Journal of Advertising

Generative AI and Advertising: Building New Theoretical Frontiers

Fittingly for a call on Generative AI, we asked ChatGPT to help envision a future where AI-generated ads are pervasive. Here’s an edited version of its response:

It’s several years from now. You receive a campaign brief, but not from a creative team. It comes from a system trained on billions of consumer signals, cultural shifts, and behavioral triggers. There’s no big idea, no storyboard, no unified message. Just an archive of thousands of generative outputs—images, scripts, video snippets, simulated conversations—most of which were never seen by more than a few dozen people. Some of it is strange. Some is beautiful. Some is tough to categorize. None of it was human-made.

Consumers engaged in unexpected ways. They lingered, skipped, reacted, shared, or ignored. Some recognized the content as AI-generated. Others didn’t. Many weren’t sure. It mattered for some. It didn’t for others.

Now you’re asked to evaluate the campaign. To explain what happened. To say why it worked - or why it didn’t.

But the models you’ve used to define persuasion, attention, trust, and recall don’t quite hold. Metrics flicker. Effects ripple across time. Patterns emerge, but not always in ways you can map. The familiar frameworks strain under the weight of content that adapts too fast, hides in plain sight, and behaves more like a system than a message.

So you pause. And ask: What exactly are we looking at? How are consumers interpreting and responding to AI-generated content? Especially when they’re unsure who, or what, created it? What new heuristics are people developing to navigate a flood of generative content? And as these shifts unfold, what still holds in our existing theories? And what must we now rethink?

Rethinking the Foundations and Future of Advertising Theory in the Era of Generative AI

We seek scholarship that doesn’t just explore how generative AI fits into existing advertising frameworks, but reimagines what advertising is, how it works, and what it could become. This themed issue invites scholars to explore, challenge, and reimagine advertising theory in light of generative AI’s rise. Rather than assuming a single path forward, we encourage diverse perspectives that embrace this era’s uncertainty, complexity, and potential.

Generative AI has already begun to revolutionize content production for brands and agencies, offering the ability to produce vast amounts of personalized and dynamic content at scale. However, this also raises critical questions: Is generative AI enhancing creativity, or will it commoditize content into homogeneous, algorithm-driven outputs? Can generative AI capture the emotional nuance and originality that resonate with audiences, or does its reliance on patterns and data make content feel generic and impersonal? Understanding the dynamics that lead to such different perceptions is essential for advertising scholars and practitioners alike.

Consumers, too, are encountering generative AI in unprecedented ways. From hyper-personalized advertisements to interactions with virtual influencers, digital clones, and AI-generated avatars, the boundaries between the real and the artificial are blurring. How do consumers process these interactions, and what are the implications for their perceptions of authenticity, trust, and personalization? Furthermore, as personalization reaches new extremes, how might consumers react when ads feel “too perfect” or uncomfortably personalized?

Generative AI is also reshaping how consumers search and retrieve information. The rise of AI-generated summaries and zero-click searches is disrupting traditional SEO strategies and revenue models. Gen AI is also pushing the industry closer to a voice-based future, potentially altering how consumers engage with brands and evaluate information. At the same time, these shifts prompt new concerns about consumer reliance on generative AI: Are people becoming overly trusting or dependent on AI-driven content? Or will increasing awareness of AI biases and motives lead to a broader skepticism of AI-generated information and generalized distrust?

This evolving landscape also underscores the need to reexamine key advertising concepts like authenticity, originality, and creativity. Generative AI has the ability to flood content ecosystems with reams of AI-created material, leaving consumers to navigate and filter this overwhelming volume of information. What heuristics and mechanisms are consumers developing to manage this deluge, and how do these processes influence their decision-making and attributions? Will generative AI drive a further erosion of trust in information, intensifying issues already seen in the social media era? Or might it spark the development of new filters and frameworks to guide how people assess the credibility of content? Parallel to the concept of persuasion knowledge, might a “generative AI persuasion knowledge” emerge among consumers? If so, what triggers this awareness, and what are its effects?

Generative AI also raises ethical and policy challenges that require attention. As AI-generated content becomes more common, questions about who owns the output and how it affects copyright and intellectual property arise. Additionally, the integration of generative AI into social media platforms could amplify existing issues, such as mental health struggles, body image concerns, and the spread of misinformation. These risks underscore the importance of understanding generative AI’s effects so that responsible frameworks and policies can be developed for its use.

At a broader societal level, generative AI’s potential to democratize content creation and access to information presents both opportunities and challenges. Will this lead to greater inclusivity and innovation, or will the sheer volume of AI-generated material foster apathy, paralysis, or a general distrust of information? And as generative AI facilitates cognitive offloading for both consumers and advertising practitioners, how might reliance on these tools shape decision-making, creativity, and critical thinking? Exploring these broader effects is essential for understanding how generative AI will shape not only advertising but also the cultural, social, and ethical landscape in which it operates.

This Call Focuses on Theory Development

This Themed Issue places a strong emphasis on advancing novel advertising theory in the context of generative AI. Whether empirical, conceptual, qualitative, quantitative, or mixed-method, all rigorous approaches are encouraged provided they contribute to the development of new theoretical insights. By spurring developing a deeper understanding of generative AI, we hope to equip advertisers, advertising practitioners, and scholars with insights needed to navigate the generative AI advertising era.

Suggested Topics

Authors are encouraged to review the following Journal of Advertising editorial on generative AI and advertising to get a sense of the suggested topics and the types of articles we are aiming to publish:

Huh, Jisu, Michelle R. Nelson, and Cristel Antonia Russell (2023), “ChatGPT, AI Advertising, and Advertising Research and Education,” Journal of Advertising, 52(4), 477-482, DOI: 10.1080/00913367.2023.2227013