HR Leaders Must Evolve Pay for Performance in the Age of AI

Q&A with Kayla Velnoskey, Director Analyst, Gartner

The rise of AI is amplifying long-standing challenges in how organizations design and deliver effective pay for performance programs. 

Kayla Velnoskey, Director, Analyst in the Gartner HR practice, discussed why it is urgent for CHROs to evolve pay for performance strategies in response to AI’s growing impact.

Q: How should organizations rethink their pay for performance strategy in an AI‑enabled workplace?

A: Effective pay for performance relies on three foundational elements: a clear philosophy, fair assessment and meaningful differentiation. 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. 

It’s necessary for organizations to adapt how they deliver the three foundational elements of successful pay for performance to adequately address the impact of AI. Specifically, CHROs should take three steps: 

1. Redefine an AI-ready performance philosophy: Clearly distinguish performance from potential when it comes to AI. While AI skills and adoption are increasingly important, paying employees for AI capability alone risks weakening the link between results and rewards.

2. Carefully apply AI for fast and fair performance assessment: Deploy AI with risk‑informed guardrails, ensure performance data is formatted for use by AI and create clear manager accountability to strengthen, not replace, human judgment.

3. Differentiate pay with human and machine intelligence: Pay for performance is most effective when pay decisions achieve high differentiation. Leverage AI to support meaningful differentiation while preserving pay equity, transparency and managers’ ability to explain outcomes.

Q: Why does pay for performance need to change as AI reshapes work?

A: Employees are motivated strongly by pay for performance. 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. 

As AI changes both how work gets done and creates new opportunities for evaluating performance, pay for performance strategy must evolve to reflect employees’ and managers’ new reality. 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.

Q: What role should AI play in performance assessment and evaluation?

A: AI can support faster and more consistent performance assessments by reducing administrative effort and synthesizing large volumes of data. 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. However, without proper guardrails, using AI in performance management can open the organization up to risk. 

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.

Q: How can organizations use AI to improve pay differentiation without eroding trust?

A: AI can help leaders explore trade‑offs in merit and bonus allocation, supporting more meaningful differentiation within constrained budgets. For example, AI can surface where small reallocations create outsized impact, flag pay outcomes that don’t align with performance data, or highlight unintended compression before decisions are finalized. Used this way, AI strengthens differentiation without replacing human judgment.

But vague or poorly explained AIdriven decisions can damage employee trust, especially in today’s era of pay transparency. To address this, organizations should combine both human and machine intelligence when using AI to help meaningfully differentiate pay. It’s important to use AI as a tool rather than a final decision maker.

Finally, CHROs must equip managers to confidently speak about how AI is being used in pay decisions, because if managers are uncomfortable explaining how AI has impacted that decision, they risk destroying trust in the fairness 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.