AI-Powered Decision Support and Employee Productivity in Technology-Driven Organizations

Abstract

Through a quantitative, cross-sectional design, this paper investigates whether AI-powered decision support influences employee productivity within technology-driven firms. A structured five-point Likert survey was used to gather responses from 280 staff members across technology-driven companies. We evaluated AI decision support across four distinct dimensions—system accuracy, decision speed, information quality, and usability—and captured employee productivity by measuring task efficiency, output quality, and on-time task delivery. The empirical analysis relied on descriptive statistics, Pearson correlations, and multiple linear regressions. According to the descriptive statistics, workers viewed AI decision support mostly positively, and they rated their own productivity as moderately high. Pearson correlation coefficients confirmed that each of the four AI decision support dimensions shared a positive and statistically significant relationship with worker productivity. Overall AI-powered decision support accounted for a substantial portion of variance, serving as a statistically significant predictor of worker productivity in the multiple regression model. When the four AI dimensions were entered into a supplementary regression, information quality and usability emerged as the leading independent predictors of worker productivity. System accuracy still mattered significantly, though to a lesser extent, whereas decision speed was no longer a significant predictor once the other three dimensions were in the model. What these results point to is that the quality and usability of the information generated by AI decision tools—not how quickly they output decisions—matter most when it comes to helping employees stay productive in tech-driven settings.