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

TL;DR

This paper explores the disconnect between organizational AI goals and worker experiences, emphasizing the need to recognize workers as central to AI integration for successful adoption.

Contribution

It identifies key barriers to AI adoption from workers' perspectives and proposes strategies to better align AI systems with real-world practices.

Findings

Workers are often invisible in AI decision-making processes

Poor usability and communication hinder AI adoption

Aligning AI with worker needs improves integration

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.