Making the Invisible Visible: Understanding the Mismatch Between Organizational Goals and Worker Experiences in AI Adoption
Christine P. Lee, Min Kyung Lee, Bilge Mutlu
TLDR
This paper examines the critical disconnect between organizational AI goals and worker experiences, revealing key barriers to successful AI adoption.
Key contributions
- Reveals that workers are often excluded from AI design and implementation decisions within organizations.
- Identifies key barriers to AI adoption, including poor usability, misaligned expectations, and limited control.
- Based on interviews with professionals using AI daily in healthcare, finance, and management sectors.
- Proposes adaptation strategies at individual, task, and organizational levels for better AI alignment.
Why it matters
This paper is crucial for understanding why AI adoption often fails in organizations. By highlighting the worker's perspective and proposing concrete strategies, it offers a path to more successful and human-centric AI integration.
Original Abstract
While AI is often introduced into organizations to drive innovation and efficiency, many adoption efforts fail as workers resist and struggle to integrate these systems. These failures point to a deeper issue: workers, the very people expected to collaborate with AI, are often invisible in decisions about how AI is designed and used. Drawing on interviews with professionals who interact with AI systems daily in healthcare, finance, and management, we examine the disconnect between organizational expectations and worker experiences. We identify key barriers, including poor usability and interoperability, misaligned expectations, limited control, and insufficient communication. These challenges highlight a gap between how organizations implement AI and the evolving worker needs, tasks, and workflows that it fails to support. We argue that successful adoption requires recognizing workers as central to AI integration and propose adaptation strategies at the individual, task, and organizational levels to better align AI systems with real-world practices.
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