Artificial Intelligence (AI) Assimilation in the Public Sector:An Attention-Based Exploration of Decision Making, Leadership, and Communication in Saudi Arabia

Abstract

The rapid development of Artificial Intelligence (AI) has opened new possibilities for public sector organisations to improve service delivery, strengthen decision-making processes, and enhance operational efficiency. However, successfully assimilating AI in government contexts presents distinct challenges that differ markedly from those faced by private sector organisations. Public institutions operate within complex frameworks shaped by multiple stakeholder expectations, stringent regulatory requirements, accountability obligations, and often risk-averse organisational cultures — all of which significantly influence technology assimilation outcomes. Despite growing interest in AI within government, there is still limited understanding of how organisational attention dynamics shape AI assimilation processes. This PhD thesis addresses this critical gap by applying the Attention-Based View (ABV) theory to explore how leadership attention allocation, communication practices, and institutional contexts influence AI integration in public sector organisations.

This doctoral thesis, structured as an article-based PhD, comprises three interrelated studies that collectively advance understanding of AI assimilation through the lens of organisational attention. The research pursues five Research Objectives (ROs): identifying organisational and governance challenges in public sector AI assimilation through a systematic literature review (RO1); investigating leadership attention allocation mechanisms in AI initiatives (RO2); examining communication channels as attention management mechanisms in public sector AI integration (RO3); analysing how national policies and institutional contexts influence AI assimilation outcomes (RO4); and providing practical insights for AI-driven governance (RO5). Methodologically, the research combines a systematic literature review with qualitative case studies conducted in the Saudi Arabian public sector, focusing on organisations implementing AI under the Vision 2030 transformation agenda.

The first study presents a systematic literature review of 61 peer-reviewed articles published between 2012 and 2023, mapping the current state of AI research in public administration. This review identifies seven major challenges including infrastructure limitations, data governance issues, workforce readiness gaps, regulatory complexities, cultural resistance, cybersecurity concerns, and resource constraints, and five primary benefits, such as enhanced decision-making, greater efficiency, cost optimisation, increased transparency, and improved citizen engagement. This study lays a foundational understanding of AI assimilation challenges and underscores the need for attention-based perspectives.

The second study applies ABV theory to examine attention-related challenges in AI assimilation within Saudi public sector organisations. Using in-depth qualitative analysis of a single case study, the research identifies five core attention-based challenges, divided into internal (situated) and external (structural) categories. Internally, challenges include fragmented leadership attention, competing priorities, and resource conflicts; externally, they involve regulatory demands, stakeholder expectations, and institutional pressures. These findings highlight the importance of understanding how attention allocation shapes AI outcomes and underscore the central role of leadership focus in managing assimilation challenges.

The third study extends this analysis by exploring how leadership practices and communication channels facilitate AI integration across multiple Saudi public sector organisations. The research shows that leaders coordinate organisational attention through structural frameworks (formal systems), situated practices (contextual engagement), and communication-mediated mechanisms (information flow management). The study introduces the concept of leaders as "attention architects" who design and manage attention structures to support digital transformation. Findings reveal how formal and informal communication channels function not only as conduits but as active mechanisms shaping attention, fostering alignment, and sustaining commitment to AI initiatives.

Theoretically, this thesis advances ABV by applying the theory to public sector AI assimilation and developing communication channels as attention regulators. It offers the first thorough application of ABV in public sector AI assimilation, highlighting distinct dynamics compared to private sector contexts. The study also underscores the role of national transformation agendas in shaping attention allocation and assimilation trajectories, providing insights relevant to Global South and developing country contexts. Furthermore, this thesis establishes a novel theoretical framework that integrates organisational attention with institutional theory, demonstrating how cultural and political factors systematically influence attention allocation patterns in complex technological transformations (Ocasio et al., 2018; Taras et al., 2020). The thesis also contributes to communication theory by conceptualising formal and informal communication networks as co-equal drivers of attention distribution, challenging traditional hierarchical models of organisational attention and proposing a more dynamic, multi-channel approach to understanding attention flows in public sector contexts (Putnam & Mumby, 2014; Cornelissen et al., 2020).

Practically, the findings provide actionable guidance for public sector leaders and policymakers. They suggest strategies for designing attention structures, managing competing demands, and leveraging communication channels to enable successful AI integration. The focus on Saudi Arabia's Vision 2030 offers valuable lessons for other governments pursuing digital transformation under complex institutional and cultural constraints.

This thesis acknowledges limitations, including its focus on a single national context, the literature review's temporal scope (up to 2023), and the qualitative nature of empirical studies. These limitations present opportunities for future work, such as cross-country comparative studies, longitudinal analyses of attention dynamics, and quantitative validation of the developed frameworks.

In sum, this thesis makes significant contributions to both theory and practice by demonstrating the critical role of organisational attention in public sector AI assimilation. It reveals that successful integration demands strategic attention management, effective communication systems, and leadership practices that align organisational focus with implementation goals. The findings offer a strong foundation for future studies on attention dynamics in technology assimilation and provide practical insights to support leaders and policymakers striving for AI-enabled governance transformation. By integrating theoretical depth with practical relevance, this PhD research advances academic understanding and offers concrete guidance for navigating public sector digital transformation.