The role of AI in performance-related pay

In organisations where performance and pay are intrinsically linked with greater transparency, artificial intelligence (AI) is introducing a complex interplay of new challenges and unprecedented rewards. Kayla Velnoskey, a director and analyst in the Gartner HR practice, underscores the urgent imperative for HR leaders to proactively evolve their pay-for-performance strategies in direct response to AI’s burgeoning impact on the modern workplace. The very foundations of effective pay-for-performance—a clear philosophy, fair assessment, and meaningful differentiation—are now under intense scrutiny and, in some cases, direct threat from the pervasive integration of AI technologies.

The Evolving Landscape of Work and Compensation

The concept of pay-for-performance is not new. Its roots can be traced back to early 20th-century industrial efficiency movements, evolving through various iterations like Management by Objectives (MBO) in the mid-century and more sophisticated performance management systems in recent decades. The core principle has always remained: rewarding employees based on their contributions and outcomes, thereby incentiv incentivising productivity and aligning individual goals with organisational objectives. However, the advent of AI represents a seismic shift, unlike any technological advancement before it.

Before the widespread integration of AI, performance evaluations largely relied on managerial observation, self-assessments, peer feedback, and a limited set of quantifiable metrics. While these methods provided valuable insights, they were often susceptible to human biases, inconsistencies, and the sheer administrative burden of data collection and synthesis. The rise of sophisticated analytics and subsequently AI has promised a future of more objective, data-driven performance insights, yet this promise comes with its own set of complexities that HR departments are only just beginning to unravel.

The current global economic climate, coupled with a heightened focus on employee experience and pay equity, further amplifies the need for robust and transparent pay-for-performance strategies. Employees, increasingly aware of market rates and internal compensation structures due to growing pay transparency legislation and cultural shifts, demand fairness and clarity in how their contributions are valued and rewarded. A December 2025 Gartner survey involving 1,622 respondents starkly revealed the motivational power of this link: when employees perceive a strong connection between their pay and performance, they are up to 17% more productive compared to when they do not. This significant productivity differential underscores why undermining this critical link with poorly implemented AI strategies could have profound negative consequences for organisational output and competitive advantage.

AI’s Double-Edged Sword: Challenges to Foundational Elements

AI’s integration into performance management is not merely an incremental change; it fundamentally reconfigures the landscape of work and evaluation. This necessitates a complete re-evaluation of the three foundational elements of successful pay-for-performance:

1. A Clear Philosophy: Traditionally, an organisation’s pay philosophy articulates what it values and rewards. With AI increasingly performing tasks or augmenting human capabilities, the lines become blurred. What should be rewarded? The human effort that leverages AI effectively? The AI-generated output itself? The innovative application of AI? This introduces new ambiguity around performance criteria. If, for instance, a sales representative uses an AI tool to generate leads and close deals far more efficiently, is their "performance" solely their human skill, or does it include their adeptness at utilising the AI? Failing to clearly define and communicate these new performance criteria risks alienating employees and eroding their belief in the fairness of the system. HR leaders must grapple with how to measure "AI proficiency" and "AI-augmented productivity" without inadvertently devaluing core human skills like critical thinking, creativity, and emotional intelligence.

2. Fair Assessment: AI-supported performance evaluations, while promising efficiency and objectivity, carry an increased risk of inconsistency and bias if not meticulously designed and managed. Algorithms are only as unbiased as the data they are trained on. Historical performance data, often laden with human biases, can perpetuate or even amplify these prejudices when fed into an AI system. For example, if past evaluations disproportionately favoured certain demographics or work styles due to implicit biases, an AI trained on this data might inadvertently replicate these patterns, leading to systematically unfair assessments. This is not merely a theoretical concern; numerous real-world examples in recruitment and lending have demonstrated how AI systems, due to biased training data, have inadvertently discriminated against certain groups. Without proper guardrails, explainable AI (XAI) principles, and continuous auditing, organisations face legal risks and severe damage to employee trust.

3. Meaningful Differentiation: In an era of tight compensation budgets and heightened scrutiny, making meaningful distinctions in pay is already challenging. AI can complicate these decisions further. While AI can identify patterns and suggest optimal allocations, if the rationale behind these suggestions is opaque or poorly understood, it can foster resentment and suspicion. The challenge lies in using AI to enhance differentiation without replacing human judgment or compromising the perception of fairness. The perception of being evaluated by an impersonal algorithm, without the nuance and context a human manager can provide, can be deeply unsettling for employees.

Leveraging AI for Enhanced Performance Assessment

Despite the challenges, AI offers substantial opportunities to enhance performance assessment when deployed thoughtfully and ethically. AI can significantly reduce administrative effort and synthesise vast volumes of data, leading to faster and more consistent assessments. The December 2025 Gartner survey illustrated this potential, with managers reporting an average of four hours saved across different parts of the performance management process when utilising AI. This time saving can free up managers to focus on more strategic coaching, development, and meaningful conversations with their teams.

Beyond mere efficiency, AI can bring a new level of data-driven insight to performance reviews. By analysing patterns in communication, project contributions, skill development, and outcome metrics, AI can provide a more holistic and continuous view of an employee’s contributions than traditional periodic reviews. For instance, AI-powered tools can:

• Identify Skill Gaps and Development Needs: By analysing project requirements and employee contributions, AI can flag areas where upskilling or reskilling might be beneficial, enabling proactive talent development.
• Provide Real-time Feedback: Instead of waiting for an annual review, AI can analyse ongoing work and provide immediate, objective feedback on performance, fostering continuous improvement.
• Detect Performance Trends: AI can identify subtle shifts in performance, both positive and negative, allowing managers to intervene early with support or recognition.
• Automate Data Aggregation: AI can pull data from various sources – project management tools, communication platforms, CRM systems – to create a comprehensive performance profile, reducing manual data entry and human error.

However, the critical caveat remains: AI must be treated as a sophisticated input to managerial judgment, not a replacement for it. Organisations must ensure that managers remain ultimately accountable for final evaluations and outcomes. This "human-in-the-loop" approach is paramount to mitigating risks and preserving trust.

The Indispensable Role of Human Oversight and Ethical AI

The ethical deployment of AI in performance management is non-negotiable. Without proper guardrails, the use of AI can open organisations up to significant legal, reputational, and cultural risks. Industry analysts and legal experts increasingly advocate for robust ethical AI frameworks within HR, encompassing several key principles:

• Transparency: Employees must understand how AI is being used in their performance assessments and pay decisions. Opacity breeds suspicion.
• Fairness and Bias Mitigation: Organisations must actively audit AI algorithms for bias, ensure diverse and representative training data, and implement mechanisms for human review and override of AI-generated insights. This includes techniques like explainable AI (XAI) to understand why an AI made a particular recommendation.
• Accountability: Human managers must retain ultimate accountability for all performance and pay decisions, even when informed by AI.
• Data Privacy and Security: The collection and analysis of vast amounts of employee data by AI systems necessitate stringent data privacy protocols and compliance with regulations such as GDPR, CCPA, and other regional data protection laws. Misuse or breaches of this sensitive data could lead to severe penalties and a catastrophic loss of employee trust.
• Human-Centric Design: AI tools should be designed to augment human capabilities and enhance the employee experience, rather than dehumanise the process or create a sense of being constantly monitored by a machine.

Leading HR technology providers are responding to these demands by developing AI tools that prioritize these ethical considerations, offering features like bias detection dashboards, clear audit trails, and customizable human override functionalities. However, the responsibility ultimately rests with the deploying organisation to implement these tools responsibly.

Differentiating Pay Fairly with AI: Building and Maintaining Trust

AI can serve as a powerful ally in optimising pay differentiation, particularly within constrained budgets. It can help leaders explore complex trade-offs in merit and bonus allocation, ensuring that differentiation is both meaningful and equitable. For example, AI can:

• Surface Outsized Impact: Identify where small reallocations of compensation could create a disproportionately large positive impact on employee motivation or retention in critical roles.
• Flag Misalignments: Highlight pay outcomes that do not align with performance data, indicating potential inconsistencies or unconscious biases that need human review.
• Identify Unintended Compression: Detect instances where salary ranges are becoming compressed, particularly for high performers, before decisions are finalised, allowing for proactive adjustments.
• Optimize Budget Allocation: By analysing market data, internal equity, and performance metrics, AI can suggest allocation strategies that maximize the return on investment for the compensation budget.

Used in this manner, AI strengthens differentiation by providing data-driven insights, allowing human leaders to make more informed, strategic, and fair decisions, rather than replacing human judgment entirely. However, the inherent risk of damaging employee trust, especially in today’s landscape of increasing pay transparency, cannot be overstated. Vague or poorly explained AI-driven decisions can rapidly erode confidence in the fairness and integrity of the compensation system.

To counteract this, organisations must actively combine both human and machine intelligence when using AI to differentiate pay. This means that while AI can crunch numbers and identify patterns, the final decision and its explanation must come from a human manager. Furthermore, CHROs bear a critical responsibility to equip managers with the skills and confidence to articulate how AI has influenced pay decisions. If managers are uncomfortable or unable to explain "how the decision was made" and "why it’s fair," the very trust in the fairness of pay decisions is jeopardised. When managers can clearly and empathetically communicate the rationale, AI transforms from a potential trust risk into a credibility enhancer, providing objective data points to support human judgment.

Actionable Strategies for HR Leaders

To navigate this evolving landscape successfully, HR leaders must adopt a proactive, multi-pronged approach:

1. Re-evaluate and Refine Pay Philosophy: Develop a clear, AI-inclusive pay philosophy that explicitly defines how AI’s contribution to productivity and outcomes will be factored into performance assessments and compensation. This involves establishing new metrics that account for AI leverage.
2. Implement Robust AI Governance: Establish clear policies and procedures for the ethical use of AI in performance management. This includes regular audits for algorithmic bias, ensuring data quality and diversity, and implementing mechanisms for human oversight and override.
3. Invest in Managerial Upskilling: Provide comprehensive training for managers on how to effectively use AI tools, understand AI-generated insights, identify potential biases, and, crucially, how to transparently and empathetically communicate AI-influenced pay decisions to their teams. This training should also cover ethical AI considerations and data privacy.
4. Foster a Culture of Continuous Feedback and Adaptive Metrics: Move away from static annual reviews towards dynamic, continuous performance management systems that can incorporate real-time AI insights. Regularly review and adapt performance metrics to ensure they remain relevant as AI reshapes job roles and responsibilities.
5. Prioritise Secure and Compliant HR Tech Infrastructure: Ensure that any AI tools used are integrated within a secure and compliant HR technology ecosystem that protects sensitive employee data and adheres to all relevant privacy regulations.
6. Champion Explainable AI (XAI): Whenever possible, advocate for and adopt AI solutions that offer explainability, allowing managers and employees to understand the rationale behind AI-generated recommendations, rather than accepting them as black-box outputs.

Broader Implications and the Future of Work

The integration of AI into pay-for-performance is more than just an HR operational change; it has profound implications for the future of work and organisational culture. It demands an evolving skill set for HR professionals, shifting towards greater data literacy, ethical AI oversight, and strategic influence. Organisations that master this integration will likely gain a significant competitive advantage, attracting and retaining top talent by demonstrating a commitment to fair, transparent, and data-driven compensation practices.

Conversely, those that fail to adapt risk fostering distrust, exacerbating employee turnover, and facing potential legal challenges. The future workplace, characterised by synergistic human-AI collaboration, requires a compensation framework that celebrates both human ingenuity and the intelligent application of technology. By strategically embracing AI as a powerful, yet carefully managed, tool, HR leaders have the opportunity to redefine pay for performance, making it more equitable, transparent, and motivating for the evolving workforce of tomorrow. The journey will be complex, but the destination – a highly engaged, productive, and fairly compensated workforce – is well worth the strategic effort.