Performance review season puts a spotlight on how managers document results, development, and expectations. This period is critical for both managers and employees as they assess achievements, set future goals, and provide constructive feedback. As annual or biannual reviews approach, leaders are seeking effective strategies to streamline the process and ensure evaluations are fair, thorough, and actionable. Today, as AI becomes central to many aspects of work, managers are increasingly turning to Artificial Intelligence (AI) tools to speed up performance evaluation writing—summarizing accomplishments, spotting themes across notes, and improving consistency from one review to the next. 

Used thoughtfully, AI can be a tremendous partner in reducing administrative time and helping leaders focus on coaching and performance improvement. Used carelessly, AI can amplify bias, introduce inaccuracies, and create documentation risk. Overall, the goal for managers shouldn’t be to “let AI write the review,” but to use AI as a drafting and analysis assistant while staying accountable for fairness, accuracy, and context. 

When managers provide high-quality inputs (e.g., goal statements, project outcomes, examples of impact, and timely feedback notes), AI can meaningfully improve the review process. Common wins include faster first drafts that managers can refine; clearer writing that translates technical work into business impact; more complete coverage of the review period by summarizing across multiple sources; and more consistent structure and language across a team, which can help reduce “style variance” that sometimes looks like unfairness. 

Conversely, the use of AI tools in the performance management space can also introduce a wide range of risks. AI tools can produce confident-sounding text that is incomplete, misleading, or poorly grounded in facts. For example, if the prompt is vague (e.g., “write an evaluation for Alex”), the output may lean on generic stereotypes, exaggerate strengths, or gloss over specific performance issues. AI can also mirror bias embedded in historical language (e.g., different adjectives commonly used for different groups) and can unintentionally shift tone—turning balanced feedback into overly harsh or overly soft wording. 

Finally, there are confidentiality and compliance concerns: performance evaluations often contain sensitive Human Resources data, heightening the need for managers to strictly follow company policy on what information can be entered into third-party tools, where data is stored, and who can access it. 

To help lower these risks, employers should assist managers by making them aware of the more common pitfalls and how best to avoid them. Below are a few key takeaways to guide the use of AI in preparing performance evaluations: 

• Use AI to summarize evidence, not to invent it: Prompt AI with concrete examples (i.e., goals, deliverables, metrics, peer feedback) and verify every claim before it goes into the final review. 

• Actively check for bias and tone drift: Review the output carefully for uneven language—for example, using “abrasive” vs. “direct,” or “helpful” vs. “high-impact.” It is also important to ensure feedback is anchored to expectations and observable behavior. 

• Protect sensitive information: Emphasis and guidance should include clear directions to follow the organization’s rules on what employee data can and cannot be entered into AI tools. Guidance should also instruct managers that when in doubt, they should remove identifiers and keep drafting inside approved systems. Key information to remove/replace includes: employee name, company name, coworker/customer/vendor names, specific locations, exact dates (if not needed), unique project code names, ID numbers, medical/leave/benefit details; anything that could identify the individual or the company.  

• Keep human accountability: Managers must own the evaluation and be held accountable; AI can speed up drafting, but only the manager can weigh context, apply judgment, and ensure fairness.