Insights
26.11.2025
Mikko Kivelä
How to use AI in performance reviews?
A balanced, research-backed overview of how AI supports performance reviews: improving consistency, transparency, and fairness while keeping human judgment at the centre.
Using AI in performance reviews no longer means manually pasting feedback into ChatGPT or managing prompts yourself. A new generation of AI-native performance tools already automates the heavy lifting, including summarising feedback, detecting patterns, reducing bias, and organising data, without requiring managers to engineer their own AI workflows. These systems integrate directly into existing performance processes, making AI practical instead of experimental.
This article explores where AI adds the most value in performance reviews, the challenges leaders must anticipate, the metrics that reveal whether AI is helping, and what the future of AI-supported performance enablement may look like.
Why does AI belong in performance reviews?
AI belongs in performance reviews because it brings structure, consistency, and analytical support to a process that is historically subjective and time-consuming. Reviews often suffer from recency bias, unclear expectations, and inconsistent writing quality. All areas where AI can reduce friction.
Research shows growing trust in AI-supported evaluations. As Neelie from AIHR notes:
"75% of employees have positive attitudes toward AI-generated performance reviews, as long as human beings assess and adjust them for accuracy."
When used responsibly, AI enhances fairness and helps managers spend less time preparing documentation and more time having meaningful development conversations.
Which parts of the review process can AI improve?
AI can improve preparation, evaluation, and post-review follow-up by organising feedback, detecting themes, and supporting calibration. It turns fragmented signals like feedback notes, goals, peer comments, project data, into a coherent performance narrative.
AI also helps normalise tone across reviews, align evaluations with role expectations, and highlight discrepancies in how similar performance is described across different teams. These capabilities make the review cycle more reliable and reduce the manual burden on managers.
What challenges should organisations expect when using AI in reviews?
The biggest challenges include over-reliance on AI outputs, insufficient transparency, and unclear governance. AI can surface insights, but it cannot understand interpersonal context, team dynamics, or organisational nuance. Leaders must set the right expectations internally.
To illustrate the common obstacles and solutions, here is a concise table:
| Challenge | Why it Happens | How to Mitigate |
|---|---|---|
| Over-reliance on AI | Managers treat summaries as decisions | Require human review & calibration |
| Lack of transparency | Employees unsure how data is used | Communicate data sources & governance |
| Skills mismatch | AI depends on structured inputs | Use clear role expectations & levels |
| Outdated frameworks | AI reflects outdated criteria | Maintain updated expectations & skills |
| Inconsistent adoption | Teams use AI differently | Provide shared guidelines & review rhythms |
What role does human judgment play when AI is used in performance reviews?
Human judgment becomes more critical, not less. AI can summarise information, but only managers can interpret nuance, intent, and interpersonal impact. AI supports fairness; humans provide context.
Industry research shows that combining automation with human review yields the best outcomes. AIHR reports high employee acceptance of AI summaries when humans remain the final evaluators, maintaining credibility while improving efficiency.
What makes AI-powered performance reviews more effective?
AI-powered reviews are most effective when expectations are clear, managers are trained, and the organisation uses a consistent performance language. This includes transparent role definitions, clear skills or competency frameworks, and a review process that supports continuous feedback, not once-a-year evaluations.
AI performs best when it can analyse structured data: goals, feedback, behaviours, and documented achievements. When these foundations are in place, AI helps managers identify patterns, avoid recency bias, and hold more grounded conversations.
For a wider context on performance tools in the market, see this blog.
What are the benefits of using AI in performance reviews?
The benefits of using AI in performance reviews include more consistent evaluations, clearer expectations, and stronger transparency across teams. AI helps managers focus on behaviours and outcomes rather than memory or writing style, giving them an objective starting point for discussion.
Harvard Business Review highlights that data-driven performance systems reduce subjective variability and make evaluations more reliable across individuals and teams by focusing on measurable patterns of work and behaviour.
The Humanyze summary of this research reinforces the same point, noting that organisations adopting data-informed performance practices see improved calibration discussions and a clearer link between evidence and evaluation.
Together, these findings show the real purpose of AI in performance reviews: reducing friction, increasing fairness, and helping managers have more grounded, evidence-based conversations, while keeping humans in full control of judgment and context.
What metrics show that AI is improving performance reviews?
AI effectiveness becomes visible through both leading indicators (such as prep time) and lagging indicators (predictable development outcomes). The most important metrics include:
Leading indicators:
- Reduced manager prep time
- Higher review completion rates
- More consistent application of criteria
Lagging indicators:
- Improved employee sentiment around fairness
- Clearer calibration discussions
- More predictable development outcomes
These metrics signal whether AI is improving structure and clarity, or simply adding another layer of complexity.
How does AI improve calibration and reduce bias?
AI improves calibration by surfacing patterns in how performance is described and evaluated across teams, making it easier to spot when similar contributions are treated differently. Research on data-driven performance management shows that when organisations ground evaluations in consistent behavioural and activity data, manager decisions become more comparable and less dependent on individual style or memory.
For example, Harvard Business Review describes how using behavioural and collaboration data helps reduce subjectivity and improves the reliability of performance discussions across groups.
AI-enabled text and language analysis can also flag biased or uneven feedback. Studies on the language of performance reviews find that men and women are often described differently for similar performance, with women receiving more personality-focused or less actionable comments.
By using natural language processing to detect patterns such as vague adjectives, gendered descriptors, or inconsistent specificity, organisations can nudge reviewers toward clearer, behaviour-based feedback and make bias easier to see and address before calibration.
How can organisations ensure transparency and trust when using AI in reviews?
Transparency matters more than technology. Employees must know what data is used, how summaries are generated, and how managers verify accuracy. Organisations should clearly communicate that AI assists with structure but does not make final decisions.
Trust increases when teams openly review AI outputs, challenge unclear summaries, and use AI as a support system—not as an authority.
How will AI change the future of performance reviews?
AI is shifting performance reviews toward continuous insight rather than episodic evaluation. Instead of annual cycles, organisations are moving toward ongoing signals: regular feedback, behavioural trends, goal tracking, and skill development patterns.
As AI improves, reviews will become checkpoints in a continuous performance enablement loop. AI will support real-time coaching insights and behavioural understanding, while human managers will remain responsible for interpretation and decisions.
What are the key insights leaders should take away from this article?
AI strengthens performance reviews by improving consistency, reducing subjective variability, and giving managers a clearer foundation for evaluation. It does not replace human judgment. Instead, it helps teams surface patterns, reduce bias, and focus conversations on evidence rather than opinion. This table summarises the core learnings leaders can apply as they modernise their review processes.
| Topic | Key Insight |
|---|---|
| Role of AI | AI supports structure, summarisation, and pattern detection — but humans remain responsible for judgment. |
| Bias reduction | NLP highlights vague, subjective, or biased language before calibration, improving fairness. |
| Calibration consistency | AI identifies discrepancies across teams, reducing rating variability and improving comparability. |
| Review preparation | Automated synthesis reduces administrative load and helps managers focus on meaningful conversations. |
| Employee trust | Transparency about how AI is used increases acceptance and strengthens credibility in the process. |
| Data foundations | AI performs best when roles, expectations, and performance frameworks are clear and up to date. |
| Future direction | Reviews shift from episodic evaluation to continuous insight, supported by real-time behavioural data. |
What to read next?
Light-touch cross-links to deepen understanding:
- How to use ChatGPT for HR
- AI in HR: Use cases and tools for 2025
- CultureAmp competitors and alternatives
FAQ
Is AI replacing managers in performance reviews?
No. AI summarises information and reduces admin work, but managers remain responsible for interpretation, judgment, and coaching.
Does AI make reviews more fair?
AI can reduce vagueness and highlight inconsistent language, but fairness still depends on clear expectations and human oversight.
Is AI safe to use for performance data?
Most modern AI systems support enterprise-grade data security. Transparency about how data is used is key to maintaining trust.
Can small companies use AI in reviews?
Yes. Even small teams benefit from AI-supported summaries and structured insights, especially when managers have limited time.