How Behavioral Science Can Improve the Return on AI Investments

Many AI projects fail because leaders treat adoption as a tech purchase instead of a behavioral change problem. People resist tools that disrupt routines, overreact to visible AI errors, and prefer familiar human judgment. As a result, even good systems fail to gain purchase. Leaders can address this problem by applying “Behavioral Human-Centered AI” across the AI adoption cycle. In the design phrase, companies should co-design with diverse users, add purposeful friction where it improves scrutiny, require beta tests with subgroup results and behavioral input. During adoption, they should frame AI as an augmenter, disclose limits and safeguards, use explainability to boost perceived control. During the management phase, they need to educate leadership, model use, track people-centric KPIs (trust, fairness, effort, opt-in usage), run disciplined pilots, and course-correct or quickly kill them. The outcome of this approach is higher trust, faster uptake, real ROI.