An employee AI approval workflow should resolve ordinary uses through the published company policy and route the remaining cases to the person responsible for that particular concern.
The weak version is familiar: an employee emails legal or security, explains half the situation, waits for follow-up questions, and eventually receives a “looks fine” reply that nobody can find six months later.
The other extreme is a committee review for every prompt. That makes the process expensive enough to avoid.
I built the Can I Use AI? workflow around a simpler split. The policy handles repeatable decisions. People handle judgment, exceptions, and unclear facts.
Step 1: receive one specific proposed use
The requester describes the task, tool, account, information, intended impact, human review, frequency, owner, and region. One request should cover one stable use.
If the tool, data, purpose, audience, or level of automation changes materially, the employee should create another request. Otherwise an approval for a harmless draft can quietly expand into permission for a customer-facing automated process.
See what an AI-use request form should ask for the intake fields.
Step 2: apply the current policy
The workflow should apply the policy version currently in force. It may resolve the use directly, return safeguards, ask for more information, or require a person to review it.
This step should be deterministic. The same facts under the same policy version should produce the same route. The employee should also see why the answer was reached and what must happen next.
Low-risk work does not gain much from sitting in a review queue. If the policy allows an approved company account to summarise public material after human review, the workflow can return those safeguards immediately.
Step 3: route the reason for review
“Approval required” is incomplete unless it names the approver.
The route should follow the issue:
- Privacy receives uses involving personal or sensitive information.
- Security or IT receives unknown tools, personal accounts, integrations, credentials, or restricted systems.
- People or HR receives hiring, monitoring, performance, promotion, discipline, and other employment uses.
- Legal or compliance receives contractual restrictions, regulated decisions, public claims, rights, and external commitments.
- The policy owner receives gaps, conflicting rules, and work outside the policy’s coverage.
A small company may assign several routes to one person. Keeping the routes separate still shows why that person is reviewing the request.
Step 4: give the reviewer enough context
The reviewer should see the submitted facts, the policy result, the rule that triggered review, and any relevant comments or references. They should be able to ask a focused question without forcing the employee to restart.
The available decisions should be explicit:
- approve as submitted;
- approve with recorded conditions;
- request more information;
- reject the use;
- record a deliberate exception within the reviewer’s authority.
An exception deserves its own rationale. It should never edit the policy result into pretending the policy allowed the use automatically.
Step 5: preserve the whole decision
The finished case should retain the request, policy result, reviewer, rationale, conditions, time, and exact policy version. Later comments, evidence references, withdrawal, expiry, and reassessment should appear as events in the same history.
This creates two useful truths:
- what the policy concluded from the original facts;
- what an authorised person decided afterward.
Combining those into a single mutable status loses the distinction between policy automation and human judgment.
Step 6: give approvals boundaries
An approval should say what it covers. Useful boundaries include the tool and account, information class, team, purpose, audience, frequency, conditions, and review date.
Some approvals can remain valid until a material fact changes. Others should expire after a pilot or scheduled review. The organisation should define those triggers before the approval queue fills up.
Step 7: learn from repeated reviews
The review queue shows where the policy is weak. If the same safe use repeatedly reaches the same reviewer, the policy may be able to resolve it with safeguards. If people repeatedly misclassify information, the examples may need rewriting.
That analysis should focus on policy gaps and bottlenecks. It should avoid turning ordinary governance into a record of employee productivity.
What the employee should experience
From the requester’s side, the process should remain small:
- Describe the proposed use.
- Receive an immediate policy answer or a named review route.
- Answer any follow-up question.
- See the final decision and conditions.
- Run a new check when the use changes.
Can I Use AI? provides that request, policy assessment, review queue, decision history, and AI-use register in one workspace. The company publishes its own rules, and each completed case keeps the version that applied.
This article provides general operational guidance. Approval authority and required reviews depend on the organisation, use, industry, contracts, and applicable law.