AI Adoption in Leadership Development Among U.S. Leaders: A Qualitative Exploratory Multiple-Case Study

Abstract

The problem addressed in this study was the need to explore the adoption and understanding of artificial intelligence (AI) in developing leadership skills and competencies within American organizations. The purpose of this qualitative exploratory multiple-case study was to investigate how leaders and senior HR/L&D stakeholders in U.S. organizations perceive and adopt AI in leadership development. The technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) served as the theoretical framework. Data were collected through semi-structured interviews with 12 leaders and senior HR/L&D, operational, and technology stakeholders across U.S. organizational contexts. Analysis used inductive coding, analytic memoing, cross-case synthesis, and post-coding comparison with TAM/UTAUT constructs; credibility was supported through transcript review, member checking, and summary validation. Findings showed that participants viewed AI adoption as conditional. AI was useful for speed, scale, coaching, simulation, practice, and personalization, but adoption depended on ethics, oversight, governance, communication, workflow fit, trust, and readiness. The study concluded that AI-enabled leadership development requires technical capability, human judgment, readiness, and responsible governance. Implications include clearer oversight, policy, communication, and implementation practices; future research should examine sector-specific adoption, employee perspectives, longitudinal outcomes, and specific AI use cases.