Marketing With AI: Unlocking New Paradigms
Margherita Pagani, PhD and Yoram Wind, PhD
Marketers live and die by new ideas that work in the real world. Artificial intelligence (AI) promises faster work and bolder ideas, but the results are uneven. Our study asks a simple, practical question with strategic consequences: when and how does AI actually make marketing more creative? We test a clear hypothesis: creativity improves when the right kind of AI is paired with the right job and guided by human judgment, and it does not improve when AI is used as a blunt, one‑size‑fits‑all tool.
Investigating Human vs. Technology
The investigation moves on two fronts. On the human side, we studied highly creative professionals (artists), as a precise stand‑in for the kind of creative thinking marketers need. We observed how these creators use both generative AI (tools that create new content based on a prompt like ChatGPT) and non‑generative AI (tools that do not produce original content but classify, predict or automate tasks like spam filters) in daily work, and what roles AI plays in their processes.
On the technology side, we audited more than a thousand integrated AI systems launched between 2015 and 2021 and nearly a quarter of a million models released from 2022 to 2024. Independent judges rated these systems on novelty (how new or surprising an idea is) and usefulness (how practical and ready for real‑world use the idea is) so human creative cognition (evaluated as the ability to produce novel and useful ideas) could be compared with AI’s creative output on common ground.
What We Learned from People
Creators turn to AI for two main reasons. The first is speed. Drafts that once took hours can now be produced in minutes. Need multiple versions or a quick tone adjustment? AI can handle it, leaving people with more time for choosing and adding taste, the work they value most. The second reason creators use AI is exploration. Generative tools let creators test dozens of directions quickly and keep the ones that show promise. More options appear in less time, and the creative process feels less pressured.
A side effect is also important. Teaching a machine to “be creative” makes people lay out their own steps more clearly than before. That process sharpens judgment, even if many still feel they don’t have full control of the tool’s outputs.
When reflecting on experiences, participants showed a mix of enthusiasm and caution. They were excited and optimistic, but also hesitant about issues such as authorship, agency, and whether they truly understood how AI generated its outputs.
What We Learned from Technology
Across the audit, systems fell into four broad groups defined by novelty and usefulness. The first were the unremarkable workhorses: low usefulness, low novelty. These are the predictable and narrow tools that rarely produce new ideas. Reliable, perhaps, but creatively stagnant.
Next came the high usefulness, low novelty systems. These were the machine’s mechanics, making everything run more smoothly, faster, more stable, and more scalable. These are essential for infrastructure, yet uninspiring when it came to creative direction.
At the other end of the spectrum stood the systems with low usefulness and high novelty. They surprise with strange and unexpected outputs, but their results often needed heavy adaptation before becoming practical.
And finally, the high usefulness and high novelty. Here we spotlight the modern multimodal and generative models. Not only could they uncover fresh patterns, but they also produced practical results that aligned with human goals. When guided by human criteria and checked with human judgment, these systems delivered the most consistent creative uplift.
Our Framework
The results point to a practical framework for using AI in creative work. First, AI makes routine tasks agile and takes over the mundane parts of the job like drafting variations, so people can focus on judgment. Non-generative tools in particular speed up feedback loops and improve everyday accuracy.
Second, AI supports human creativity. Generative tools act like collaborators in brainstorming, expanding possible ideas, making evaluation criteria explicit, and allowing for quick cycles of testing and revision. Still, people provide the direction, deciding what fits the assignment and what feels right for the brand.
Finally, AI encourages fresh perspectives. It can make evident approaches that teams might not consider under tight deadlines. But novelty by itself is not enough, people must edit and adapt the outputs to make them relevant and accurate.
Across all uses, some boundaries are essential. Bias and privacy must never be compromised, and any information that appears factual must be checked before shared.
Real Estate Implications
In real estate, speed is everything. A property’s story, a neighborhood’s angle, or a marketing hook can turn cold almost overnight, and AI has the potential to give agents and marketers a competitive edge.
Start with automation. Use tools that handle routine tasks like routing leads in a CRM, A/B testing headlines, cleaning up listing photos, or auto-filling metadata. These save hours and free people to focus on higher-level work.
Next, use reliability tools and creative wildcards. Reliability tools (high usefulness, low novelty) include AI assistants that check compliance, flag data errors, or resize content for different platforms. They are practical, safe, and time-saving. On the other hand, creative wildcards (low usefulness, high novelty), where image generators like DALL-E stand, need heavy adaptation of outputs, but they often spark fresh ideas marketers wouldn’t have considered under deadline pressure.
Finally, lean into full generative AI platforms like ChatGPT or MidJourney to build polished storylines, image sets, property tour videos, or social-ready copy. Done right, you can test these assets in days instead of weeks.
The winners will be the teams that stop asking “AI or human?” The real advantage is AI and human, where machines widen the option set while people are steering the wheel and making the final call.
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Recommended Reading
Pagani, Margherita and Yoram Wind (2025), “Unlocking Marketing Creativity Using Artificial Intelligence” Journal of Interactive Marketing, 60(1), 1-24. https://doi.org/10.1177/10949968241265855
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About the Authors
Margherita Pagani, PhD
Professor of Digital & AI in Marketing. Director, SKEMA Center for Artificial Intelligence, SKEMA Business School (France)
Dr. Margherita Pagani (PhD – Université Jean Moulin Lyon 3 and HDR – Université Évry, France) studies human-centered AI, digital marketing, and computational creativity, with a focus on how generative and non-generative systems augment ideation, personalization, and strategic decision-making. She published several books, two encyclopedias, and articles in leading international journals, including MISQuarterly, MIT Sloan Management Review, Harvard Business Review, British Journal of Management, Journal of Business Research, Information & Management, Journal of Interactive Marketing, Journal of International Advertising, and Psychology & Marketing, among others. Dr. Pagani is the recipient of the 2023 International Marketing Trend Award, the 2009 Mobile Marketing Association Global Award “Academic of the Year,” and multiple other research distinctions throughout her career. She has been recognized among the top 2% of highly cited scientists globally for the years 2022, 2023, and 2024 by Elsevier.
Yoram Wind, PhD
Lauder Professor Emeritus and Professor of Marketing, University of Pennsylvania
Dr. Yoram “Jerry” Wind (PhD – Stanford University) is a pioneering scholar of marketing strategy, innovation, and creativity. He founded the Wharton “Think Tank” – The SEI Center for Advanced Studies in Management and directed it for three decades. Among his many innovations at Wharton, he led the development of the Wharton Executive MBA, the Lauder Institute, the new MBA curriculum of 1990, The Wharton Fellows, Wharton School Publishing, and various research programs, including The Future of Advertising. He has edited top marketing journals and published over 300 articles, manuscripts, and chapters. He authored, co-authored, or edited 30 books and received major marketing awards. He has consulted with over 100 companies and still testifies in intellectual property cases. He is a member of advisory boards of various companies and nonprofit organizations, including the Lauder Institute (since he founded it in 1984), American Friends of Reichman University (since its founding in 1995), QS (since 2018), and the Scientific Committee of SKEMA AI School of Business (since its founding in 2022). He is a trustee of the Philadelphia Museum of Art, the Curtis Institute of Music, and Grounds for Sculpture. He co-founded Reichman University, the first private, nonprofit university in Israel, formerly known as The Interdisciplinary Center (IDC) Herzliya and the 2021 recipient of their Honorary Doctorate. His current research explores marketing-driven business strategy, creativity, and innovation. His current publications include a Coursera Creativity Course for all ages, professions, and countries, an edited, special issue of MBR on AI for Customer engagement, and the book “Creativity in the age of AI.” His other recent books include: “Transformation in Times of Crisis” (2020), “Can Art Resolve Conflict?” (2018), “Beyond Advertising: Creating Value Through All Customer Touchpoints” (2016), and “The Network Imperative: How to Survive and Grow in the Age of Digital Business Models” (2016).
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