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Artificial intelligence (AI) has disrupted human resource management (HRM) and, in particular, performance management systems (PMSs). Using AI for PMS can allow data analytics, machine learning, and automation to create objective, transparent, and efficient means of performance evaluation. As is well known, the IT industry shows a faster pace of adopting new technologies, quickly investing in the automation of performance management tools to optimize workforce productivity while simplifying appraisal tools. On the other hand, acceptance and effectiveness of these AI systems significantly depend on the perception of employees on the same. This research intends to comprehend the perception of IT professionals of Chennai towards AIenabled performance management systems, in terms of trust, satisfaction, and concerns on these aspects. The research issues considered are the effect of AI-based PMS on employee retention; perceived accuracy and fairness of evaluations conducted by the AI system; and problems faced by the employees in adjusting to AI evaluations. Comprehensive insights will lead to an understanding of factors that influence employee acceptance of AI-powered PMS, such as algorithmic biases, concerns about data security, automated decisionmaking without some form of human empathy, and transparency in decision-making. The study results will go ahead in adding practical value to the IT organization whose objective is to install AI-driven PMS in their systems for employee fear curtailment when the implementation of AI technologies is undertaken in their HR activities. As emphasized in the research title, AI should be giving support to, not essentially replacing, human judgments in performance evaluations. Transparency, fairness, and ethics in AI will install trust in employees and will contribute to the more effectual and inclusive AI-based performance management system. Here lays yet another ingredient for building the arguments of AI into HRM with empirical evidence in the field of employee perceptions saluting the fine-tuning of AI strategies of organizations in becoming more involved in making a pronounced direction for a much more human-focused AI use in performance evaluation in the IT arena.