LANDMARK · 6 MIN · STEWARD
Building an AI inventory
After this landmark, you can describe why an AI inventory is the prerequisite to every other governance step, and what it needs to capture.
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This concept is shared, but the Everyday lens is less central here.
You can still explore it. We’re showing the shared explanation and a related practical view without hiding the knowledge.
Governance assumes visibility, and most organizations don’t have it: AI capabilities arrive embedded in existing software (a CRM’s new ‘smart’ feature, a spreadsheet tool’s AI column), adopted by individual teams without central review, or used informally by employees with personal accounts. An AI inventory is the exercise of finding and cataloguing all of it (what’s in use, by whom, on what data, for what decision), before any risk classification, procurement policy, or EU AI Act obligation can be meaningfully applied, because you cannot classify or govern a system you don’t know exists.
This is the canonical concept. It stays the same across learner lenses so personalization never changes the underlying facts.
◌ EVERYDAY LENS
What this looks like for you
If you use an AI feature at work that IT or leadership doesn’t know about, that’s a gap in the inventory; flagging it, even informally, closes a blind spot that governance depends on.
MAKE A DECISION
A company wants to start governing its AI use responsibly. What should come first?
CARRY THISFor your own team or organization, list every AI tool or feature you personally know is in use. Compare it against any official inventory that exists; is there a gap?
- Artificial Intelligence Risk Management Framework (AI RMF 1.0)National Institute of Standards and Technology (NIST) · 2023-01-26
First-pass citations, limited to primary sources; a reviewer will broaden and verify these before this entry leaves draft.