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Artificial Intelligence and Job Design: A Dynamic Framework
Amin Shirangi, Todd L. Pittinsky

Artificial Intelligence and Job Design: A Dynamic Framework

Author / Organization:
Science & Mathematics Published 2026 Verification Pending

Overview & Synopsis

This study aims to deepen the understanding of artificial intelligence's (AI) impact on work by introducing and describing the AI-Job Dynamics Framework. Drawing from sociotechnical systems theory, complexity theory, and organizational learning theories, we conceptualize AI integration as a dynamic, non-linear process with three key phases. The framework, supported by theoretical propositions, identifies how this process affects organizational decision-making structures and key job characteristics over time, emphasizing the balance between centralization and decentralization of decision-making power. AI integration initially reduces job autonomy but may lead to greater autonomy in later phases as AI becomes more embedded. Throughout this process, AI reshapes skill requirements from routine to complex cognitive and adaptive skills. In the advanced phase of AI integration, we introduce the alternative paths and varied trajectories through which AI, organizational structures, and individual work experiences evolve. We conclude by discussing the implications of this framework for future research on AI and job design.

Indexed from repository: arXiv (arxiv.org)
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Document Information

Document Title Artificial Intelligence and Job Design: A Dynamic Framework
Author / Organization Amin Shirangi, Todd L. Pittinsky
Subject & Discipline cs.CY
Categories Science & Mathematics
Publication Year 2026
License / Rights arXiv Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website arXiv (arxiv.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-269061
Original Source Link Visit Source Page on arxiv.org »