Unlocking Workforce Potential: AI-Powered Predictive Models for Employee Performance Evaluation

Artificial intelligence (AI) in human resource functions is rewriting the way organisations evaluate employee performance.Or traditional performance evaluation techniques are subjective in nature, time consuming & biased which add to inaccurate measures [1].AI-driven predictive models offer a data-driven way to assess employees in organizations to leverage large datasets, identify patterns and drive manager decisions.These models employ complex methodologies that are machine learning, deep learning, and natural language processing to evaluate employee productivity, engagement, and potential [2].In this paper, the work done on AI based predictive models in evaluation of employee performance is presented as a detailed survey.It studies numerous AI methodologies, ranging decision trees up to neural networks and sentiment analysis to learn more on how these tools make assessment of workplace better [3,4].The other part of our paper describes use-cases in practice of AI nowadays, from talent acquisition to performance monitoring and employee retention etc [5,6].The paper also brings to light some of the ethical issues (bias in algorithms, transparency, etc) and challenges related to evaluation driven by AI systems [7].And our literature review maps where AI for employee performance evaluation is heading, flagging the need for explainable AI (XAI), federated learning and approaches to mitigate bias [11].We find that despite these benefits of AI in workforce management, it is important ethical considerations and transparency are considered to achieve fair, responsible AI adoption [8].The takeaways from our paper will be to help organizations leverage AI as critical enabler with different capabilities from making more informed decisions to uplifting employee engagement and workforce potential.