Technology Committee
Enterprise AI strategy. Reference architectures. Platform economics and model routing. Agentic-system reliability. The operating model that turns AI investment into measurable capability. I have architected and operate governed AI platforms at global public-company scale and am a named inventor on two granted U.S. patents.
I can evaluate technology decisions at the depth an engineering team makes them and at the altitude a board must defend them. The translation between those two altitudes is the single most frequent failure mode in board-level AI conversations — and it is exactly the translation I make every week in my operating role.
Audit & Risk Committee
AI governance design. Model risk management. Evidentiary traceability and provenance of agentic outputs. Third-party AI risk. The control environment for AI-assisted decisions. I have worked inside the audit stack for regulated enterprises. My research program includes dedicated papers on evidence blocks and a governed lifecycle for agentic engineering work.
I can read an AI control narrative the way an auditor reads a control narrative, and I can translate that between engineering teams and the committee without losing either audience. That is the voice audit and risk committees need in 2026, and it is rare.
Cybersecurity & Data Committee
Private AI for regulated environments. Data sovereignty. Master Data Management as a control surface, not just a reporting input. Identity and access in agentic systems. The architecture of trust for AI that touches customer data. My Northrop Grumman tenure — Northrop Grumman Fellow, Chief Enterprise Architect, Chief Data Scientist, with Top Secret clearance during that era — was spent inside security-first environments. That posture is not a style I adopted; it is the operating default I built from.
The boards most exposed to cyber and data risk benefit most from directors who know how to reason about security before they know how to reason about AI. I bring that ordering by construction.
Innovation Committee
How to separate experimentation from production at enterprise scale. How to build an intake model for AI that does not create shadow-AI chaos. How to operationalize responsible AI without stopping the business. How to allocate innovation budget against a coherent platform thesis rather than against a proliferation of pilots.
Every enterprise AI program I have run was designed against exactly those constraints. I can tell the difference between a pilot that will scale and a pilot that will die — at the pilot-proposal stage, before the committee funds it.
Compensation & Human Capital Committee
AI's effect on organizational design. Operating-model shifts between technology and business functions. The economics of augmented roles. The cultural operating system that makes AI adoption durable. I founded a 1,200+ member internal Superworker community inside a global public company and have written publicly on what separates programs that compound from programs that do not.
The compensation committees that will matter most this decade are the ones that can govern the re-wiring of human work. That is a conversation I can carry.