Why Your AI Governance Committee Needs a Confirmed Inventory Before Approving New Use Cases
Should your governance committee be approving new AI use cases without confirming what the current ones are doing?
An approximate inventory produces approximate governance - and approximate governance is what slows every deployment decision that follows.
The Situation
Most organizations using AI today have the structural governance foundation in place: a committee, an acceptable use policy, and a working sense of which tools are in use. What they don't have is a complete inventory, mapped to data access, a business owner, and oversight level. A tested inventory is what turns a governance committee into a fast-approval mechanism: without it, every new use case request requires estimating the deployment landscape before the committee can evaluate where the request fits and what governance it needs.
The Exposure
The Grant Thornton 2026 AI Impact Survey found 78% of leaders lack full confidence their organization could pass an independent AI governance audit within 90 days. That number largely reflects the inventory gap: without a central record of which AI systems are running, what data each one accesses, and what outputs they're producing, governance decisions rest on informed guesses rather than documented facts. McKinsey's The State of AI in 2025 found 88% of firms already use AI, most of it deployed before there was a formally built inventory. The decisions your governance committee is making right now are being made against a picture of AI deployment that no one has fully confirmed.
The Judgment Call
Companies are hesitating to initiate the work of building a complete inventory because the AI governance model is almost always still being established, and very few have a formal Head of AI role or defined responsibility yet. Most companies I speak with now are in the process of filling that role, but it’s slow going because it’s rare to find AI expertise combined with business knowledge. As a result, it’s easier to wait on the inventory until the role is staffed, and commissioning an inventory before that risks producing a deliverable that the new leader won’t buy in to, or that they feel they have to re-validate before they can use it. However, that instinct trades program speed for a clean handoff. Every organization wants to deploy AI quickly to deliver value faster, and deployments accelerate when the governance is already in place. If the position isn’t filled yet, then the right person to commission the baseline inventory is the executive the Head of AI role will report to. That executive is already invested in the AI program's outcomes, and incoming candidates are more likely to treat it as a fact than as a debatable proposition if their executive management signed off on it.
Risk: Building the AI inventory requires a full review of existing deployments, which may be perceived organizationally as a waste of time, resulting in AI deployment delays instead of acceleration.
Benefit: A complete and confirmed inventory enables the approval and deployment of new use cases faster, with the business benefiting from AI sooner.
This Week’s Action
What to do: Task the Head of AI, or if that role is currently unfilled, the hiring executive, with commissioning a definitive AI use case inventory.
Who to involve: Your CAE, CCO, or COO to lead the inventory work, under the formal direction of whoever holds accountability for the AI program - the Head of AI if the role is filled, or the commissioning executive if it isn't.
What outcome to achieve: A documented baseline inventory that can be used and acted on immediately by the governance committee when evaluating use case performance and approvals.
Time required: 45 minutes with IT and Procurement to initiate the tool and SaaS review, assign a compiler, and set the scope; then 60 minutes two weeks later with the AI leader, CAE, or CCO to review it, complete the ownership and oversight columns, and flag gaps requiring immediate remediation.
Artifact
Create one use case card for each AI system currently active, including approved platforms and AI features embedded in approved SaaS tools with the following:
Section 1 - What's Running
1. Tool or feature name:
2. Business owner (named individual, not department):
3. Data this AI can access - confirmed with vendor, not assumed:
4. Approximate user count and primary use case:
5. Is human review required before the AI's output is acted on? YES / NO / DEPENDS
Section 2 - What's Governed
6. Was this system or feature approved through a formal intake process? YES / NO
a. If YES: date approved and name of approving authority:
b. If NO: is there a remediation plan? YES / NO
7. If a SaaS tool, does the current vendor contract cover this specific AI feature by name? YES / NO / UNKNOWN
Section 3 - Highest Priority
For the single highest-risk AI system:
8. What is the worst realistic output failure: incorrect, biased, or harmful?
9. Who is personally accountable when it occurs?
10. Does an AI incident response plan exist that covers this system? YES / NO
Any use case card with three or more NO or UNKNOWN answers in Sections 1 or 2 is a first-week priority. Any use case card with a NO in question 10 is a day-one escalation.
When the stakes exceed your internal capacity:
AI Exposure Diagnostic: A 2-hour strategic evaluation for risk, compliance, and legal leaders to identify your highest-priority governance gaps and deliver a 90-day remediation roadmap.
12-Week Governance Sprint: Translate regulatory requirements into audit-ready policies, control frameworks, and accountability structures.
Ongoing Advisory Retainer: Embedded judgment for policy updates, vendor assessments, and board prep as regulations and technology evolve.
Reply with "Diagnostic" or “Sprint” to schedule a conversation for next month.
Chris Cook writes Judgment Call weekly for compliance and risk officers navigating AI governance.
Former IBM Vice President and Deputy Chief Auditor. Published in the AI Journal, speaker at Yale.
Chris Cook
Managing Partner & Founder
Blackbox Zero
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