From Blind Spot to Behavioral Clarity: AI Adoption Masterclass
Eugene Chan
PhD behavioral scientist for AI.
Why Your AI Adoption Numbers Lie — and What to Do About It
Your organization has deployed AI tools. The dashboard looks fine. And yet — decisions aren’t improving, workarounds are multiplying, and the productivity gains your implementation promised haven’t fully materialized.
The problem isn’t the technology. It’s the behavior happening around it.
AI deployments fail not because tools are inadequate, but because they were designed for technical integration, not behavioral integration. Employees override recommendations without documentation. Parallel systems emerge outside IT’s visibility. Scrutiny of AI outputs drops precisely as output quality improves. Accountability for AI-assisted decisions disperses across so many parties that no one feels genuinely responsible for the outcome.
These patterns are predictable. They are grounded in decades of behavioral science research. And they are almost entirely invisible to standard adoption monitoring.
This masterclass gives you the diagnostic vocabulary, the research grounding, and the intervention framework to see what your dashboards can’t — and to actually fix it. You will leave with a behavioral profile of your own deployment and a concrete playbook for addressing the failure modes it reveals.
What you’ll learn
Master the behavioral mechanisms behind AI adoption failure — and gain the diagnostic and intervention tools to fix what your metrics can’t
Learn the psychological mechanism behind each failure mode, grounded in peer-reviewed research
Distinguish behavioral failure from technical failure — and why standard metrics conflate the two
Apply precise diagnostic language to patterns you have already observed in your own organization
Complete a proprietary 10-item behavioral diagnostic instrument live during the session
Interpret your failure mode profile and identify which modes represent your highest-priority risks
Understand what drives variation in profiles across deployment contexts and industries
Walk through a mode-by-mode intervention playbook grounded in choice architecture and behavioral design
Learn which intervention levers are most effective for each failure mode and why
Distinguish high-leverage from low-leverage interventions so you prioritize correctly
Understand why AI deployments structurally diffuse responsibility and how to reverse it
Apply principles of behavioral accountability design to your own governance structure
Learn what explicit, personally legible accountability looks like in practice
Move beyond usage metrics to behavioral indicators that track genuine adoption quality
Learn a framework for connecting behavioral failure mode reduction to measurable business outcomes
Design a monitoring approach that surfaces behavioral risk before it compounds
Translate behavioral science findings into language that resonates with senior stakeholders
Frame governance proposals around risk reduction, not compliance
Leave with a vocabulary that positions you as the person in the room who understands the human side of AI
Workshop agenda
Why Your Adoption Metrics Are Lying to You
Usage rates, completion scores, and satisfaction surveys measure interaction — not integration. We establish what genuine AI adoption looks like behaviorally and why standard dashboards miss it.
The Five Behavioral Failure Modes
A deep dive into Override Drift, Shadow AI Formation, Workaround Economics, Automation Bias, and Accountability Drift — mechanism, research evidence, and real-world signature for each.
Live Diagnostic: Your Behavioral Failure Mode Profile
Participants complete the 10-item behavioral diagnostic instrument in real time and learn to interpret their organizational profile across the five failure mode dimensions.
The Intervention Playbook: Mode by Mode
For each failure mode: the behavioral levers that work, the ones that don’t, and how to sequence interventions for maximum effect using choice architecture and behavioral design principles.
Accountability Architecture and Governance Design
How to build decision structures that keep responsibility personal, visible, and actionable as AI takes on more of the reasoning load in organizational decisions.
Measuring Behavioral ROI and Making the Internal Case
A framework for tracking genuine adoption quality, connecting behavioral improvement to business outcomes, and translating findings into governance proposals that senior stakeholders will act on.
Learn directly from Eugene
Eugene Chan
Full Professor and founder of Behavieural. 90+ papers on AI trust and adoption.
Who this workshop is for
You’re responsible for AI tool rollout and adoption — and you’re the first to hear when something isn’t working but can’t explain why.
You own the deployment. You’ve hit the usage targets. Now you need to understand why the promised performance gains aren’t showing up.
You’re building the case internally for AI investment — and you need the behavioral evidence to anticipate failure and protect the ROI.
What's included
Live sessions
Learn directly from Eugene Chan in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Frequently asked questions
Maven for Teams
Reimbursement
Get your company to pay
Everything L&D needs: email template, receipts, and certificate of completion.
Get reimbursedTeam discount
Learn with your teammates
Save 20%+ when 2 or more teammates enroll in the same cohort.
Save 20%+ with a teamPrivate cohort
Run a cohort for your org
A dedicated cohort with a custom schedule and curriculum, tailored to your team.
Book a private cohort$247
USD
1–3:30pm EST
·2 cohorts