How artificial intelligence will change the future of marketing

By T. Davenport, Abhijit Guha, Dhruv Grewal, Timna Breßgott. Journal of the Academy of Marketing Science, 2019

1,929 citations recorded by the source corpus.

In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.

Subfields: Artificial Intelligence, Information Systems, Safety Research

View Paper: https://www.semanticscholar.org/paper/1013a3e5b2352d2cbe9d8c6291c6406ac504d8e7

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Citations in the corpus

Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda (2020, background): “, the entire network is the level of analysis) that addresses how collaborative, systemic aspects of AI should be managed (Davenport et al., 2020; Randhawa et al., 2016; Rangaswamy et al., 2020).”
Customer experiences in the age of artificial intelligence (2020, background): “In the context of AI-enabled services, two further non-monetary sacrifices have to be considered: A lack of human interaction and the potential for social isolation (Davenport, Guha, Grewal, & Bressgott, 2020), both of which can have a negative impact on the customer experience.”
Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making (2021, background): “…Schoemaker and Tetlock, 2017) or enabling infrastructure (Ransbotham et al., 2017), otherwise using AI “ may be limited by legacy infrastructures ” (Davenport et al., 2020, p.29); and that using AI requires development of employees ’ technology skills (e.g. McKinsey, 2017; Ransbotham et al., 2018;…”
The future of technology and marketing: a multidisciplinary perspective (2019, methodology): “…AI is gaining greater power to address marketing and business challenges, across a vast number of usage contexts, including not just healthcare but also driverless cars (e.g., Tesla, Waymo), retailing, customer service (e.g., the Pepper robot), and so forth (see Table 1 in Davenport et al. 2020).”
A strategic framework for artificial intelligence in marketing (2020, background): “It has even been asserted that AI will change the future of marketing substantially (Davenport et al. 2020; Rust 2020).”
Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution (2020, background): “Rapid advances in statistical machine learning techniques have broadened the scope for AI applications, and commercial uses now span diverse areas such as marketing ( Davenport et al., 2019 ), molecule discovery ( Gawehn et al., 2016 ), automotive manufacturing (Luckow et al., 2018) and beyond.”
An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance (2021, background): “It is clear from the findings that AI is changing the marketing landscape as previously indicated by Davenport et al. (2019).”
Artificial intelligence in drug discovery and development (2020, methodology): “In addition, these methods use natural language-processing tools to analyze keywords entered by customers and relate them to the probability of purchasing the product [111,112].”
How Technology is Changing Retail (2021, background): “Unlike Davenport et al. (2020), Grewal, Haenlein t al. (2020) and Grewal, Kroschke et al. (2020) and Shankar 2018) who focus on AI, we examine different technologies, nly some of which may be driven by AI. Finally, while Hoyer t al. (2020) focus on customer experience, we cover the impact f technolo”