AI is making performance easier to measure than ever. Deciding what that performance is worth is still up to people.
Artificial intelligence is giving organizations something they have long struggled to achieve: a clearer, more continuous view of employee performance.
Instead of relying on periodic reviews or manager recall, companies can now draw on real-time data generated through day-to-day work, including progress toward goals, collaboration patterns and tangible outputs that were once harder to capture. For employers looking to strengthen pay-for-performance models, that shift carries significant weight.
Still, better data on its own doesn’t guarantee better decisions. As organizations begin weaving AI into performance management and compensation processes, the challenge extends beyond measurement. It lies in how that information is interpreted, communicated, and ultimately used to determine pay.
“AI is impacting both sides of the coin, both how we decide what performance is and how we measure it,” said Kayla Velnoskey, Director and Analyst at Gartner. “It can be used to capture information about how people are performing, summarize feedback, and even act as an input for making pay decisions.”
Performance is becoming more visible
AI is already reshaping how organizations approach performance measurement, moving beyond manager observations or employee self-reporting toward insights drawn directly from the work itself. That might include pulling signals from collaboration tools, tracking goal progress in real time, or identifying contributions that might otherwise go unnoticed.
It can also take on more administrative tasks, such as summarizing feedback or drafting performance reviews. This gives managers more time to focus on interpretation and discussion rather than documentation.
“Can AI find progress toward goals that maybe the employee hasn’t updated yet? Can it pull out the data that shows someone has completed an activity or moved something forward?” said Gretchen Alarcon, SVP/GM of HCM and Pay at UKG.
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The shift, however, isn’t just about efficiency. It also reflects a broader move toward improving the quality and consistency of performance insights. With the ability to surface patterns over time, highlight overlooked achievements, and piece together a more complete picture of employee contributions, AI is helping organizations see performance in ways that were previously fragmented.
That added visibility can also change how employees experience performance itself. Instead of relying on periodic feedback or unclear expectations, more continuous data can make it easier for employees to understand how they’re performing and where they might need to improve.
In fact, Corinne Post, the Fred J. Springer Endowed Chair in Business Leadership and Professor of Management at the Villanova School of Business, pointed to research by an international team that found employees have more confidence in A than in their human managers when it comes to performance assessment. By drawing on larger, more continuous data sets, AI can reduce some of the bias that often shapes performance and pay decisions.
Even so, many organizations are still figuring out how to use that information effectively. While experimentation is widespread, full integration into compensation decisions remains limited.
“There’s an opportunity to call out efficiencies gained with AI, but we don’t want to lose sight of the connection between how work gets done and what’s actually achieved,” Velnoskey said. “We really want performance to still be about outcomes that matter to the organization.”
Better data doesn’t settle pay decisions
Beyond improving measurement, AI has the potential to strengthen the connection between performance and pay, which is at the core of any effective compensation strategy.
Post points to the importance of clarity in pay-for-performance systems. Employees need to understand how their efforts translate into results and how those results influence compensation. When that relationship is unclear, motivation can quickly erode.
AI, in theory, can help to close that gap.
“What AI can do is tell me exactly how I’m doing,” Post said. “It can track performance in real time and provide feedback, so you’re not working hard and then discovering at the end of the day that it didn’t work. It can help employees see that connection between performance and reward more clearly.”
That level of visibility can be powerful. Instead of waiting for periodic reviews, employees gain a clearer, ongoing view of how their work contributes to outcomes and how those outcomes may be reflected in rewards.
Why judgment still matters
Even as AI improves how performance is measured, it doesn’t remove the need for human judgment. If anything, it makes it more important.
Managers remain responsible for interpreting data, applying context, and making final decisions about pay. Without that oversight, decisions can also become difficult to explain or defend, particularly when employees question how AI-generated insights are being applied to pay.
“I still think in any of these cases a human has to be in the loop. This can’t be a case where we just let AI write the document, do the ranking, and the assessments, and give compensation adjustments. There have to be humans in every single part of that process,” Alarcon said.
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Trust ultimately determines whether these systems succeed. Employees need to understand how performance is measured and how pay decisions are made. Without that transparency, even the most advanced tools can undermine confidence.
“There’s a chance people just learn what AI is tracking and optimize on those indicators,” Post said. “You also have situations where one employee is using AI and producing more, while another isn’t. That could affect how performance is evaluated, even if the underlying contribution isn’t that different.”
AI isn’t replacing pay for performance; it’s reshaping how it’s executed.
Better data can support better decisions, but only when paired with clear thinking, strong management, and open communication. Organizations that treat AI as a tool to enhance judgment, rather than replace it, are more likely to strengthen the connection between performance and pay.
But that balance is critical. As Post and Alarcon both suggest, the success of these systems ultimately depends on whether employees see them as fair, transparent, and grounded in human judgment.
Those that rely on it too heavily risk losing the context and trust that make the system work in the first place.
