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

Decision-maker background matters significantly: The study found that experts' professional training, experience, and expertise levels directly influence whether they choose to use AI tools, suggesting that AI adoption isn't just about the technology itself but about who is using it and their existing knowledge base.

Trust in AI varies dramatically across domains: Interviews across medicine, law, journalism, and public sector revealed that perceptions of AI reliability and accuracy differ substantially by field, indicating that one-size-fits-all approaches to AI deployment will likely fail and domain-specific considerations are essential.

Consequences for the decision-maker are a primary concern: The research identified that professionals are more likely to adopt AI when they understand how it affects their own work and accountability, rather than being motivated solely by efficiency gains—a finding that challenges assumptions about what drives AI adoption.

Stakeholder implications influence adoption decisions: Decision-makers consider how AI use affects not just themselves but other stakeholders (patients, clients, the public), meaning responsible AI deployment requires addressing broader societal concerns, not just individual user preferences.

Context-specific adoption strategies are necessary: The cross-domain analysis revealed that factors driving AI adoption in medicine differ from those in law, journalism, and public sector work, demonstrating that effective AI implementation requires tailored approaches rather than generic deployment strategies.