The Impact of Artificial Intelligence Adoption On Employee Productivity: A Study Of IT Companies In Silicon Valley
Abstract
This study investigates the impact of Artificial Intelligence (AI) adoption on employee productivity in IT companies in Silicon Valley. Using a descriptive research design, data were collected from 150 employees across five major IT firms through structured questionnaires. The study measured variables including employee productivity, job satisfaction, technological adaptability, and job stress. Descriptive statistics, correlation, and multiple regression analyses were conducted using SPSS. Findings revealed that AI adoption significantly affects productivity: technological adaptability and job satisfaction positively influenced productivity, while job stress negatively impacted it. Specifically, productivity showed strong positive correlations with technological adaptability (r = 0.62, p < 0.01) and job satisfaction (r = 0.58, p < 0.01), and a negative correlation with job stress (r = –0.42, p < 0.01). Regression analysis confirmed that technological adaptability (β = 0.47, p < 0.001) and job satisfaction (β = 0.33, p = 0.018) were significant positive predictors, whereas job stress (β = –0.29, p = 0.003) was a significant negative predictor. The study concludes that optimizing AI adoption requires strengthening employee adaptability and satisfaction while managing stress. The findings provide practical guidance for managers and policymakers on implementing AI to maximize workforce productivity.
Keywords: Artificial Intelligence, Employee Productivity, Technological Adaptability, Job Satisfaction, Job Stress, IT Companies, Silicon Valley
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