Catalysts of generative AI adoption in policing: the primacy of transformational leadership and organizational capacity

Purpose This study examines the organizational determinants of generative AI adoption in US law enforcement, addressing a gap in systematic evidence on factors driving AI innovation within police agencies. Design/methodology/approach A total of 140 valid responses to an online survey of Texas police chiefs were analyzed. Generative AI engagement (from planning to extensive use) and four hypothesized drivers—transformational leadership style, prior body-worn camera deployment, presence of a formal AI policy, and agency capacity—were measured, with chiefs' demographic characteristics included as controls. Logistic and probit regression models were employed for data analysis. Findings Transformational leadership, larger budgets, and chiefs' advanced educational attainment each show significant positive associations with generative AI engagement. In contrast, prior BWC deployment and the mere presence of a formal AI policy do not predict AI adoption. Practical implications To foster responsible AI integration, agencies should invest in leadership development that cultivates transformational competencies, secure adequate funding, and support advanced education. Policymakers and grantors should prioritize capacity-building over standalone policy directives. Originality/value This study is among the first large-n quantitative analyses of generative AI adoption in policing, underscoring the primacy of visionary leadership and institutional capacity—rather than historical technology experience or policy frameworks alone—in driving AI innovation.