Revolutionizing Human Resource Management With Cognitive Computing And AI-Driven Insights For Advanced HR Decision-Making And Efficiency

Abstract

AI has applied largely in various organizations and its contribution to Human Resource Management has received tremendous interest. To the best of the author’s knowledge, scant research work has been dedicated to elucidating the effects of AI adoption on certain HR performance indicators. This study investigates the impact of AI adoption on four critical HR dimensions: decision-making efficiency, operational effectiveness, employee engagement, and strategic readiness. Therefore, the strength and importance of these relations can be measured using a detailed quantitative approach. This paper presents empirical models that supports the relationship between AI adoption and the multidimensional HR outcomes which provides a systematic framework of how AI is a strategic asset within the HR field. Unlike such studies that paid much attention to only one or the other, this study adopts them both to achieve practical objectives. As discussed in the previous section, four research hypotheses linking AI adoption with each below HR outcome was developed, for which our use of multiple linear regression (MLR) approach is aligned. The data were gathered from interview surveys and statistically processed using regression coefficients, model fit (R², adjusted R²), F-test, t-test, and normality tests. From the findings, all the hypotheses were therefore postulated and confirmed with statistical significance. The regression analysis revealed that there was a significant positive correlation between the adoption of AI and each of the HR outcomes; the regression coefficients, β₁ was obtained as 0.511 and 0.651 and the coefficient of determination (R²) was obtained as 0.48 and 0.61. AI enhances HR in decision making domain, operations, engagement and strategic fit.