Institutt for strategi og entreprenør­skap

Journal of Accounting and Public Policy 57 s. 1-31 Doi: https://doi.org/10.1016/j.jaccpubpol.2026.107433

The increasing adoption of Artificial Intelligence (AI) technologies in accounting practices is reshaping traditional processes, offering unprecedented opportunities while also posing significant challenges for organizations. However, previous research has primarily focused on large accounting firms, leaving AI adoption patterns in non-accounting firms, where 76% of accountants are employed, largely underexplored. This study addresses this gap by conducting the first comprehensive comparative analysis of AI adoption, uncovering novel insights into levels, drivers, and barriers of AI integration and how they differ between accounting and non-accounting firms. Based on 35 semi-structured interviews, the study reveals an interplay between organizational-level and individual-level factors that shape adoption of different types of AI: robotic process automation (RPA), analytical AI (i.e., machine learning), and generative AI (i.e., large language models (LLMs)). It applies the Technology-Organization-Environment (TOE) framework as analytical lens and extends it by incorporating an individual dimension drawn from the UTAUT framework. The analysis reveals distinct patterns. Accounting firms predominantly employ RPA and ML for assurance-driven tasks such as transaction testing, anomaly detection, and regulatory compliance, while non-accounting firms adopt AI largely for predictive financial forecasting and process optimization. Key barriers, including rigid business models in accounting firms and organizational silos in non-accounting firms shape adoption trajectories. This study contributes to the field of accounting and auditing research by offering a nuanced understanding of how organizational context and professional logics uniquely influence AI adoption. It provides actionable insights for academics, practitioners, and policymakers, emphasizing the need for cross-functional collaboration, regulatory frameworks, and AI literacy initiatives to foster effective adoption across diverse sectors.

The journal of business & industrial marketing 41(13) s. 1-10 Doi: https://doi.org/10.1108/JBIM-01-2025-0016

Journal of Rural Studies 122 Doi: https://doi.org/10.1016/j.jrurstud.2025.103994

Construction Management and Economics Doi: https://doi.org/10.1080/01446193.2026.2660109

IEEE transactions on engineering management 73 s. 1071-1085 Doi: https://doi.org/10.1109/TEM.2025.3647495