partner in the New York office.

The authors wish to thank Pedro Abreu, Rohit Agarwal, Steven Aronowitz, Arun Arora, Charles Atkins, Elia Berteletti, Onno Boer, Albert Bollard, Xavier Bosquet, Benjamin Braverman, Charles Carcenac, Sebastien Chaigne, Peter Crispeels, Santiago Comella-Dorda, Eleonore Depardon, Kweilin Ellingrud, Thierry Ethevenin, Dmitry Gafarov, Neel Gandhi, Eric Goldberg, Liz Grennan, Shivani Gupta, Vinay Gupta, Dan Hababou, Bryan Hancock, Lisa Harkness, Leila Harouchi, Jake Hart, Heiko Heimes, Jeff Jacobs, Begum Karaci Deniz, Tarun Khurana, Malgorzata Kmicinska, Jan-Christoph Köstring, Andreas Kremer, Kathryn Kuhn, Jessica Lamb, Maxim Lampe, John Larson, Swan Leroi, Damian Lewandowski, Richard Li, Sonja Lindberg, Kerin Lo, Guillaume Lurenbaum, Matej Macak, Dana Maor, Julien Mauhourat, Marco Piccitto, Carolyn Pierce, Olivier Plantefeve, Alexandre Pons, Kathryn Rathje, Emily Reasor, Werner Rehm, Steve Reis, Kelsey Robinson, Martin Rosendahl, Christoph Sandler, Saurab Sanghvi, Boudhayan Sen, Joanna Si, Alok Singh, Gurneet Singh Dandona, François Soubien, Eli Stein, Stephanie Strom, Michele Tam, Robert Tas, Maribel Tejada, Wilbur Wang, Georg Winkler, Jane Wong, and Romain Zilahi for their contributions to this report.

For the full list of acknowledgments, see the downloadable PDF .

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