Factors relevant to organizational readiness for AI in diagnostic pathology: a regional pre‑implementation survey in Western Norway

Background Artificial intelligence (AI) is becoming increasingly integrated into digital pathology (DP). In the Western Norway Regional Health Authority, two CE‑marked AI tools, Ki67 quantification in breast cancer and prostate cancer detection and grading, are scheduled for deployment in 2026 across four pathology departments. As successful adoption requires organizational preparedness in addition to technical implementation, this study examined factors relevant to organizational readiness for AI implementation and identified key enablers, barriers, and unmet needs. Methods Two pre-implementation surveys were conducted among pathology personnel ( N = 42) and managers ( N = 14) across four regional pathology departments. A tailored questionnaire informed by Weiner’s Organizational Readiness for Change (ORC) theory was used to examine AI-related attitudes, willingness to adopt AI, knowledge, confidence, competence, and implementation-related factors. Quantitative findings were supplemented by an inductive thematic analysis of free-text responses. Results Both participant groups expressed strong willingness and motivation toward AI adoption. Among pathology personnel participants, 88.1% indicated that they would like to use AI in their work, while self-reported knowledge, confidence, and competence varied across respondents. Nearly all personnel participants (97.6%) preferred hands-on training. Managers expressed strong motivation to facilitate implementation but identified major barriers, including integration with laboratory information systems (71.4%), limited departmental resources (71.4%), and insufficient AI knowledge and technical expertise among pathology personnel (64.3%). Ethical concerns included automation bias, potential loss of diagnostic skills, and uncertainty regarding responsibility for AI-related errors. Conclusions Pathology personnel participants and managers expressed strong motivation toward AI adoption, while personnel participants reported variable knowledge, confidence, and competence. These findings identify key determinants of organizational readiness and underscore the need for targeted hands-on training, structured implementation strategies, clear accountability frameworks, and measures to minimize automation bias while maintaining diagnostic competence throughout the implementation process.