INTRODUCTION

We examine how artificial intelligence (AI) and pay information disclosure (PID) — “the communication of relevant pay information between and among actors” (Brown et al. 2022: 1662) — combine to shift the foundational assumptions of total rewards (TR) system design and administration. Historically, TR systems have been grounded in the interconnected foundational principles of individual motivation and equity as well as organizational justice and trust (Adams and Freedman 1976; Colquitt, Scott, and LePine 2007; Colquitt 2012; Gerhart and Fang 2015; Lawler and Suttle 1973).

Yet, the practical design and implementation of these systems is often constrained in management and communication (Fulmer and Chen 2014; Gerhart 2024). This has led to a preference for simpler structures, which are easier to administer and explain (Gerhart and Rynes 2003). However, the evolving competitive talent market along with a more competitive business environment necessitates more nuanced and customizable TR strategies to effectively reward high performers, get a high ROI from TR systems and maintain a competitive advantage (Conroy et al., 2015; Gerhart and Weller 2019; Nyberg, 2010).

Organizations have historically provided minimal transparency about the inner workings of pay administration and other pay details, in part, to maintain an advantage in employee negotiations, maintain employee privacy and avoid criticism of pay practice exceptions or inequitable pay practices (Brown et al. 2022). A confluence of factors, however, is now driving a shift toward greater transparency in pay information, leading to more pressure to disclose pay details (Brown et al. 2022; Peng 2022). This shift is fueled by heightened interest from various stakeholders, including employees, companies, governments, the media and scholars (Arnold and Fulmer 2018; Bamberger 2021, 2023). It is facilitated by digital advancements and evolving legal mandates for transparency of pay information to assist in making critical decisions. Such transparency is likely to affect employee decisions, such as which organization to join, whether to stay, and how much effort to exert (Gerhart and Rynes 2003).

The ubiquitous use of generative AI, driven by access to large language models, is promising to fundamentally transform the way we implement pay. The increasing automation of work processes will influence PID as AI and modern computing capabilities can revolutionize TR system design, enabling the mass customization and communication of pay (Nyberg et al. 2023). AI’s capacity to process vast amounts of data and convey intricate information can aid in educating stakeholders, management and employees about more sophisticated pay systems.

The integration of AI in PID initiatives promises to elevate trust in AI technologies while fostering a culture of transparency and understanding around TR systems (Charlwood and Guenole 2022; Privacy Forum 2018). Additionally, the symbiosis between AI and PID is poised to challenge and redefine traditional pay administration approaches focused on simplicity and uniformity, paving the way for more nuanced, individualized and transparent approaches, starting a new evolution in how pay information is disclosed and pay is designed and managed.

In this article, we explore the integration of AI and PID as complementary forces that can redefine the TR landscape. We highlight relevant challenges and opportunities, including concerns around data privacy, trust and regulatory compliance. Through five illustrative use cases, we demonstrate the potential of AI and PID to revolutionize TR systems, enhancing transparency and fairness. We also provide actionable recommendations for navigating the complexities of this integration, with a focus on achieving employee trust and pay equity.

Our discussion aims to chart a course for the future of compensation management, highlighting the strategic benefits of embracing this transformative synergy. See Table 1 for a summary of use cases.

CURRENT LANDSCAPE OF AI IN TOTAL REWARDS

AI in HR encompasses a wide spectrum of tools, each at different stages of maturity. These tools range from knowledge management systems, which organize and process information, to cognitive computing models that mimic human problem-solving capabilities, including natural language processing for text and speech (Howard 2019; Wang and Siau 2019). Each serves a distinct function, processing specific types of information to produce desired outcomes. Such tools can be integrated into broader networks or platforms to support organizational or cross-organizational functions (Strohmeier and Piazza 2015).

From basic analytics and automation to advanced predictive and prescriptive analytics, AI’s evolution is enhancing HR operations, streamlining tasks such as resume screening and candidate sourcing, and enabling more strategic resource allocation (Strohmeier and Piazza 2015; Charlwood 2021; Guenole and Feinzig 2018).

AI’s role in decision making ranges from full automation to hybrid models where AI and human decisions are made in tandem. IBM’s implementation of a machine learning system for salary recommendations, which boasts a 95% manager acceptance rate and has led to a 50% reduction in attrition, exemplifies AI’s potential in compensation discussions (Sammer 2019). Similarly, the use of algorithmic selection tests in hiring processes has demonstrated that managers who follow AI recommendations tend to make better hiring decisions, underscoring AI’s utility in improving selection outcomes (Hoffman, Kahn, and Li 2018).

One current AI adoption trend in HR is the experimentation and increasing use of generative AI tools such as ChatGPT to help in daily work. While these tools primarily help individuals in limited tasks, they foreshadow the power of AI tools that are fully designed and integrated with the requisite data (Budhwar et al. 2023). The ongoing and future integration of AI in total rewards promises to revolutionize the field by automating administrative tasks, providing personalized pay explanations, and offering HR professionals unprecedented predictive insights, trend analyses and sophisticated TR methodologies (Nyberg et al. 2023).

CHALLENGES AND OPPORTUNITIES IN AI-PID INTEGRATION

Challenges

Despite the growing adoption of generative AI in the HR space, organizations face hurdles in data collection and integration. Effective AI deployment requires a delicate balance of cost, quality and quantity of data. Moreover, employee trust is crucial if the potential of AI to transform TR practices is to be fully realized.

Employees must be able to understand how AI processes data and how decisions are made regarding their pay. However, the “Black Box Dilemma,” where the inner workings of AI algorithms are not easily understood, complicates this transparency, potentially leading to confusion and mistrust (Brooks 2021; Kim and Bodie 2021). Balancing the need for transparency with privacy concerns is also critical, especially in TR systems handling sensitive data. The evolution of AI technology also introduces potential regulatory challenges as laws struggle to keep pace with technological advancements (Kim and Bodie 2021, Rampersad 2020).

Opportunities

Despite these challenges, there are several key opportunities for AI and PID. AI’s capability for customization stands out. AI can tailor pay packages and performance feedback to individual employee needs, offering a level of personalization previously impractical. This personalization extends from communication to the administration of pay packages, enhancing the transparency and understanding of PID processes. Advanced AI systems can demystify how pay decisions are made, thereby fostering trust and engagement among employees.

AI’s analytical prowess also has the potential to transform pay negotiations, providing access to comprehensive data for more informed discussions. By leveraging data-driven insights, HR can foster equitable pay structures, aligning organizational objectives with employee expectations, reducing the ambiguity in pay negotiations. When implemented thoughtfully, AI can promote pay transparency, helping to develop a culture of trust when pay and performance are well connected. However, there will be a premium to working toward removing bias in the algorithms that learned from prior human decisions (see Table 2).

THE FUTURE OF AI, PID AND THE NEW TR LANDSCAPE

As we navigate the future of AI and PID within total rewards, several key themes emerge that will redefine this landscape:

Pay customization could become standard. AI’s predictive analytics could enable the crafting of pay packages that are highly personalized, aligning with individual needs and market dynamics. This level of customization, from individual pay to performance feedback, has the potential to not only enhance employee satisfaction but also drive organizational performance by making pay feel more tailored and equitable (Maltarich et al. 2017; Abdulsalam et al. 2021). The complexity of pay plans, previously a barrier due to administrative constraints, can be mitigated by AI, allowing for sophisticated designs that accommodate a multitude of variables. This shift toward customization, including individualized pay and performance arrangements (Maltarich et al. 2017) mirrors the trend of mass customization seen in marketing (Da Silveira, Borenstein, and Fogliatto 2001). It is increasingly demanded by consumers in a wide variety of products, from smaller items such as customized clothing, to larger items such as customizing homes. AI’s ability to tailor pay based on a wide range of compensable factors promises a more holistic approach to rewards. Critical to making such plans effective will be to ensure that even customized plans are aligned with the strategic organizational goals (Nyberg and Reilly 2019).

Performance management systems will increasingly incorporate real-time information. Real-time feedback will increase and the accumulation and sorting of large amounts of data from many different perspectives will become easier to manage (Budhwar et al. 2023). Facilitated by both direct and indirect data monitoring, real-time feedback can empower employees with immediate insights into their contributions and the workings of the pay system. Even if managers still adhere to annual reviews, the ability to pull from and sort through vast quantities of data should overcome one of the larger annual review problems, namely recency bias (Stone, Lukaszewski, and Johnson 2024). Vast amount of employee-specific data are generated, yet much of it remains untapped. This abundance of data makes the transition to a more advanced performance management system increasingly viable.

Electronic surveillance may also facilitate data collection, where indirect observation provides insights into employee activities. This can include, for instance, the analysis of anonymous metrics such as log-in frequencies, which may be initially collected without personal identification but later correlated with individual profiles (West 2019; Krapp 2022; O’Leary 2013). Companies specializing in electronic surveillance collect data via internet browsers, websites and mobile applications, often without full user awareness. They can leverage this information to infer work habits and productivity levels, while triangulation techniques enable the prediction of an employee’s identity based on data patterns, akin to profiling (Krapp 2022). For example, without explicit personal information, a digital footprint anchored by a single data point, such as a phone number, can reveal significant details about an individual’s work and personal life through location and usage tracking (Krapp 2022).

In combination, AI tools can use internally monitored data, as well as data acquired directly or indirectly from third parties, to construct a comprehensive profile of an employee’s work performance. For example, companies might use data from internal surveillance, such as monitoring in warehouses (e.g., Amazon) or communications platforms (e.g., email, Zoom, Slack) (Lacoma 2023), to refine performance assessments (Delfanti and Frey 2021), while external data providers could offer additional insights to enrich algorithms that estimate performance- or pay-related metrics.

Pay transparency could become more common. With AI’s capability to calculate and communicate pay determinants, and as the use of AI increases, organizations will face increased expectations for openness. AI can simultaneously address demands for transparency, offering clear, individualized explanations of pay decisions and processes, thereby fostering a more transparent workplace (Nyberg et al. 2023).

AI-enabled, bias-reducing decision making holds substantial promise. AI has potential to reduce human biases in performance evaluations and pay-related decisions (Stone, Lukaszewski, and Johnson 2024). Through careful training and regular audits for fairness, AI can contribute to more objective decision-making processes, identifying and correcting systemic inequities (Stone, Lukaszewski, and Johnson 2024). However, this is an area fraught with potential problems, as AI learns from prior results, so bias built into the system could permeate if not carefully monitored (Lukaszewski and Stone 2024).

AI and PID can play a central role in pay negotiations. AI tools can equip both employers and employees with detailed data during pay negotiations, increasing the likelihood that discussions informed by data can drive the negotiation.

Employees will become more empowered with pay knowledge. Increasingly available AI tools and PID will place more power into the hands of employees. From helping employees understand where they are in terms of pay compared to peers, to assessing future career paths and associated pay, AI can provide actionable insights and new perspectives.

Ethical challenges with AI and PID will persist. The integration of AI and PID in HR processes brings with it ethical considerations around privacy, transparency and fairness. HR professionals must navigate these challenges, addressing concerns such as data privacy, the perpetuation of biases through AI and the impact of AI on employment. The balance between technological advancement and the human element of HR will be crucial, ensuring that AI tools augment rather than replace the nuanced, human-centric aspects of HR strategy and delivery.

In summary, the future of total rewards with increased AI and PID has the potential for greater efficiency, equity and transparency. HR professionals will need to navigate this new landscape with a balance of technological competency and human touch. While AI tools coupled with increased PID offer unprecedented capabilities, AI tools should augment, not replace, the human element of HR strategy and delivery. Table 3 summarizes these future trends.

FUTURE USE CASES

Use cases offer a forward-looking approach to navigate the AI landscape. They can help define the functional requirements to be addressed and formulate a clear vision for measurable and achievable outcomes. Recognized as a standard practice in IT development and project management to bridge the gap between technical potential and practical application (Larson and Larson 2004), the use case methodology is a useful tool in planning AI-PID strategy.

The commitment to AI entails substantial financial investment, necessitating an understanding of requirements and anticipated benefits to define the ROI before deciding where to allocate scarce resources (Jarrahi 2018).

Benchmarking methods of determining application design and their underlying workflows can fall short due to their inability to accurately capture the dynamic and rapidly evolving nature of a technology. This weakness is particularly relevant to AI applications due to AI’s rapid evolution, underscoring the importance of a proactive approach. The speed of adoption and implementation of a disruptive technology can meaningfully affect an organization’s competitive advantage (Christensen 2013; Moore 1999; Reis 2011).

In Table 1, we listed five use cases focused on total rewards, illustrating where innovative and strategic AI and PID applications could address real-world TR systems and pay administration challenges. Below, we provide a description of these use cases. After presenting each scenario, the purpose of the application is clarified by defining the goals of what it aims to achieve. The use case then specifies the preconditions that must exist before the solution can be implemented and the postconditions that determine successful implementation.

Use Case 1: Proactive Pay Scale and Equity Adjustments

Scenario: A global company with a diverse workforce unexpectedly discovers pay disparities due to varying local and regional job roles, and the absence of standardized pay scales; however, the company wants to be a leader in addressing gender and racial pay gaps.

AI-PID application solution: An AI-driven system uses a wide range of data, including regional benchmarks, specific job roles and performance metrics, to continuously assess and adjust pay scales to identify pay disparities and recommend adjustments, to increase the likelihood of consistently applying the company’s pay structure.

Goal: To ensure pay scales are consistently aligned with the organization’s strategy, market standards, employee performance and regional differences, while also identifying and correcting pay disparities across the organization due to gender, race or other non-performance-related factors. Thus, promoting pay equity and a culture of fairness and inclusivity.

Preconditions: 1. The organization has access to comprehensive market data, including regional indices and industry pay standards for relevant roles. 2. Individual pay and job-related data are available and updated. This includes employee demographics, job responsibilities and performance data. 3. The organization has established clear criteria for what constitutes equitable pay for similar roles and responsibilities, accounting for experience and performance.

Success criteria: 1. Pay scales are adjusted in real time or at predefined intervals to reflect market changes and differences. Discrepancies in pay due to market changes or internal inconsistencies are minimized. Pay equity is achieved across employee demographics, with no unjustified disparities. 2. Enhanced transparency and understanding among employees regarding how pay scales are determined, fostering trust in the pay administration process. 3. The organization is recognized internally and externally for its commitment to equity and inclusivity, potentially enhancing its reputation and employer brand. 4. Employees have confidence in the organization’s commitment to fairness, as evidenced by feedback and satisfaction surveys.

It should be noted that such adjustments are possible now (many organizations are attempting to do so), and with generative AI, these actions are likely to become much simpler. Simultaneously, with generative AI, it is possible that employees will be able to check on management practices much more easily, placing greater pressure on organizations to act.

Use Case 2: Real-time Performance Feedback System

Scenario: A large enterprise finds assessing individual and collective performance management challenging, and that it is a lagging indicator, often leading to biases during evaluations. Additionally, managers complain that annual reviews require compiling too much information, employees complain that the feedback is too superficial, and management recognizes cognitive limits of annual reviews (e.g., recency bias).

AI-PID application solution: An AI-enabled system that monitors both individual outputs and collaborative efforts in real time, capturing a wide range of performance-related activities and interactions to provide a holistic view of employee contributions.

Goal: To develop and implement a real-time performance feedback system that enhances employee development, engagement and productivity by providing immediate, actionable feedback based on a comprehensive analysis of individual and team performance metrics.

Preconditions: 1. The organization has an infrastructure capable of capturing real-time data on employee activities, outputs and interactions. 2. Employees and managers are trained on how to interpret and use real-time feedback for personal development and team improvement. 3. Clear performance metrics and goals are established for all relevant roles.

Success criteria: 1. A system is in place that provides employees with real-time feedback on their performance, allowing for quick adjustments and improvements. 2. Employee engagement and productivity show improvements resulting from timely and relevant feedback. 3. Managers, individuals and teams use the feedback system to support ongoing development, fostering a culture of continuous improvement and learning.

Such information gathering may include analyzing social media and intra-organization communications (e.g., email exchanges). This would help capture both what performance outcomes can be captured as well as an indicator of how that performance was achieved.

Use Case 3: Addressing Employee Concerns and Pay Education

Scenario: At a mid-sized tech company that recently acquired a smaller competitor, employees are increasingly concerned about both the internal consistency of their pay and how it compares to market standards. The acquisition has led to questions about how the integration of new employees will affect the existing pay structure and whether their pay remains competitive.

AI-PID application solution: An AI-powered chatbot that provides employees with clear, personalized explanations of their pay and how it compares to market standards. The chatbot answers questions about salary, bonuses and pay equity. Additionally, an online portal offers AI-enabled educational modules on the company’s TR philosophy and market trends.

Goal: To enhance communication and understanding around pay policies and structures, addressing employee concerns transparently and educating them on how pay decisions are made, thereby increasing trust and satisfaction in pay.

Preconditions: 1. The organization's HR and payroll systems are integrated, ensuring that accurate and up-to-date information on pay policies, individual pay details and market comparisons are available for both the AI system and employees. 2. The organization has detailed information on pay philosophies and pay policies, including how pay levels are determined, criteria for raises and the rationale behind pay structures. 3. The organization has a process in place for collecting and categorizing employee questions and concerns and their respective answers, which will be used to help train the AI system.

Success criteria: 1. Employees demonstrate a better understanding of the TR system, as indicated by reduced confusion and increased confidence in the TR system and their own pay-related decisions. 2. Concerns or complaints about pay decreases, reflecting increased clarity and satisfaction. 3. There are improved trust and transparency scores on internal surveys.

To achieve these beneficial employee outcomes, it will require that the organization links pay systems with performance. In contrast, the organization may be better served leaving greater opaqueness in the system if pay and performance are not well linked.

Use Case 4: Custom Pay Structures and Pay Packages

Scenario: A multi-state, multi-site corporation struggles with retention. Exit interviews often mention total rewards as a reason for leaving. However, the analysis of why individuals are dissatisfied with their pay varies widely.

AI-PID application solution: An AI tool considers multiple variables to create equitable pay structures and individualized pay packages.

Goal: To create personalized and equitable pay packages that reflect individual employee contributions, roles, preferences and circumstances to enhance job satisfaction and retention.

Preconditions: 1. The organization has detailed data on employee roles, performance, preferences and personal circumstances that could affect TR needs and desires. 2. The organization understands its different pay elements and the impact on employee satisfaction and retention. 3. Guidelines for equitable pay practices are established to promote equity cross pay structures. 4. The organization has a strong pay system in place. This generally means a system that connects performance and pay. The organization may be better served keeping pay information secret if pay is not explained by performance.

Success criteria: 1. Employees receive pay packages that are tailored to their individual situations, leading to increased satisfaction and perceived value of their rewards. 2. The organization improves its retention rates and positive employer brand perception. 3. Disparities in pay due to personalization are based on a broad but consistent set of criteria, ensuring that custom packages are fair and based on objective criteria.

Use Case 5: AI-Enhanced Job Matching and Compensation Benchmarking

Scenario: An organization in an industry disrupted by new technology and a rapidly evolving labor market, struggles to match its jobs to the changing market. Thus, it struggles to keep pay packages competitive to attract top talent without overpaying.

AI-PID application solution: AI monitors multiple sources of pay data in real time, matching jobs and providing recommendations for adjusting pay scales, bonuses and benefits.

Goal: Achieve automated, dynamic and accurate job matching and benchmarking of pay packages against industry standards and competitors, ensuring the organization remains competitive in attracting and retaining top talent.

Preconditions: 1. The organization has clear definitions and documentation for internal job roles, including detailed descriptions and required qualifications. Strategic jobs and key data points for benchmarking comparisons have been identified. 2. The organization has access to external benchmarking data sources, including job titles, descriptions and pay data across the industry. 3. Regularly updating pay structures based on benchmarking insights has been scheduled.

Success criteria: 1. Internal roles are accurately matched with external benchmarks, providing a reliable basis for pay decisions. 2. Proactive adjustments are made to the organization’s pay packages. keeping them consistently in line with the organization’s pay policies as relevant to labor market competitors. 3. Employees recognize and value the competitiveness of their pay, contributing to higher satisfaction and lower turnover.

AI-PID MATURITY LEVELS

The AI-PID journey will be long. Understanding desired end states and recognizing growth opportunities along the path can assist in strategic planning and decision making. While each organization starts its journey from a different place, some even starting by sharing the most basic pay information, each can progress to sophisticated AI-powered compensation integration and high levels of PID. Organizations can progress along this journey through adopting more detailed and individualized pay data, integrating AI technologies for pay analysis and predictive modeling, and moving toward comprehensive transparency and real-time market adjustments. Progress will result in increased efficiency, accuracy and personalization, as well as the ability to address privacy concerns and ethical implications associated with AI integration. Table 4 maps out the maturity levels of AI-PID integration, providing organizations with a framework to assess their current stage and guide their development plans in this area.

RECOMMENDATIONS FOR ORGANIZATIONAL AND HR LEADERSHIP

Considering the transformative potential of AI and PID in reshaping TR strategies, we offer the following recommendations for organizational and HR leaders. These guidelines aim to facilitate the strategic, ethical and effective integration of AI and PID into TR management.

Jointly embrace PID and AI. AI is a tool that can improve communications. Use AI combined with increased PID to offer insights into how pay is determined and help employees understand their pay. AI’s analytical capabilities can promote and increase the link between pay and performance and communicating how to address disparities. Be clear with employees about what AI is used for and what employee data is exposed to AI to increase employee trust.

Create an AI competency development plan. Given AI’s rapid evolution, HR professionals should prioritize ongoing education on AI’s functionalities, applications and integration of best practices, particularly in HR contexts. Establishing partnerships with educational institutions can accelerate learning and practical application. Encourage hands-on experiments with AI projects and maintain continuous feedback to refine AI applications.

Focus on trust and transparency. Transparency is fundamental to trust in AI and TR systems (Day 2011, Privacy Forum 2018) and is likely a critical factor mitigating employee concerns about artificial intelligence’s negative impact on jobs (Brougham and Harr 2018). Therefore, it will be useful to be proactive in communicating with and listening to employees. Initiate discussions about AI to promote employee understanding of its role in decision making, especially concerning PID. Provide transparency regarding the AI processes that are related to pay. Establish communications channels where employees can provide feedback on AI-driven decisions, offering opportunities to adjust and refine processes.

Prioritize data privacy and security. Implement robust data protection measures to safeguard personal information within AI systems. Practice informed consent, clearly communicating data collection practices and securing explicit approval from individuals. Be transparent about data usage and continuously update privacy protocols.

Engage in active collaboration with trusted partners. The learning curve with AI is steep, and the resources required for implementation can be considerable. Use cross-functional teams by assembling employees from different departments to gain diverse insights into AI integration challenges and solutions. Collaborate with external resources by engaging with other businesses or industry groups to share insights and best practices to address challenges.

View AI as an augmentative tool, not a replacement for people. While AI can process and analyze data, the human touch remains irreplaceable in areas such as interpersonal communication and emotional intelligence. Ensure that AI tools complement human judgment and interpersonal interactions, adhering to principles of interactional justice. Carefully look at decision chains and decision protocols to have the proper human involvement.

Continuously update AI systems. The accuracy and effectiveness of AI tools depend on the quality and currency of the data they process. Regularly update AI systems with fresh data and involve qualified individuals in monitoring and evaluating AI’s performance and alignment with organizational objectives.

Strategically invest in AI. Assess AI investments based on potential ROI, focusing on applications that promise significant benefits in efficiency, effectiveness and quality within TR management. Consider the specific requirements and potential impacts of AI tools before committing resources (Furneaux 2012). Understanding each tool’s purpose informs whether the tool will meet the task requirements and the technology functionality as well as provide a sufficient ROI (Goodhue and Thompson 1995).

Be regulatory active. Monitor evolving regulatory landscapes related to AI and HR, preparing for changes in transparency and privacy regulations. Advocate for favorable regulatory guidelines and champion ethical AI practices, promoting compliance and ethical integrity in AI.

These recommendations are posited to help with navigating the complexities of integrating AI and PID into TR strategies, while maximizing the benefits of these technologies, maintaining ethical standards and fostering trust among employees.

SUMMARY

The integration of AI and PID may transform the landscape, particularly as related to pay. As organizations embrace the rapid growth of AI and the call for greater transparency, human resources must play a pivotal role. Despite the challenges associated with AI, such as data collection complexities, the opacity of AI algorithms and measurement hurdles, the opportunities AI presents are unparalleled. AI has the potential to streamline pay administration, improve pay-related communication and enhance the overall efficiency of TR programs.

HR professionals can harness these opportunities by recognizing the ROI of AI, enhancing AI literacy, overcoming PID barriers and building trust in AI-driven systems. The evolution of AI and PID has the potential to offer sophisticated pay methodologies, break down information silos and provide more nuanced, emotionally resonant communication around pay.

AI’s application in total rewards is still emerging, with practical insights gleaned from use case criterion. From automating compensation analyses to facilitating job matching and predictive modeling for budgeting, AI’s capabilities are likely to redefine pay administration.

This shift to AI-enhanced processes, coupled with a societal push for pay transparency, presents an opportunity for meaningful transformation. The potential benefits of integrating AI into HR practices, through fostering transparency, efficiency and innovation, are substantial.

By adopting AI tools, HR professionals can secure a competitive advantage, navigating the changing corporate landscape with agility and foresight.

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