Performance reviews are meant to help employees understand where they stand, where they’re going and how to get there. But in most organizations, they’ve become a source of stress, confusion and broken trust.

Leaders see one reality while employees experience another. Acorn found that 66% of executives believe their performance metrics are fair, yet most workers leave performance reviews feeling anxious or stressed. One in four employees question their value to the organization afterward. Recent research from CalypsoAI found that 38% of workers would rather have an artificial intelligence (AI) manager than a person. When more than a third of your workforce prefers an algorithm to their actual boss, something fundamental has broken.

Organizations need performance systems that actually work — systems that develop capability and retain talent. However, many are spending a fortune every year on approaches that push their best people toward the exit.

AI can help fix what’s broken, but only if it’s used to make managers more effective at the human parts of their job, not to replace human judgment.

Reviews Measure the Wrong Things

Performance reviews have become backward-looking exercises that document the past rather than develop for the future. Even when reviews include development conversations, they’re often disconnected from actual growth opportunities or career paths within the organization.

One of the core problems is that HR’s own functions operate in isolation from each other. Recruitment evaluates candidates based on resumes and interviews. Learning and development (L&D) tracks course completions. Performance management measures goal achievement. Each function uses different platforms, different metrics and different definitions of success.

This disconnection results in organizations being unable to answer basic questions like “What is this person actually capable of doing?” or “What do they need to learn to be ready for the next role?” Without a shared framework for capability across hiring, development and performance, even HR can’t connect the dots between where someone is and where they could go.

Without integration, even well-designed frameworks collapse under manual workarounds and misaligned data. Leaders know the system is broken; 80% of senior leaders admit employees must leave their company to get promoted or earn higher pay, according to Acorn’s research. When performance reviews focus on past outcomes instead of future capability, they can’t do what they’re supposed to do: Help people grow.

AI Can Help Managers Focus on People

Employees are more open to AI in performance management than leaders expect. According to The Predictive Index’s 2025 AI at Work survey, 81% of employees feel positive about AI’s role in their careers. But openness doesn’t mean employees want AI to make decisions about their performance. They want it to make their managers better at the human parts of the job.

Think of AI as a productivity tool for people management. It can:

• Transcribe performance conversations and help managers translate what they heard into clear, actionable development plans.
• Surface behavioral patterns that might signal disengagement before small issues turn into resignation letters.
• Help managers document what matters without spending hours writing performance reviews from scratch.

Organizations can’t and shouldn’t try to automate judgment but instead remove the administrative burden that keeps managers from developing their people. When managers spend less time on paperwork and more time on meaningful conversations, performance reviews can actually be the tools they were always supposed to be.

What Forward-Looking Reviews Actually Require

Performance reviews need to shift from evaluating the past to building for the future. That means rethinking what they’re actually designed to accomplish. Here’s what works:

• Two-way conversations, not one-sided evaluations. Employees don’t want to be talked at; they want dialogue about their growth and their manager’s role in supporting it.
• Focus on capability. Ask what this person can do, what they need to learn and how that moves the business forward. This shifts the conversation from outputs to potential.
• Use behavioral insights. Understand how people work best, not just what they produce. Some employees thrive with autonomy, while others need structure. Performance conversations should account for these differences.
• Create transparency around growth paths. When employees can’t see how to advance without leaving, they leave. Clear capability frameworks show what’s required for the next role.
• Move to continuous development. Ongoing conversations throughout the year keep development front and center instead of making it an annual afterthought.

Building Trust Takes Time

These changes require intention, but they don’t demand perfection. Organizations don’t need to overhaul everything at once to build lasting progress.

Start with teams and managers who are willing to try something different before jumping the gun on company-wide mandates. Let early wins build momentum rather than forcing adoption. When skeptical managers see their peers having better conversations and spending less time on administrative work, they’ll be more willing to try it themselves.

And address skepticism directly. Employees need to understand how AI will be used and what it won’t do. Be clear that AI augments manager judgment for better employee support, not replaces it or surveils employees. When organizations are transparent about what AI does and doesn’t do, and when employees see that it makes their managers more helpful rather than more intrusive, adoption becomes easier.

Better performance conversations start with better tools and a willingness to change. Trust doesn’t rebuild overnight, but every good conversation is a step forward.