Journal Article
Machine Learning

How generative AI Is shaping the future of marketing

Dhruv Grewal(University of Bath), Cinthia B. Satornino(University of New Hampshire), Thomas H. Davenport(University of Virginia), Abhijit Guha(Florida Atlantic University)
December 14, 2024Journal of the Academy of Marketing Science174 citations
PDFDOI

174

Citations

10

Influential Citations

Journal of the Academy of Marketing Science

Venue

2024

Year

Abstract

Abstract Generative AI (Gen AI) is shaping the future of marketing. In the next decade, Gen AI will influence how marketers interact and communicate with customers, help create and deliver marketing content (text, images, and video), and inform methods for researching and developing new products and services. In both service and sales settings, Gen AI will affect customers directly and significantly. Therefore, marketers, researchers, and public policy makers require a clear understanding of Gen AI and its potential, as well as its limitations. To assist marketers in thinking through the adoption and implementation of Gen AI, the current article presents a four-quadrant organizing framework that highlights trade-offs in both the nature of Gen AI inputs and the extent of human augmentation needed to deliver Gen AI–generated outputs. This framework provides guidance for the selection and implementation of Gen AI tools, as well as recommendations for further research.

Analysis

Why This Paper Matters

This paper addresses a critical gap in marketing practice: how to systematically adopt generative AI (Gen AI) without over-relying on automation or underutilizing human expertise. As Gen AI tools proliferate, marketers face decisions about when to use fully automated content generation versus when to augment with human oversight. The authors provide a clear, actionable framework that balances efficiency with quality and ethical considerations.

The significance lies in its timeliness—published in a top marketing journal with 174 citations already—and its practical orientation. It moves beyond hype to offer a decision-making tool that can be applied across industries, from retail to services.

Technical Contributions

  • Four-Quadrant Framework: Organizes Gen AI applications along two axes: nature of inputs (structured vs. unstructured) and level of human augmentation (low vs. high).
  • Trade-off Analysis: Explicitly maps the tension between automation benefits (speed, scale) and human augmentation benefits (accuracy, creativity, ethics).
  • Implementation Guidance: Provides specific recommendations for each quadrant, such as using fully automated Gen AI for routine content (e.g., product descriptions) and human-augmented Gen AI for high-stakes customer interactions.
  • Research Agenda: Identifies key open questions, including measurement of Gen AI effectiveness and long-term customer trust impacts.

Results

The paper does not present empirical results but rather a conceptual model. Its primary output is the framework itself, which has been cited 174 times, indicating strong early adoption by the research community. The framework's utility is demonstrated through illustrative examples across marketing functions (content creation, customer service, product development).

Significance

This work provides a foundational structure for integrating Gen AI into marketing strategy, influencing both academic research and industry practice. By highlighting the need for human augmentation, it addresses concerns about job displacement and ethical risks. The framework can guide future empirical studies and help policymakers develop guidelines for responsible AI use in marketing.

generative-aimarketinghuman-augmentationframeworkcontent-creationcustomer-interactionproduct-development

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