Journal Article
Machine Learning

How artificial intelligence will change the future of marketing

Thomas H. Davenport(Babson College), Abhijit Guha(University of South Carolina), Dhruv Grewal(Babson College), Timna Breßgott(Maastricht University)
October 10, 2019Journal of the Academy of Marketing Science2,422 citations
PDFDOI

2.4k

Citations

85

Influential Citations

Journal of the Academy of Marketing Science

Venue

2019

Year

Abstract

Abstract 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.

Analysis

Why This Paper Matters

This paper addresses a critical gap in marketing literature by integrating three dimensions—intelligence levels, task types, and robot embodiment—into a single framework for understanding AI's impact. As AI rapidly permeates marketing, practitioners and researchers need a coherent structure to navigate the complex interplay between technology and customer behavior. The paper's emphasis on augmentation over replacement resonates with ongoing debates about human-AI collaboration, making it highly relevant for strategic decision-making.

By grounding its framework in both academic research and practical insights, the paper bridges theory and application. Its forward-looking research agenda also anticipates pressing policy questions around privacy, bias, and ethics, which are increasingly central to AI deployment. This positions the paper as a foundational reference for future studies on AI in marketing.

Technical Contributions

  • Multidimensional framework: Combines three dimensions—AI intelligence levels (e.g., assisted, augmented, autonomous), task types (e.g., repetitive, creative), and embodiment (embedded in a robot or not)—to categorize AI applications in marketing.
  • Research agenda: Proposes specific questions for future research on how AI changes marketing strategies (e.g., personalization, pricing) and customer behaviors (e.g., trust, adoption).
  • Policy focus: Highlights privacy, bias, and ethics as critical areas requiring investigation, moving beyond purely technical considerations.
  • Augmentation principle: Argues that AI systems designed to augment human managers (rather than replace them) lead to better outcomes, providing a design guideline for practitioners.

Results

As a conceptual paper, no empirical results are reported. The main output is a structured framework and a set of research propositions. The paper has garnered 2422 citations, indicating substantial influence in the marketing and AI communities.

Significance

This paper has shaped subsequent research on AI in marketing by providing a common vocabulary and structure. Its multidimensional framework helps practitioners assess which AI applications fit their strategic needs, while the augmentation principle offers a human-centric design philosophy. The explicit attention to policy issues also encourages responsible AI development. Overall, it serves as a key reference for academics and practitioners navigating AI's transformative role in marketing.

artificial-intelligencemarketingframeworkcustomer-behaviorprivacyethicsaugmentation

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