Using AI to Write Performance Reviews: Everything You Need to Know

Performance reviews are possibly one of the most dreaded things on every manager’s calendar (aside from that weird icebreaker game at the company offsite where precisely nobody’s facts are fun, that is).

They’re time-consuming, high-pressure, and the ensuing data hunt can often become a total needle-in-a-haystack exercise.

“There isn’t an immediate payoff,” said Kim Minnick, founder and principal consultant of Code Traveller HR. “A manager’s role is helping to develop their team, and reviews are just water stops on a very long career marathon.”

Generative AI has already helped managers write reviews faster. But AI-assisted performance reviews are only useful when they improve the quality of the feedback, not just how quickly it can be churned out.

When done well, AI can help managers synthesize performance data, identify trends, and create stronger first drafts. Getting it right depends on setting clear guardrails, being transparent on AI use, and manager ownership.

Why AI-Assisted Reviews Have Become a Real Need

Performance reviews may be disliked by many, but their value has never been in question. Lattice’s 2027 State of People Strategy Report found that 45% of HR are running review cycles monthly or quarterly, up from 37% last year. 

And with 2026 Gallup research finding that managers’ spans of control are only getting wider with more direct reports and work than ever, running reviews for a team of ten or more direct reports can quickly become unsustainable.

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“We ask way too much of reviews, which makes them wildly overwhelming for [the] team,” Minnick said. “It's not just a review of your performance but an outsized data point for compensation decisions, promotion decisions, and what comes next in your career. This is a huge amount of pressure for individuals and their managers to take on.

“We pile them on without removing or pausing any of the work that needs to get done,” she added. “We ask folks to remember key events that happened months ago — and I don’t even remember what I had for dinner yesterday. It’s a mental powerlifting competition.”

That pressure on all sides is changing how HR and managers look at review season. In Lattice’s 2027 State of People Strategy Report, 74% of HR said managers are using AI to support writing performance reviews, and most agree the outcomes are better.

That means AI can make a real difference in reducing the burden of reviewing and summarizing a whole year’s worth of information, and frees up even more time for companies that are moving toward continuous performance programming. 

‍Discover how Lattice AI can raise the bar for performance reviews with our interactive tour.

What AI Can (and Can’t) Do in the Employee Performance Review Process

When implemented as part of a continuous feedback culture, AI can help HR and managers take some of the pressure off when review season rolls around. Beyond just faster drafting, it can support higher-quality outcomes for feedback and review prep:

• Synthesize performance and feedback: Identify employee feedback themes, summarize key achievements and goals, spot trends in performance data, and see how employee contributions ladder up to strategic goals. 
• Improve review writing: Increase speed, optimize language precision and specificity, generate draft content, and tailor the tone and format of reviews.
• Strengthen feedback quality: Detect bias and inconsistencies, connect feedback to relevant role competencies, and ensure feedback stays actionable and relevant.
• Support future growth: Suggest new goals and employee development areas, and recommend learning plans or next steps for career progression.

This makes AI-powered performance reviews especially useful for helping busy managers write reviews that feel differentiated and include specific examples that relate to each employee. But the quality of that starting point hinges entirely on the quality of the information behind it.

“When we’re just giving scattered notes [to AI], we aren't really giving the full picture and we're leaving it up to the robot to fill gaps,” Minnick said. “When we share more data, hopefully it becomes easier for our AI partner to recognize themes and patterns.”

In layman’s terms: garbage in equals garbage out. If managers are using AI trained on tidbits of information to assist on performance reviews, they’ll get a generic output that doesn’t feel connected to employees’ actual performance or development opportunities. It’s not just something AI would struggle with — it’s the same deal for a human.

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But when AI tools can synthesize rich context from years of performance and growth data, plus insights from peer feedback and weekly one-on-one notes, managers are more likely to get a result that feels accurate, tailored, and tonally on-point.

However, this still doesn’t remove the need for human oversight.

While you can make the machine smarter with the right data, AI can’t observe the dynamics of our relationships with our peers (for now). It doesn’t know that when an employee remarked that they “got their OKRs over the line” in the meeting transcript, they were being ironic. That makes human judgment and sign-off non-negotiable.

“AI can be fed lots of information and point out themes,” said Minnick, “but it's the manager's job to question those themes and pull back the layers of the onion.”

Introducing Evidence-Based AI Reviews: Richer Context, Better Judgment

If we ran a quick poll, we reckon few managers would say they have enough time and space to think clearly during review season. Running a dozen or more reviews means many spend more energy hunting down data, rather than applying the human judgment needed to make feedback thoughtful and meaningful.

Removing that sense of overwhelm means giving managers a better place to start from — one where they don’t need to pan through a year’s worth of information to find the gold.

Lattice was really helpful for writing mid-year reviews because it helped me focus my thoughts. Streamlining and organizing ideas is one of the best uses for AI tools in Lattice. A process that could have taken hours was simplified into about 30 minutes.

Gretchen Willard, Waterford employee

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With Lattice’s Evidence-based AI Reviews managers start with a first-pass review grounded in real performance data across past performance cycles, growth areas, one-on-one notes, feedback, and more.

This takes the drafting process from a black box to a traceable evidence chain. Sources are cited alongside the draft, so that managers can see exactly where the information is coming from, and verify it within context.

And that rich context layer really matters, because performance data rarely tells one clean story in isolation. Viewing a missed goal through the lens of declining employee engagement, for example, means a simple performance metric can end up looking very different.

“Low engagement often equals low performance,” Minnick said. “Scattered notes might show low performance at face value, but with more and richer data to draw from, we might find that low engagement stems from changing priorities, a disconnect from the work and the mission, or a lack of a growth path. This information can fundamentally change AI output and therefore how the review is written.”

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With Evidence-based AI Reviews, managers have full control over everything in the review and have the data to shape something better. That raises the quality bar for performance reviews, meaning ratings and progression stay consistent, actionable, and defensible across the whole team. Meanwhile employees have context-driven insight on what they’re doing well, and next steps on how to move up.

And when organizations make better decisions on employee progression and growth, employees feel more engaged, more fairly rewarded, and more likely to stay.

How AI-Assisted One-on-Ones Feed Better Reviews

Performance reviews are just one point of the performance management cycle. But the processes you run around that cycle matter just as much for making sure your AI-powered reviews stay meaningful and fair.

One-on-ones are one of the richest sources of data to feed into AI-assisted annual reviews. They’re a year-round process that provides a consistent source of information on goal progress, challenges, and self-evaluations on growth. 

But because the data from one-on-one conversations is unstructured and qualitative, the most useful insights often end up buried in meeting notes and passing comments — making it hard for managers to find them quickly. 

AI-powered one-on-ones can help make this information capture easier. Lattice’s AI Agent can join 1:1s to capture meeting notes, highlight key insights and action items, and sync everything back into Lattice, so managers can focus on listening, rather than writing things down. This gives them a continuous data trail that feeds directly into Lattice’s Evidence-based AI Reviews.

Meanwhile, Writing Assist helps synthesize that information — plus the information from employee feedback, previous reviews, and development and growth needs — to generate a high-quality first draft for managers to review and edit.

The more structured and consistent the one-on-ones, the better the data to inform the performance review content — meaning the prep work is no longer a scrabbled rush at the start of review season, because it’s done well ahead of time.

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Responsible AI in Reviews: What HR Teams Need to Get Right Before Review Season

Performance reviews shape employee growth, pay, progression, and how valued employees feel. And when those decisions impact someone’s livelihood, bringing AI into the process without due care can quickly undermine trust. 

Responsible AI use needs to start before the next performance review period hits, and give both HR teams and managers time to integrate both the tech and the behaviors needed to get the best possible result. 

1. Set clear rules for where AI can and can’t be used.

Before any AI-powered tools are rolled out, HR teams need to be very clear on how AI feeds into the process, and draft a clear set of rules to guide appropriate use.

“[HR] teams should make decisions about the guardrails,” said Kim Rohrer, founder and principal at Patchwork Portfolio. “Where are managers expected to use AI? Where is it okay to use AI? Where should they not use AI? For example, use AI to surface data, use AI to organize your thoughts, don’t use AI to write the review for you, and never send someone feedback without carefully reviewing the output.”

That accountability needs to be made explicit. Managers need to know that their name on the review means they own the final content and judgment — whether they penned it by hand, or AI gave an assist. 

Guidelines should also include the basic ‘what’ and ‘why’ of rolling out AI-assisted reviews in simple language that managers can share with their direct reports. You need clear usage guidelines of dos and don’ts or an employee-facing FAQ, as well as some scenario-based examples to help managers apply the right thinking in different contexts. 

Setting the ground rules on AI-assisted reviews

To get clear on where AI fits into your performance management processes, ask:

• What can AI assist with in the performance review process — such as trend analysis, feedback synthesis, suggestions for improvement, or drafting review content?
• What can AI explicitly not assist with — such as performance ratings, compensation decisions, and role progression?
• Do we require all managers to use AI to write performance reviews?
• What do managers need to check before submitting a performance review?
• How will we make sure managers have the skills to understand what high-quality AI-assisted reviews look like? 
• How can managers and HR deal with cases where the AI judgment is in conflict with the manager’s?

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2. Decide what data sources the AI can draw from.

Performance review data is among some of the most sensitive information your organization holds about your workforce. It can include performance feedback and self-evaluations, compensation and promotion history, and professional development progress.

That makes data privacy principles a core part of any rollout.

Deciding what data feeds into your AI-assisted reviews, and understanding how your tools handle, store, and protect that data before roll-out is critical if you want to keep your workforce’s trust.

But the source selection is equally as important to create trust in the final product itself. If you limit too much of what the AI can use, it won’t produce the high-quality reviews needed for either managers or employees to invest in the process.

“AI is only as good as the data it has, so if you give it poor data — or don’t give it data at all — you’re not going to get accurate reviews, much less helpful ones,” Rohrer said. “Know your sources: notes from one-on-ones, shoutouts from peers, or feedback from a launch postmortem. HR should clarify which sources will be feeding the AI, and ensure managers and peers are actively contributing to those sources, so that when review time comes around, the AI has all of the material it needs to consolidate, look for trends, and surface insights.”

3. Make the process visible to employees.

Without transparency on how decisions are made, performance reviews can often feel opaque to employees. The same goes for AI. Combine the two in a labor market that’s already feeling the squeeze from AI-related job erasure, and you’ve got the perfect recipe for a big anxiety spiral.

“Whatever your process is, the important thing is that employees understand where the feedback is coming from and how AI is involved,” Rohrer suggested. “And managers actually need to follow the process and follow up as humans — not just use AI to summarize a collection of disparate notes and then call it feedback.”

Rohrer suggested organizations walk through a typical workflow of where data is gathered and how: “For example, [tell employees] that we proactively push feedback into our Slack postmortem channel, AI collects transcripts from one-on-ones and then gives managers a monthly summary of their team’s performance that they can add to while things are still fresh. It’s about being really clear about the process.”

Beyond explaining data collection, teams should also flag how AI-assisted reviews will be audited and calibrated, and where employees or managers can give anonymous feedback if they have concerns. 

4. Audit and recalibrate as needed.

AI tooling evolves constantly — and even with the latest intel, any AI guidelines will become out of date. This is why as managers start using AI in live review cycles, HR should keep an eye on how the process and final product are landing.

While no human resources team should be expected to check over every review, auditing AI-assisted reviews for bias may help provide an early warning that your tool needs some recalibration. Segmenting these by different demographics may help spot pockets where the AI is exhibiting particular biases compared to other groups.

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Meanwhile at the employee level, tracking performance drivers as part of regular engagement surveys will help teams see how employees’ perception of fairness, reward, and progression change. This will give you a read on how well employees trust the process. 

Broader employee lifecycle data can also be a sign that something’s not working, Minnick noted: “I have a hunch that the same data we always look at — goal attainment, retention rates, engagement — will reflect if it’s landing well or not.”

Correlation isn’t always causation, but if these metrics start trending in the wrong direction post-launch, HR teams have a reason to look more closely at how the process is being used and experienced.

Monitoring the Efficacy of AI Reviews

When running an audit on your AI-assisted reviews, ask:

• Are there any aspects about AI-assisted performance reviews that are causing concern, confusion, or extra risk among managers?
• Have we seen any feedback in engagement or pulse surveys that suggests employees are finding the new process inconsistent, confusing, or unfair?
• Are employees clear on how AI is involved in the process?
• Are managers clear on how they should be using AI in performance processes?
• Are there any new edge cases that we need to assess — or some that keep coming up?
• Have we had any reports of misuse, poor quality reviews, or improper application of AI?
• Are review outcomes moving in the right direction?

How Managers Should Be Using AI In Reviews

Once you have the guardrails in place, you need to tell managers where the line is for practical use.

One foundational principle to using AI well during the review process, said Theresa Fesinstine, an HR executive turned AI educator for HR teams and founder of peoplepower.ai, is “to think of AI as your assistant, not your replacement. Using it to spit out generic, one-size-fits-all feedback won’t do your team any favors,” she cautioned. 

In practice, this involves avoiding a few common pitfalls:

• Mistaking polished language for useful feedback: Generative AI tools can often produce text that feels composed on the surface, but is actually error-laden, exaggerated, or vague on closer inspection.
• Generating feedback based on incomplete data: AI can only work with the data it’s given. If the underlying notes and performance data are incomplete or limited, it could skew the output — potentially amplifying bias in decision-making. In Lattice, feedback summaries are only generated when there’s enough feedback to create a meaningful analysis. But if there isn’t enough data, no summary is created.
• Over-relying on AI summaries without human review: AI can help synthesize feedback themes and retrieve performance data, but it’s not a substitute for human judgment.
• Lack of transparency on how AI is used: If managers can’t explain how and where AI was used in the performance management process, what information it used, or basic information on how the model works, they’ll quickly lose trust and motivation from their team.

The underlying theme is that employees need to feel like their feedback came from an actual beating-heart human who knows their work — even if the basis for the review started with AI.

“I don’t think most employees hate AI being in the loop of feedback,” Minnick said. “It can objectively discover themes, recognize patterns, and point out things humans might miss because of personal biases. [But] we value human feedback when it’s delivered with respect to the person receiving it. Nobody wants to hear, ‘ChatGPT said…’ when discussing feedback — they trust that their manager knows them better than an [AI tool].”

Updated AI Prompts for Managers and Employees

Once the conditions for responsible AI-assisted performance reviews are in place, the right prompts can help managers and employees wrangle scattered thoughts and notes into a coherent review.

Fesinstine suggested the first step is to keep an ongoing bulleted list of successes and challenges for each member of your team. 

“Then, in order to get the most out of AI tools like ChatGPT and Claude, the trick is in asking the right questions,” she said. Fesinstine recommended managers try the following prompt to kick things off:

“How can an employee improve in areas such as teamwork? Take into account that they were coached on Y feedback this year.”

Just like performance reviews, the quality of your input reflects the quality of your output, so make sure your prompts are specific and detailed. We’ve put together a few templates for prompts below.

Performance Review Prompts for Managers

• "Summarize the key themes from the following one-on-one notes and feedback for [employee name]. Highlight strengths, recurring challenges, and any patterns in how they work with others. Flag any areas where more context or evidence may be needed.”
• "Based on these performance notes [insert notes], write a draft performance review that is specific, behavior-focused, and avoids vague language. Flag any areas where the language might be interpreted as biased."
• "This employee received constructive feedback on [specific area] earlier this year. Draft a review section that acknowledges their progress, cites specific evidence, and identifies concrete next steps."

Performance Review Prompts for Employees

• "Based on the following accomplishments and goals from this year [insert details], help me write a self-review that connects my work to broader team and company objectives."
• "Review this self-evaluation draft for clarity and tone. Flag anywhere the language undersells my contributions, overstates impact, or is too vague."
• "Help me identify two or three development goals for the next review cycle based on the feedback I've received this year [insert feedback themes]. Include why each goal matters, and what meaningful progress could look like.”

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