Institutional pressures on AI adoption in management accounting: Evidence from SMEs and implications for education

This qualitative study examines how normative institutional pressures influence the adoption of artificial intelligence (AI) in management accounting and how these pressures shape the transformation of management accounting education. Drawing on institutional theory, the study focuses on normative isomorphism arising from professional norms, standards, and expectations that guide organizational behavior. It also distinguishes between normative and coercive pressures and considers their interaction in shaping organizational responses. Empirical evidence is based on 12 semistructured interviews with management accounting and AI experts from small and medium-sized enterprises (SMEs) and selected large firms used for comparison in Austria, Germany, and the United States. The findings reveal that professional bodies, regulatory frameworks, and industry norms play a central role in legitimizing AI use in management accounting by promoting ethical guidelines, competency frameworks, and standardized practices. AI adoption is thereby understood as a socially embedded process shaped by institutional expectations rather than purely technological considerations. These pressures not only affect organizational adoption strategies but also translate into new educational requirements, particularly regarding data literacy, critical thinking, ethical awareness, and interdisciplinary skills. The analysis identifies key thematic patterns and highlights how SMEs interpret and respond to institutional expectations under resource constraints. Cross-country comparisons highlight context-specific variations in the intensity and sources of institutional pressures, while comparisons between SMEs and large firms reveal variation in the scale and formality of AI-related educational responses. The study contributes to the literature by providing an empirically grounded and conceptually structured analysis of institutional pressures as a driver of AI adoption in management accounting and by outlining implications for the redesign of management accounting education. It further develops a conceptually structured framework linking institutional pressures, organizational responses, and capability development. The findings offer practice-oriented insights for educators, professional bodies, and policy makers seeking to prepare future management accountants for an AI-driven professional environment.