Enhancing employee performance management : an AI driven approach to overcome key challenges
Tambekar, Nikhil (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025100925834
https://urn.fi/URN:NBN:fi:amk-2025100925834
Tiivistelmä
Employee Performance Management (PM) plays a vital role in aligning individual goals with strategic organizational objectives. It is a continuous process that comprises the identification, measurement, and development of employee performance. However, many organizations have faced challenges in performance data collection due to manual processes, lack of standardization, and limited managerial time. These issues have often resulted in biased evaluations, inaccurate ratings, and reduced employee engagement.
The aim of this thesis was to examine the key challenges faced by managers in collecting accurate and actionable employee performance data. The study leveraged theoretical frameworks and current research in performance management to investigate how Artificial Intelligence (AI) could be utilized to automate and enhance the data collection process, thereby enabling more effective performance feedback.
The research was conducted using a mixed-methods approach, combining quantitative surveys and qualitative interviews with people managers in the Information Technology (IT) sector. The theoretical framework examined the performance management process, its benefits and limitations, as well as foundational AI concepts and associated ethical considerations.
Key findings revealed that accurate data collection is the most challenging and resource-intensive aspect of performance management, primarily due to non-standardized processes and difficulties in capturing qualitative contributions. As a result, performance data often remained insufficient, fragmented, and prone to bias. While respondents were optimistic regarding the potential of AI to enhance fairness and efficiency in evaluations, concerns persisted around ethical issues, particularly algorithmic bias, data accuracy, data privacy, and security.
The thesis concludes by recommending the adoption of AI-driven techniques to automate data collection and enable real-time access to performance data. Such advancements are expected to improve both the efficiency and accuracy of performance management processes.
The aim of this thesis was to examine the key challenges faced by managers in collecting accurate and actionable employee performance data. The study leveraged theoretical frameworks and current research in performance management to investigate how Artificial Intelligence (AI) could be utilized to automate and enhance the data collection process, thereby enabling more effective performance feedback.
The research was conducted using a mixed-methods approach, combining quantitative surveys and qualitative interviews with people managers in the Information Technology (IT) sector. The theoretical framework examined the performance management process, its benefits and limitations, as well as foundational AI concepts and associated ethical considerations.
Key findings revealed that accurate data collection is the most challenging and resource-intensive aspect of performance management, primarily due to non-standardized processes and difficulties in capturing qualitative contributions. As a result, performance data often remained insufficient, fragmented, and prone to bias. While respondents were optimistic regarding the potential of AI to enhance fairness and efficiency in evaluations, concerns persisted around ethical issues, particularly algorithmic bias, data accuracy, data privacy, and security.
The thesis concludes by recommending the adoption of AI-driven techniques to automate data collection and enable real-time access to performance data. Such advancements are expected to improve both the efficiency and accuracy of performance management processes.