Seven Myths about AI and Productivity: What the Evidence Really Says

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.