AI Performance Review Policy: A Practical Template

An AI performance review policy tells employees and managers where AI use is encouraged, where it is prohibited, and who is accountable for its outputs. Without one, teams often improvise with public AI tools, paste in sensitive employee data, or assume generated text is accurate and unbiased. A written policy makes the safe path the obvious path.

The template is a starting point, not legal advice. Adapt it with employment counsel, privacy, security, and employee representatives where your company operates.

Why AI Performance Reviews Need a Policy

AI-assisted reviews affect employee data, workplace trust, and decisions that impact careers. A policy sets consistent boundaries before a problem occurs. It should distinguish low-risk assistance, such as organizing verified notes, from consequential uses, such as recommending ratings, pay, promotion, discipline, or termination.

The NIST AI Risk Management Framework organizes responsible AI work around governance, mapping risks, measuring them, and managing them. Responsible use begins by defining the context and consequences of an AI system, then assigning people to manage those risks. That accountability remains with the employer whether it builds the system or buys it from a vendor, which is why a policy is necessary in either case.

Define Approved AI Uses

An AI performance review policy should list permitted purposes rather than granting broad permission to “use AI.” Low-risk uses usually include retrieving verified accomplishments, organizing manager notes, identifying missing evidence, suggesting questions, and drafting text for human revision. Any use outside the list should require approval from HR, privacy, and security owners.

Suggested policy language:

Employees may use company-approved AI systems to organize authorized performance information, retrieve relevant work examples, suggest discussion questions, and produce draft review language. AI output is a working draft. The reviewer must verify every factual claim, add relevant context, and take responsibility for the final review.

Link approved uses to specific tools and data sources. “Approved AI” should never mean any chatbot an employee happens to have open.

Prohibit High-Risk Uses

The policy should prohibit AI from making final employment decisions or creating unsupported judgments about personality, intent, health, protected characteristics, or future potential. It should also ban covert monitoring and the use of unapproved consumer tools for confidential employee information. These boundaries should apply even when a vendor markets the output as objective.

Suggested policy language:

AI may not independently assign performance ratings or decide compensation, promotion, discipline, performance-improvement plans, or termination. Users may not ask AI to infer protected characteristics, medical conditions, emotions, honesty, personality, or intent. AI output may not be cited as evidence unless the underlying source is available and relevant.

If AI informs a consequential decision, document the human rationale and the evidence reviewed.

Set Rules for Employee Data

Employee information should enter only approved systems under defined access, retention, and deletion rules. The policy should identify allowed data sources, prohibit unnecessary sensitive data, and apply least-privilege access. Public chatbots should not receive review text, names, private messages, medical information, compensation details, or investigation material.

University of Wisconsin–Madison’s HR guidance cautions HR staff against entering personally identifiable, confidential, or sensitive information into public generative AI systems. A company policy should go further by naming the approved system, its contractual safeguards, where data is stored, whether inputs train models, and how long inputs and outputs remain available.

Require Human Review and Evidence

Human review must be substantive, not a manager clicking “accept.” The reviewer should open the underlying evidence, confirm that it belongs to the correct employee and period, test whether contrary evidence is missing, and rewrite conclusions in their own judgment. The final review should identify a human owner who can explain every consequential statement.

A workable review checklist is:

• Is each factual claim supported by a source the reviewer can inspect?
• Does the review cover the full period rather than only recent work?
• Are outcomes separated from assumptions about attitude or intent?
• Has the reviewer considered constraints, role changes, leave, and team context?
• Would the reviewer defend the same conclusion without mentioning the AI?

For a fuller workflow, see how to use AI for performance reviews.

Disclose AI Use to Employees

Employees should receive a plain-language notice before AI is used in their review process. The notice should explain the tool’s role, data sources, limits, access rules, and correction process. Avoid saying only that a process is “AI-powered”; employees need enough detail to understand how information about them is produced and used.

Suggested notice:

We use an approved AI system to help retrieve and organize authorized work context and draft review language. Your manager reviews and edits the output and makes all ratings and employment decisions. You may ask what sources informed your review and request correction of inaccurate information through [process or contact].

Repeat the notice when the tool, purpose, or data sources materially change.

Test for Bias and Unequal Impact

AI output should be tested before launch and monitored after each review cycle. Examine missing evidence, error rates, rating changes, and outcomes across relevant employee groups. Bias testing should include the entire process, because unequal data coverage or inconsistent manager edits can create disparities even when the model behaves consistently.

Start with a limited pilot. Compare AI-assisted drafts with the source evidence, record common errors, and check whether some roles generate richer data than others. During calibration, flag inconsistent standards and unsupported language without assuming the software has eliminated bias. Our guide to performance review bias explains why human judgment and outcome audits remain necessary.

Create a Correction and Appeal Process

Employees need a practical way to correct factual errors and challenge consequential conclusions. The process should name a contact, set a response timeline, preserve the disputed version, and prevent retaliation. A human who was not solely responsible for the original decision should review material disputes involving ratings or employment actions.

The policy should separate two requests:

• Correction: the source or generated statement is factually wrong, incomplete, or belongs to someone else.
• Appeal: the facts may be accurate, but the employee disputes the interpretation, rating, or resulting decision.

Record corrections so the same bad information does not reappear in later drafts. Do not require employees to prove how the model produced an error before the company investigates it.

Assign Owners, Retention, and Review Dates

An enforceable policy names owners and dates. HR should own the employment process, Security and Privacy should approve tools and data flows, Legal should review jurisdiction-specific obligations, and managers should own final review content. The policy itself should be reviewed at least annually and whenever tools, purposes, or laws change.

For every approved system, record:

• business owner and technical owner;
• permitted users and data sources;
• vendor review and contract date;
• input, output, and audit-log retention periods;
• model or feature changes that require reassessment;
• incident and employee-contact procedures.

Retain only what serves a defined business or legal purpose. “The vendor stores it” is not a retention policy.

Copyable AI Performance Review Policy Template

This short AI performance review policy template can be copied into a handbook or AI-use standard, then expanded to match local law and company practice. Replace every bracketed field, attach the approved-tools list, and have the final language reviewed by the people responsible for employment, privacy, security, and legal compliance.

Purpose: [Company] permits approved AI systems to assist with defined administrative parts of performance reviews while preserving human judgment, employee privacy, and fair treatment.

Permitted uses: Authorized users may use [approved tools] to retrieve approved work context, organize verified notes, suggest questions, and draft language for human review.

Prohibited uses: AI may not independently determine ratings, compensation, promotion, discipline, performance-improvement plans, or termination. Users may not enter prohibited data into unapproved systems or ask AI to infer sensitive traits, emotions, personality, or intent.

Human accountability: [Role] must verify sources, correct errors, add context, and approve the final review. The human decision-maker remains accountable for every rating and employment decision.

Transparency and correction: Employees will receive notice of AI use and may request the sources informing their review, correct inaccurate information, or appeal a consequential decision through [process].

Monitoring: [Owner] will test the process for accuracy and unequal impact before launch and after each review cycle. [Owner] will review this policy by [date] or sooner if tools, uses, or legal requirements change.

Put the Policy Into the Workflow

A policy works only when the software and review process reinforce it. Approved data sources, permissions, source links, manager sign-off, calibration, and correction steps should be built into the review cycle. Training and an acknowledgment box cannot compensate for a workflow that encourages managers to accept unsupported output.

Windmill collects authorized work context throughout the year, generates editable review drafts, and keeps managers responsible for the final judgment. Its performance review workflow and calibration tools help teams examine evidence and rating differences before decisions are finalized.

Book a Windmill demo

Pilot one review cycle, audit the results, and revise the policy. Responsible AI governance is a repeatable way to notice when a system is not safe.

Frequently Asked Questions

What should an AI performance review policy include?

An AI performance review policy should define approved tools and purposes, prohibited data, human-review requirements, employee disclosure, bias testing, correction and appeal procedures, retention limits, access controls, and accountable owners. It should also state which employment decisions AI may inform and which it may never make automatically.

Can employers use AI for performance reviews?

Employers can use AI to assist with performance reviews, but existing employment, privacy, and anti-discrimination rules still apply. The safest approach is to use AI for evidence retrieval, organization, and drafting while requiring an accountable human to verify the content and make every consequential decision.

Should employees be told that AI is used in performance reviews?

Yes. Employees should know what AI does, which data sources it uses, what it does not monitor, who can access the output, and how to correct inaccurate information. Clear disclosure improves trust and gives employees a meaningful opportunity to challenge errors.

Can AI make performance ratings or promotion decisions?

AI should not make final performance ratings, promotion, compensation, discipline, or termination decisions. It may surface evidence or patterns for review, but a named human decision-maker should evaluate context, document the rationale, and remain accountable for the outcome.