The role of leadership and communication in AI assimilation: case studies from Saudi Arabia’s public sector organizations

Purpose This study aims to examine the influence of leadership practices and communication channels on the assimilation of artificial intelligence (AI) in Saudi Arabia’s public sector. It investigates how leaders allocate attention and use formal and informal mechanisms to implement AI and generative AI (GenAI) initiatives. Design/methodology/approach This study adopts a qualitative multiple case study approach, using the attention-based view (ABV) theory. Data were collected through semi-structured interviews with senior decision-makers in three Saudi government organizations and analyzed thematically. Findings The findings reveal that leadership attention serves as a pivotal force in determining organizational priorities, resource distribution and approaches to institutional and technological hurdles. Furthermore, the relationship between leadership decisions and AI adoption is evident throughout the study. Strong leadership support not only facilitated the integration of AI, but also helped overcome institutional resistance. This study highlights how leadership-driven attention and targeted communication directly accelerate the assimilation of GenAI, chatbots, analytics and biometric systems, thereby transforming public service delivery and organizational effectiveness. Originality/value This study advances the ABV theory by demonstrating that targeted leadership communication, when strategically aligned with national development priorities, significantly accelerates AI assimilation in non-Western public sector organizations. By identifying how leaders prioritize AI initiatives and shape organizational attention through both formal channels and informal influence, this research uncovers mechanisms unique to these institutional contexts. These insights offer actionable guidance for policymakers and organizational leaders seeking to foster effective AI adoption beyond Western settings and lay a foundation for future research exploring context-sensitive strategies for technology integration.