Human–AI synergy: finding cognitive balance in idea generation for product innovation

This study examines how innovators and AI work together during idea generation for product innovation. It examines how varying levels of reliance on AI impact cognitive engagement and, in turn, influence the quantity, originality and feasibility of ideas as well as innovators’ overconfidence. The study highlights AI’s role as a cognitive amplifier, showing how human intuition and AI's analytical power interact to support creativity and innovation. A controlled experiment was conducted with 123 product innovators, testing three conditions: no AI, moderate AI assistance and high AI assistance, to measure cognitive engagement, number of ideas generated, originality, feasibility and innovator overconfidence. ANOVA, polynomial regression and mediation tests were performed to determine the effects of AI assistance on innovative idea generation. The results reveal an inverted U relationship between AI assistance, cognitive engagement and the generation of ideas for product innovation. Moderate AI assistance optimally enhances cognitive engagement, producing the highest number of original and feasible ideas. In contrast, excessive AI assistance may foster automation bias, reducing originality and increasing overconfidence. At the same time, the absence of AI constrains idea generation due to cognitive limitations in relying only on human abilities. The findings show that moderate AI use maximizes the quantity, originality and feasibility of ideas while minimizing overconfidence. Innovation managers should structure ideation sessions to cap AI interactions, promote critical evaluation of AI outputs and combine them with human insight. This balanced approach enables firms to optimize cognitive engagement and generate higher-quality product innovations. This research uniquely contributes to product innovation literature by explicitly focusing on human–AI synergy, highlighting AI’s optimal role as a cognitive enhancer rather than a substitute. It elucidates conditions that maximize innovative outcomes through balanced human–AI collaboration, providing actionable managerial guidelines for structuring AI integration to amplify creativity and mitigate biases in idea generation for product innovation.