Trisociation is a creativity technique that combines three disparate concepts to generate novel, coherent ideas — extending Arthur Koestler's classic bisociation method by adding a third element. While the technique has existed in theory since Victor Papanek referenced it in Design for the Real World, it remained largely obscure due to the cognitive overload it imposes on human thinkers. The article argues that Large Language Models like ChatGPT and Claude have unlocked trisociation's potential, as they can seamlessly process and blend three unrelated concepts simultaneously without the cognitive biases or mental fatigue that limit human ideation.
The authors distinguish between open-ended trisociation (combining three random concepts for maximum novelty) and focused trisociation (anchoring one or two concepts to a specific domain like a product category). They demonstrate how the strategic selection of related versus unrelated concepts allows businesses to calibrate the balance between novelty and feasibility — choosing all unrelated terms yields breakthrough but potentially impractical ideas, while related terms produce incremental but commercially viable innovations. Practical applications span new venture ideation, product concept development, and attention-grabbing visual advertising, illustrated with examples like origami-inspired digital wellness platforms and bear-themed pizza ads.
Notably, companies like Ipsos and HYPE Innovation are already integrating trisociation into their creative workflows. The authors provide concrete implementation guidelines: define goals first, select concepts strategically, use AI outputs as raw starting points for human refinement, and maintain curated concept databases. The piece draws on 22 months of research and teaching across more than 10 organizations in Asia and North America.
This California Management Review article by Dritjon Gruda and Brad Aeon systematically debunks seven common myths about AI and productivity using evidence from recent meta-analyses and systematic reviews rather than individual studies or vendor claims. The authors challenge the assumption that AI is a universal productivity enhancer, showing that gains are highly context-dependent — benefiting junior workers far more than experts, and often introducing quality regressions that offset speed improvements. Perhaps most strikingly, a meta-analysis of 106 experiments found that human-AI collaboration frequently underperforms either agent working independently, except in open-ended creative tasks like brainstorming.
The article also addresses AI's impact on creativity, finding that while AI-augmented humans achieve modestly higher novelty scores, this comes at the cost of dramatic declines in idea diversity — a homogenization effect that undermines innovation. At the macroeconomic level, a meta-analysis of 371 estimates found no robust relationship between AI adoption and aggregate productivity gains once methodological factors are controlled, echoing a modern version of the Solow paradox. Additional myths around automation bias, where high-reliability AI systems actually increase certain types of human error by 12%, and the stress paradox, where AI creates new forms of technostress rather than reducing workplace burden, round out the analysis.
The authors conclude with practical guidance: organizations should conduct evidence audits with task-specific metrics, implement a "creation versus evaluation" matrix for task triage, deploy adaptive governance structures with confidence disclosures, and maintain an equity lens to track distributional impacts across skill levels. The core message is that AI's productivity dividend is real but narrow — it materializes only for specific users, specific tasks, and specific workflow designs, demanding far more analytical rigor than most organizations currently apply.
