AI in Performance Management: Development Tool or Surveillance Mechanism?

Artificial intelligence is now moving beyond recruitment into the internal management of employees, especially in performance management. In many organisations, AI-based systems are being used to track productivity, analyse work patterns, support appraisal decisions and generate performance insights. From one perspective, this looks like a major improvement. AI can process large volumes of performance data quickly, identify patterns that managers may miss and support more continuous feedback than traditional annual appraisal systems. Varma (2024) argues that AI has the potential to reshape each stage of the performance management system, including goal setting, feedback, evaluation and development planning. This suggests that AI could make performance management more evidence-based and responsive.

The attraction is understandable in a fast-changing global business environment. Organisations increasingly want real-time information on employee output, collaboration and skill development, especially where work is dispersed across teams, time zones and digital platforms. AI tools can help managers identify performance trends, personalise feedback and link development needs to organisational goals. In theory, this aligns with strategic HRM because it supports better workforce planning and more targeted employee development. Recent reviews of AI and HRM also show that predictive analytics and employee-experience personalisation have become major themes in the field, reflecting the wider move toward data-driven people management.

However, the central problem is that performance management is not only a technical system. It is also a social and ethical process shaped by trust, fairness and power. The International Labour Organization warns that digital technologies are increasingly being used for workplace monitoring and decision-making, allowing employers to track workers’ behaviour, output and even patterns of risk in unprecedented detail. The ILO notes that these systems can intensify work, reduce privacy and weaken worker autonomy if they are introduced without proper safeguards. This is particularly important in people management because employees may experience AI-enabled performance tracking less as helpful feedback and more as constant surveillance.

There is also evidence that monitoring is linked to poorer employee experience. The American Psychological Association reports that 45% of electronically monitored workers say monitoring has a negative impact on their mental health, compared with 29% of workers who are not monitored. In addition, 56% of monitored workers reported feeling tense or stressed at work, compared with 40% of those not monitored. These figures matter because performance systems are supposed to support improvement and engagement, yet heavy monitoring may instead create anxiety and reduce trust. In this sense, AI can shift performance management from a developmental tool toward a control mechanism.

This debate becomes even more serious when AI influences employment-related decisions rather than only producing information. The EU AI Act treats AI systems used in employment, worker management and access to self-employment as high-risk applications subject to strict legal requirements. European institutions have also highlighted concerns that AI and algorithmic management may increase psychosocial risks, reduce decision-making autonomy and contribute to technostress. These are not minor side issues. They show that in modern HRM, performance analytics cannot be separated from questions of employee wellbeing, dignity and accountability. For multinational organisations, this is especially important because systems used across borders may face different legal expectations and different employee responses depending on the cultural context.

At the same time, it would be too simplistic to conclude that AI in performance management is always harmful. There are cases where AI-supported systems may enhance fairness and development. For example, AI can potentially reduce some forms of line-manager inconsistency by using broader evidence across time rather than relying only on recent impressions. It can also support continuous coaching by identifying skill gaps and performance trends early. Emerging research on AI and employee wellbeing suggests that AI adoption does not necessarily reduce wellbeing directly; rather, its effects depend on how it changes work-related conditions such as task optimisation and safety. This means implementation matters more than technology alone.

In my view, AI in performance management is useful only when it is clearly governed as a developmental aid rather than an automated control system. The most sustainable model is not “AI replaces managerial judgement,” but “AI supports informed and accountable managerial judgement.” Employees need to know what is being measured, why it is being measured and how the information will be used. They also need mechanisms for explanation, challenge and human review. Without these protections, organisations may gain more data but lose the trust and engagement needed for long-term performance.

Overall, AI has the capacity to improve performance management by making feedback more continuous, data more visible and development planning more targeted. But it also carries a real risk of turning people management into surveillance. The key distinction is whether AI is used to help employees grow or simply to watch them more closely. In the next post, I will examine another important HR function by looking at whether AI-driven learning and development creates more personalised growth opportunities or a new form of digital dependency.

Reference 

American Psychological Association (2023) Electronically monitoring your employees? It’s impacting their mental health. Available from APA workplace research pages.

European Economic and Social Committee (2025) Opinion on AI and algorithmic management in the workplace. Available via EUR-Lex.

European Union (2024) Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (AI Act). Available via EUR-Lex.

International Labour Organization (2024) Workplace monitoring and decision-making technologies. Geneva: ILO.

Úbeda-García, M., Claver-Cortés, E., Marco-Lajara, B. and Zaragoza-Sáez, P. (2025) ‘Artificial intelligence, knowledge and human resource management: mapping the intellectual structure of a research field’, European Management Journal.

Valtonen, A., et al. (2025) ‘AI and employee wellbeing in the workplace: An empirical study’, Journal of Business Research.

Varma, A. (2024) ‘Artificial intelligence and performance management’, Organizational Dynamics.