AI Based HR Analytics and Employee Retention in the IT Sector

Abstract

In the information technology (IT) sector, employee retention has a strategic dimension because IT staffs are generally specialised, projects are ongoing, teams need to work together, and the skill set can quickly shift. Human resource (HR) analytics powered by artificial intelligence can help recognise patterns linked with employee turnover, estimate the probability of turnover, and assist in more timely retention decisions. This critical review covers predictive approaches, retention practices in technology workforces, translating analytics into managerial action, and ethical considerations for responsible use. Searches using target keywords like public sources and citation checking to find traceable research literature on turnover prediction by machine learning, IT sector employment in India, retention in technology-based teams, explainable AI, and AI-based HRM in general. Supervised machine-learning techniques, notably tree-based ensembles, are commonly employed for attrition prediction, and job satisfaction, work experience, compensation, overtime, and career-related variables are also commonly included in predictive models as per literature. There is plenty of evidence from IT-focused studies that shows not only how to model attrition (or job tenure). The usefulness of such forecasts, though, relies on how clean data is, how they work in other settings, how the risk can be explained, and how appropriate HR responses are designed. A prediction-to-retention framework is suggested, involving data readiness, risk estimation, human review, supportive intervention, and outcome evaluation under a continuous governance process. The authors identify the following key factors for organisations that want to use analytics to aid with employee retention: career development, supervisor support, workload quality, internal mobility, privacy, and fairness.