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The rise in AI usage is disrupting pay for performance models, bringing fresh challenges around how organizations define, measure, and reward contribution.
Kayla Velnoskey, Director, Analyst at Gartner, said: “Effective pay for performance relies on three foundational elements: a clear philosophy, fair assessment, and meaningful differentiation.”
As AI becomes embedded in workflows and performance management, it is both adding efficiencies and amplifying existing weaknesses.
Velnoskey said: “AI threatens these foundations by amplifying longstanding challenges, such as introducing new ambiguity around what should be rewarded, increasing the risk of inconsistency and bias in AI-supported performance evaluations, and complicating already tight compensation decisions.”
Turning workforce data into early warnings for high-cost employees
It forces CHROs to revisit the mechanics of pay for performance at a structural level, rather than treating AI as a bolt-on to existing systems.
AI disrupts pay for performance foundations
“Employees are motivated strongly by pay for performance,”Velnoskey says. “A December 2025 Gartner survey of 1,622 respondents revealed that when employees believe there is a strong link between pay and performance they are up to 17% more productive versus when they do not.”
Maintaining that link is becoming harder as AI reshapes both work and evaluation processes. If performance arrangements fail to capture AI-driven output, or if AI introduces bias into assessments, the consequences extend beyond process failures to impact employee motivation.
Velnoskey explains: “If, for example, performance criteria are unable to capture real productivity gains from using AI, or if AI use in performance reviews creates biased assessments, organizations risk destroying this critical link between performance and pay that drives motivation.”
Pay for performance can no longer rely on static definitions of output or contribution. Organizations are now rethinking how performance is defined in environments where human and machine input are increasingly intertwined.
Balancing AI efficiency with trust and fairness
AI’s role in performance management presents both an opportunity and a risk. While, on the one hand, it can streamline processes and improve consistency, on the other, it raises questions about accountability and transparency.
“AI can support faster and more consistent performance assessments by reducing administrative effort and synthesizing large volumes of data,” explains Velnoskey.
“According to the December 2025 Gartner survey, managers reported an average of four hours saved across different parts of the performance management process when using AI.”
Without clear guardrails, however, AI can become a risky source of bias rather than a tool for objectivity.
Velnoskey expands: “Organizations should treat AI as an input to managerial judgment rather than a replacement for it, ensuring managers remain accountable for final evaluations and outcomes.”
In other words, while AI can help identify patterns and optimize outcomes, it cannot replace the human judgment required to maintain fairness and trust.
“AI can help leaders explore trade-offs in merit and bonus allocation, supporting more meaningful differentiation within constrained budgets. But vague or poorly explained AI-driven decisions can damage employee trust, especially in today’s era of pay transparency.”
The challenge, as ever, is how to integrate AI without eroding confidence in the system, placing new demands on managers, who must be able to explain both the process and the outcome of pay decisions.
“When managers can clearly explain ‘how the decision was made’ and ‘why it’s fair,’ AI becomes a credibility enhancer rather than a trust risk.”
