Abstract:
The hardest problem in enterprise AI right now is not model performance. It is the last mile between a successful pilot and a workforce that actually uses the technology with confidence. In regulated industries such as healthcare, legal, and finance, most generative AI pilots never reach production. The usual explanation is technology readiness. My field experience says the real culprits are fear, unclear governance, and training designed for engineers instead of the people who do the work.
Over the past two years, I have trained more than 500 non-technical professionals: executives and radiologists at a national teleradiology practice, nonprofit teams, rural healthcare cooperatives, and small business owners through a 26-session generative AI accelerator delivered in partnership with the Minnesota Department of Employment and Economic Development. Same tools, wildly different adoption outcomes. This case study session shares what separated the organizations that scaled from the ones that stalled.
I will walk through three frameworks born from that work: a maturity model for diagnosing where an organization sits on the path from AI chaos to AI capability, a plain-language data audit method that surfaces the outdated and duplicative data that quietly sabotages AI initiatives, and a governance structure sized for organizations that do not have a Chief AI Officer and never will. I will also share what failed, including training formats that produced enthusiasm in the room and zero behavior change afterward.
Audience:
This session is for business leaders, AI program owners, and practitioners responsible for driving adoption inside their organizations, especially in regulated or risk-averse environments. No technical background required.
Takeaways:
A diagnostic they can run on their own organization in under an hour to locate their real adoption blockers,
a governance starter structure appropriate for mid-market and regulated organizations, not just the Fortune 500, and
training design principles that convert non-technical skeptics into confident daily users, so teams operate with confidence instead of crossed fingers.
Register here: https://sites.google.com/umn.edu/2026-aihub-wiads/home?authuser=0