Leadership and Organizational Change, United States
Despite the widespread adoption of artificial intelligence (AI) technologies, organizations struggle to realize promised benefits like efficiency and cost savings. Current approaches prioritize technical capabilities and potential cost savings before establishing clear system objectives, leading to misalignment between AI implementation and organizational requirements. This paper proposes a framework for AI integration that emphasizes socio-technical system functionality through structured stakeholder involvement. Evidence from industry reports (2022-2024) and organizational case studies demonstrates that successful outcomes depend on aligning AI requirements with system capabilities. The proposed framework combines divergent and convergent thinking approaches to help organizations establish appropriate integration objectives and goals. By extending socio-technical systems theory to address AI integration, this study provides both theoretical insights and practical guidance for organizational leaders across industries and contexts. The findings show that when integration priorities address both technical system functionality and social subsystem dynamics, organizations can effectively implement AI.
