Enterprise AI Analysis
Ideation with Generative AI—in Consumer Research and Beyond
Authors: Julian De Freitas, Gideon Nave, Stefano Puntoni
Publication: Journal of Consumer Research, Vol. 52, 2025
This article explores the transformative potential of generative AI (genAI), particularly Large Language Models (LLMs), in creative ideation within consumer research. It integrates human creativity literature with LLM capabilities, distinguishing between LLMs as 'key ideators' versus humans as 'key ideators' assisted by LLMs. The core argument is that genAI's productivity and semantic breadth, analogous to human persistence and flexibility, can drive innovation. While LLMs excel in incremental improvements, their true potential for groundbreaking ideas is realized through human-AI collaboration. The article provides a framework of 'ideation roles' for LLMs and practical guidelines for integrating genAI to foster radical insights.
Executive Impact: Key Takeaways
Understand the critical implications of integrating GenAI into your research and innovation workflows, with insights drawn directly from this cutting-edge study.
- GenAI enhances creative ideation through productivity and semantic breadth.
- LLMs can act as 'Designer' or 'Writer' (AI as key ideator) or 'Interviewer' or 'Actor' (human as key ideator).
- Human-AI collaboration is crucial for radical innovation, moving beyond incremental improvements.
- Practical guidelines emphasize collaboration and strategic prompting for diverse and original ideas.
- The field must navigate challenges like idea homogeneity and bias to maximize genAI's impact.
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Increased Creative Productivity (Zhou & Lee, 2024)
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Original Ideas from LLMs before Plateau (Meincke et al., 2024a)
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More Persuasive than Humans in some domains (Hackenburg & Margetts, 2024)
Deep Analysis & Enterprise Applications
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LLM Productivity & Semantic Breadth
LLMs leverage productivity (analogous to human persistence) to generate a large volume of ideas efficiently, and semantic breadth (analogous to human flexibility) to connect disparate knowledge categories for originality. This dual capability allows for both deep exploration and broad concept generation.
Dual Pathway To CreativityEnterprise Process Flow
Generate N ideas with LLM using base prompt (x 40 separate sessions)
→
Team up sessions into groups of 4 (120 ideas total per group)
→
Group refines to 10 best ideas
→
Aggregate 10 best ideas from each group
| Role | Primary Function | Key Benefits |
|---|---|---|
| The Designer | Improving stimulus selection for experiments |
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| The Writer | Enhancing idea expression and persuasiveness |
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LLMs can serve as 'key ideators' by acting as a Designer for experimental stimuli or a Writer for enhancing idea articulation and persuasiveness. Each role addresses specific research needs and leverages distinct LLM capabilities.
| Role | Primary Function | Key Benefits |
|---|---|---|
| The Interviewer | Prompting human thought and creativity |
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| The Actor | Simulating consumer perspectives |
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When humans are the 'key ideators', LLMs can facilitate the process by acting as an Interviewer to prompt human creativity or as an Actor to simulate consumer perspectives, providing valuable qualitative insights.
The Prisoner's Dilemma of LLM Adoption
Widespread LLM adoption without strategic intervention risks idea homogeneity, creating a 'prisoner's dilemma' where individual gains in creativity could lead to a collective decrease in diversity. This necessitates proactive strategies like those outlined in the article to maintain field-wide originality.
- Individual vs. Collective Creativity: While LLMs boost individual creativity, they might reduce collective idea diversity, potentially leading to a 'prisoner's dilemma'.
- Navigating 'Small' vs. 'Big' Ideas: Ideas considered 'big' pre-LLM might seem 'small' post-adoption, pushing researchers to seek truly radical insights.
- The Role of Peer Review: Peer review can act as a natural selection mechanism to filter out homogenous ideas, fostering original contributions.
- Beyond Originality: Future LLM applications could focus on predicting 'impact' and 'relevance' of ideas, not just originality, using real-world metrics.
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