How AI Helps Manage Underperformance: A Manager's Guide

Addressing underperformance is one of management’s hardest responsibilities. 69% of new managers feel unprepared for tough performance conversations, and one-third of employees feel unsafe having difficult discussions with their managers. The result? Performance issues fester until they become termination-level problems.

AI performance reviews change this dynamic by identifying struggling employees earlier, providing objective evidence for difficult conversations, and helping managers balance empathy with accountability.

The Problem with Traditional Underperformance Management

Traditional performance reviews create a dangerous gap between when problems start and when managers address them.

Annual reviews catch problems too late. By the time a manager sits down for an annual review, an underperforming employee has already spent months struggling. What could have been early coaching becomes a formal performance improvement plan.

Managers avoid difficult conversations. Research shows 70% of employees regularly avoid difficult workplace conversations, and managers are no exception. Without concrete data, it’s easier to hope an employee will improve independently.

Documentation is sparse. When managers need to address underperformance formally, they scramble to reconstruct months of issues from memory. Weak documentation undermines both coaching effectiveness and legal protection.

How AI Identifies Underperformance Earlier

AI performance reviews monitor work patterns continuously, detecting warning signs before they become crises.

Pattern Recognition Across Time

AI tracks performance indicators year-round—project completion rates, collaboration frequency, goal progress. When metrics decline, AI flags the change.

A sales rep who consistently closed 12 deals per quarter but drops to 8 triggers an alert. An engineer whose pull requests decrease by 40% gets flagged. These pattern breaks signal issues early.

Multi-Source Data Aggregation

AI pulls data from project management tools, communication platforms, code repositories, and CRM systems to build a complete picture.

When an employee stops contributing in channels, misses deadlines, or receives less peer feedback, AI surfaces these signals even if they still appear engaged in meetings.

Goal Tracking in Real Time

AI monitors goals continuously, identifying at-risk objectives weeks before deadline. Managers get alerts in August when progress stalls—leaving time for course correction instead of discovering failures in December.

Using AI to Document Performance Issues

When underperformance conversations become necessary, documentation protects both manager and employee. AI makes this documentation objective and comprehensive.

Specific, Timestamped Evidence

Instead of “Jane has been struggling with deadlines,” AI provides: “Jane delivered 6 of 12 projects late in Q3 (average delay: 8 days), compared to 2 of 12 late in Q2.” This specificity makes feedback undeniable and actionable.

Proper documentation is legally critical. PIPs require concrete examples of performance deficiencies, and AI provides this evidence without relying on manager recall.

Bias Detection

AI analyzes feedback language to identify unconscious bias patterns. 72% of survey respondents believe AI can lead to fairer performance evaluations, especially regarding age, gender, and racial biases. Objective data helps managers focus on work output rather than personality preferences.

Performance Trends Over Time

AI distinguishes temporary dips from sustained underperformance. An employee struggling for two weeks after a family emergency looks different from someone declining over six months, helping managers respond appropriately.

Creating AI-Assisted Performance Improvement Plans

When coaching hasn’t worked and formal intervention is needed, AI helps create effective PIPs.

Data-Driven Goal Setting

Only 38% of employees complete PIPs successfully, often because goals are vague or unrealistic. AI helps set specific, measurable objectives based on historical performance and role benchmarks.

Instead of “improve communication skills,” AI suggests: “Respond to customer emails within 24 hours (current: 48-hour average, measured via Zendesk).”

Clear Performance Baselines

PIPs require current performance level, target level, and measurement method. AI provides baselines automatically: “Current: 8 deals/quarter averaging $45K. Target: 12 deals/quarter averaging $60K.”

Progress Monitoring

PIPs with frequent check-ins improve success rates by 30%. AI automates progress tracking, showing whether employees are trending toward improvement rather than waiting until the 90-day deadline.

Balancing AI Objectivity with Human Empathy

AI provides data; managers provide context and care. Both are essential.

When AI Helps

Removing emotion from assessment. AI confirms or challenges manager intuition with objective data, preventing both premature intervention and delayed action.

Ensuring consistency. AI applies the same standards across all employees, preventing leniency or strictness bias.

Creating accountability. Documented patterns make it harder for managers to avoid necessary conversations or for employees to dismiss concerns.

When Human Judgment Matters

Understanding root causes. AI shows output decreased but can’t explain why. Managers must investigate: burnout, unclear expectations, personal challenges, skill gaps, or disengagement.

Providing context. An employee working 60-hour weeks but missing targets has a different problem than someone coasting at 30 hours.

Deciding on response. Not all underperformance requires PIPs. Sometimes the right response is training, role adjustment, or clearer expectations.

Delivering feedback with care. Research shows recipients of purely negative feedback often doubt both the accuracy and the messenger. Managers must deliver data-backed feedback with empathy.

Practical Steps for Managing Underperformance with AI

1. Establish Performance Baselines

Configure your AI system during onboarding. Document expected output levels and success metrics for each role. Clear standards make underperformance easier to identify.

2. Act on Early Warnings

When AI flags declining performance, schedule a conversation: “I noticed your project completion rate has shifted. Is everything okay? How can I help?”

Early coaching often resolves issues before formal intervention becomes necessary.

3. Gather Multiple Perspectives

AI should analyze output metrics, peer feedback, and collaboration patterns. Sometimes “underperformance” signals broken processes or unclear requirements.

4. Document Coaching Conversations

After performance discussions, document what you discussed, actions agreed upon, and follow-up timeline. This protects both parties.

5. Create Specific, Measurable PIPs

When formal PIPs are needed, use AI baselines to set concrete goals with current state, target state, specific actions, support provided, check-in frequency, and timeline (30-90 days).

6. Monitor Progress Transparently

Share weekly progress reports with the employee. Transparency builds trust and gives employees agency in their improvement journey.

Legal and Ethical Considerations

Documentation Requirements

Employment law requires clear documentation throughout the PIP process, including performance issues, feedback sessions, and employee responses. AI systems provide timestamped, objective records meeting these requirements.

Avoiding Discrimination

PIPs must not unfairly target employees based on protected characteristics. AI helps by applying consistent standards, but human oversight is essential—AI can perpetuate historical biases.

Regular audits across demographics ensure the system isn’t biased.

Privacy and Transparency

Employees should know how AI monitors performance. Transparent policies build trust. AI should track work outputs—projects completed, goals achieved—not personal behavior.

Real-World Impact

Organizations using AI for performance management report measurable improvements:

Earlier intervention. Problems get identified 2-3 months earlier on average, giving employees more time to improve before reaching PIP stage.

Higher success rates. Companies effectively using structured performance improvement see 46% success rates rehabilitating underperformers, versus 38% with traditional PIPs.

Better manager confidence. Objective data helps managers feel prepared for difficult conversations.

Clearer expectations. Evidence-based feedback helps employees understand exactly what needs to change.

When Underperformance Isn’t the Real Problem

AI reveals patterns that sometimes point to systemic issues rather than individual failure:

Entire teams underperforming? The problem is likely unclear expectations, inadequate resources, or poor processes.

High performers suddenly struggling? Look for burnout, role misfit, or personal circumstances requiring support.

Consistent gaps in one skill area? The employee may need training or role adjustment.

AI data helps managers distinguish individual performance issues from organizational problems.

Making Difficult Conversations More Constructive

Lead with curiosity. Start by asking what the employee is experiencing. “I’m seeing some changes in output over the past few months. Help me understand what’s happening from your perspective.”

Present data objectively. Share specific metrics without judgment: “Your project completion rate has been 60% this quarter compared to 90% last quarter.”

Collaborate on solutions. Frame improvement plans as partnerships: “What support do you need to get back on track?”

Separate performance from worth. Underperformance is about fit, skills, or circumstances—not the employee’s value as a person.

The Bottom Line

AI performance reviews transform underperformance management from an annual reckoning into continuous support. Early detection, objective evidence, and structured improvement plans help managers address issues before they escalate—while treating struggling employees with dignity.

The most effective approach uses AI for data gathering, pattern identification, and progress tracking—while managers provide empathy, context, and coaching.

75% of employees support AI-generated performance reviews when humans review them. The key is balance: let AI handle the mechanics while you focus on the relationship.

Tools like Windmill automate continuous data gathering, giving managers visibility into performance patterns before problems become crises.

Done right, AI doesn’t make underperformance conversations easy—but it makes them earlier, fairer, and more likely to end in improvement rather than termination.