Organizations bring me in when an AI initiative is too consequential to improvise — when the system will write to systems of record, operate inside regulated workflows, or act where failure carries consequences beyond the enterprise.
I work the seam where complex AI initiatives actually fail: between executive intent and engineering reality. The frameworks I publish — Human-in-Command, the MV-HIC evidence standard, and the operating models for AI governance boards and AI Centers of Excellence — are the same ones I implement.
Engagements
AI Initiative Leadership
End-to-end leadership of complex AI programs: strategy, data architecture, governance, and delivery. For organizations standing up a VP of AI mandate or a CAIO charter that need the function built before — or instead of — the full-time hire.
Governance Architecture
Design and stand-up of the structures that make AI adoption defensible at scale: governance boards that operate as clearance systems, Centers of Excellence that operate as transformation engines, and Human-in-Command control planes for agentic systems.
Executive Advisory
Standing counsel for COOs, division presidents, and Chief AI Officers navigating the agentic transition — AI procurement against the MV-HIC evidence standard, regulatory readiness, and the institutional design of human command authority.
How I Work
Every engagement begins with structure: decision rights, evidence standards, and testable criteria for done. Governance is not a brake on adoption — it is the architecture that makes adoption defensible at scale, and defensibility is what lets organizations move quickly without betting the enterprise.
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