Neutral or Positive? Understanding Finance Professionals' Attitudes Toward <scp>AI</scp> Adoption

ABSTRACT Using a combination of quantitative (sentiment analysis) and qualitative (grounded analysis) methods, this study examines how finance professionals perceive the adoption of artificial intelligence (AI) and machine learning (ML). Drawing on 76 semi‐structured interviews with Canadian finance professionals, we examine how two key determinants of AI perception (i.e., familiarity and knowledge) shape overall sentiment toward AI adoption, and how professionals with neutral versus positive sentiment orientations differ in their articulation of AI's benefits and concerns. Quantitative results indicate that greater AI familiarity and knowledge are strongly associated with more positive attitudes toward AI/ML. Complementing these results, qualitative findings show that while both groups recognize common issues such as human oversight, compensation erosion, and efficiency gains, their emphases diverge: neutrals stress high‐level organizational factors and job security whereas positives focus on specific technical hurdles and express optimism about advanced applications. These findings have important implications for organizations, suggesting that fostering familiarity and enhancing knowledge may support AI adoption, but that implementation strategies should be tailored to different perception profiles. This study contributes to the accounting and finance literature by providing first‐hand evidence of professionals' attitudes toward AI and by introducing a mixed‐method approach that integrates sentiment analysis with grounded coding.