Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption

2025-08-01

AI Summary

This study investigates the key motivators and barriers influencing decision-makers’ voluntary adoption of artificial intelligence (AI) tools in healthcare, law, journalism, and public administration. Drawing on cross-sectoral in-depth interviews and qualitative thematic coding, we identify four core dimensions shaping adoption behavior: decision-maker background, AI literacy, anticipated personal consequences, and stakeholder influence. From this analysis, we develop the first cross-domain comparable “Four-Dimensional AI Adoption Framework” and a context-sensitive adoption checklist. Moving beyond a purely technical lens, the framework explicates how socio-technical factors differentially drive adoption across sectors, thereby enhancing the responsibility, contextual fit, and ethical alignment of AI deployment. The framework provides practitioners with an actionable assessment tool and delivers empirically grounded insights to inform policy design and governance mechanisms for responsible AI adoption.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Humanities & Computational Social Science

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Growing excitement around deploying AI across various domains calls for a careful assessment of how human decision-makers interact with AI-powered systems. In particular, it is essential to understand when decision-makers voluntarily choose to consult AI tools, which we term decision-maker adoption. We interviewed experts across four domains -- medicine, law, journalism, and the public sector -- to explore current AI use cases and perceptions of adoption. From these interviews, we identify key factors that shape decision-maker adoption of AI tools: the decision-maker's background, perceptions of the AI, consequences for the decision-maker, and perceived implications for other stakeholders. We translate these factors into an AI adoption sheet to analyze how decision-makers approach adoption choices through comparative, cross-domain case studies, highlighting how our factors help explain inter-domain differences in adoption. Our findings offer practical guidance for supporting the responsible and context-aware deployment of AI by better accounting for the decision-maker's perspective.

Problem

• Factors influencing AI adoption by decision-makers across domains
• Understanding when and why decision-makers choose AI tools
• Comparative analysis of AI adoption differences between domains

Innovation

• Cross-domain expert interviews for AI adoption insights
• AI adoption sheet to analyze decision-maker choices
• Comparative case studies highlighting inter-domain differences