How AI Shapes Creativity: Expanding Potential or Narrowing Possibilities?

On September 25, 2025, Wharton Human-AI Research (WHAIR) hosted an AI Horizons webinar exploring how AI impacts the quality and diversity of creative ideas. The discussion was led by Kartik Hosanagar, Faculty Co-Director of Wharton Human-AI Research, joined by Anil Doshi, Associate Professor, UCL School of Management, Oliver Hauser, Professor of Economics, University of Exeter, and Léonard Boussioux, Assistant Professor, University of Washington.

Each of the speakers drew from recent research they conducted, and together their panel examined how AI influences innovation across industries, highlighting both opportunities and risks.

Here are the key takeaways:

Creativity combines novelty and usefulness

Creativity is often defined along two dimensions: novelty (how rare or original an idea is) and usefulness (how valuable or applicable it is). As Oliver Hauser explained, “Some ideas are wildly novel but useless, others useful but unoriginal. The best innovations balance both.” For professionals, this means evaluating AI-generated ideas not just for originality but also for practical business value.

AI boosts productivity and raises baseline quality

Across studies, people using AI complete tasks faster and often produce outputs rated as higher quality. In storytelling experiments, AI support “supercharged” less-creative individuals, enabling them to produce work judged more novel and useful than before. For organizations, this means AI can democratize creativity by raising the floor of idea quality across teams.

But AI can reduce diversity of ideas

A consistent finding is that while AI improves individual output, it narrows the range of ideas overall. As Hauser noted, when people start with an AI suggestion, “they get anchored to it,” leading to outputs that are more similar to each other. For businesses, this raises a competitive concern: if everyone uses the same tools in the same way, ideas risk converging rather than diverging.

Collaboration design matters

Kartik Hosanagar’s research shows that when humans generate initial ideas and AI supports evaluation or refinement, diversity is preserved. But when AI is used in early ideation, outputs converge. The implication: leaders should structure workflows so that humans drive the earliest creative stages, while AI assists with scaling, editing, or selection. This design choice can make the difference between breakthrough innovation and homogenized outcomes.

Guarding against over-reliance

Panelists cautioned against treating AI as a full “copilot.” Over-dependence risks weakening human creativity—much like pilots who lose manual skills when autopilot is overused. Instead, organizations should build “mindful friction” into workflows, ensuring employees practice core creative thinking before turning to AI. This helps preserve the human “muscle” of creativity while still leveraging AI’s advantages.

Strategies to maintain innovation edge

To counter homogenization, companies can:

• Use multiple AI models, agentic systems with advanced reasoning and tool capabilities, or varied prompting strategies to expand the idea space.
• Encourage independent ideation before AI input.
• Introduce collaborative systems where AI critiques, rather than generates, ideas.
• Establish organizational norms around when AI should complement versus substitute for human creativity.

Bottom line for business leaders: AI can augment the creative process and unlock new possibilities, but unmanaged use risks producing sameness. The organizations that win will be those that design collaboration carefully, leveraging AI to scale and refine, while protecting the uniquely human capacity for diverse, breakthrough ideas.