AI Workforce Governance Checklist

Decision context

The right design depends on the kind of decision being made and the operating environment around it. Use both perspectives before selecting tools or expanding permissions.

A checklist is a verification aid, not evidence that a control works. For each item, distinguish documented intent, manual practice, technical enforcement and tested effectiveness. A checked box without an owner, runtime proof or recent test should remain an open risk in the purchase or pilot decision.

For an AI workforce, the unit of design is the business outcome rather than the individual prompt. Roles need distinct responsibilities, tools and limits, while a human owner retains authority over the goal. Evaluate the trace across roles so locally good outputs do not hide a poor end-to-end result.

Scope and boundaries

Use these boundaries before deciding how much work an agent may own:

• Every consequential action has a named human authority.
• Approval applies to the exact action and content reviewed.
• Missing policy, evidence or provider confirmation stops execution.

Evaluation criteria

A useful evaluation separates outcome quality from the controls that make the result safe to use:

1. 01

   Goal and accountable owner are documented.

   Ask what evidence supports this criterion, who owns it and how often it is reviewed.
2. 02

   Agent and user identities are attributable.

   Define an acceptance threshold before the pilot so a persuasive example cannot move the goalposts.
3. 03

   Data and tool scopes follow least privilege.

   Include exceptions and rejected outputs; they show the real review and recovery cost.
4. 04

   High-impact actions have explicit approval rules.

   Record the decision and rationale so a later scope change can be evaluated against the same baseline.
5. 05

   Budgets, quotas and stop conditions are enforceable.

   Ask what evidence supports this criterion, who owns it and how often it is reviewed.
6. 06

   Audit records connect sources, decisions and receipts.

   Define an acceptance threshold before the pilot so a persuasive example cannot move the goalposts.

Implementation sequence

Move from a narrow, observable starting point to broader responsibility only when evidence supports it:

1. 1

   Score each control as absent, manual, enforced or verified.Retain the baseline, owner and approved scope.

2. 2

   Prioritize identity and execution boundaries before adding autonomy.Keep source references and the policy version used.

3. 3

   Run tabletop tests for expired approval and provider failure.Record validation results, exceptions and corrections.

4. 4

   Assign remediation owners and dates.Bind any human decision to the exact proposed action.

5. 5

   Repeat the review whenever scope or integrations change.Verify the final state and attach provider evidence.

Failure modes to test

Test the negative path deliberately. These patterns usually reveal a weak operating model:

• Treating a policy document as proof of runtime enforcement.
• Recording model output without the source or decision context.
• Letting temporary pilot exceptions become permanent defaults.