Harnessing AI for Aligning Human Resource Management with Sustainability Goals to Enhance Workforce Productivity

Purpose: This research examines the influence of Artificial Intelligence-driven sustainable Human Resource Management (HRM) practices on employee engagement and performance within the Indian Information Technology sector. Design/Methodology/Approach: A cross-sectional survey was performed to collect data from 1,280 employees across prominent IT hubs in India, employing the Triple Bottom Line (TBL) and Ability-Motivation-Opportunity (AMO) frameworks. Findings: The findings reveal that Sustainable Organisational Uncertainty (SUO) and Sustainable Uncertainty Investment (SUI) significantly enhance EEG, which, in turn, positively influences Employee Performance (EPF). Research Limitations: The study is limited to the Indian Information Technology (IT) sector and employs a cross-sectional design, which restricts causal inferences. Practical Implications: The study provides practical implications that managers and policymakers should take into consideration for the adoption of AI tools in HRM to ensure sustainable employee engagement and personalised performance improvements. Personalised AI-based strategies can promote workforce diversity and support organisational sustainability objectives. Originality/Value: The paper helps pave the way for the growing area of AI-enabled sustainable HRM by connecting the underlying theories with the empirical analysis. It provides new ways to connect HR practices with sustainability objectives, which play a role in enhancing engagement and performance in changing markets. Keywords: Artificial Intelligence-driven HRM; Work Performance; Sustainability Initiatives; Sustainable HRM Practices; Structural Equation Modelling Citation: Prakash, K., Franklin, M., Singh, A. P. and Rohini, V. Prasanna, S. L. (2026): Harnessing AI for Aligning Human Resource Management with Sustainability Goals to Enhance Workforce Productivity. World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 22, Nos 1-2, pp. 89-109.