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This study investigates the psychological costs experienced by software professionals during the adoption of AI technologies in software engineering workflows, focusing on a case study from a large development services company one year post-adoption. Through qualitative interviews, the research identifies key issues such as accountability anxiety, disruption of craft identity, and erosion of job satisfaction, highlighting that these psychological strains are significant and often overlooked in discussions about AI integration. The findings suggest that while organizations pursue productivity gains from AI, the human impact of these changes necessitates a more nuanced understanding of AI adoption as a complex human transition rather than a purely technological shift.
Software professionals face significant psychological strains like accountability anxiety and identity disruption as AI becomes integrated into their workflows, challenging the notion that AI adoption is cost-free.
Artificial intelligence (AI) is increasingly used to augment software engineering (SE) workflows. While code generation remains the main use case, organizations are actively seeking AI integration in other practices such as test cases generation and code reviews. Organizational AI adoption strategies seem to focus on tangible outcomes such as productivity. However, AI is a disruptive force, introduced into settings where role identity, team norms, and the sources of job satisfaction were well established before the recent advances in generative AI. Historically, technological disruptions have caused psychological and social strains in workplaces, ranging from anxiety and eroded meaning to deskilling and disrupted professional identities. The assumption that AI for SE is cost-free may not be accurate. Therefore, in this study we sought to understand the psychological costs software professionals experience during organizational AI adoption. We carried out a case study in a large software development services company, one year after the company launched its AI adoption. We collected qualitative data through meetings and semi-structured interviews (N = 21). We found that software professionals experience accountability anxiety, craft identity disruption, meaning and satisfaction erosion, cognitive and workload intensification, and uncertainty distress. Practitioners manage these costs through practices that restore control, mitigate them through protective and identity-preserving adaptations, or absorb them, carrying what neither can resolve. We contribute to AI-human collaboration in SE by repositioning AI adoption as a human transition, not only a technological and organizational one.
