Can AI Be Trusted With Pay Raises and HR Decisions?

By: Zenia Pearl V. Nicolas

As organizations adopt artificial intelligence for routine administrative work, discussions are now turning toward its use in high-stakes human resource functions, including pay raises, promotions, and performance evaluation. AI-powered tools promise speed and consistency, but they also raise questions around fairness, transparency and accountability in decisions that affect people’s livelihoods.

AI in HR: Efficiency Gains and Emerging Tools

AI in HR initially focused on administrative automation, such as scheduling and payroll processing. Today, many HR platforms include capabilities that go further, offering data-driven recommendations for pay adjustments, promotion readiness and workforce planning. Tools such as Workday’s Payroll Agent are designed to reduce manual data errors and free HR teams from repetitive tasks. These systems scan payroll records to catch mistakes, notify managers of compliance concerns, and help standardize routine HR workflows.

Managers are also increasingly using general AI tools, including large language models to assist with performance reviews and draft evaluation narratives. A survey cited in recent reporting found more than 60 % of managers using AI to inform employee decisions, and over half saying they involve AI in deliberations around raises and promotions. While AI can provide rapid insights, reliance on undertrained tools and unmonitored outputs has sparked concern about quality and unintended outcomes.

Bias, Algorithmic Decisions, and Employee Trust

A central challenge lies in how algorithms make decisions. Algorithmic bias, the systematic skewing of outcomes based on statistical patterns in data, is a well-documented issue in automated systems. When training data contain skewed historical practices, such as past unfair pay decisions, an AI system can inadvertently replicate those patterns. This can lead to outcomes that appear neutral but actually reinforce existing disparities in pay or advancement opportunities.

Organizational behavior researchers have also documented algorithm aversion, a psychological tendency for people to distrust algorithmic recommendations, especially when stakes are high. This preference can be especially strong in areas such as compensation, where perceptions of fairness and control play a significant role in career satisfaction and engagement.

Ethical Frameworks and Best Practices

Recognizing these risks, experts advise that organizations treat AI as supportive rather than authoritative in HR decision-making. Ethical frameworks for AI deployment emphasize principles such as fairness, transparency, and human accountability. These principles widely cited in organizational AI governance discussions, encourage clarity about how systems work, what they are trained on, and how results are interpreted by people.

A balanced approach means that while AI can highlight patterns and flag potential issues, the final decision on pay changes and promotions remains explicitly within the purview of trained HR professionals or managers with appropriate context and oversight. This practice helps ensure that decisions reflect both quantitative insight and qualitative judgment.

Signals From Organizational Practice

Real-world employer feedback underscores this hybrid model’s appeal. HR leaders increasingly develop AI governance guidelines that define where AI can be used, how results should be reviewed, and where human judgment must prevail. Many organizations also integrate bias audits and model validations to ensure that algorithmic outputs align with fairness goals over time rather than reinforcing past inequities.

This responsible deployment supports both efficiency and trust, enabling HR teams to leverage AI’s analytical power without ceding accountability to opaque systems.

Subtle Strategic Perspective

The current moment in 2026 suggests a transition: AI in HR is no longer experimental but entering mainstream practice. Yet, elevating AI’s role must be matched by governance, fairness checks, and transparent communication. When implemented with these guardrails, AI becomes a complement to human insight, helping organizations make better-informed decisions while preserving fairness and ethical standards.

References

Idaho Business Review. (2026, February 5). AI agents reshape payroll and HR decision-making. https://idahobusinessreview.com/2026/02/05/ai-agents-hr-payroll-decisions/

Online Harvard Business School. (2024, June 26). Building a responsible AI framework: 5 key principles for organizations. https://professional.dce.harvard.edu/blog/building-a-responsible-ai-framework-5-key-principles-for-organizations/

Wikipedia. (2026). Algorithmic bias. https://en.wikipedia.org/wiki/Algorithmic_bias

Wikipedia. (2026). Algorithm aversion. https://en.wikipedia.org/wiki/Algorithm_aversion