Eugene Chan

I%27m a behavioral scientist and Full Professor (Van Norman Chair of Business) at Tyndale University, with an academic record of 90+ peer-reviewed publications and an h-index of 31. Over the past several years I%27ve moved that research into commercial application through Behavieural, my consultancy, where I built the AI Trust Axis — a diagnostic framework measuring how organizations earn or lose trust in AI deployment across five dimensions: cognitive alignment, autonomy safety, fairness comprehension, interaction effort, and failure recovery intelligence — along with a broader behavioral governance practice (B-GRIT) that treats institutional design, not policy language, as the actual lever for changing how humans behave around AI systems.

What I bring to a board or advisory seat is the ability to translate between two languages that rarely understand each other: the technical and regulatory vocabulary of AI deployment, and the behavioral reality of how employees, customers, and regulators actually respond to it. Most governance and trust failures in AI-driven companies aren%27t technical — they%27re behavioral: automation bias, adoption stalls, credibility gaps with enterprise buyers, and misjudged trust signals with end users. I diagnose those mechanisms with the same rigor I%27d apply to a peer-reviewed study, and I%27ve built proprietary tools to measure them rather than relying on frameworks borrowed from adjacent fields.

I%27m best suited to companies at the intersection of AI and trust-sensitive deployment — health tech and health systems, financial services, insurance, legal, and other regulated or high-stakes verticals where the gap between %22the model works%22 and %22people will actually adopt and trust it%22 is the thing standing between a product and its market.