GENERATIVE ARTIFICIAL INTELLIGENCE ADOPTION AND EMPLOYEE PRODUCTIVITY: IMPLICATIONS FOR ORGANIZATIONAL PERFORMANCE
DOI:
https://doi.org/10.69980/zqkmpa73Keywords:
Generative Artificial Intelligence, Employee Productivity, Organizational Performance, AI Adoption, Business InnovationAbstract
Generative Artificial Intelligence (GenAI) is becoming more popular in businesses as a way of increasing the efficiency of the workforce, aiding decision-making processes, and improving the overall performance of the business. The research was conducted using a quantitative approach based on secondary data to explore the correlation between the use of GenAI and employee productivity as well as organizational performance. 2,000 observations of organizations in different industries, company sizes, regions and stages of AI adoption were analysed. Descriptive statistics, correlation analysis, group comparison and multiple linear regression were used to evaluate the pattern of use of GenAI and its association with productivity and performance outcomes. The results indicated that the variables of AI maturity, task automation, time savings, and variables of intensity of adoption and employee productivity are positively related. Overall, the greatest productivity change was due to AI maturity and task automation services. Increases in revenue, decreases in cost, and Customer Satisfaction were also positively correlated with productivity improvement, while AI adoption and maturity was positively correlated with Innovation performance. This implies that organizational value of GenAI is not merely about access to AI tools, but about how they're integrated, the readiness of the workforce, and strategic implementation. The study findings include augmenting AI capabilities to improve productivity and performance, helping employees adapt, and embedding GenAI into relevant business processes. Further research is needed over time and at the employee level to explore cause-and-effect relationships and the impact on the organization over time.
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Copyright (c) 2025 Dr. Elena Fischer, Dr. Alexander Morgan
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