Exploring Financial Implications of Artificial Intelligence-Powered Human Resource Analytics across Industries: A Smart PLS Multi-Industry Empirical Study in Delhi-NCR

Abstract

Aim/Objective: This paper governs financial consequences of Artificial Intelligence (AI)-powered Human Resource (HR) analytics in Delhi-NCR, concentrating on its effect on financial performance of HR initiatives.

Methodology/Approach: The paper usages Partial Least Square-Structure Equation Modelling (PLS-SEM) to analyse data collected from 401 respondents using convenience sampling. Key independent variables comprise AI-powered HR analytics, industry type, HR budget allocation, and employee productivity.

Findings: Findings show that AI-powered HR analytics meaningfullyincreases financial outcomes of HR initiatives, with industry type working as a moderator. Besides, HR budget allocation and employee productivity subsidize positively to financial performance. In this study, the data were gathered from 401 respondents (HR professionals) across industries including manufacturing, IT, finance and healthcare.

Conclusion and Recommendation: Results highlight serious role of AI in enhancing HR functions, improving decision-making, and enhancing organizational productivity, finally leading to financial improvements. This paper provides appreciated understandings for organizations looking for to leverage AI in HR, highlighting importance of industry context and resource allocation for exploiting financial performance.