Weekly AI insights: research, frameworks, and practices for purposeful, responsible use.
AI-human collaboration boosts accuracy today but quietly erodes the skills it depends on tomorrow. Research shows smart task allocation can add 20 accuracy points while reducing human judgement through lost practice, leaving no one capable when AI fails.
AI systems fail not because of poor models, but because organizations ignore how humans think under real conditions. Studies show identical AI can boost performance by 40% or cut it by 20%, and time pressure halves our ability to catch AI errors while confidence rises.
AI success hinges on unconscious habits, not tools. Studies of consultants and organizations show that how people argue with, delegate to, or defer to AI determines learning, accuracy, and adoption—while anxiety silently shapes outcomes leaders never measure.
AI training fails because we measure activity, not thinking. Studies of designers and students show clicks, usage, and AI knowledge don’t predict better judgment. Transformation happens between stimulus and action—and today’s metrics can’t see it.
AI rollouts fail not because of technology, but psychology. Research shows that mismatched AI personalities degrade performance and managers penalize workers for AI use even when quality holds. Ignoring collaboration dynamics and credit attribution turns AI adoption into hidden dysfunction.