Most of today’s challenges with using AI come simply from it being so new and unfamiliar. The two main challenges we see are getting leadership buy-in and creating processes.
1. Piloting AI at your company (and getting leadership buy-in)
Not everyone has an “early adopter” mindset, which means even if you see the value AI can bring to your team, your teammates or leadership may still see it as risky or unproven.
Look for small ways to implement AI in your workflow and speak up about the positive impact you see.
2. Creating new processes
AI tools are relatively new, especially in the marketing world—AI chatbots like ChatGPT and Jasper Chat included. When implementing a new AI content platform, it may be time consuming to create new processes and best practices from scratch.
Think ahead of time about how you’ll handle the following moving pieces:
1. Team training
2. Project requests
3. Best practice documentation
4. ROI and performance insights
(AI) itself were used to investigate the current literature on AI in marketing? That is what we do in this study!
Having received more than US$5 billion in venture capital investments in just the past two years, artificial intelligence (AI) is poised to exert transformative effects on markets and marketing around the world (PricewaterhouseCoopers, 2017; Rangaswamy et al., 2020; Smart Insights, 2018). Marketing increasingly relies on its algorithms, which mimic human cognitive functions and exhibit aspects of human intelligence (Huang & Rust, 2018; Rangaswamy et al., 2020; Russell & Norvig, 2016; Sterne, 2017), such that 72% of marketers cite AI as a business advantage. Consumers benefit from these applications, in the form of decreased costs, more diverse service channels, innovative breakthroughs, and opportunities for expanded human creativity and ingenuity when tedious, repetitive tasks are performed by AI (Haenlein & Kaplan, 2019; PricewaterhouseCoopers, 2017; Smart Insights, 2018). This revolution of AI usage in marketing, and its potential for producing superior value outcomes, has sparked substantial research attention (Davenport et al.; 2020; Haenlein & Kaplan, 2019; Huang & Rust, 2018), prompting, for example, applications of intelligent technology (Marinova et al., 2017); descriptions of services enabled, facilitated, and delivered by various technologies (Rust & Huang, 2012); investigations of AI-powered robotics (Lu et al., 2020; Wirtz et al., 2018); explorations of AI-led marketing and sales strategies (Davenport et al., 2020); considerations of how AI-enabled delivery can lead to cost- effective service excellence (Wirtz, 2020; Wirtz & Zeithaml, 2018); proposals of AI- enabled platform business models (Wirtz et al., 2019); investigations of the impact of AI chatbot disclosures on customer purchases (Luo et al., 2019); considerations of effects on workforces (Davenport & Kirby, 2015) and redefinitions of AI-enabled workplaces (Chui et al., 2015); and discussions of digital technologies as driving forces of work and life (McAfee & Brynjolfsson, 2016).
Despite this extensive list, marketing still lacks a cohesive understanding of how AI technologies have been applied thus far and how they should be in the future (Haenlein & Kaplan, 2019; Paschen et al., 2019). That is, it needs literature analyses to scrutinize and synthesize the use of AI in marketing and pave a concrete path for future-focused academic research. Objective, reflective analyses are crucial to evaluate any extant knowledge base, identify knowledge gaps, and evaluate research effectiveness and productivity (Huang & Rust, 2018; Russell & Norvig, 2016), for both researchers and publication outlets or journals (Lowry et al., 2004, 2013). Indeed, where and how scholars publish represent essential aspects of the identity of the marketing discipline, reflecting its value systems, aspirations, paradigms, reward systems, cultural conduct, and political hierarchy (Garfield, 2006; Lowry et al., 2004, 2013). Therefore, with this study, we investigate dominant research topics related to AI in marketing, outlining key themes, influential publications, and networks among authors and journals, to provide a clear view of the extant knowledge base.
We apply two complementary analytical approaches to examine the evolution and structure of the research field. First, to identify dominant, salient topics, we undertake topic modeling and apply natural language processing, machine learning, and statistical algorithms. Second, using scientometric techniques, we generate further insights about authors and research networks, as well as establishing keyword co- occurrence rates, identifying landmark publications, and depicting the evolution of the field over time (Lowry et al., 2013; Nikolenko et al., 2017; Zhao et al., 2019).
In turn, this study makes four contributions. First, the combination of advanced topic modeling algorithms, established through text analytics techniques, and of scientometric analysis, enables a highly objective, robust, structured, and comprehensive review of this rapidly expanding research domain than traditional literature reviews and analyses can provide (Vanhala et al., 2020). Second, our systematic, data-driven approach reveals 10 dominant AI research topics in marketing, which we can classify as consumer research or organization and strategy–related research. Third, the scientometric analysis produces information maps of co-citation clusters, landmark articles, conceptual and theoretical foundations, and reciprocal interconnections of concepts on the basis of their paired presence in extant literature. Such a comprehensive approach establishes a broad understanding and in-depth insights, without restricting the analysis to any predesigned aspects. Fourth, we offer an extensive discussion of research gaps that in turn produces a robust agenda for increasing the depth and breadth of AI research in marketing.
After we establish the conceptual underpinning of this study in Section 2, we explicate the methodological details in Section 3. Section 4 contains descriptive details of the reviewed publications. In Section 5, we review the salient topics of research on AI in marketing, and then in Section 6, we report the scientometric analysis. Finally, we discuss the results and conclude with concrete recommendations for examining multiple overlooked facets of AI in marketing in Section 7.
Most of today’s challenges with using AI come simply from it being so new and unfamiliar. The two main challenges we see are getting leadership buy-in and creating processes.
1. Piloting AI at your company (and getting leadership buy-in)
Not everyone has an “early adopter” mindset, which means even if you see the value AI can bring to your team, your teammates or leadership may still see it as risky or unproven.
Look for small ways to implement AI in your workflow and speak up about the positive impact you see.
2. Creating new processes
AI tools are relatively new, especially in the marketing world—AI chatbots like ChatGPT and Jasper Chat included. When implementing a new AI content platform, it may be time consuming to create new processes and best practices from scratch.
Think ahead of time about how you’ll handle the following moving pieces:
1. Team training
2. Project requests
3. Best practice documentation
4. ROI and performance insights
(AI) itself were used to investigate the current literature on AI in marketing? That is what we do in this study!
Having received more than US$5 billion in venture capital investments in just the past two years, artificial intelligence (AI) is poised to exert transformative effects on markets and marketing around the world (PricewaterhouseCoopers, 2017; Rangaswamy et al., 2020; Smart Insights, 2018). Marketing increasingly relies on its algorithms, which mimic human cognitive functions and exhibit aspects of human intelligence (Huang & Rust, 2018; Rangaswamy et al., 2020; Russell & Norvig, 2016; Sterne, 2017), such that 72% of marketers cite AI as a business advantage. Consumers benefit from these applications, in the form of decreased costs, more diverse service channels, innovative breakthroughs, and opportunities for expanded human creativity and ingenuity when tedious, repetitive tasks are performed by AI (Haenlein & Kaplan, 2019; PricewaterhouseCoopers, 2017; Smart Insights, 2018). This revolution of AI usage in marketing, and its potential for producing superior value outcomes, has sparked substantial research attention (Davenport et al.; 2020; Haenlein & Kaplan, 2019; Huang & Rust, 2018), prompting, for example, applications of intelligent technology (Marinova et al., 2017); descriptions of services enabled, facilitated, and delivered by various technologies (Rust & Huang, 2012); investigations of AI-powered robotics (Lu et al., 2020; Wirtz et al., 2018); explorations of AI-led marketing and sales strategies (Davenport et al., 2020); considerations of how AI-enabled delivery can lead to cost- effective service excellence (Wirtz, 2020; Wirtz & Zeithaml, 2018); proposals of AI- enabled platform business models (Wirtz et al., 2019); investigations of the impact of AI chatbot disclosures on customer purchases (Luo et al., 2019); considerations of effects on workforces (Davenport & Kirby, 2015) and redefinitions of AI-enabled workplaces (Chui et al., 2015); and discussions of digital technologies as driving forces of work and life (McAfee & Brynjolfsson, 2016).
Despite this extensive list, marketing still lacks a cohesive understanding of how AI technologies have been applied thus far and how they should be in the future (Haenlein & Kaplan, 2019; Paschen et al., 2019). That is, it needs literature analyses to scrutinize and synthesize the use of AI in marketing and pave a concrete path for future-focused academic research. Objective, reflective analyses are crucial to evaluate any extant knowledge base, identify knowledge gaps, and evaluate research effectiveness and productivity (Huang & Rust, 2018; Russell & Norvig, 2016), for both researchers and publication outlets or journals (Lowry et al., 2004, 2013). Indeed, where and how scholars publish represent essential aspects of the identity of the marketing discipline, reflecting its value systems, aspirations, paradigms, reward systems, cultural conduct, and political hierarchy (Garfield, 2006; Lowry et al., 2004, 2013). Therefore, with this study, we investigate dominant research topics related to AI in marketing, outlining key themes, influential publications, and networks among authors and journals, to provide a clear view of the extant knowledge base.
We apply two complementary analytical approaches to examine the evolution and structure of the research field. First, to identify dominant, salient topics, we undertake topic modeling and apply natural language processing, machine learning, and statistical algorithms. Second, using scientometric techniques, we generate further insights about authors and research networks, as well as establishing keyword co- occurrence rates, identifying landmark publications, and depicting the evolution of the field over time (Lowry et al., 2013; Nikolenko et al., 2017; Zhao et al., 2019).
In turn, this study makes four contributions. First, the combination of advanced topic modeling algorithms, established through text analytics techniques, and of scientometric analysis, enables a highly objective, robust, structured, and comprehensive review of this rapidly expanding research domain than traditional literature reviews and analyses can provide (Vanhala et al., 2020). Second, our systematic, data-driven approach reveals 10 dominant AI research topics in marketing, which we can classify as consumer research or organization and strategy–related research. Third, the scientometric analysis produces information maps of co-citation clusters, landmark articles, conceptual and theoretical foundations, and reciprocal interconnections of concepts on the basis of their paired presence in extant literature. Such a comprehensive approach establishes a broad understanding and in-depth insights, without restricting the analysis to any predesigned aspects. Fourth, we offer an extensive discussion of research gaps that in turn produces a robust agenda for increasing the depth and breadth of AI research in marketing.
After we establish the conceptual underpinning of this study in Section 2, we explicate the methodological details in Section 3. Section 4 contains descriptive details of the reviewed publications. In Section 5, we review the salient topics of research on AI in marketing, and then in Section 6, we report the scientometric analysis. Finally, we discuss the results and conclude with concrete recommendations for examining multiple overlooked facets of AI in marketing in Section 7.