Making the Invisible Visible: Understanding the Mismatch Between Organizational Goals and Worker Experiences in AI Adoption

2026-05-04

AI Summary

This study addresses the frequent failure of AI system adoption in organizations, often stemming from a disconnect between organizational objectives and frontline work practices due to insufficient attention to end-users’ real-world needs. Through in-depth interviews with practitioners across healthcare, finance, and management sectors, the research employs qualitative methods to systematically examine workflows, control dynamics, and communication mechanisms in human-AI collaboration. It reveals, for the first time, the critical yet overlooked role of “invisible workers” in successful AI implementation. The study identifies key barriers—including poor usability, inadequate interoperability, misaligned expectations, loss of user control, and ineffective communication—and proposes a multi-level co-adaptation framework spanning individual, task, and organizational dimensions. This approach advances a human-centered paradigm for AI integration, enhancing system effectiveness in authentic operational contexts.

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.

Problem

• AI adoption
• worker experience
• organizational goals
• human-AI collaboration
• implementation gap

Innovation

• AI adoption
• worker-centered design
• organizational-technical mismatch
• human-AI collaboration
• workplace integration