There is no shortage of commentary on the state of performance management. Much of it centers on dissatisfaction—processes that feel outdated, overly burdensome, or disconnected from how work actually gets done.

At the same time, organizations face an increasingly complex reality. As AI becomes embedded in everyday workflows, it is changing not only how work is executed but also reshaping how value is created—often in ways that are less visible, less attributable, and more distributed than traditional systems were designed to capture.

Which raises a more fundamental question: When AI co-produces work, what does meaningful contribution look like—and how should it be measured and rewarded?

Our latest research (full reporting coming in late May which will be published on our Future of Work Research Lab Library ) brings together a global survey of more than 4,000 employees with a multi-session customer consortium of 25 performance and rewards leaders to explore this question in depth.

What emerges is not a simple call to replace existing systems, but rather a set of patterns that point to how they may need to evolve.

Performance management still carries significant weight

One consistent finding across the data is that performance management remains closely tied to how employees interpret their broader experience at work.

Our survey data illustrates that perceptions of performance management effectiveness are strongly and systematically associated with:

• Trust in leadership
• Perceived fairness
• Sense of value and contribution
• Job satisfaction
• Organizational resentment

As employees’ perceptions of performance management improve, so do reported levels of fairness, value, satisfaction, and trust—while resentment decreases. As they decline, the opposite pattern emerges.

From a psychological perspective, this is not particularly surprising. Performance management systems do more than assess outcomes; they signal how the organization evaluates people, allocates resources, and makes decisions. In that sense, they function as a visible expression of organizational values.

Further, what is entirely clear from our data is the importance of how employees interpret the purpose of performance management.

When employees experience their performance system as oriented toward improvement or development, they are significantly more likely to report higher levels of fairness, trust, and perceived value. When it is experienced as primarily focused on tracking or evaluation, these outcomes were less likely—and notably, organizational resentment becomes more likely.

This is a relatively subtle distinction, but an important one. It suggests that the impact of performance management is shaped not only by what the system is designed to do, but how it is enacted and experienced in practice.

Shifts in work are challenging existing measurement assumptions

Alongside these findings, there is broad agreement among both employees and HR leaders that the nature of work is changing—and that performance management will need to evolve alongside it.

Several forces are shaping this shift:

• The increasing pace and fluidity of work
• More distributed and cross-functional collaboration
• Greater reliance on digital tools and AI
• Shorter half-lives of skills

Within this context, some of the core assumptions embedded in performance management systems begin to show strain. For example, many systems implicitly assume that:

• Output is a reasonable proxy for value
• Contributions can be clearly attributed to individuals
• Performance can be evaluated within fixed cycles
• Managers have sufficient visibility to assess performance accurately

In practice, however, these assumptions are becoming increasingly untenable.

In our customer consortium, HR leaders consistently pointed to three areas where this friction is most pronounced:

1. Identifying impact. Organizations can often track activity and deliverables, but struggle to determine which work meaningfully influenced outcomes or advanced strategic priorities.
2. Capturing long-term value. Contributions that unfold over longer time horizons (such as building capabilities, improving systems, or reducing future risk) are not always well represented in annual or quarterly cycles.
3. Interpreting performance signals. Managers remain central to performance evaluation, yet their visibility is often partial, especially in distributed or asynchronous environments where much of the work happens outside direct observation.

These challenges are not necessarily new. But as work becomes more complex, interconnected, and increasingly mediated by AI, they are becoming more pronounced and difficult to navigate.

Toward a broader view of contribution

To better understand how organizations are making sense of these shifts, we asked HR leaders to reflect on how the definition of performance will need to evolve in the future.

What emerged was not a single, unified model, but a set of recurring themes that point toward a broader view of what constitutes meaningful contribution.

Several directional shifts stood out:

• From output to impact—moving beyond what is produced to what meaningfully changes as a result.
• From short-term results to longer-term value creation—accounting for durability, downstream effects, and cumulative contributions over time
• From individual achievement to system contribution—recognizing how individuals enable others and improve the performance of the broader system
• From task completion to quality of judgment—placing greater emphasis on decision-making, prioritization, and discernment, particularly in ambiguous or AI-augmented contexts
• From periodic reviews to more continuous sensing—reflecting the ongoing and dynamic nature of work
• From manager observation to a broader set of signals—incorporating data and patterns that extend beyond what any one individual can directly observe
• From static goals to more adaptive alignment—acknowledging that priorities shift and that goals may need to evolve accordingly

Taken together, these shifts reflect a gradual reorientation: from measuring what is visible and attributable, to understanding what actually creates value.

Importantly, these are not mutually exclusive, nor are they universally adopted. Rather, they illustrate the range of directions organizations are exploring as they work to better align performance measurement with how value is created today and tomorrow.

More data does not necessarily resolve ambiguity

One potential response to these challenges is to increase the volume and granularity of performance data.

Advances in AI and digital tools now make it possible to capture a wide range of behavioral signals, from communication patterns and collaboration networks to time-on-task metrics to workflow interactions.

However, both empirical literature and our own research point to an important tension. While additional data can provide new sources of insight, it can also introduce:

• Noise and ambiguity
• Challenges in interpretation
• Concerns around privacy and surveillance
• Risks to perceived fairness and autonomy

Empirical work suggests that systems perceived as opaque or overly intrusive can undermine trust, even when technically accurate. Our discussions with HR leaders echo this concern—particularly around how performance signals are generated, how they are weighted, and whether employees have visibility into or recourse over how those signals are used.

As a result, the question shifts from simply how much data is available to how that data is interpreted, communicated, and governed—placing increasing importance on transparency and explainability, as well as preserving space for human judgment.

The relationship between performance and rewards is also evolving

Changes in how performance is defined and measured have clear implications for how it is rewarded.

Our survey data reinforces that compensation remains a central factor in how employees engage with their work. On average, employees attribute approximately 72% of their motivation at work to pay, with a meaningful subset attributing nearly all of their motivation to compensation. Further, between 74-80% of employees report that an increase in pay – holding all else constant—would have a meaningful or major impact on key work outcomes including motivation, engagement, discretionary effort, performance, and retention. Across all five measures, only a very small minority (5–8%) say a pay increase would have no impact.

Together these findings underscore the continued salience of compensation as a behavioral driver.

At the same time, the relationship between performance and pay is becoming more complex, reshaped by several forces:

• Greater transparency around compensation
• Increased interest in skills-based pay models
• The rise of team-and network-based contributions
• Expectations for more personalized reward experiences

These developments raise important questions about how organizations will need to define fairness and allocate rewards in environments where contribution is more distributed, less visible, and more difficult to attribute to individuals.

They also point to a broader evolution in the role of rewards. Beyond reflecting past performance, compensation increasingly functions as a mechanism for shaping behavior and signaling organizational priorities—particularly in environments where what counts as contribution is itself evolving.

Looking ahead

Ultimately, these findings suggest that organizations are navigating a period of meaningful transition.

The core objectives of performance management—aligning effort with strategy, supporting development, and informing rewards—remain largely unchanged. What is changing is the context in which these objectives must be achieved.

As AI continues to shape how work is produced, organizations will need to place greater emphasis on:

• Understanding impact, not just activity
• Recognizing contributions that extend beyond formal roles
• Incorporating multiple sources of data and perspective
• Establishing credible, dynamic signals of individual skills and skill growth over time
• Balancing technological capability with human judgment and trust

For HR leaders and technology providers, this presents both challenges and opportunities—particularly in how performance and reward systems are designed, interpreted, and experienced.

Watch this space for the full research report, coming in late May where we explore these implications in greater depth, including emerging measurement models, design considerations for performance and reward systems, and potential directions for HR technology.

For now, one observation offers a useful starting point: As the very definition of performance evolves, so too must the systems used to understand and reward it—grounded in how value is created today and adaptable to how it will be created in the future.