AI pilots need governance answers before they can scale
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AI pilots often stall because organisations cannot show what they reach, who owns them or what happens when access goes wrong.
TL;DR
AI pilots stall at scale when security and risk teams cannot answer what each system can reach, who owns it, or what happens if it touches something it should not. The source recommends one cross-provider inventory covering users, costs, access and named ownership, distinguishing production systems from pilots.
Original by James Westlake, on Rencore. Read the original
This is our own summary, not a republication or full translation.
Governance takeaway
- Makers: Record the system owner and intended users for each AI pilot so responsibility does not disappear when the pilot expands.
- Admins/CoE: Build an inventory across providers showing usage, cost, ownership and reachable content or systems, because provider consoles reveal only part of the estate.
- Security/Compliance: Require evidence for reach, ownership and unintended access before approving scale; an unanswered question can keep a working pilot from production.
- Leadership/Business: Count active production use and stalled pilots against business-case assumptions, because renewing low-use pilots obscures value and weakens future funding.
Frequently asked questions
Which three questions stop AI pilots from moving into production?
People ask what the system can reach, who owns it and what happens if it touches something it should not.
What should an AI inventory record for each system?
Four things: the number of active users, the cost, the content and systems it can reach, and a named owner.
Why do Microsoft Purview and Entra not give a complete AI overview on their own?
Purview classifies and protects data across Microsoft 365 and audits Microsoft 365 Copilot interactions, but its reach into non-Microsoft services depends on connectors.