Exploring AI Adoption in Performance Management

Abstract

The adoption of artificial intelligence (AI) in performance management, particularly among knowledge workers in traditional organizational settings, is the focal point of this thesis. The study aims to understand the current state of AI adoption, anticipated benefits, challenges, and effective strategies for adoption, considering the critical role of employees in this process. Performance management is crucial for employee motivation and overall performance, and its measurable nature aligns with AI's capabilities to enhance efficiency and objectivity. However, there is a gap in AI's adoption and perceived utility, indicating considerable resistance and skepticism. From the outset, this study faced a unique challenge: no companies using AI in performance management agreed to participate. This underscores a broader reality; the topic is complex and shrouded in both excitement and secrecy. This reluctance highlights the sensitive nature of performance management. Moreover, the adoption of AI into human-centric processes is far from neutral. This study employs an exploratory qualitative research design, with data collected through semi-structured interviews with 15 HR managers and professionals who indirectly represented employees. Participants were selected from sectors actively engaging with AI technologies through direct implementation or consultancy services to capture diverse perspectives. The findings reveal a nuanced perspective on the role of AI in performance management. While there is some use of generative AI tools, such as ChatGPT, the overall adoption remains minimal among companies in the Netherlands. HR professionals recognize AI’s potential to enhance decision support, personalization and engagement, operational efficiency, and strategic planning. However, there is skepticism about AI's ability to fully capture employee performance, highlighting objectivity as both a benefit and a challenge. Key challenges identified include technology-, organization-, people-, and environment-related aspects. Among the challenges are diverse workforce perceptions, resistance to AI, and data quality issues. Assessing non-quantitative performance dimensions, such as competencies and skills, the variability in performance criteria, and the subjective nature of evaluations, remain significant hurdles. While AI offers data-driven insights, it does not yet solve these fundamental challenges in performance management. Despite optimistic expectations, significant challenges persist in early stages of AI adoption in performance management, indicating the need for further research before widespread implementation can be achieved. Practically, this research provides valuable insights for HR managers, guiding them to critically assess their current technological landscape and evaluate the applicability of AI within their specific organizational context compared to other technologies. Companies should assess their performance management goals and criteria to determine if AI can effectively address these needs. The development of AI technology for performance management should be watched closely, as advancements could enable AI to automate more tasks. Effective AI adoption in performance management requires comprehensive strategies integrating technology, organization, people, and environmental factors during the initiation and adoption phases. Robust data management practices are essential to ensure the reliability and value of AI applications. Including employees in the process is vital for successful AI adoption. Additionally, maintaining a human touch is essential to ensure that technology enhances human capabilities rather than replaces them. Shaped by the nascent stage of AI adoption in performance management, both in literature and practice, this study focused on theoretical explorations and anticipated challenges rather than real world experiences. Future studies should focus on organizations actively adopting AI to assess whether the anticipated potentials, challenges, and strategies identified in this study hold in practice.