Turn risk into a decision
Define the use, consequence, evidence, accountable owner, and the condition that changes what may ship.
About
Regulated-program leadership, applied to AI
I have spent 25 years leading consequential work where licenses, safety, privacy, budgets, and operating continuity depended on decisions that could be explained and documented.
Since July 2023, I have applied that operating discipline directly to AI systems: 35 built, 25 in production, and three in the care sector. I work from intended use and authority through evaluation, release, monitoring, incident response, and change review.
The bridge
Direct AI operating work plus long regulated-program leadership
My AI governance work is owner-side. My earlier leadership spans financial services, state-licensed operations, environmental and safety programs, commercial delivery, and multidisciplinary remediation. The common work is making authority explicit, separating facts from preferences, assigning evidence and owners, and recording a decision that survives the meeting.
Healthcare is personal to me. I grew up around hospital administration and I have been a family caregiver for years. I bring that motivation with respect for the clinical, legal, privacy, security, compliance, technical, and operational functions that own their judgments.
Transferable experience
Facilitation
One decision record for competing obligations
I led multidisciplinary regulated programs across legal, licensing, environmental, engineering, contractor, and operating constraints. I converted competing requirements into a shared view of evidence, owner, dependency, cost, schedule effect, and approval authority, then used that record to sequence work and move specialist decisions to the people who owned them. The programs included 10-plus-person crews and one remediation project delivered on budget. The transferable method is direct: one decision record, explicit authority, visible open questions, and no requirement disappearing between meetings.
Working method
Five practices that make a decision usable
Make policy reach the system
Trace a material rule to its control, test, evidence, exception path, and next review trigger.
Keep expert authority explicit
Clinical, legal, privacy, security, compliance, technical, and operational judgments stay with their named owners.
Watch for action, not activity
A monitored signal names its bound, owner, first response, escalation path, and condition for reopening the decision.
Build controls teams can keep using
Risk-based review depth, reusable evidence, and automated completeness checks protect judgment from avoidable queue work.
The problems I want next
High-consequence AI that needs an operating system
I am drawn to mature teams working through review throughput, local validation, lifecycle evidence, agentic observability, decision rights, and the practical friction that determines whether governance survives delivery pressure.
Next step