Managing employee performance is not always easy, especially for companies with large and diverse workforces. Managers need to know whether employees are meeting their goals, where performance is falling short, and what support employees may need. Doing this manually can become difficult when a company has thousands of employees and large amounts of performance data to analyze.
AI can help companies make sense of this information by identifying performance trends, tracking goals, highlighting training needs, and giving managers additional information when making performance decisions. However, using AI to monitor employees also creates concerns about privacy, fairness, and whether automated systems can accurately measure meaningful work.
Amazon provides a useful example of these challenges. As a U.S.-based multinational company operating across e-commerce, cloud services, digital services, and other technology-related sectors, Amazon has a large and diverse workforce. Its scale makes employee performance management a useful example of how companies can use AI to improve performance while still maintaining human judgment and employee trust.
The Case of Amazon
Amazon Inc. is a U.S.-based multinational corporation that deals in e-commerce, cloud services, digital services, and other technology-related sectors. Due to the large and diverse workforce at Amazon, a performance-management issue that Amazon might encounter is to make sure that the productivity of employees is assessed in a proper and fair manner, especially when it comes to remote employees. Ravid et al. (2023) note that AI-driven monitoring can assist managers in monitoring performance, but overuse of monitoring can trigger issues regarding fairness, privacy, and trust between employees.
HR leaders could use AI to assess the task completion rates, project completion rates, goal achievement rates, workload, attendance, turnover, customer-service performance, and performance trends. AI is able to detect trends throughout this data and point out those employees or teams that might need more assistance. In the case of Amazon, AI can also be used to enable managers to process large volumes of workforce data more effectively and make evidence-based management decisions, as stated by Menon et al. (2025).
The insights provided by AI could assist Amazon in understanding where it should hire more workers, which workers need to be trained, where the workload should be balanced, and where to improve performance. To illustrate, the inability to meet deadlines on a regular basis could be a sign of too much work or a lack of skills of the employees, instead of low effort. This information might be used by the HR to train, change the working staff, or redistribute the workload. This enables Amazon to base its workforce decisions better and minimize the use of subjective decisions.
AI tools can assist in goal setting, feedback, and performance evaluation by recognizing performance patterns and aiding managers in setting performance-based goals. The feedback created by AI can be used to give timely information on areas that employees might require training or coaching. Nonetheless, human managers can still be utilized since robotic systems might not detect personal situations or draw inaccurate assessments.
Remote employees may attempt to manipulate AI-monitored systems by generating artificial mouse or keyboard activity, keeping applications open to appear active, or focusing on easily measured activities instead of meaningful work. The study by Ravid et al. (2023) has shown that the impact of electronic performance monitoring on work outcomes may be both positive and negative, illustrating the critical role of a well-designed monitoring system. The HR is not supposed to be dependent on activity monitoring alone. Rather, organizations should gauge the qualification of work, the attainment of goals, teamwork, and overall performance. The HR should also be clear regarding practices that need to be monitored, protect the privacy of employees, regularly audit AI systems against discrimination and lies, and offer employees the chance to challenge inaccurate judgments. All these protective measures can facilitate faith and enable AI to underpin competent performance management.
Sources
Menon, P., Arjun, K., Nisha, R., & Thomas, K. (2025). Leveraging artificial intelligence for decision-making and managerial effectiveness: A case study of Amazon. GRJESTM, 1(4), 166-172.
Ravid, D. M., White, J. C., Tomczak, D. L., Miles, A. F., & Behrend, T. S. (2023). A meta‐analysis of the effects of electronic performance monitoring on work outcomes. Personnel Psychology, 76(1), 5–40.
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