How Generative AI is Changing the Future of Marketing
Artificial intelligence, and generative AI in particular, is permeating everyday activities and disrupting jobs—especially in marketing. Yet most of these changes are occurring in a slapdash way, featuring a patchwork of individual initiatives. Few managers have a full understanding of the different uses of AI across their organization; even fewer have any real control over whether and when it gets implemented. In an effort to help marketing managers gain greater understanding, the JAMS article, How Generative AI is Changing the Future of Marketing, first defines generative AI, relative to analytical AI, then articulates some benefits and perils of using it. We also highlight key considerations for managers faced with the question of what and how to use gen AI.
These considerations can be summarized in a framework, in which we map the type of input (general or customized) used to train gen AI, together with the level of human augmentation required (how much humans intervene and augment the output from gen AI) before its delivery to the end user or deployment in the field. The resulting quadrants reveal inherent trade-offs, with respect to privacy versus data security, tolerance for bias versus risk of hallucinations, and regulatory and ethical issues. All these complex and interdependent risks can be mitigated though, depending on the functional needs and risk tolerance of the firm. High-stake tasks and decisions need to be treated differently than low-risk functions and tasks. Then, depending on which risks a firm is willing to incur and the task for it is deploying gen AI, it can benefit from greater speed, accuracy, and control. The framework also provides a structured way to find a balance between the risks of AI automation and the costs of human augmentation, while avoiding regulatory and ethical pitfalls.
With these insights, this article can inform both academia and managers. For managers, we provide an initial framework they can leverage to build out their gen AI capabilities, especially for marketing applications. For academia, we offer a forward-looking research agenda that ideally will guide and inspire scholars to examine important issues that will determine how gen AI ultimately affects marketing, now and over the medium term.
What inspired you to focus on this topic?
Gen AI is reshaping marketing by transforming interactions with customers, efforts for content creation, and decision-making processes. As marketing scholars and professors, we constantly work to stay abreast of the changes wrought by gen AI, and through such efforts, we identified a clear need for guidance. We have witnessed significant changes and also have been working, together and separately, on research pertaining to technology and AI for years. Tom and Dhruv especially have been deeply involved in this subject area. From that vantage point, we were able to spot a missing tool in managers’ strategic toolkits—namely, a useful, organizing framework for considering the choices linked to selecting and deploying gen AI.
How did you choose the current approach for the manuscript?
Our approach is largely embedded in our own work on AI. In conversations with managers of firms representing various industries and sizes, from Fortune 100 through to small and mid-sized companies, we heard many of the same concerns, fears, and hopes. We leveraged our deep understanding of AI, based on research we have conducted in the past, then drew on recent scholarship in the field, to formulate the organizing framework. Inspired by the simplicity and utility of the BGC Growth Share Matrix and Ansoff’s matrix, we developed our four-quadrant framework.
What key challenge or questions were you hoping to address with this paper?
Gen AI is and will continue to evolve rapidly. It can be difficult for managers to keep up with individual tools and offerings. By using our product-agnostic framework, managers can navigate this turbulent technological market disruption more strategically. We hope managers will choose their gen AI tools strategically, as well as invest in training and governance mechanisms, including careful measures of the returns, so that they can stay ahead of emerging technologies and applications. We also hope this articles guides and inspires academics to conduct continued research into gen AI and marketing but also pursue research that can help managers directly, such as by framing their AI strategies.
