How to Use AI for Performance Reviews in 2025
Learn how to use AI for performance reviews to save time, reduce bias, and create better evaluations. Step-by-step guide with best practices, tools, and implementation tips.
Performance reviews consume hours of manager time while often missing key contributions. Research shows 49% of managers struggle to review a year of feedback, and 42% find the process burdensome. AI changes this, letting managers complete reviews in 6 minutes instead of 6 hours.
This guide covers how to use AI for performance reviews effectively, from selecting tools to balancing automation with human judgment.
Why use AI for performance reviews?
AI solves three key problems in traditional performance management:
Time efficiency. Managers spend 3-6 hours per review gathering notes and crafting feedback. AI automates data collection and generates drafts, reducing this to minutes.
Bias reduction. Human reviewers suffer from recency bias, focusing on recent weeks rather than the full evaluation period. AI analyzes performance data year-round for a balanced view.
Real-time insights. Annual reviews capture old performance data. AI tracks accomplishments as they happen, enabling timely coaching.
What AI can do for performance reviews
Automated data collection. AI integrates with tools like Slack, GitHub, Jira, and Salesforce to track contributions continuously. It captures project completions, code commits, meeting participation, and task completion without manual input.
Bias detection. AI analyzes review language to identify unconscious bias patterns, flags inconsistent standards, and highlights when reviews favor certain demographics.
Draft generation. Managers get AI-generated draft reviews based on year-round data and feedback. Drafts include key accomplishments, development areas, and specific examples to refine.
Goal tracking. AI monitors goals in real time, flagging at-risk objectives early for intervention. This shifts reviews from backward-looking to forward-focused coaching.
Continuous feedback. AI prompts managers and peers to give feedback after key collaborations when details are fresh. These moments build a rich performance record without lengthy surveys.
How to use AI for performance reviews
1. Audit your current process
Map your existing workflow: How many hours do managers spend per review? Where do bottlenecks occur? What tasks are repetitive? This reveals where AI delivers the most value.
2. Set AI guidelines
Establish clear policies: Will you disclose AI use to employees? (75% of employees support AI-generated reviews when humans review them.) Ensure GDPR/CCPA compliance. Require managers to review all AI content. Commit to regular bias audits.
3. Choose your tools
Specialized platforms like Windmill, Lattice, and Betterworks offer native integrations, year-round data collection, built-in workflows, and compliance certifications.
General AI tools like ChatGPT or Claude can generate review language and suggest goals, but lack continuous data gathering.
For full performance management, specialized platforms work best.
4. Integrate with your tools
Connect AI to communication platforms (Slack, Teams), project management (Jira, Asana), code repos (GitHub), CRM systems (Salesforce), and calendars. More integrations mean better AI summaries.
5. Enable continuous feedback
Configure AI to request feedback after key events like pull requests, project completions, and collaborations. Use conversational AI for brief questions (under 30 seconds) that achieve 85%+ response rates. Schedule weekly check-ins where AI gathers updates.
6. Generate AI-assisted reviews
Self-reviews: AI compiles accomplishment lists, prompts reflection questions, and drafts narrative sections employees can edit.
Peer reviews: AI suggests relevant peers based on collaboration patterns, asks about specific projects, and aggregates feedback into themes.
Manager reviews: AI synthesizes metrics and feedback into drafts highlighting achievements, development areas, and goal progress. Managers refine and personalize these drafts.
7. Add human judgment
Managers must personalize language, add context AI can’t understand (team dynamics, challenges), provide coaching vision, build relationships, and verify all AI-generated facts to avoid errors.
Best practices
Use AI as an assistant, not replacement. AI gathers data and generates drafts. Humans add context, relationships, and judgment. Combine both for best results.
Maintain transparency. Tell your team how AI is used in reviews. This builds trust and helps employees understand how their data contributes.
Train managers. Provide training on AI capabilities, limits, and best practices. Managers should know when to override AI suggestions.
Never copy-paste AI outputs. Always verify and personalize AI content. Unchecked AI can produce errors or sound generic.
Don’t over-rely on metrics. AI tracks measurable outputs, but meaningful contributions like mentorship and culture-building need human capture.
Audit for bias regularly. AI can perpetuate historical biases. Analyze review outcomes across demographics and adjust when bias appears.
Keep feedback specific. Reference concrete examples and clear improvement paths. Avoid vague statements like “needs better communication.”
Common challenges
Employees feel surveilled. Emphasize that AI tracks work outputs (projects, goals), not personal behavior. Frame it as preserving accomplishments.
AI generates errors. Require human review of all AI outputs. Train managers to fact-check before finalizing reviews.
Reviews sound generic. Use AI drafts as frameworks. Add personal anecdotes and observations from 1-on-1s.
Privacy concerns. Choose platforms with SOC 2 Type II and GDPR compliance. Set clear data retention policies.
Low engagement. Make feedback brief (under 30 seconds) and conversational. Trigger prompts after real work events. Tools like Windmill achieve 85%+ response rates in Slack.
Manager resistance. Show time savings and frame AI as making jobs easier, not replacing roles. Start with a pilot program.
Real-world results
Organizations using AI-powered performance management report major improvements:
97% time savings. Tools like Windmill cut review time from 3+ hours to 6 minutes per review.
300% faster calibration. Leadership teams calibrate entire organizations in days instead of weeks.
80-90% response rates. Conversational AI achieves much higher completion rates than traditional surveys (50-60%).
Better preparedness. Managers enter conversations with comprehensive year-round context instead of relying on memory.
89% employee satisfaction. More than double the rate without AI support.
18% faster problem resolution. AI surfaces blockers multiple sprints sooner.
Windmill: AI-powered performance reviews
Purpose-built platforms like Windmill deliver end-to-end automation:
Year-round context. Windy (Windmill’s AI assistant) lives in Slack, chatting weekly to understand priorities, accomplishments, and blockers. By review time, 90% of the review is written.
Conversational reviews. Employees complete self-reviews through natural Slack conversations. Windy asks follow-up questions and drafts their review.
Smart peer feedback. Windy analyzes collaboration patterns in GitHub, Jira, and Salesforce to suggest relevant peers, then collects feedback via brief Slack chats.
Manager drafts. Managers get comprehensive drafts with key wins, development areas, and examples from tools, peers, and check-ins. They can edit and publish in minutes.
Calibration intelligence. Windy identifies rating gaps, generates pre-reads, and flags discussion areas—speeding up fairness reviews.
Companies using Windmill cut review time from weeks to days while improving satisfaction with quality and fairness.
Frequently asked questions
Is AI review data secure? Choose platforms with SOC 2 Type II and GDPR compliance like Windmill. Ensure tools don’t train public models on your employee data.
Will employees trust AI reviews? Research shows 75% of employees support AI-generated reviews when humans review them. Transparency and personalization are key.
Can AI reduce bias? AI reduces recency bias by analyzing full evaluation periods. But it can perpetuate historical biases, so regular audits are essential.
What if AI generates errors? Human oversight is mandatory. Managers must verify all content before finalizing to avoid trust damage and legal issues.
Do I need special training? Modern platforms like Windmill are designed for non-technical users. Provide initial training and ongoing support.
Can we customize AI prompts? Yes. Leading platforms let you customize questions and workflows to match your culture and framework.
How long to implement? Setup takes hours to days. Full benefits appear after one review cycle when AI has gathered continuous data.
Start using AI for performance reviews
AI makes reviews actually work by automating data collection, reducing bias, and enabling continuous feedback. Reviews shift from dreaded paperwork to meaningful coaching conversations.
Use AI as an assistant for mechanical work while managers provide judgment, context, and relationship-building.
Ready to start? Visit gowindmill.com to see how Windmill automates your performance management process in the tools your team uses.