<![CDATA[The rapid deployment of Generative Artificial Intelligence (GenAI) across enterprise environments represents a foundational shift in how work is structured, cognitive tasks are allocated, and individual productivity is realized. This critical review systematically synthesizes empirical and conceptual literature published between 2021 and 2026 to examine the multi-dimensional impact of GenAI on employee productivity and job redesign. Guided by Job Demands-Resources (JD-R) theory, Socio-Technical Systems (STS) theory, and the Task-Technology Fit (TTF) framework, this paper analyzes findings across 16 benchmark studies encompassing randomized field experiments, structural equation modeling, and qualitative case investigations. Results indicate that GenAI integration yields significant task-level productivity gains—ranging from 14% to 40% across professional domains—predominantly by automating routine cognitive tasks, reducing time-to-completion, and elevating output quality for lower-skilled workers. However, operationalizing these productivity gains requires comprehensive job redesign, characterized by cognitive task unbundling, skill reallocation toward prompt engineering and critical evaluation, and the mitigation of emerging techno-stressors. The review synthesizes core mechanisms into a structural path model and outlines a strategic HR governance framework to optimize human-AI collaboration.]]>
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