Artificial intelligence is increasingly, yet subtly, ascending the hierarchy of HR functions poised for transformation, particularly within the critical domain of pay, benefits, and total rewards. While early stages of adoption prevail, recent data indicates a significant uptick in experimentation, signaling a future where AI plays an integral, albeit carefully managed, role in how organizations compensate their workforce. This evolution is driven by the promise of enhanced efficiency and consistency, yet it is tempered by profound concerns regarding legal compliance, data integrity, and the pervasive specter of algorithmic bias.
The Rise of AI in Compensation: A Shifting Landscape
The journey towards AI integration in HR compensation is a natural progression of technological advancements that have steadily reshaped human resources over decades. From the advent of enterprise resource planning (ERP) systems in the 1990s to the proliferation of cloud-based HR information systems (HRIS) in the 2000s, HR functions have consistently sought digital solutions to streamline operations and improve decision-making. The current wave of AI, particularly generative AI and large language models (LLMs), represents the next frontier. Initially, AI applications in HR focused on areas like recruitment (applicant tracking, resume screening), talent management (performance prediction, learning recommendations), and employee engagement. However, the complex, data-rich nature of compensation has made it a prime, albeit more challenging, candidate for AI intervention.
A February Korn Ferry survey of HR and total rewards professionals revealed a notable increase in experimentation with AI in pay, benefits, and total rewards between 2025 and the early months of 2026. Despite this surge in exploration, the broader landscape suggests a cautious approach, with a significant 57% of respondents indicating they had not yet commenced experimenting with AI in total rewards. This measured pace underscores the intricate challenges and inherent risks associated with automating such a sensitive and legally fraught area. The global market for AI in HR is projected to grow substantially, with various analyses forecasting compound annual growth rates exceeding 20% over the next five to seven years, highlighting the strategic importance and investment potential in this sector, even if compensation applications lag behind other HR functions.
Current State of Adoption and Experimentation
The hesitation in widespread adoption is not unfounded. Gordon Frost, Global Rewards Solution Leader at Mercer, articulated to HR Dive that the integration of AI for setting pay has been deliberately slow, primarily due to "concerns about risk." Nevertheless, the technology is undeniably carving out a niche within many organizations by augmenting the capabilities of compensation professionals. Rather than replacing human judgment, AI is currently being deployed as a sophisticated tool for data synthesis and analysis.
Setting competitive and equitable pay structures necessitates the meticulous aggregation and interpretation of myriad data points, encompassing both internal organizational metrics and external market benchmarks. AI’s nascent role is to accelerate this process, making data collection faster, analysis more straightforward, and insights more consistent. "We’re seeing people start to use it from that perspective," Frost observed, highlighting AI’s utility in handling the sheer volume and complexity of compensation data.
Jamie Eisner, an attorney at Offit Kurman, clarified the current scope of AI deployment in this context. Employers are generally not utilizing large language models, such as OpenAI’s GPT or Anthropic’s Claude, to directly assign specific dollar figures to roles or individual employees. Instead, AI is more frequently observed as "a system that shapes the data, assumptions and decision-making frameworks that ultimately influence pay outcomes." This distinction is crucial, as it positions AI as an analytical engine rather than an autonomous decision-maker, a nuance that impacts both its utility and its regulatory implications.
Britney Torres, Senior Counsel at Littler Mendelson, emphasized that employers are strategically integrating AI into a broader, holistic compensation strategy. The objective extends beyond mere data processing; it involves leveraging AI to help determine which factors are most relevant in setting pay and to enhance consistency across an organization, particularly in large, geographically dispersed workforces. The ultimate goal, Torres noted, is to "improve the accuracy of your wage and compensation setting program… to accurately reward the employee." In this regard, the allure of AI lies in its potential to "improve the quality of that output," leading to more precise, fair, and defensible compensation decisions.
Foundational Prerequisites: The Data Imperative
Before organizations can effectively integrate AI into any HR process, particularly compensation, a significant amount of foundational work is indispensable. As Frost elaborated, a critical precursor is the consolidation of all relevant organizational data into a centralized repository. More importantly, this data must be rigorously cleaned, validated, and purged of errors and unintended biases. This data preparation phase, which often spans years, is paramount because the efficacy and fairness of any AI system are directly contingent on the quality and impartiality of the data it processes.
The complexities involved in this foundational cleanup are considerable. Organizations that have undergone multiple mergers and acquisitions, for instance, often contend with disparate job titles, inconsistent coding systems, and multiple employees performing similar roles under different classifications. "All of that needs to be cleaned up, simplified and aligned before you can use AI or do sophisticated data analysis," Frost stressed. Failure to address these underlying data inconsistencies can lead to the amplification of existing biases, rendering AI outputs unreliable and potentially discriminatory. This "garbage in, garbage out" principle is particularly potent in compensation, where flawed data can perpetuate systemic inequities and expose employers to significant legal and reputational risks.
Navigating the Regulatory Minefield: Legal and Compliance Challenges
The application of AI in setting pay introduces a labyrinth of legal and compliance challenges, reflecting the nation’s increasingly complicated AI regulatory landscape. The potential for AI-influenced pay structures to implicate various federal laws is a primary concern. Eisner pointed out that employers risk violating the Fair Labor Standards Act (FLSA) if AI leads to employee misclassification, minimum wage breaches, or errors in overtime pay calculations. These fundamental labor laws are non-negotiable, and AI systems must be designed and monitored to ensure strict adherence.
Furthermore, AI systems trained on historical pay or performance data pose a substantial risk of perpetuating or exacerbating existing pay disparities along protected characteristics such as gender or race. Such outcomes can create significant liabilities under federal equal employment opportunity laws, including Title VII of the Civil Rights Act and the Equal Pay Act. The insidious nature of algorithmic bias means that even without explicit intent, AI can inadvertently codify and amplify historical discrimination.
While federal regulations specifically targeting AI in compensation are still evolving, several states have proactively enacted laws restricting AI’s use in hiring, some of which contain provisions that could extend to pay systems. Torres highlighted that California’s privacy regulations, for example, mandate pre-use notice requirements for certain employers, informing consumers (including employees) about the use of automated tools in areas like compensation. Other state statutes require employers to conduct risk assessments of their AI tools or maintain detailed data pertaining to AI usage.
States have been slower to adopt laws directly addressing AI’s role in setting wages, but proposals are emerging from local legislators. While the precise contours of future legislation remain uncertain, Torres advises employers to identify "big-picture concepts" to prepare for forthcoming requirements. A recurring theme in emerging AI legislation is the demand for anti-bias assessment requirements. These clauses aim to ensure that automated tools make determinations that are free of discrimination and are legitimately based on factors pertinent to the employee’s role.
Other state AI laws, though not directly about pay, can indirectly affect compensation strategy. Maryland’s AI law, for instance, addresses the use of facial recognition technology during the hiring process. Laws of this nature may impose consent or usage restrictions, depending on how an AI tool functions and what data it collects. Eisner unequivocally stated, "The key point is that AI does not shift legal responsibility away from the employer. Employers remain accountable for wage outcomes, regardless of whether a human or an algorithm make the recommendation." This principle underscores the enduring need for human oversight and ultimate accountability.
Mitigating Bias and Ensuring Fairness in AI-Driven Pay
The critical challenge of avoiding discriminatory AI outputs extends beyond simply instructing a tool to ignore protected characteristics. As Eisner explained, employers must adopt an intentional approach to AI governance, recognizing that the technology’s efficacy is directly tied to the quality and ethical considerations embedded in the information provided. "Employers should clearly define what inputs are permissible and explicitly exclude inputs that may lead to biased outputs or pay disparities," she emphasized.
Even seemingly neutral factors can subtly skew results and disadvantage certain groups. Eisner cited examples such as an employee’s pay history, which can perpetuate past discriminatory wages; assumptions about an employee’s career path, which might reflect societal biases; location data, which can correlate with demographic factors; and performance history, if the metrics used for evaluation are themselves biased. Furthermore, relying on "overly blunt" metrics like keystrokes and mouse movement creates significant risk, as such measures can inadvertently discriminate based on protected characteristics like disability, national origin, or ethnicity.
Torres stressed the importance for employers to critically evaluate what factors might serve as a proxy for bias, reiterating that pay determinations must be based on nondiscriminatory and nonretaliatory reasons. Performance, while a seemingly sensible metric, requires careful consideration in its measurement. If performance evaluations incorporate audio or visual surveillance data, for example, this could implicate protected characteristics such as an employee’s national origin, race, ethnicity, or disability. "It’s not just about configuring the tool to comply with the law," Torres asserted. "You also want to plan for that meaningful human oversight and regular anti-bias assessments to protect against discriminatory patterns that may develop as a proxy." This proactive stance is essential for ethical and legally compliant AI implementation.
Safeguarding Sensitive Information: Data Security and Privacy
Beyond regulatory compliance and bias mitigation, data security and privacy represent another "major" consideration for employers contemplating AI in compensation. The reluctance to share sensitive HR data with AI tools is widespread. Frost from Mercer noted that organizations are generally hesitant to embrace AI for pay unless they are absolutely confident that the tool adheres to their organizational firewalls and is designed exclusively for private, internal use.
"That’s one reason why we’re not seeing widespread use of [AI in pay] yet," Frost commented, adding a stern warning that HR teams must never feed employees’ pay information into a publicly available AI model. The inherent risks of data exposure and potential misuse are too great. "Just knowing what the security parameters are and what the privacy guardrails are is super important." This highlights the critical need for secure, enterprise-grade AI solutions.
Torres emphasized the importance for compensation professionals to ensure that the data they use is anonymized wherever possible and to carefully consider how legal privilege might apply to decisions influenced by AI, even if its recommendations are not directly implemented. The potential for AI outputs to become discoverable in legal proceedings necessitates rigorous documentation and adherence to best practices.
AI tools often draw from a wide array of data sources beyond just pay figures, including human resources information systems (HRIS) software that tracks employee data, performance, engagement, and other indicators. Eisner advises employers to maintain transparency with employees regarding the types of data collected, how that data is utilized, and the legitimate business necessity for its collection. This transparency fosters trust and can mitigate legal challenges related to privacy.
Vendor relationships also introduce additional HR risks. The original article mentions the Kronos ransomware attack as an example of vendor-related vulnerabilities. When engaging AI vendors, employers must meticulously review their data handling practices, contract language concerning data ownership, and the security safeguards in place. "A guiding principle here should be data minimization: don’t collect or process data you don’t actually need because unnecessary data collection may create legal exposure without delivering corresponding value," Eisner concluded, underscoring a fundamental tenet of data privacy and security.
The Indispensable Human Element: Oversight and Governance
A central tenet underpinning the ethical and effective use of AI in compensation is the unwavering necessity of human oversight. Frost unequivocally stated that HR professionals must ultimately review AI outputs and make the final determinations on all compensation decisions. He drew an analogy to using Microsoft Excel: a human operator must validate and approve the output based on their knowledge and experience. "We’re not just completely giving it to AI and absolving ourselves of responsibility," he emphasized, reinforcing the concept of AI as a powerful tool that augments human capability, rather than replacing human accountability.
This point underscores the critical need for robust governance procedures. Torres noted that even humans overseeing AI outputs can struggle to identify bias without specific training and established protocols. Therefore, employers must invest in comprehensive training programs, budget for regular audits, and conduct thorough risk assessments to ensure their pay processes remain compliant and equitable. This systemic approach is vital for detecting and correcting discriminatory patterns that may emerge, even as a proxy for protected characteristics.
Before an organization commits to adopting AI in its compensation practices, Eisner recommends a strategic preliminary phase. This involves clearly articulating the specific objectives of introducing the technology, identifying the data necessary for successful implementation, and defining the core values the organization wishes to see reflected in its pay practices. This thoughtful preparation ensures that AI deployment is aligned with organizational goals and ethical standards. "AI should be treated as a decision-support tool, not as an autonomous decision-maker," Eisner firmly advised, encapsulating the philosophy that should guide all AI integration in sensitive HR functions.
Conclusion: The Future Trajectory of AI in Compensation
The quiet ascension of artificial intelligence in HR pay practices represents a significant evolution in how organizations approach compensation. While the promise of increased efficiency, consistency, and accuracy is compelling, the journey is fraught with complex challenges related to regulatory compliance, data integrity, and the persistent threat of algorithmic bias. The current landscape is characterized by cautious experimentation, driven by a clear understanding that the benefits of AI must be carefully balanced against its inherent risks.
Looking ahead, the trajectory of AI in compensation will likely see continued, yet deliberate, adoption. Organizations that succeed will be those that prioritize foundational data work, invest in robust governance frameworks, and commit to continuous anti-bias assessments. The legal and ethical imperative for human oversight will remain paramount, positioning AI as an invaluable decision-support tool rather than an autonomous decision-maker. As regulatory bodies at federal and state levels continue to grapple with the implications of AI, proactive engagement from employers, coupled with a commitment to ethical AI principles, will be essential for shaping a future where technology truly enhances fairness and equity in the workplace. The integration of AI into compensation is not merely a technological upgrade; it is a profound organizational transformation demanding strategic foresight, meticulous preparation, and an unwavering commitment to human-centric values.
