Artificial intelligence (AI) is increasingly reshaping government institutions by changing how public organisations process information, organise administrative activities and deliver public services (Mishra et al., 2024). This development represents a further stage in the evolution of digital government, which has progressed from electronic access to information towards integrated, data-driven and intelligent administrative systems. Recent scholarship emphasises that public-sector digital transformation is not simply a technological process but a broader socio-technical and organisational phenomenon involving changes in institutional practices, organisational capabilities and public value creation (Jonathan, 2026). The integration of AI is particularly significant because its analytical, predictive and generative capabilities can extend beyond the automation of existing processes to influence organisational routines, structures and institutional practices. Tangi et al. (2025) therefore argue that AI integration in public administration requires corresponding transformation in social and technical systems, including organisational routines, practices, structures, governance and culture. AI consequently represents an important development in the continuing transformation of government.
The growing use of AI also creates new conditions for government leadership. As intelligent technologies become incorporated into administrative environments, leaders increasingly operate within organisations characterised by extensive data availability, technological expertise and changing patterns of organisational coordination. Traditional leadership approaches that emphasise vision, authority, motivation and administrative control remain relevant, but AI-enabled environments require leaders to respond to new organisational and technological conditions. Leaders must increasingly understand technological possibilities, facilitate organisational adaptation, coordinate specialised expertise and ensure that AI is integrated consistently with institutional objectives and public-sector responsibilities. The OECD (2025) identifies leadership, skills, organisational capacity and responsible implementation as important considerations in government AI adoption. This indicates that AI-driven transformation should be understood not only in terms of technological capability but also in relation to the changing requirements placed upon those responsible for directing and managing public institutions.
The United Arab Emirates (UAE) provides a particularly significant context for examining these developments because AI has become closely associated with its national digital transformation agenda. The UAE has pursued ambitious strategies for digital government and artificial intelligence, seeking to integrate advanced technologies into governmental operations and public-service delivery. Its National Strategy for Artificial Intelligence 2031 established a long-term framework for expanding AI applications and strengthening government performance, while subsequent digital-government initiatives have continued to emphasise data-driven and technology-enabled administration (UAE Government, 2017, 2025). This policy environment provides an important institutional setting for examining how rapid technological transformation interacts with established organisational structures and leadership practices. The UAE therefore offers a relevant context for investigating the implications of AI for government leadership within a public sector characterised by strong institutional commitment to technological innovation.
Despite these developments, a significant research problem remains. Existing literature has generated substantial knowledge concerning digital transformation, AI adoption, organisational capabilities, institutional change and public-sector technological challenges, while emerging studies have also examined leadership within digitally transforming organisations. However, the literature provides insufficient understanding of how the increasing integration of AI is transforming government leadership within UAE institutions. In particular, limited attention has been given to how government leaders experience changes in their roles, leadership practices and competencies, and how they respond to the organisational, institutional and governance challenges associated with AI-driven transformation. Much of the existing evidence has also emerged from contexts outside the UAE, while studies within the UAE have frequently examined AI adoption, digital transformation, organisational capability and leadership through quantitative approaches. Consequently, there remains limited understanding of how government leaders themselves experience and interpret the transformation of leadership as AI becomes increasingly embedded within government institutions. Moreover, the relationship between AI, leadership, organisational transformation and institutional change requires greater analytical integration. Accordingly, the study is guided by the following overarching research question: What is the changing nature of government leadership in AI-enabled government institutions in the UAE, particularly in relation to AI-driven organisational transformation, institutional change and emerging leadership challenges? The corresponding objective is: To examine the changing nature of government leadership in AI-enabled government institutions in the UAE, with particular attention to AI-driven organisational transformation, institutional change and emerging leadership challenges. By addressing these dimensions through qualitative inquiry, the study seeks to provide a context-sensitive understanding of how leadership is being reconfigured as AI becomes increasingly embedded within UAE government institutions.
This study adopts an exploratory qualitative research design to examine Artificial Intelligence and the Transformation of Government Leadership: A Qualitative Inquiry into UAE Government Institutions. A qualitative design is appropriate because it facilitates an in-depth interpretation of complex organisational and institutional processes that cannot be adequately captured through statistical measurement (Creswell & Poth, 2018; Denzin & Lincoln, 2011). The study examines how artificial intelligence is transforming leadership roles, practices, organisational relationships and institutional processes within UAE government institutions. Rather than testing causal relationships, it interprets documentary evidence to understand how AI-driven transformation is represented, implemented and institutionalised within government.
The study relies exclusively on secondary and documentary sources, including peer-reviewed journal articles, scholarly books, government publications, policy documents and institutional reports. Priority is given to literature indexed in recognised academic databases and authoritative UAE government materials. Particular attention is given to the UAE National Strategy for Artificial Intelligence 2031 and related digital-government and AI-governance frameworks, which identify AI adoption, government performance, leadership capabilities, governance and regulation as important components of national transformation (UAE Government, 2018). The literature reviewed addresses artificial intelligence, government leadership, organisational transformation, institutional change, public-sector innovation and digital governance.
Sources are selected purposively according to relevance, credibility, academic quality, contextual suitability and contribution to the research objectives. Recent peer-reviewed studies are prioritised to capture developments in AI and public-sector transformation, while seminal works are incorporated to establish the theoretical foundations of Institutional Theory and organisational change. This approach follows the principle that qualitative documentary research requires systematic and critical engagement with sources rather than treating all documents as equally authoritative (Bowen, 2009). The analysis therefore considers the methodological quality, theoretical contribution, empirical basis and institutional authority of each source.
The study employs thematic analysis following Braun and Clarke (2006), who established thematic analysis as a systematic approach to identifying, analysing and interpreting patterns within qualitative material. The process involves familiarisation with selected documents, generation of initial codes, identification and refinement of themes, and interpretation of relationships among themes. Key themes include AI-driven leadership transformation, changing leadership roles and competencies, organisational adaptation, institutional pressures, AI adoption, accountability, governance and public-sector digital transformation. These themes are interpreted through Institutional Theory, particularly the institutional environment, legitimacy and isomorphic pressures identified by Meyer and Rowan (1977) and DiMaggio and Powell (1983).
Analytical rigour is enhanced through source triangulation and systematic comparison of academic, governmental and institutional materials. Triangulation enables the researcher to compare different forms of documentary evidence and identify areas of convergence, divergence and emerging patterns (Denzin & Lincoln, 2011). The study also applies explicit source-selection criteria and a consistent thematic procedure to enhance transparency and maintain alignment between the research questions, objectives, theoretical framework and findings. As a qualitative documentary study, the research does not seek statistical generalisation. Instead, it aims to provide a theoretically informed and contextually grounded interpretation of how artificial intelligence is reshaping government leadership, organisational practices and institutional relationships within UAE government institutions.
This study adopts Institutional Theory as its theoretical premise for examining Artificial Intelligence and the Transformation of Government Leadership: A Qualitative Inquiry into UAE Government Institutions. The theory originates from Philip Selznick’s work on institutionalisation, particularly Foundations of the Theory of Organization (1948) and Leadership in Administration (1957), which established that organisations are shaped by institutional environments, values and leadership processes. Its neo-institutional development by Meyer and Rowan (1977) and DiMaggio and Powell (1983) emphasised legitimacy, institutional environments and organisational conformity. Five key assumptions of the theory are: organisations are shaped by institutional environments; legitimacy supports organisational survival; coercive, normative and mimetic pressures influence organisational behaviour; organisations may adopt practices for legitimacy as well as efficiency; and institutionalised practices become embedded within organisational structures and routines.
Recent scholarship demonstrates the relevance of these institutional processes to AI transformation in government. Selten and Klievink (2024), in a qualitative study of public organisations, found that AI adoption creates tensions between established bureaucratic structures and the flexibility required for technological innovation. Their findings showed that public organisations respond through structural separation or contextual integration of AI capabilities, demonstrating that AI adoption requires organisational adaptation rather than technological implementation alone. This supports an institutional perspective in which technological transformation interacts with established structures, routines and organisational expectations.
The researcher adopts Institutional Theory because the study examines AI as a transformative force within government institutions rather than merely as a technological tool. Government leadership operates within regulations, administrative norms, professional expectations, accountability requirements and national policy priorities. Institutional Theory therefore provides a suitable lens for explaining how these conditions may shape AI adoption and contribute to changes in leadership roles, practices and organisational relationships. This perspective is particularly relevant to the UAE, where AI development is embedded within broader governmental digital-transformation priorities.
The assumption concerning institutional pressures provides the strongest basis for the study’s scientific contribution. Coercive pressures may arise from government policies and regulations, normative pressures from professional standards and responsible-AI expectations, and mimetic pressures from emulating leading institutions. The scientific contribution of the study lies in extending Institutional Theory beyond its conventional emphasis on organisational conformity and technology adoption by proposing that AI-related institutional pressures can also transform government leadership roles, practices and organisational relationships. The study consequently develops a theoretically grounded explanation of how institutional pressures may shape the transformation of government leadership in AI-enabled UAE government institutions. Accordingly, the following diagram summarises the five key assumptions underpinning Institutional Theory and highlights their relevance to the analysis of AI-driven transformation in government leadership.
| Institutional theory |
|---|
| Organisations are shaped by institutional environments; legitimacy supports organisational survival |
| Coercive, normative and mimetic pressures influence organisational behaviour |
| Legitimacy supports organisational survival |
| Organisations may adopt practices for legitimacy |
| Institutionalised practices become embedded within organisational structures and routines |
Source: Author’s construction based on Selznick (1948, 1957), Meyer and Rowan (1977), and DiMaggio and Powell (1983).
The evolution of digital government represents a progressive shift in how public institutions employ technology to organise administrative processes, deliver public services and interact with citizens (Gaie & Mehta, 2024). Earlier e-government approaches focused largely on placing government information and selected services online, whereas digital government has increasingly emphasised integrated data, digital platforms, interoperable systems and citizen-centred service delivery. Recent scholarship argues that digital transformation should not be understood simply as technological adoption, but as a broader socio-technical process involving changes in organisational structures, institutional practices, administrative routines and public value creation (Jonathan, 2026). This distinction is particularly important with the emergence of artificial intelligence (AI), whose capabilities can influence how governments generate information, allocate resources, identify patterns and support administrative activities.
The incorporation of AI therefore represents a significant stage in the evolution of digital government. Unlike conventional information technologies, AI can analyse complex datasets, identify patterns, automate selected processes, generate information and support forecasting and administrative decision-making. The OECD (2025) observes that governments are increasingly applying AI across public service delivery, internal administration, fraud detection, policy evaluation and financial management. Digital government is consequently moving beyond the digitisation of existing processes towards more intelligent and adaptive forms of administration. Jonathan (2026), in a systematic review of public-sector digital transformation research, similarly demonstrates that the growing prominence of generative AI is expanding debates beyond technological adoption towards organisational capabilities, institutional adaptation and public value.
Recent literature further challenges the assumption that AI can be introduced into existing government structures without substantial organisational consequences. Tangi et al. (2025) conceptualise AI integration in public administration as a socio-technical transformation in which technological systems interact with organisational structures, routines, practices and institutional arrangements. The effectiveness of AI therefore depends partly on corresponding changes in organisational structures, governance arrangements and institutional culture. This perspective positions AI not merely as an administrative instrument but as a technology capable of altering relationships between people, organisational processes and technological systems. Digital transformation consequently becomes an ongoing process of institutional adaptation rather than a discrete technological project.
AI-enabled government also creates new expectations concerning governmental capacity and responsiveness. AI can potentially enable public institutions to process information rapidly, improve administrative efficiency, personalise services and identify emerging problems through data-driven analysis. However, technological potential does not automatically produce effective governmental transformation. The OECD (2025) identifies challenges involving data quality, skills shortages, legacy systems, transparency, accountability and public trust. These challenges demonstrate that AI-driven transformation is simultaneously technological, organisational and institutional, requiring governments to integrate AI into existing administrative environments while maintaining public-sector values and accountability.
This transformation is particularly significant in the United Arab Emirates (UAE), where digital government development is closely connected to national strategies for AI and advanced technologies. The UAE provides an important institutional context for examining the transition from conventional digitalisation towards AI-intensive administration. Nevertheless, existing literature remains stronger in explaining technological and organisational dimensions of AI adoption than in explaining how AI transforms government leadership. Comparatively less attention has been given to how technological and organisational changes reshape the roles, responsibilities and expectations of government leaders. This gap is significant because leaders remain central to interpreting technological possibilities, managing organisational adaptation and aligning AI-driven change with institutional objectives. The literature therefore establishes AI-enabled digital government as an essential foundation for examining the changing nature of leadership within government institutions.
The increasing incorporation of artificial intelligence (AI) into public institutions is reshaping government leadership by influencing how leaders formulate strategies, coordinate organisational resources, manage employees and respond to data-intensive administrative environments (Addai et al., 2026). Traditional leadership perspectives emphasise human agency, interpersonal influence, organisational vision and the capacity to motivate followers towards institutional objectives. Transformational leadership, for example, highlights vision, innovation and organisational change, while distributed and shared leadership perspectives emphasise the diffusion of leadership responsibilities across organisational actors. These perspectives remain relevant in AI-enabled environments, but intelligent technologies introduce dimensions of leadership that cannot be adequately explained through conventional leader-centred approaches. Recent scholarship therefore suggests that effective public-sector AI adoption requires leadership combining strategic direction, technological understanding, organisational adaptability and responsible oversight (OECD, 2025).
One important transformation concerns the movement from leadership based primarily on managerial control towards leadership that facilitates technological and organisational adaptation (Cortellazzo et al., 2019). In conventional public administration, leaders often operate through established hierarchies, formal procedures and defined areas of authority. AI-enabled environments require leaders to coordinate technical specialists, policy professionals, data experts and administrative personnel whose expertise may extend beyond that of senior decision-makers (Sacavém et al., 2026). This creates a more complex leadership environment in which leaders must facilitate collaboration across professional and organisational boundaries. The OECD (2025) emphasises that strong leadership is essential for establishing a coherent vision for AI adoption and creating organisational conditions for responsible innovation. Leadership therefore increasingly involves enabling and coordinating specialised expertise rather than simply directing subordinates.
Leadership practice is also being transformed by AI-supported information and analytical capabilities. AI can process large volumes of information, identify patterns and support forecasting, potentially changing the informational foundations of managerial judgement (Sacavém et al., 2026). However, technologically generated information does not eliminate the need for human leadership. Rather, leaders must interrogate AI-generated outputs, recognise their limitations and determine whether recommendations are appropriate within particular institutional and policy contexts. This creates an important tension between technological augmentation and human agency. The OECD (2025) cautions that excessive reliance on AI can reproduce errors and weaken accountability, suggesting that effective leadership requires a balance between technological assistance and human responsibility.
The changing environment also raises questions about the competencies required of public-sector leaders. AI competence does not necessarily require senior leaders to become technical specialists. Instead, emerging literature points towards a combination of technological literacy, strategic capability, data awareness, ethical reasoning, communication and organisational change competence. Misuraca et al. (2025) propose a multidimensional framework encompassing technological, management and design, and policy, legal and ethical competencies. Sandoval-Almazan et al. (2024) similarly identify competency gaps among public managers concerning AI implementation. Leadership capability therefore involves understanding AI’s possibilities and limitations, mobilising appropriate expertise and ensuring that technological applications remain aligned with institutional purposes.
A further debate concerns whether AI strengthens or potentially displaces aspects of managerial authority. AI may augment leaders’ analytical capacity and improve access to information, but dependence on algorithmic systems may also shift practical influence towards technological infrastructures and specialised experts. This raises particular concerns in government, where decisions must remain connected to transparency, accountability and institutional legitimacy. Leaders must therefore determine appropriate boundaries for AI while preserving meaningful human responsibility for consequential decisions (OECD, 2025). Nevertheless, the literature provides limited insight into how government leaders themselves experience and interpret these changes. Existing competency frameworks identify required capabilities but offer less understanding of how leaders negotiate changing authority, professional identity, responsibility and leadership practices. This leadership-specific gap provides a strong justification for qualitative inquiry into how government leaders understand and experience the changing nature of leadership in AI-enabled government institutions.
The integration of artificial intelligence into government institutions represents organisational transformation rather than a purely technological intervention (Fan, 2025). While organisational change traditionally involves modifications to structures, processes, roles and coordination, AI introduces new forms of analytical, predictive and administrative capability that can alter how public organisations organise work and distribute responsibilities. AI adoption should therefore be understood as a socio-technical process in which technological capabilities interact with organisational arrangements and established institutional practices (Tangi et al., 2025). Its significance consequently extends beyond technological performance to include changes in organisational routines, responsibilities and institutional objectives.
Given the above, institutional theory provides a useful framework for examining these changes because government organisations operate within established rules, norms, values and expectations. The introduction of new technology does not automatically transform institutions; rather, technological innovations interact with existing arrangements and may either reinforce established practices or create pressure for change. This is particularly important in government institutions characterised by formal procedures, hierarchical structures and accountability requirements. AI-enabled automation, data-driven coordination and algorithmic decision support may challenge established administrative arrangements, requiring organisations to reconcile technological innovation with professional standards, institutional norms and public-sector responsibilities.
Furthermore, organisational culture also shapes the outcomes of AI-driven transformation. Employees’ perceptions of AI, their willingness to adapt and the extent to which organisations encourage experimentation can influence implementation. Cultures that support learning, collaboration and innovation may facilitate adoption, whereas rigid norms may reinforce existing routines. Tangi et al. (2025) emphasise that AI transformation requires changes in organisational practices and culture, while the OECD (2025) highlights organisational capabilities, skills and institutional readiness as important conditions for effective government AI adoption. AI transformation therefore requires cultural adaptation alongside technological development.
AI may further reshape organisational structures and patterns of authority. Traditional bureaucratic systems distribute responsibility through defined positions and hierarchical relationships, whereas AI implementation can require collaboration among administrators, data specialists, technology professionals and policy actors. Organisations must also determine how responsibilities are shared between human personnel and AI systems, potentially producing new roles, revised workflows and interdisciplinary coordination. Such developments require institutional adaptation rather than technological implementation alone (Jonathan, 2026). Resistance to AI represents another important dimension of organisational change. Employees may experience uncertainty regarding changing responsibilities, professional autonomy, technological competence and the reliability of algorithmic systems. Resistance can therefore reflect concerns about organisational identity and professional roles rather than simple opposition to innovation. The OECD (2025) stresses the importance of skills, trust, transparency and accountability in government AI adoption. Effective transformation consequently requires communication, employee participation, training and institutional confidence.
Overall, AI can reshape government institutions through changes in organisational routines, culture, structures, competencies and authority. However, existing literature gives comparatively less attention to how these changes interact with established institutional arrangements and bureaucratic continuity. This gap is particularly relevant to the UAE, where ambitious AI strategies are being implemented alongside established governmental structures. Examining these organisational and institutional dynamics is therefore essential for understanding how government institutions can integrate AI while maintaining accountability, legitimacy and human responsibility.
The United Arab Emirates (UAE) provides an important context for examining government leadership in an increasingly AI-enabled public sector (Dafri, 2023). Over the past decade, the UAE has incorporated artificial intelligence into its broader digital transformation agenda. The National Strategy for Artificial Intelligence 2031 identifies AI as an instrument for enhancing government services, developing national capabilities and strengthening the country’s position as a global AI leader. It also emphasises leadership, institutional capabilities, data, infrastructure, talent and regulatory arrangements as important conditions for AI adoption (UAE Government, 2017). AI is therefore increasingly positioned not simply as an information technology but as an integral component of governmental transformation.
Empirical research indicates that digital transformation is already influencing leadership and organisational performance in UAE public-sector institutions. Almazrouei and Alnahhal (2026) found that transformational leadership is positively associated with employee performance, with digital transformation and organisational agility contributing to this relationship. Similarly, Razmak and Farhan (2026), in their study regarding UAE leaders and managers, demonstrated the growing importance of leadership capabilities in digitally transforming organisations. However, these studies primarily examine digital transformation broadly rather than how AI specifically changes the meaning, practices and responsibilities of government leadership.
Recent UAE-based AI research further demonstrates the country’s rapidly developing AI environment. Almheiri et al. (2025), studying 344 public managers, found that organisational AI capability can strengthen dynamic capabilities, creativity and organisational performance. This suggests that AI capability is becoming an organisational resource rather than merely a technical function. Other research has identified challenges involving trust, algorithmic bias and the delegation of leadership-related functions to AI (Sacavém et al., 2026). Similarly, research in UAE municipalities has highlighted ethical and governance concerns surrounding AI-enabled public services, particularly data protection and institutional safeguards (Alyileili & Opoku, 2025). Collectively, these studies confirm that AI is becoming increasingly embedded within UAE organisational and governmental practices.
Nevertheless, the existing literature provides only a partial understanding of how this transformation affects government leadership in the UAE. Research has frequently examined digital transformation, AI capability, employee performance, adoption and ethical implementation as separate dimensions. Less attention has been devoted to how government leaders themselves experience and interpret the changing responsibilities, expectations and pressures associated with AI. This issue is particularly important in the UAE, where rapid technological development occurs alongside strong institutional coordination and ambitious national AI strategies. Findings from Western public administrations cannot therefore be assumed to fully explain how leadership is changing within the distinctive institutional environment of the UAE. A more context-sensitive examination is consequently required.
Moreover, the nature of existing research also leaves important questions concerning how leaders actually navigate AI-driven organisational change. Much of the UAE literature employs quantitative approaches to examine relationships among leadership, digital transformation, AI capability and organisational performance (Almazrouei & Alnahhal, 2026; Almheiri et al., 2025). While such studies provide valuable evidence of associations between variables, they offer less insight into how leaders interpret changing authority, manage uncertainty, respond to employee concerns and balance AI-generated capabilities with human judgement. Understanding these processes requires attention to leaders’ experiences, perceptions and interpretations rather than relationships between variables alone. A qualitative approach can therefore provide a deeper understanding of the organisational and institutional processes through which AI is incorporated into government leadership.
The literature also suggests that existing theoretical perspectives do not fully capture the changing nature of leadership in AI-enabled government. Transformational leadership, technology acceptance, resource-based and dynamic capabilities perspectives have contributed significantly to explaining digital transformation and AI adoption. However, less attention has been given to the intersection of AI capability, leadership, organisational transformation and institutional context. Recent research indicates that digital transformation leadership involves both strategic direction and organisational implementation (van Roekel et al., 2025), but the implications of AI for the role, authority and identity of government leaders remain insufficiently explored. This indicates the need to examine leadership not only as a factor influencing technological adoption but also as a phenomenon being reshaped by AI itself.
Taken together, the literature demonstrates that AI is reshaping digital government, leadership expectations and organisational structures. UAE evidence confirms that these changes are already emerging within a government environment strongly committed to AI innovation. However, existing scholarship remains fragmented across technology, leadership, organisational capability and governance, with comparatively limited qualitative attention to how government leaders experience and navigate these developments. The present study responds to this limitation by examining the intersection of AI, government leadership and organisational transformation, focusing on how leaders in UAE government institutions perceive and navigate the changing nature of leadership within an AI-enabled governmental environment.
The thematic analysis reveals that artificial intelligence represents a significant stage in the evolution of digital government, moving public administration beyond conventional digitalisation towards more intelligent, integrated and adaptive forms of governance. The findings indicate that AI enables government institutions to process information, identify patterns, support prediction and automate selected administrative activities. However, the analysis demonstrates that AI transformation extends beyond technological implementation because it requires changes in organisational structures, administrative routines, institutional practices and governance arrangements. AI therefore emerges as both a technological and organisational force capable of influencing how government institutions coordinate activities and respond to administrative demands.
The analysis further demonstrates that AI-driven digital transformation creates new expectations concerning institutional capacity, administrative responsiveness and government leadership. The findings indicate that effective AI integration requires institutional readiness, appropriate governance arrangements, relevant expertise, reliable data and accountability mechanisms. Within the UAE context, AI is increasingly embedded within broader government digital-transformation priorities, creating expectations for institutions to strengthen technological and organisational capabilities. Consequently, AI is not merely an administrative instrument but an institutional force capable of reshaping leadership responsibilities, organisational practices and relationships. Leadership transformation therefore emerges as an important dimension of AI-enabled digital government.
Consequently, the third assumption of Institutional Theory, concerning coercive, normative and mimetic institutional pressures, aided the analysis by explaining how government institutions may respond to AI transformation. Coercive pressures may arise from government policies and regulatory requirements, normative pressures from professional expectations and responsible-AI practices, while mimetic pressures may encourage institutions to emulate successful AI practices. Applying this assumption enabled the analysis to examine AI transformation beyond technological capabilities by identifying the institutional pressures that may influence changes in leadership practices, organisational routines and expectations. The findings therefore provide a theoretically grounded explanation of AI-driven leadership transformation within UAE government institutions.
The thematic analysis reveals that the integration of artificial intelligence (AI) is changing the nature of leadership in government institutions from predominantly hierarchical control towards coordination, technological adaptation and responsible oversight. The reviewed literature indicates that government leaders increasingly work with technical specialists, data professionals and policy actors whose expertise may extend beyond conventional administrative knowledge. AI-supported analytical capabilities also expand the volume and speed of information available to leaders, thereby altering traditional approaches to managerial judgement and organisational coordination. The findings therefore suggest that effective leadership in AI-enabled government increasingly depends on the ability to integrate technological capabilities with human judgement rather than relying exclusively on established bureaucratic authority.
Furthermore, the finding is consistent with existing literature which emphasises technological literacy, strategic capability, ethical reasoning and organisational adaptability as emerging leadership requirements. AI does not eliminate the role of leaders; rather, it changes the basis on which leadership authority is exercised by requiring leaders to evaluate AI-generated information, coordinate specialised expertise and determine how technological outputs should inform institutional decisions. This transformation is particularly significant because government leadership must remain connected to accountability, legitimacy and public-sector values. Consequently, leadership effectiveness increasingly depends on maintaining an appropriate balance between technological assistance and human responsibility.
From an Institutional Theory perspective, the finding reflects the assumption that organisations respond to institutional rules, norms and expectations in seeking legitimacy. Meyer and Rowan (1977) argue that organisations incorporate institutionalised practices partly to maintain legitimacy and stability. Applied to AI-enabled government, this assumption suggests that leaders must not only adopt AI for its technical capabilities but also ensure that its use conforms to institutional expectations concerning accountability, transparency, ethical conduct and responsible governance. The finding therefore demonstrates that AI is reshaping not only leadership practices but also the institutional conditions within which leadership authority is exercised. This provides an important basis for examining how government leaders experience changing responsibilities, authority and decision-making practices within AI-enabled institutions.
The thematic analysis reveals that AI adoption is generating organisational transformation in government institutions by altering administrative routines, structures, responsibilities and patterns of coordination. The reviewed documents indicate that AI-enabled automation, data-driven processes and algorithmic decision-support systems are encouraging new forms of collaboration between administrators, technology professionals, data specialists and policy actors. The findings further suggest that these changes extend beyond technological implementation because organisations must modify established workflows and redistribute responsibilities between human personnel and AI-supported systems. AI adoption therefore represents a socio-technical transformation in which technological capabilities interact with existing organisational arrangements and institutional practices.
Furthermore, the finding demonstrates that organisational culture and institutional norms significantly influence the extent to which AI-driven transformation can occur. Organisations characterised by learning, collaboration and innovation are more likely to accommodate technological change, whereas rigid administrative routines may reinforce established practices. Existing literature similarly emphasises organisational readiness, skills, trust and institutional capabilities as important conditions for effective public-sector AI adoption (Tangi et al., 2025; OECD, 2025). Resistance to AI also appears to arise from concerns regarding changing professional roles, autonomy, competence and accountability rather than from opposition to technology alone. Consequently, successful AI transformation requires organisational adaptation, employee participation, appropriate training and institutional confidence.
From an Institutional Theory perspective, these findings support the assumption that organisations are influenced by institutional rules, norms and expectations in maintaining legitimacy. AI implementation must therefore be reconciled with established governmental procedures, professional standards, accountability requirements and public-sector values. This assumption aided the analysis by explaining why technological adoption does not automatically produce institutional transformation: government organisations may adapt AI to existing structures while simultaneously modifying those structures in response to technological pressures. The findings consequently indicate that AI can become a mechanism for institutional change when technological innovation challenges established routines and responsibilities. This provides an important basis for examining how UAE government institutions negotiate technological transformation while maintaining institutional continuity, legitimacy and human responsibility.
The thematic analysis reveals that the UAE’s rapid integration of artificial intelligence (AI) into government is creating new leadership expectations and organisational challenges. The reviewed documents indicate that AI has become an important component of the UAE’s broader digital government transformation, with increasing emphasis on organisational capability, technological competence, data governance and institutional readiness. The findings further suggest that AI is influencing leadership by expanding analytical capabilities, changing patterns of coordination and creating new responsibilities for managing technological innovation. However, leaders must simultaneously address concerns relating to trust, accountability, ethical use of AI and the appropriate balance between automated capabilities and human judgement.
Furthermore, the finding is consistent with UAE-based studies showing that digital transformation, organisational AI capability and leadership capabilities are increasingly associated with organisational adaptability and performance. However, much of the existing evidence examines digital transformation, AI capability or leadership as separate variables, providing comparatively limited insight into how leaders themselves experience these changes. The findings indicate that the UAE context presents distinctive leadership challenges because rapid technological development occurs alongside established governmental structures, institutional expectations and ambitious national AI objectives. Consequently, effective AI-enabled leadership requires more than technological adoption; it involves managing organisational change, coordinating specialised expertise, addressing employee concerns and preserving human responsibility for consequential decisions.
From an Institutional Theory perspective, the findings reflect the assumption that organisations adapt to institutional pressures and expectations in seeking legitimacy and organisational stability. This assumption aided the analysis by demonstrating how UAE government institutions must reconcile rapid AI innovation with established administrative norms, accountability requirements and expectations of responsible governance. The findings therefore suggest that AI is not simply transforming government technology but is also reshaping the institutional environment within which leadership operates. Leaders are increasingly required to negotiate between innovation and institutional continuity, technological capability and human judgement, and efficiency and accountability. This provides a basis for the present study to examine how UAE government leaders perceive and navigate these emerging leadership challenges within an AI-enabled governmental environment.
This study adopts a qualitative inquiry to examine artificial intelligence and the transformation of government leadership in UAE government institutions. The first part covers the conceptual and contextual foundations of artificial intelligence, digital government transformation and leadership within contemporary public administration. It establishes how the growing incorporation of AI into government is creating new opportunities and challenges for leadership and explains why understanding these changes is important within the UAE’s rapidly developing digital government environment.
The second part delves into the changing nature of leadership in AI-enabled government. It demonstrates that the integration of AI is moving leadership beyond conventional hierarchical control towards greater technological awareness, coordination, adaptability and responsible oversight. Government leaders increasingly need to engage with specialised expertise, interpret AI-supported information and determine how technological capabilities can be integrated into administrative practices. Leadership consequently involves balancing technological innovation with human judgement, accountability and institutional responsibility.
The third part addressed AI, organisational transformation and institutional change. The study shows that AI adoption can influence organisational structures, routines, responsibilities, competencies, workplace culture and patterns of coordination. These transformations occur within established governmental institutions and therefore require organisations to reconcile technological innovation with existing rules, professional norms, administrative procedures and expectations of legitimacy. The findings demonstrate that AI-driven transformation is consequently both technological and organisational in nature.
The fourth part contains an examination of AI-driven government transformation and emerging leadership challenges within UAE government institutions. The study highlights how the UAE’s strong commitment to AI and digital government is creating new expectations for government leaders while also generating challenges relating to technological adaptation, institutional continuity, accountability, ethical responsibility, trust and changing leadership roles. These developments demonstrate that AI is not simply supporting existing leadership practices but is contributing to their transformation.
Finally, the study adopts a qualitative methodology to provide an in-depth understanding of how AI is transforming government leadership within the UAE institutional context. Institutional Theory provides the theoretical foundation for interpreting how government institutions respond to technological change while maintaining established rules, norms and expectations of legitimacy. The significance of the study lies in demonstrating that effective AI-enabled leadership requires the integration of technological capability with human judgement, organisational adaptability and institutional responsibility, thereby contributing to a deeper understanding of the evolving nature of government leadership in AI-enabled public institutions.
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