AI Governance Workshop: An Agenda That Produces Decisions

An AI governance workshop is a working meeting where people agree what AI may do, which actions need approval, and who owns the next steps. The useful output is a small set of decisions that someone can apply to real work, with unresolved questions clearly separated.

Use the agenda below for a two-hour session about one workflow. Tools like fenbs keep the resulting rules and tasks outside the meeting chat, so connected assistants can read the same agreed boundaries afterward. Start with a process your team actually uses, rather than debating every possible use of AI.

What should you prepare before the workshop?

Send participants a one-page description of the workflow, a redacted example, and three decisions you need from the meeting. State the current problem: perhaps AI drafts customer replies, but nobody has agreed whether it may send them. Ask participants to bring one failure they worry about and one benefit they want to preserve.

Invite the workflow owner, someone doing the work, someone reviewing its outputs, and the person who can approve the boundaries. Add security, privacy, or legal expertise when the workflow needs it. One person facilitates; another records the exact decisions. Do not make the busiest speaker the approver by default.

• Choose a specific start and end: for example, from receiving a customer question to sending the reply.
• Use fictional or safely redacted material for exercises; do not circulate real confidential records as meeting examples.
• Ask the approver to confirm which decisions they can make and which need another person.
• Agree that an unanswered question leaves the relevant action paused until someone resolves it.

The NIST AI Risk Management Framework↗ (opens in a new tab) is a voluntary reference for managing AI risks. You can use its concerns to shape questions without presenting this workshop as a NIST assessment or a compliance approval.

What does a useful two-hour agenda look like?

Keep the same example on screen throughout the meeting. A broad conversation becomes easier to settle when every proposed boundary must work for the same request, input, and output. The schedule below is a suggested facilitation plan; adjust the time when participants need specialist advice.

Ask for quiet individual notes before discussion. This gives the person checking outputs space to raise a problem before the team settles around the first proposal. Put interesting questions outside the scope in a separate list, with no implication that they are approved.

For guidance on day-to-day work before the meeting, share how to use AI at work. Keep that background reading out of the decision time.

How do you turn a vague concern into a usable rule?

Replace broad words with a trigger, an action, and an accountable person. “Be careful with customer data” does not tell someone what to do. “Use only the approved sample records in this trial; the workflow owner must approve any change to the input source” gives them a boundary they can follow.

Question to settle:
Workflow and scope:
Decision: permitted / approval required / paused
Exact boundary:
Person who decided:
Reason and evidence considered:
How the boundary will be implemented:
Owner and next step:
Review date or change trigger:
Unanswered questions:

Keep implementation separate from agreement. If the group requires approval before sending a reply, assign someone to check that the sending system actually supports that restriction. A statement in meeting notes cannot stop software from sending. Record the rule now and keep the workflow paused if the required control is missing.

The NIST Playbook Govern guidance↗ (opens in a new tab) discusses documented responsibilities and review processes. Use that as a prompt to ask who can check, challenge, and stop the proposed workflow. In fenbs, Decisions and rules give the agreed boundary a shared home.

How should the group test its decisions?

Say a small home-goods retailer wants AI to draft answers to shipping questions. In the first hypothetical scenario, the approved order information shows that a package shipped yesterday. Ask each participant to describe what the assistant may draft, what the reviewer must check, and who sends the answer.

In the second scenario, the customer requests a refund and the order information is incomplete. A useful boundary might allow a draft asking for missing information while requiring a person to decide any refund. The assistant must not treat missing information as evidence that the customer qualifies or fails to qualify.

1. Give the scenario to the group without explaining your preferred answer.
2. Ask the person doing the work to apply the proposed rule step by step.
3. Ask the reviewer to identify the evidence they would need before approving the output.
4. Revise any rule that produces conflicting answers or depends on information the team cannot obtain.

Write the expected outcome beside each scenario. Repeat the exercise after a rule changes. The point is to expose ambiguity before the rule becomes an instruction that different people and assistants interpret differently.

What happens when people cannot agree?

Capture disagreement as an open decision, with the competing options and the missing evidence. Give someone the next action: obtain the relevant vendor documentation, ask the appropriate specialist, or run a bounded test. Assign a date for returning to the question and state what remains paused meanwhile.

Do not ask an assistant to break the tie. It can summarize options and record what a person decides. It cannot supply the authority the meeting lacks. For broader follow-through, use AI governance training; this session should end with concrete choices rather than becoming a general course.

How to do this with fenbs

1. Create a free account and add a project for the workflow you are reviewing.
2. Record the decisions people made, naming the decider. Mark continuing instructions as rules and leave unresolved proposals open.
3. Add follow-up tasks for implementing and checking each boundary. Include the owner, next step, and evidence needed to call the work finished.
4. Connect a supported assistant through Integrations, then ask it to read the context before preparing follow-up work.
5. Save progress before stopping, including what is waiting on a person and where the next session should begin.

fenbs does not enforce restrictions inside your customer-service system or certify that a workflow is safe. If you need that enforcement, configure it in the system that takes the action. fenbs keeps the shared work record; people still approve decisions and verify implementation.

How do you know the workshop produced useful results?

Ask a colleague who missed the meeting to apply one rule to a fresh example. They should identify the permitted action, approval point, owner, and unresolved limitation without asking someone to reconstruct the discussion. If they cannot, clarify the record while the participants still remember the reasoning.

Schedule a short review after the first real use. Compare what happened with the expected scenario outcomes, and turn missing checks into tasks. The guide to AI agent governance for small teams covers the ongoing boundaries; the AI governance hub connects the wider topics.