Quantitative Productivity Analysis, Workforce Integration Dynamics, and Sentiment Classification while Leveraging Generative AI for Enterprise Optimization

Abstract: Generative Artificial Intelligence (Gen AI), like other business technologies, has rapidly expanded worldwide. It has transformed organizational tasks and management, emphasizing the need to explore its effects on productivity and employment dynamics. When used as a data processing tool, Gen AI integrates various tasks with professional activities. Consequently, its adoption impacts employees' experience, workload, autonomy, scope of work, skill deployment, and other factors. We have studied the impact of systematically adopting Gen AI on performance metrics and employee well-being, identifying indicators such as productivity gains and challenges in workplace transformation. Using a multi-dimensional, high-volume dataset of 100,000 companies across 14 countries and various sectors, we find evidence of an average increase of approximately 18.47% in productivity, with significant variations across industries such as Defense and Retail. Conflicting reactions and feelings among employees were prevalent alongside productivity gains and concerns about employment preservation. The results showed no statistically significant relationship between training hours and productivity change, emphasizing the importance of strategic application. We employed a hybrid methodological framework combining quantitative and qualitative analysis techniques. Applying descriptive statistics, sentiment analysis, and clustering techniques to examine metrics such as productivity change, employee impact, training hours, and thematic evidence. This study aims to measure the pragmatic justifications of Generative Artificial Intelligence. Further, the study aims to examine cross-sectoral and regional heterogeneity testing emotional responses of the employees via sentiment analysis Reviewing the existing empirical evidence highlights the importance of developing an operational understanding, fostering problem-solving skills, and promoting collaboration with employees, as well as sharing benefits with both employers and workers. This approach enhances the advantages of implementation while carefully addressing concerns related to human capital. This study, through an in-depth analysis, makes a meaningful contribution to the existing literature on Artificial Intelligence-driven organizational adjustment. It also offers specific recommendations to policymakers and industry experts navigating the complexities of technological globalization.

Keywords: Generative AI, Enterprise Productivity, Workforce Adaptation, Sentiment Analysis, Machine Learning