How Generative AI is Reshaping Work, Skills, and Organizational Strategy

Abstract

This research examines how generative artificial intelligence, large language models, and machine learning are transforming work across industries. Through a mixed-methods approach comprising interviews with 42 organizational leaders (January-June 2024), a survey of 157 knowledge workers, and comparative analysis of 18 implementation case studies, the study develops an integrated sociotechnical framework for understanding AI's impact. Findings indicate that generative AI is shifting from task automation to cognitive augmentation, with implementation success varying significantly based on organizational context and approach. Quantitative results show that organizations employing comprehensive strategic approaches achieved 3.2 times higher ROI than those with narrower implementations. Distributional analysis reveals uneven impact patterns, with knowledge workers in higher-status positions experiencing 27% greater augmentation benefits than those in routine roles. The research contributes to sociotechnical systems theory by identifying specific mechanisms through which technological capabilities interact with organizational factors to reshape work. Implications for theory, practice, and policy are discussed, with particular attention to how organizations can implement these technologies while mitigating potential workforce inequalities.