Guide
Generative AI governance for the enterprise project manager
Frameworks, guardrails, and the PM’s role in scaling AI responsibly.
For project managers and leaders responsible for how AI is used on their projects and teams.
Guide · 17 pages · PDF
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What’s inside
- Why AI governance is the PM's job now
- Copyright: training, output, and ownership
- The regulatory landscape: executive orders, state laws, and the EU AI Act
- Security: AI as the tool, AI as the attacker
- Frameworks and standards, and where the PM fits
- Your AI policy: eight components and five human checkpoints
- Tokens, cost, and agentics
- Making it stick: an intake card and your governance audit
The five-question AI intake card
Before any project where AI touches the work, answer these in writing.
- Data. What data class does this touch? Is any of it leaving our tenant?
- Source. Is the AI working from our own documents and code, or pulling from the open internet?
- Decision. Does the output affect hiring, credit, benefits, safety, or enforcement?
- Review. Who is the named human reviewer, and where does that step sit on the schedule?
- Cost. What is the token budget for the pilot, and the projected burn at production volume?
Legal and regulatory details are current to late September 2026. They change quickly; check primary sources before relying on them.