Developers find AI tools helpful but limited under workplace pressures
Helpful but Fallible: Developer Experiences of AI Tools Under a Coordinated Industrial Roll-out
Software Engineering
Summary
AI tools designed to help software developers are being used widely in some big companies, but we don’t know much about how developers actually feel about these tools during the rollout. The authors studied a telecom company in Sweden where these AI tools were introduced across multiple sites and talked to developers about their experiences. They found that while the tools can be useful, developers often face frustrations and feel pressure because management has high expectations. The study shows that developers think a lot about the risks and drawbacks of AI tools, which current technology acceptance models don’t fully capture.
What this means in practice
- •For software engineering managers: Better plan and support AI tool roll-outs by understanding how developers perceive risks and pressures beyond productivity metrics and surveys.
- •For it department leads: Shape organizational expectations and training programs to align with developers’ realistic experiences of AI tools’ benefits and limitations.
Authors
Andreas Bexell, Rushali Gupta, Lo Gullstrand Heander, Emma Söderberg, Per Runeson, Sigrid Eldh, Wei Shi, Konstantin Malysh
Abstract
AI-enabled software development tools (AI-devtools) are being industrially adopted under strong expectations of productivity gains, yet developers' experiences of such roll-outs are underexplored. Organizations commit budgets, evaluate staff, and revise practice on a partial picture, since the evidence base is mainly tool evaluations, productivity metrics, and surveys, with few qualitative in-situ accounts of ongoing, coordinated roll-outs. We report a case study of a coordinated roll-out of AI-devtools at a large Swedish telecommunications company, investigating how developers experience the roll-out and how they anticipate their profession will change. We conducted semi-structured interviews with 12 software professionals across three sites, analyzed with process coding and thematic analysis, and interpreted through the extended Technology Acceptance Model (TAM2) as a post-hoc analytical lens. Our findings on use cases, productivity, frustrations, and tool limitations corroborate prior survey work. Beyond corroboration, the interviews surface a management-developer expectation gap that maps onto the TAM2 constructs of subjective norm and voluntariness, and show that participants weigh perceived risk heavily, a factor that TAM2 and similar acceptance models do not represent. AI-devtools emerge as helpful but fallible assistants whose value is shaped by organizational expectations, system scale, and developers' skills.