AI in enterprise management: determinants of purchase intention among CEOs without AI experience

Purpose This study introduces a novel model of AI adoption focused on purchase intention by CEOs with no prior AI experience – a key shift from traditional usage-based approaches. It addresses how executives make strategic investment decisions under uncertainty, emphasizing trust and perceived value over readiness factors. Design/methodology/approach Survey data from 252 CEOs were analyzed using PLS-SEM and necessary condition analysis (NCA). The model evaluates the effects of security, perceived value, response costs, organizational compatibility and facilitating conditions, explaining 73.7% of purchase intention variance (R2 = 0.737). Findings Security and perceived value emerged as the strongest drivers of AI purchase intention, while response costs act as a significant deterrent. Although facilitating conditions and organizational compatibility are relevant, their impact is secondary at the pre-adoption stage. The model also identifies perceived value and organizational compatibility as necessary – but not sufficient – conditions for adoption. Originality/value This research makes three key contributions: (1) it reconceptualizes AI adoption as a staged strategic process centered on purchase intention; (2) it applies upper echelons theory to explain how CEOs’ lack of AI experience shapes their cognitive evaluations of risk, cost and strategic value during early-stage adoption decisions, offering a novel context for its use in AI adoption research and (3) combines PLS-SEM and NCA to identify both drivers and prerequisites for early-stage AI investment.