Surveys & Reports
Original evidence on how people actually use, trust and oversee AI.
Behavieural runs original surveys of professionals who work with AI and publishes the findings, so organizations can see where leaders and users diverge and what shapes real adoption.
The white papers argue. The frameworks measure. The reports show what the data says: findings from original surveys, with the methods and limitations stated.
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The Adoption Gap
Psychological factors predicted AI use more strongly than any other category tested.
At a glance: 523 professionals · 3 sectors · October 2026
The study. An original survey of professionals who use AI at work in healthcare, finance and insurance, and manufacturing and operations. It tests what best predicts how often people use AI and whether they intend to keep using it, across five categories of explanation: psychological factors, technical quality, incentives and culture, regulatory and compliance concerns, and change management.
What it found. Taken together, psychological factors were the strongest predictor of AI use and intent to continue, roughly twice as strong as technical quality or incentives and culture. The same pattern held in each of the three sectors. Technical quality and incentives and culture mattered too, with the mix varying by sector. Regulatory concerns were borderline, and change management showed no significant independent effect once the other factors were accounted for.
What it means. Adoption programs built around training, change communication and technical fixes may leave the largest driver untouched. The report maps each finding to the Behavieural engagement designed to address it.
What the data can and can't show. Respondents are current AI users, so the findings say nothing about people who have never adopted. The design is cross-sectional and self-reported, so it shows association, not cause. Data were collected in waves, with results checked as the sample grew, and the report discusses what that does and doesn't mean for the conclusions. It also tests whether differences in the number of items per category could explain the result.
Who it's for. Leaders of AI programs, transformation and change teams, and product and operations leaders trying to understand why deployed tools aren't used.
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The Confidence Gap
Leadership thinks AI oversight is working. The people actually using it disagree by up to 30 points.
At a glance: 770 professionals · 3 sectors · September 2026
The study. An original survey of professionals involved in evaluating or overseeing AI in healthcare, finance and insurance, and manufacturing and operations. It compares how managers and executives see AI oversight with how individual contributors, who work with the systems daily, experience it.
What's inside. The study design, the findings and what they mean for oversight. Each finding is mapped to the Behavieural engagement designed to address it.
Who it's for. Executives, risk and governance leaders and operational teams who rely on reports from the top of the organization about how AI is performing.