SIGNATURE TALKSTopics I Speak On
Governing AI at Enterprise Scale
What does it actually take to build governance infrastructure inside a large organization? The gap between framework theory (NIST AI RMF, EU AI Act) and operational reality, drawn from experience building AI review boards and accountability frameworks across Fortune 10 organizations.
The Builder-Gatekeeper Gap
The most expensive problem in enterprise AI isn't a bad model. It's the disconnect between the teams building AI and the teams responsible for legal, compliance, and risk oversight. Names the gap, explains why it persists, and walks through the practical structures that close it.
AI Governance for Higher Education
Research universities operate some of the most complex data environments in the country. Between academic publishing, student lifecycle, research data, alumni networks, and institutional operations, the governance challenge is unlike any other sector.
From AI Pilot to AI Program
Many organizations have successful AI pilots. Far fewer have successful AI programs. The difference is operational infrastructure. Intake processes, prioritization frameworks, stakeholder coordination, and accountability structures.
What I Built: Intelligent Automation in Practice
A live walkthrough of the automation systems I've designed and deployed, from market intelligence pipelines to content production dispatchers to Slack approval workflows. Equal parts inspiration and instruction.
PAST EXPERIENCEWhere the Work Has Taken Me
My professional experience includes AI program leadership and governance work at Harvard Business School, Harvard Business Publishing, McKesson, Meta, and Carrier. I’ve operated at the intersection of AI build teams, executive leadership, and compliance and risk functions across higher education, healthcare data operations, consumer technology, and Fortune 500 industrial organizations.
I speak from the perspective of someone who has sat in the rooms where these decisions get made. Not someone who has read about them.
