AI governance, operating models, and what actually holds up under examination. Published on LinkedIn.
Comprehensive visibility is not auditing for violations — it is mapping for strategic intelligence. Federal agencies recently discovered they had three to five times more AI than leadership believed. That visibility gap is strategic blindness with measurable bottom-line impact.
Decision authority in agentic systems. When an agent executes against money, the accountability question is not whether a policy existed — it is which named human authorised the decision, and whether that authorisation can be evidenced.
The institution does not choose when the examiner arrives, when a vendor ships an AI feature into a licensed platform, or when a model drifts out of intent. Those events set the rhythm — and a governance programme that ends is a programme that fails the second year.
If most of the Fortune 500 runs critical workflows on the same foundation model version, a single silent failure or bad update could cascade across industries. Model diversity is a business continuity question, not only a performance one.
A white paper on building AI governance that survives regulatory examination — inventory, consequence-based classification, accountable ownership, and the evidence trail examiners actually ask for.
Read the white paper →