UAE Leads Global Shift to Chief AI Officers, Driving Higher AI ROI, IBM-DFF Study Finds

AI governance is rapidly reshaping how organisations approach artificial intelligence, and the United Arab Emirates is emerging as a global benchmark. A recent IBM Institute for Business Value study, conducted with the Dubai Future Foundation, reveals that the UAE leads the world in appointing Chief AI Officers (CAIOs). The global survey of more than 600 CAIOs across 22 countries and 21 industries shows that UAE organisations have embraced CAIO leadership at a higher rate than any other nation, with a meaningful impact on return on investment (ROI) from AI. The study highlights not only the prevalence of CAIOs but also the tangible business value they unlock, including notable ROI improvements when CAIOs oversee centralized or hub-and-spoke operating models. This evolving governance framework underscores a broader cultural and institutional shift toward making AI a core driver of performance and value across sectors.

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UAE leads in CAIO adoption and ROI impact

The global study indicates a clear UAE advantage in appointing CAIOs. In the UAE, 33 percent of organisations have installed a CAIO, compared with a global average of 26 percent. This leadership position signals a deliberate national strategy around AI governance and senior-level accountability for AI outcomes. The data suggest that having a CAIO at the helm is associated with revenue enhancements from AI investments. Organisations with a CAIO report an average ROI that is 10 percent higher than those without, underscoring the financial payoff of formal AI leadership. Even more striking is the implication of organisational design: when CAIOs lead a centralised or hub-and-spoke operating model, ROI can rise by as much as 36 percent. These figures point to a strong alignment between governance structure and measurable AI value, reinforcing the strategic case for elevated AI leadership in driving enterprise performance.

Embedded within the UAE’s numbers is a broader vision that ties governance to national priorities. The study’s foreword is written by His Excellency Omar Sultan Al Olama, UAE Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications. He stresses that AI leadership is not a fleeting technical advancement but a cultural, institutional, and habitual transformation. The CAIO, in his view, is more than a technologist; he is a translator who converts vision into execution, bridging strategy and science while stewarding value across the enterprise. This framing situates CAIOs as pivotal agents for implementing AI across diverse domains, including public administration, healthcare, education, and logistics, and signals a cultural shift toward sustained AI-enabled performance.

The UAE’s cross-sectoral approach to AI leadership is further illustrated by the participation of public agencies such as the Roads and Transport Authority (RTA) and Dubai Customs in the study. Their involvement demonstrates a cohesive, nation-wide effort to articulate and execute an AI strategy that spans mobility, border management, and trade. The inclusion of these entities provides a multi-faceted view of how AI leadership can be deployed in very different environments—ranging from regulated, safety-critical transport systems to dynamic, high-volume customs operations—while maintaining a common governance thread and performance metrics.

Dubai’s stance on CAIOs is characterised by a quick, forward-looking public sector ethos. Saeed Al Falasi, director of the Dubai Center for Artificial Intelligence, notes that Dubai’s early adoption of the CAIO role reflects a national commitment to a responsible, future-ready government. The emphasis is not only on technological adoption but also on building a governance framework that supports responsible AI deployment, ethical considerations, and measurable benefits across city services and public administration. In parallel, the UAE’s private and public partnerships—like the collaboration between IBM and the Dubai Future Foundation—underscore a shared belief that CAIOs can act as strategic enablers, accelerating AI maturity through well-designed tools, governance processes, and scalable capabilities.

In addition to highlighting the UAE’s leadership, industry perspectives from IBM reinforce the message of CAIOs as strategic catalysts. Shukri Eid, vice president and general manager for IBM Gulf, Levant, and Pakistan, emphasises that embedding CAIOs across organisations makes AI a strategic enabler across sectors. This reflects a broader optimism about the UAE’s foresight in shaping a future-ready economy, underlining IBM’s ongoing commitment to supporting organisations as they scale AI capabilities to achieve lasting, measurable impact. Lula Mohanty, IBM’s managing partner for the Middle East and Africa in IBM Consulting, notes that early CAIO appointments, coupled with visibility and budget control, lay a solid foundation for enterprise AI. The next phase, she says, involves moving beyond pilots, integrating AI into core business functions, and delivering measurable ROI. These leadership insights align with the UAE’s ambition to translate AI initiatives into scalable, value-driving outcomes.

This section of the study, therefore, reframes CAIOs not just as a new executive role but as a central axis around which the UAE’s AI-enabled transformation pivots. The evidence suggests that CAIOs are more than custodians of technology; they are performance leaders who align AI investments with strategic objectives, finance AI initiatives, and ensure that AI programs progress from experimental pilots to integrated, enterprise-wide implementations. The UAE’s approach provides a concrete, real-world model for other nations and organisations seeking to achieve high-impact AI outcomes while maintaining governance, accountability, and strategic alignment.

Subsection: The centrality of a hub-and-spoke operating model

Within the UAE’s AI strategy, a central theme is the use of hub-and-spoke operating models under CAIO leadership. When CAIOs coordinate a central AI function while linking to business units, data teams, and operations across the organisation, AI initiatives tend to move more efficiently from concept to value realization. The study’s data indicate that ROI rises substantially in this configuration, suggesting that a strong central AI governance layer can harmonise AI programs across disparate divisions, standardise data practices, and accelerate decision-making cycles. In a hub-and-spoke setup, the CAIO acts as a central coordinator who can prioritise initiatives, allocate budgets, and ensure consistent measurement frameworks, while business units retain the domain-specific expertise, domain knowledge, and operational context necessary to translate AI into concrete outcomes.

The UAE’s emphasis on CAIOs and centralized governance aligns with broader national goals that seek to embed AI across critical sectors—health, education, energy, and smart city infrastructure—so that AI is not a siloed function but a pervasive capability that informs policy, service delivery, and economic growth. The ROI benefits associated with hub-and-spoke models in the UAE context should be interpreted as evidence of governance effectiveness: when a CAIO ensures coherence across programmes, reduces duplication, and aligns technology choices with enterprise strategy, the resulting ROI can be meaningfully enhanced. These dynamics are indicative of a maturation path for AI governance, where leadership, structure, and accountability co-evolve with capabilities and data maturity.

The CAIO role: from technologist to strategic translator

The UAE study presents the CAIO as a pivotal bridge between vision and execution, a role that goes beyond technical proficiency to embrace strategic stewardship. CAIOs are described as translators who convert high-level AI ambitions into actionable roadmaps, budgets, and metrics. In this framing, CAIOs not only identify opportunities for AI but also design governance models that ensure alignment with public policy objectives, clinical standards, educational outcomes, and logistical efficiency. They serve as conduits between strategy and science, ensuring that AI initiatives are anchored in solid evidence, ethical considerations, and scalable value.

In the UAE, the CAIO’s responsibilities appear to be broad and strategically oriented. Across sectors, CAIOs are positioned to influence capital allocation, decision rights, and cross-functional collaboration. They are expected to drive measurable outcomes while navigating the complexities of data governance, privacy, and security in an environment of rapid digital transformation. The emphasis on central governance and cross-sector collaboration demonstrates that the CAIO’s impact is not limited to one department or project; rather, the CAIO shapes the enterprise’s AI maturity trajectory, aligning capabilities, data assets, and business processes with overarching strategic aims.

The study’s foreword reinforces this view, with the UAE’s leadership articulating AI as a cultural and institutional habit. The CAIO is described as a steward who fosters a value-driven approach to AI, ensuring that investments yield durable benefits across the enterprise. This perspective suggests that CAIOs are expected to cultivate organizational learning, scale capabilities, and embed AI into core decision-making processes. The leadership narrative frames the CAIO as a catalyst for cultural change, promoting an enterprise-wide mindset that treats data, models, and AI-enabled workflows as essential assets rather than isolated technology projects.

The UAE’s cross-sectoral collaboration—featuring public bodies such as the RTA and Dubai Customs—also points to a broader governance model in which CAIOs coordinate with multiple stakeholders to align AI efforts with public service objectives, regulatory requirements, and operational realities. The involvement of high-profile institutions in this study signals a long-term commitment to building an AI governance ecosystem in which CAIOs are empowered to set priorities, oversee budgets, and measure impact across a spectrum of services, from urban mobility to trade facilitation. The voices from industry leaders underscore a shared belief in CAIOs as strategic enablers whose influence extends beyond technology to business outcomes, policy implications, and national competitiveness.

Subsection: Leadership perspectives on CAIO impact

The study features several resonant viewpoints from leading voices within the UAE and from IBM about the evolving role of the CAIO. Dubai’s early embrace of the CAIO position is framed as a practical step toward creating a responsible, future-ready government. The emphasis on responsible AI governance highlights the important balance between ambition and accountability, ensuring AI deployments deliver benefits while addressing privacy, ethics, and safety concerns. IBM’s leadership emphasizes that CAIOs help ensure AI is not merely a pilot project but an integral component of strategic execution. The belief that CAIOs drive scalable, measurable AI impact across key sectors aligns with the UAE’s ambition to modernise public services and accelerate economic growth through AI-enabled efficiency and innovation.

Together, these perspectives illustrate a coherent narrative: CAIOs are not optional add-ons but central to the design of AI-enabled organisations. When CAIOs are given the visibility, authority, and budgetary control to prioritise initiatives and drive execution, they create the conditions for AI to move from experimentation to enterprise-wide adoption. The UAE’s approach demonstrates how a government-led and industry-supported governance structure can create a powerful alignment between public policy goals, business value, and technological capability. The result is a robust platform for both national competitiveness and sustainable, long-term socio-economic benefits.

UAE CAIOs: leadership strength, strategic scope, and implementation challenges

A thorough look at the UAE CAIO landscape reveals several core themes about leadership, scope, and the challenges that come with embedding AI at scale. The study’s key findings show that UAE CAIOs enjoy stronger leadership support compared to their global peers, a broader strategic remit, and a deeper operational orientation. Yet, these advantages are accompanied by notable implementation challenges that require ongoing attention and targeted action.

First, CAIOs in the UAE report higher levels of senior leadership backing. About 90 percent say they receive sufficient CEO support, versus 80 percent globally. This indicates that the CEO’s backing is not merely symbolic but a practical facilitator of AI initiatives, enabling CAIOs to secure resources, align priorities, and navigate competing demands across the organisation. In addition, 86 percent of UAE CAIOs report broader C-suite backing, compared with 79 percent globally. Enabling cross-functional collaboration and ensuring that AI programmes are integrated with other strategic initiatives appears to be a hallmark of the UAE approach to AI governance.

Second, internal appointments are common in the UAE, with 69 percent of CAIOs being appointed from within the organisation, compared with 57 percent globally. This internal promotion trend points to a governance model that values institutional knowledge, domain expertise, and established relationships with business units. It implies a culture of internal capacity-building and continuity, in which seasoned professionals who understand the organisation’s data, processes, and strategic priorities are elevated to lead AI transformation.

Third, UAE CAIOs operate with an expansive scope, reflecting a strategic shift toward AI as a core business capability. A striking 79 percent report control of the AI budget, versus 61 percent globally. This level of budgeting authority signals a strong link between leadership, financial autonomy, and the ability to prioritise AI initiatives that align with strategic goals. It also indicates a recognition that effective AI deployment requires dedicated funding, a clear roadmap, and ongoing cost management.

Fourth, UAE CAIOs place a heavy emphasis on business cases, with 62 percent prioritising the building of solid business cases, compared to 45 percent globally. This focus on rigorous justification reflects a data-driven approach to investment and an emphasis on measurable value. It suggests that UAE organisations are striving to connect AI projects to tangible outcomes such as revenue uplift, cost reduction, productivity improvements, or enhanced customer experiences.

Fifth, the UAE CAIO role encompasses hands-on implementation responsibilities. About 50 percent oversee direct implementation of AI, which aligns with the UAE’s practical, execution-oriented approach to governance. This involvement in real-world deployment indicates that CAIOs are not only strategists but also operational leaders who ensure that AI programmes translate into concrete results. However, this can accompany challenges, as the same group reports difficulties in implementation: 38 percent describe implementation as “very difficult,” higher than the global average of 30 percent. This tension illustrates the complexity of moving from strategy to scalable, reliable AI systems across diverse functions and domains.

Sixth, the UAE CAIO cohort brings deep operational experience. While 69 percent have a background in data, mirroring global figures, a larger share—48 percent—come from operations, compared with 38 percent globally. This indicates a preference for executives who understand the day-to-day dynamics of business functions, processes, and workflows. An execution-oriented mindset is embedded in this profile, suggesting that CAIOs in the UAE are better positioned to translate AI concepts into practical actions that improve operational performance.

Despite these strengths, the study recognises persistent tensions between experimentation and accountability. UAE CAIOs are not waiting for perfect metrics to act: 76 percent say their organisation risks falling behind without measurement of AI impact, just slightly higher than the global figure of 72 percent. Meanwhile, 74 percent initiate AI projects even if results aren’t fully measurable yet, compared with 68 percent globally. This willingness to pursue action, even amid incomplete measurement, reflects a pragmatic approach to innovation: organisations are willing to take calculated risks to accelerate learning and capture early value, while continuing to develop more robust metrics over time.

The UAE’s AI maturity trajectory shows room for growth despite momentum. A substantial 76 percent of UAE organisations remain in the pilot stage, compared with 60 percent globally. This suggests that while CAIO leadership is accelerating AI initiatives, the region is still advancing toward scale and enterprise-wide deployment. The study’s findings point to a coordinated push required to move AI from pilots to integrated processes that endure, are scalable, and deliver consistent outcomes across functions and sectors.

Subsection: Implications for execution and governance

The combination of strong leadership support, budget authority, and a data-and-operations-oriented talent pool positions the UAE to push AI forward with disciplined governance. Yet, the higher rate of perceived difficulty in implementing AI projects signals the need for mature governance frameworks, robust change management, and scalable integration strategies. Organisations pursuing similar paths can draw several practical implications:

Invest in governance structures that align CAIOs with senior leadership, ensuring clear decision rights around data governance, model risk management, and cross-functional accountability.
Prioritise building business cases early in the AI journey, with rigorous value propositions and measurable KPIs that link AI activities to concrete outcomes.
Support CAIOs with access to the right capabilities, including data engineering, platform engineering, and operations-oriented AI specialists, to balance theoretical potential with practical deployment.
Foster a culture of proactive experimentation complemented by disciplined monitoring, enabling teams to iterate while maintaining governance, risk oversight, and ethical considerations.
Plan for scale from the outset by designing AI architectures and data ecosystems that can support increasing volumes, more diverse data sources, and more complex models while maintaining reliability, security, and privacy.

Balancing experimentation with accountability: measurement, pilots, and value

A central theme in the UAE study is how CAIOs balance the need for experimentation with the imperative to deliver accountability and measurable impact. The data show that senior leadership support translates into a governance environment where AI initiatives can proceed with clear ownership and a framework for monitoring outcomes. Yet the tension between rapid experimentation and rigorous measurement remains a fundamental challenge. The UAE’s approach recognises the value of launching AI projects even when results are not yet fully measurable, while simultaneously emphasising the importance of impact measurement to maintain momentum and justify ongoing investment.

From a governance perspective, this balance is achieved through several practices. First, organisations prioritise developing business cases that articulate the anticipated value of AI investments and tie them to strategic objectives and financial metrics. Even when initial results are uncertain, a strong case can guide prioritisation, resource allocation, and risk management. Second, organisations pursue a pragmatic measurement philosophy, starting with early indicators that can be refined as data quality and modelling maturity improve. This approach enables learning and iteration without delaying deployment to the point of stagnation.

In parallel, CAIOs emphasise accountability by maintaining tight control over budgets and ensuring alignment with enterprise priorities. The ability to shape investments and track progress against defined KPIs helps translate AI pilots into repeatable, scalable operations. The UAE’s focus on both experimentation and accountability reflects a modern understanding of AI maturity: it requires a staged journey from pilot to scale, with governance mechanisms that accommodate evolving data ecosystems, model risk considerations, and ethical guidelines.

Subsection: The role of measurement in enterprise AI strategy

Measurement is not merely a reporting exercise; it is a strategic instrument for driving continual improvement and governance discipline. In the UAE context, CAIOs view measurement as essential for identifying which AI initiatives generate the most value, understanding the drivers of ROI, and guiding future investment decisions. The data indicate that leaders recognise gaps in measurement and accept that some initiatives may advance without perfect metrics at the outset. This pragmatic stance supports a culture of experimentation, wherein teams can explore new AI opportunities, capture early benefits, and refine measurement approaches over time to achieve higher confidence in results.

Organisations pursuing similar trajectories can adopt several practices to support effective measurement:

Build a clear measurement framework that links AI initiatives to specific business outcomes, with tiered metrics that capture near-term and long-term value.
Invest in data quality and governance to ensure that metrics are accurate, reliable, and actionable across different units.
Establish feedback loops that translate measurement findings into actionable changes in strategy, resource allocation, or process redesign.
Create transparent communication channels that share progress, learnings, and insights with executive leadership, thereby reinforcing accountability and alignment.
Develop a culture of continuous improvement, recognising that AI maturity is a dynamic process that evolves with data, technology, and operating contexts.

The path to scale: AI maturity and pilot-stage dynamics

Despite strong momentum, the UAE’s AI maturity profile indicates that most organisations remain in pilot or early deployment stages. A striking 76 percent of UAE organisations report that they are still in the pilot phase, compared with 60 percent globally. This statistic highlights significant growth potential in operationalising AI at scale, particularly when weighed against the positive indicators of CAIO leadership, cross-functional collaboration, and budgetary control. The translation from pilot success to enterprise-wide adoption requires a combination of technical readiness, governance maturity, process integration, and cultural alignment.

To accelerate scale, organisations may focus on several levers:

Strengthen data infrastructure and data governance to enable scalable data-sharing, data lineage tracing, and model monitoring across diverse business units.
Standardise AI platforms and tooling to reduce friction in deployment, enable rapid iteration, and ensure consistent risk management and compliance.
Invest in change management and workforce enablement to address skills gaps, foster AI literacy, and cultivate a culture that embraces data-driven decision making.
Align AI initiatives with enterprise architecture and IT strategy to ensure seamless integration with core systems, processes, and governance frameworks.
Promote cross-functional collaboration by establishing governance forums that include business leaders, data professionals, IT, compliance, and risk management representatives.

These strategies can help UAE organisations translate pilot successes into durable, scalable value across sectors, aligning with the nation’s longer-term AI goals.

Subsection: The UAE’s AI Strategy 2031 and the national vision

The study situates CAIO-led governance within the broader frame of the UAE’s AI Strategy 2031, which envisions the country becoming a global leader in artificial intelligence across essential sectors such as health, education, energy, and smart cities. This national roadmap provides a top-level ambition that resonates with the CAIO-driven governance model described in the study. By aligning CAIO leadership with strategic priorities and national objectives, the UAE aims to create a cohesive, scalable AI ecosystem that supports innovation, efficiency, and socio-economic advancement.

The collaborative research between IBM and the Dubai Future Foundation offers an evidence-based contribution to the national dialogue on AI governance. It demonstrates how CAIOs can become central to achieving the 2031 vision by bridging strategic intent with operational execution, ensuring that AI investments translate into tangible outcomes for citizens, businesses, and government operations. The findings underscore the importance of a coordinated, multi-stakeholder approach to AI governance—one that relies on strong leadership, clear budgets, rigorous measurement, and a commitment to scale.

UAE CAIOs as catalysts for city and national advancement

The UAE study frames CAIOs as catalysts for the broader policy and economic agenda of the city and the nation. By embedding CAIOs in organisations and providing them with the authority to shape budgets and strategic direction, the UAE is constructing an AI-enabled economy that emphasises measurable impact, cross-sector collaboration, and rapid implementation. The early adoption of CAIO roles, supported by public-private partnerships and cross-government coordination, creates a model for how AI governance can accelerate performance improvements while maintaining quality, accountability, and ethical standards.

For Dubai, the CAIO model aligns with the city’s ambition to be a global hub for innovation, technology, and smart city solutions. The leadership’s confidence in CAIOs as strategic enablers supports the city’s vision of scalable, measurable AI impact across key sectors. By empowering CAIOs with the right tools, governance, and budgets, Dubai aims to translate AI capabilities into meaningful benefits that touch every facet of urban life—from mobility and logistics to services and public safety.

IBM’s perspective reinforces the UAE’s leadership trajectory. The company notes that embedding CAIOs across organisations strengthens AI as a strategic enabler across sectors and signals a forward-looking approach to economic development. IBM’s ongoing collaboration with the Dubai Future Foundation further demonstrates a shared commitment to helping organisations scale AI capabilities and achieve lasting, measurable outcomes. The messages from IBM leadership echo the UAE’s emphasis on practical execution, governance, and value creation as core features of a mature AI ecosystem.

National and regional implications for AI governance

The UAE’s CAIO-driven governance framework does more than optimise internal efficiencies. It provides a model for how governments and organisations can structure leadership, governance, and measurement to maximise AI value while maintaining ethical standards and public accountability. The cross-pollination of ideas between public agencies, industry players, and global technology partners demonstrates a robust ecosystem in which AI governance evolves through collaboration, learning, and shared goals.

The study’s findings suggest that CAIOs can drive more than performance gains—they can become integral to national competitiveness, policy implementation, and service delivery. By elevating the status of AI leadership and linking it to strategic budgetary control and measurable outcomes, the UAE is building the muscle memory needed to sustain AI-driven improvements over the long term. The results offer a blueprint that other nations and organisations can study, adapt, and implement within their own governance frameworks.

Implications for global AI governance and enterprise value

The UAE’s leadership in CAIO adoption has several important implications for global AI governance and enterprise value:

Elevating the CAIO role can improve alignment between AI strategy and business outcomes, enabling organisations to realise ROI more consistently and at scale.
Governance models that empower CAIOs with budgetary authority and cross-functional influence can reduce silos and accelerate AI deployment across departments and sectors.
A strong emphasis on measurement, disciplined experimentation, and enterprise-wide adoption supports sustained value creation and long-term AI maturity.
Cross-sector collaboration, including partnerships with research institutions and industry leaders, can accelerate knowledge sharing, capability building, and the dissemination of best practices.
A national strategy that positions AI as a key driver of economic growth and public service excellence can attract investment, talent, and international partnerships, reinforcing competitive advantages in a changing global landscape.

The UAE case demonstrates how a well-structured governance framework—anchored by CAIO leadership, robust budgeting, and a culture of measured experimentation—can create durable value while advancing national objectives. As more countries and organisations grapple with the governance of AI, the UAE’s experience offers practical insights into how to translate AI potential into real-world outcomes, reinforcing the idea that governance, strategy, and execution must be tightly integrated for AI to deliver its maximum benefits.

Conclusion

The new IBM Institute for Business Value study, conducted alongside the Dubai Future Foundation, places the United Arab Emirates at the vanguard of AI governance. The UAE’s higher CAIO adoption rate, coupled with strong executive sponsorship, internal pathways to leadership, and a strategic, budget-driven approach to AI initiatives, signals a mature and ambitious path toward enterprise-wide AI maturity. The evidence that CAIO-led, hub-and-spoke operating models yield higher ROI reinforces the value of central governance in aligning AI investments with strategic objectives. The study’s findings about CAIOs’ roles—as translators, planners, and execution leaders—underscore the importance of equipping them with the tools, budgets, and organizational support necessary to scale AI responsibly and effectively.

Dubai’s cross-sector collaboration, with inputs from entities like the Roads and Transport Authority and Dubai Customs, illustrates how a city can align AI strategy with public service outcomes, safety, efficiency, and growth. The UAE’s AI Strategy 2031 provides a national framework for ambition and coherence, connecting CAIO leadership to macro-level goals in health, education, energy, and smart cities. The voices of public and private sector leaders—ranging from government ministers to IBM executives—collectively emphasize CAIOs as strategic enablers whose work drives measurable, long-term impact across sectors.

For organisations worldwide, the UAE’s experience offers a comprehensive reference on how to structure AI governance to accelerate value while maintaining accountability, ethics, and strategic alignment. The move from pilots to enterprise-scale AI requires disciplined measurement, strong leadership, and deliberate investment in people, processes, and platforms. With CAIOs guiding the way, AI governance can mature into a core strategic capability that transforms operations, powers innovation, and sustains competitive advantage in an increasingly AI-enabled economy.