Does anyone actually look forward to a performance review? For a process meant to help people grow, it gives everyone plenty to dread. Employees spend hours proving they did their jobs. Managers spend hours trying to remember how well they did them. And somehow a full year of work ends up as a meeting both people would happily reschedule.

Work comes with immediate deadlines. Help improving at it can apparently wait another few months. That is the problem with the traditional performance review: By the time employees hear what they should have done differently, they have already spent months doing it. AI can help managers prepare for useful conversations throughout the year. Whether that improves the process depends on whether managers make time.

Your Performance Review Arrives After You Needed It

An annual review can establish where an employee stands. It cannot go back and help them handle the assignment they struggled through four months ago. Yet the formal review can become the place where an employee first learns that their manager was dissatisfied.

In Gallup’s August 2025 U.S. employee data , just 22% strongly agreed they had received meaningful feedback in the previous week. Only 20% strongly agreed that their performance was managed in a way that motivated them to do outstanding work.

Replacing the annual review with a quarterly version does not automatically solve this. A shorter wait is still a wait if the employee needs help preparing for tomorrow’s meeting. The process needs to make room for coaching when there is still something the employee can do with it. Formal reviews can then assess progress instead of introducing concerns that should have been discussed months earlier.

An AI Coach Still Isn’t Your Boss

Will Foussier, CEO and cofounder of leadership coaching company AceUp, sees a practical reason those conversations get postponed.

“The coaching gap isn’t a desire problem; it’s a capacity problem,” he says. “Managers are expected to coach while still being accountable for everything else they need to deliver, and human coaches can’t be available every time someone needs support.”

AI can help when an employee needs guidance and their manager is unavailable. Someone who struggled through a presentation would benefit from an AI coach to prepare for the next one. They would have somewhere to work through their approach while the experience is fresh.

But their manager still needs to discuss what happened. The employee may think they explained the material poorly when the real problem was that they had been given conflicting instructions. A conversation gives both people a chance to understand what went wrong and agree on what would help. The manager may need to change something, too.

“More context can make AI a better coach. It doesn’t make it the manager,” Foussier says.

That conversation is part of the working relationship. Employees need to hear what their manager expects them to do differently. An AI coaching session cannot settle an expectation the boss has never explained.

“Decisions about someone’s performance, compensation or promotion need to remain with a human who can explain the decision and be accountable for it,” Foussier says.

Use AI To Help Managers Pay Attention

AI can also help the manager prepare for their side of that conversation. Sachin Goyal, founder of employee development platform ClarityLoop, sees a problem with asking managers to reconstruct months of work.

“The fundamental problem was not the annual review form itself,” Goyal states. “It was that the process asked the wrong question, too late: ‘What can you remember about this person’s performance over the past six or twelve months?’ By then, the evidence is scattered across projects, conversations and systems. Recent or highly visible moments tend to dominate, quieter contributions disappear, and something that could have been addressed constructively months earlier can become a formal performance issue.”

He describes a system that draws on context from everyday communication and collaboration. Direct feedback provides another source of information. The system helps highlight examples of someone’s work while the details are still fresh.

The presentation example shows how this works for the manager, too. Before their next one-to-one, the manager should review relevant feedback about that assignment rather than rely on a general recollection that it went badly. They would have a starting point for asking what happened. The employee can then explain the conflicting instructions that contributed to the problem.

The conversation could end with a specific agreement: The manager will clarify the brief before the next presentation. At the following check-in, they can discuss whether that helped.

By reducing the work of finding and organizing examples, AI can help managers spend more of the conversation discussing what the employee needs . The manager still needs to check those examples and give the employee room to explain what happened.

“In some organizations, continuous performance management has simply turned one large annual form into a smaller form that people are asked to complete more often,” Goyal says.

Companies should resist using AI to produce more of the same paperwork. The useful question is whether it helps the manager have a conversation they would otherwise have postponed or handled poorly.

Goyal emphasizes that employees should be able to inspect and challenge the evidence behind AI-generated observations. He also rejects communication volume alone as a measure of performance. Being the busiest person in the team chat does not establish who is doing the best work. Companies should explain what information the system uses and who can access its output.

What The Early Research Shows

ClarityLoop’s July 2026 white paper describes its CollabSense research with Edinburgh Napier University. The study examined more than 3,300 public GitHub collaboration interactions to test whether they could produce useful coaching material.

Human reviewers judged 85% of sampled growth opportunities to be at least partially actionable. They judged 90% to be at least partially supported by the original evidence. Those findings support the potential to give managers useful starting points for coaching conversations. They do not establish whether that coaching subsequently improved employee performance.

Goyal is careful about separating those findings from the longer-term outcomes the company wants to examine.

“The stronger test will be longitudinal: whether managers prepare better, whether feedback becomes more specific, whether agreed actions are completed, whether employees experience fewer review surprises, and whether perceptions of fairness and trust improve over time,” he says.

Employers evaluating AI coaching should apply that standard, too, examining whether feedback leads to action and whether managers provide the support they promised.

Keep The Review. Change What Happens Before It

The formal review can stay on the calendar. Between reviews, managers should discuss important work while the details are fresh and record what they agreed to do next. AI can help organize the material for those conversations. At review time, both people should be examining progress against expectations they have already discussed.

To put that into practice, companies should:

  • Explain expectations before the assignment. Employees should understand what successful work looks like before discovering at review time that they missed the mark.
  • Discuss important work while the details are fresh. Use the conversation to understand what happened and agree on one useful next step.
  • Use AI to prepare for conversations. Managers should check the underlying examples and invite the employee to supply missing context.
  • Record the manager’s commitments. If a manager agrees to provide support, revisit whether it arrived. The employee’s follow-through should not be the only follow-through under review.
  • Explain how AI-generated material will be used. Employees should know whether coaching information can enter a formal assessment.
  • Check for review surprises. A significant concern raised for the first time during a formal assessment should prompt an examination of why it was not discussed sooner.

The formal review still has a job to do. It can assess progress and explain decisions about an employee’s future. It becomes more credible when both people can trace that assessment through conversations they actually had. AI may help a manager remember the work. Employees still need a manager who makes time to talk about it before the rating is due.