Gartner’s latest findings reveal a quiet yet profound upheaval in how enterprise AI is led at the highest levels. A growing majority of Chief Data and Analytics Officers now steer AI initiatives, signaling a move away from traditional CIO- and CTO-led paradigms. This shift underscores data as a strategic asset and elevates data governance, cross-functional collaboration, and business outcomes as central to AI success. As organizations race to harness AI capabilities, the CDAO role has evolved from a compliance and data quality function into a core driver of AI strategy and value delivery. The implications are sweeping: leadership structures, decision rights, and performance expectations are all being redefined in light of data-centric leadership models.
The Rise of the CDAO as the Central Leader for AI Strategy
The enterprise landscape is undergoing a notable transition in the way technology and data leadership intersect with business strategy. Historically, Chief Information Officers (CIOs) concentrated on the reliability and efficiency of IT infrastructure, while Chief Technology Officers (CTOs) focused on accelerating innovation pipelines. In parallel, data execution tended to be a support function, tethered to operations rather than positioned as a strategic differentiator. Yet as data volumes balloon and AI technologies mature from experimental pilots to mission-critical investments, organizations have begun to recognize a fundamental truth: data management, data governance, and analytics capability are at the heart of competitive advantage in an AI-powered economy.
This recognition has birthed the Chief Data and Analytics Officer (CDAO) role, initially centered on compliance, data quality, and foundational analytics. Over time, CDAOs have broadened their remit to encompass the design and execution of enterprise AI strategies. They now operate with cross-functional visibility that enables them to translate AI potential into tangible business value. The Gartner data indicates that seven out of ten CDAOs now hold primary responsibility for AI strategy development and the establishment of operational frameworks within their companies. This marks a decisive shift away from the erstwhile dominance of CIOs and CTOs in strategic decision-making and toward a model in which data assets and AI-enabled processes steer corporate outcomes.
This redistribution of leadership authority aligns with a broader recognition: data — and the capacity to leverage it through AI systems — is foundational to competitiveness in today’s market. The shift is not merely about where AI projects sit within an organization, but about who has the authority to shape, fund, and govern those initiatives across functions. The evidence is visible in organizational reporting structures. By 2025, Gartner notes that 36% of CDAOs reported directly to the Chief Executive Officer, up from 21% the year prior. This step-change in reporting lines signals that data leadership is no longer a back-office function; it has become a strategic driver at the top tier of corporate governance. It also highlights a growing expectation that AI initiatives be subject to executive-level scrutiny, alignment with business strategy, and measurable impact on performance.
Sarah James, who serves as a Senior Director Analyst at Gartner, emphasizes the strategic moment for CDAOs. She describes 2025 as a pivotal year when AI presents an opportunity for CDAOs to cement their rank in AI leadership. In James’s view, the CDAO’s broad exposure across the organization, coupled with their deep data and analytics expertise, positions them uniquely to guide and challenge the business in delivering value from AI. This is more than technical proficiency; it is about translating data assets into strategic bets and ensuring that AI investments produce measurable results. The shift thus reflects a new reality in which data leadership is not a peripheral function but a central, influential force in AI strategy and execution.
The stakes for CDAOs have never been higher. Gartner projects that by 2027, 75% of CDAOs who are not perceived as essential to AI implementation success will lose their C-level status within their organizations. This projection underscores the high expectations placed on CDAOs to demonstrate strategic indispensability. The CDAO role’s emergence as a strategic function is tied to the mounting complexity and ubiquity of data, along with AI’s expanding range of applications and value across the enterprise. CDAOs must not only possess technical prowess but also the ability to translate that expertise into business outcomes that boards and CEOs can see and measure.
What this means for organizations
The elevation of CDAOs reshapes how companies approach AI governance, data strategy, and cross-functional collaboration. With data becoming a central enterprise asset, organizations are redesigning operating models to ensure that data governance, master data management, and analytics are integrated into product development, customer experience, supply chain optimization, and risk management. The shift also calls for new metrics and accountability structures that tie AI performance to business outcomes, rather than merely to technical milestones. As the CDAOs assume greater influence, CIOs and CTOs increasingly find their traditional technical domains expanding into collaborative sectors where policy, risk, and value creation converge. The result is a more holistic approach to technology leadership, one that places data quality, governance, and AI-enabled decision-making at the core of enterprise strategy.
The practical implications for leadership teams
Organizations must consider how to recalibrate reporting lines, governance councils, and performance incentives to reflect the new AI leadership paradigm. Boards will increasingly expect CDAOs to articulate AI roadmaps in business terms, justify data investments with ROI scenarios, and establish governance frameworks that address risk, ethics, and regulatory compliance. Furthermore, the evolving role demands that CDAOs cultivate the skills to convene and align multiple stakeholders, from data engineers and data stewards to product managers and line-of-business leaders. This cross-functional orchestration is essential to build a sustainable data-driven culture capable of delivering durable AI value.
Evidence of Change: Reporting Lines, Influence, and Early Impacts
The ongoing transition is not theoretical; it is visible in how organizations structure authority and allocate accountability for AI outcomes. The data shows a clear trend toward elevating data leadership within the corporate hierarchy. The rise in CDAOs reporting directly to the CEO indicates a strategic re-prioritization of data and AI as enterprise-wide concerns rather than IT-centric initiatives. This structural change signals to the market that data governance and AI-enabled business value are executive-level priorities requiring direct oversight at the highest levels of governance.
As CDAOs assume a more central role, their influence extends beyond technical governance to strategic decision-making across departments. Their visibility within the organization—and the depth of their collaboration with C-suite peers—enables them to integrate AI considerations into product design, customer experience, operations, and risk management. This cross-functional orientation helps ensure that AI initiatives are not siloed projects but are embedded into the enterprise roadmap. In practical terms, this means CDAOs contribute to setting enterprise-wide AI standards, policies, and measurement frameworks that guide how AI is developed, tested, deployed, and monitored.
The boardroom implications are substantial. Investors and senior leaders are increasingly seeking transparency on how AI investments translate into measurable business outcomes, including revenue growth, cost optimization, and risk mitigation. CDAOs, with their enterprise-wide vantage point and technical acumen, are well-positioned to provide such visibility. They can articulate the interdependencies between data quality, AI readiness, and business value, making a compelling case for sustained investment in data platforms, governance mechanisms, and workforce capabilities. The result is a more coherent and accountable approach to AI at scale, where governance, ethics, and performance are aligned with strategic business goals.
The three evolution paths Gartner identifies
Gartner highlights three distinct trajectories for CDAOs, reflecting different strategic foci and organizational needs. Each path requires a unique blend of competencies, governance approaches, and collaboration patterns. Organizations may find that different divisions or product lines align with different CDAO paths, or that a single CDAO gradually traverses multiple trajectories as the enterprise’s AI agenda matures.
The Expert Data and Analytics Leader
In this path, the CDAO functions as the central authority on data matters, acting as the ultimate steward of business intelligence systems and master data management processes. The primary responsibility is ensuring data integrity and consistency across the organization’s assets. This role typically reports to the IT department, reinforcing the alignment between data platforms, data quality, and enterprise analytics capabilities. The expert data and analytics leader emphasizes governance best practices, data lineage, metadata management, and the establishment of standardized analytics tools and methodologies. The aim is to create a robust data foundation that makes analytics reliable, scalable, and trusted across departments.
This trajectory yields clear advantages: a unified data fabric across the enterprise, easier enforcement of data standards, and a strong risk management posture. The potential drawbacks include concentration of power within IT governance and the risk of slower business alignment if the CDAO is perceived as too technically oriented or disconnected from commercial outcomes. To mitigate this, the expert data and analytics leader must actively translate data governance activities into tangible business benefits, partnering with product, marketing, and operations leaders to demonstrate how data-driven insights drive performance.
Key characteristics and capabilities of this path include deep expertise in data modeling, data quality frameworks, master data management, and the orchestration of enterprise BI platforms. The role requires strong collaboration with data engineers, data stewards, data scientists, and business analysts to ensure data assets are designed and maintained for analytics needs across the organization. It also demands an attention to regulatory compliance and risk controls, given the centrality of data governance in enterprise risk management.
The Connector CDAO
The second pathway envisions the CDAO as a bridge builder who connects C-suite strategy with the technical data and AI teams that execute on those strategies. This mediator role prioritizes embedding analytics capabilities into products and services while advancing the AI agenda across the organization. The connector is less about owning the data platform and more about ensuring that analytics capabilities are integrated into the product lifecycle, customer experiences, and operational processes. The CDAO in this path collaborates closely with chief product officers, chief marketing officers, chief operating officers, and other executives to ensure data-driven insights inform strategic choices.
This trajectory emphasizes communication, collaboration, and influence rather than pure governance or data stewardship. The ability to translate business questions into data and analytics requirements, and then translate analytics findings into actionable business actions, is central. A successful connector helps teams across the enterprise adopt AI responsibly, accelerates experimentation, and expands the application footprint of analytics capabilities. The main risk is potential diffusion of focus if the CDAO lacks sufficient authority to align cross-functional teams or if analytics initiatives compete for scarce resources without a clear prioritization framework. To combat this, the connector must cultivate a clear roadmap that links AI-enabled product enhancements to business outcomes, with measurable milestones and executive sponsorship.
The Pioneer CDAx
The third direction Gartner identifies is the “pioneer CDAx,” a transformation-oriented executive who blends data, analytics, and AI leadership into a single, transformative mandate. This role emphasizes ethical principles, governance frameworks, and cross-functional innovation while actively driving organizational change. The pioneer is charged with shaping the enterprise-wide AI strategy, fostering adoption of AI in diverse business units, and ensuring that AI development adheres to ethical standards, security requirements, and regulatory expectations. This path places a premium on strategic influence, change management, and the ability to mobilize disparate teams around a shared AI vision.
A pioneer CDAx must be comfortable operating at the intersection of business strategy, technology, and governance. They lead efforts to establish enterprise-wide AI governance practices, promote responsible AI development, and champion governance that protects privacy, fairness, accountability, and security. They also coordinate cross-functional innovation initiatives that accelerate AI adoption while maintaining alignment with corporate risk appetite and strategic priorities. This path demands a broad set of capabilities, including advanced strategic thinking, stakeholder management, change leadership, and the ability to reconcile competing priorities to achieve cohesive, ethical, and scalable AI outcomes.
The skill sets required across paths
Each path requires a distinct mix of talents, yet several core capabilities are universally valuable for CDAOs across trajectories. Business acumen and the ability to translate technical concepts into business value remain essential. Strong communication skills are critical for ensuring that data and AI initiatives resonate with executives and line-of-business leaders alike. Technical expertise remains important, but the emphasis shifts toward governance, data quality, and the ethical and responsible application of AI. Change management and the ability to lead cross-functional teams through transformation are also crucial as organizations scale AI initiatives. Finally, a robust understanding of risk, compliance, and regulatory considerations ensures that AI programs operate within enterprise risk tolerances.
Sarah James notes that D&A leadership is likely to diverge further in the near future, and both current and aspiring CDAOs should tailor their skill development to align with their chosen path. This strategic alignment helps CDAOs solidify their role and strengthen their position in AI leadership. The overarching message is that there is no one-size-fits-all CDAO model. Instead, organizations should recognize the spectrum of paths and empower CDAOs to chart and pursue the route that best fits their strategic goals, culture, and risk posture. A CDAO who can navigate this spectrum and demonstrate tangible value will become indispensable in the AI era.
Building a Data-Driven Organization: Capabilities and Governance
Successful CDAOs must cultivate a data-driven capability across the enterprise. This includes building AI-ready data foundations to ensure data quality, reliability, and accessibility for AI workloads. It also involves establishing governance frameworks that address privacy, security, ethics, and compliance while enabling rapid experimentation and responsible scale. The ability to articulate the value of data and AI in business terms is essential to secure ongoing investment and alignment with strategic priorities. Collaboration across silos becomes a defining competence, with the CDAO serving as a catalyst for unified action rather than a gatekeeper of separate functions.
Organizations should invest in the people, processes, and technology that enable data-driven decision-making. This includes data catalogs and lineage to improve transparency, metadata management to support governance, and standardized analytics tools that promote consistency across business units. It also means implementing mechanisms to measure AI impact, track performance against business outcomes, and adjust strategies in response to evolving market conditions and regulatory expectations. As CDAOs assume an expanded leadership role, they must balance the need for speed and experimentation with the imperative to manage risk and protect stakeholder interests.
Practical steps for CDAOs and their teams
- Define clear AI strategy objectives tied to business value, with measurable KPIs and governance standards that executives can monitor.
- Develop or strengthen data foundations, including data quality programs, metadata management, data lineage, and master data governance, to support reliable analytics and AI outcomes.
- Create cross-functional governance bodies that include representatives from IT, data, product, legal, risk, and business units to ensure alignment and accountability.
- Invest in AI ethics, bias mitigation, privacy protections, and security controls to foster responsible AI development and deployment.
- Build a culture of data literacy across the organization, enabling broader adoption of data-driven decision-making and enabling teams to leverage AI insights more effectively.
The role of leadership in driving cultural change
CDAO leadership extends beyond technology and data into organizational culture and change management. champions of data-driven decision-making must communicate a compelling narrative about AI’s value to the business, demonstrate quick wins, and build trust with stakeholders who may be skeptical or risk-averse. This involves translating complex data concepts into business language, aligning incentives with AI outcomes, and ensuring that teams have the training and resources to adopt data-driven practices. Leadership must also address ethical considerations, ensure governance clarity, and create inclusive processes that invite diverse perspectives in AI development and deployment.
Implications for Boards, Executives, and the Enterprise
The governance, investment, and performance expectations surrounding CDAOs are material. Boards increasingly look to CDAOs to articulate not only the technical feasibility of AI programs but also the strategic rationale, risk management, and economic value they deliver. The emergence of data leadership as a strategic function requires rethinking how organizations allocate capital for AI, how they measure success, and how they govern risk across data assets and AI systems.
For executives, the CDAO’s role offers an opportunity to align AI initiatives with broader corporate strategy and to maximize the impact of data assets. It requires a clear understanding of how data governance, data quality, and AI readiness influence the ability to scale AI across the enterprise. As AI becomes more pervasive, the demand for cross-functional collaboration grows, and the CDAO is uniquely positioned to facilitate these collaborations, translate between technical teams and business units, and ensure that AI investments generate durable business value.
The organizational design implications are significant. The rising prominence of the CDAO suggests that enterprise AI leadership will be distributed across multiple paths, depending on strategic priorities and organizational culture. Some companies may consolidate data governance under a single CDAO, while others may maintain distinct leadership pods for data management and AI product development. In all cases, the emphasis should be on creating coherent governance, robust data foundations, and a shared sense of purpose around AI’s role in achieving strategic objectives.
What boards should expect from CDAOs
Boards should expect CDAOs to present a clear AI strategy linked to measurable business outcomes, an evidence-based governance framework for data and AI, and transparent metrics that demonstrate ROI and risk management. They should seek assurance that the organization has established AI ethics and governance standards, data quality programs, and security controls that protect stakeholder interests. Boards should also look for evidence of cross-functional coordination, executive sponsorship, and the ability to scale AI initiatives across business units with consistent methodology and accountability.
The Path Forward: Strategies for CDAOs to Succeed
To maximize the impact of the CDAO role and ensure AI programs deliver tangible value, organizations should embrace a holistic approach that prioritizes data assets, governance, ethical considerations, and cross-functional collaboration. CDAOs must continue to evolve their capabilities to lead in a rapidly changing AI landscape, balancing technical depth with strategic execution and governance discipline.
Key strategic priorities for CDAOs include:
- Strengthening data foundations: Invest in data architecture, quality, lineage, and governance to enable scalable, trustworthy AI and analytics.
- Defining a compelling AI roadmap: Link AI investments to business outcomes with clear milestones, budgets, and accountability.
- Building ethical, secure AI programs: Establish governance frameworks for fairness, privacy, security, and compliance, and embed them in AI development cycles.
- Fostering cross-functional collaboration: Create channels for continuous alignment among IT, data teams, product, marketing, operations, and risk management.
- Demonstrating measurable value: Develop metrics that quantify AI impact on revenue, cost savings, customer experience, and risk reduction.
Conclusion
The landscape of enterprise AI leadership is shifting decisively toward data-driven governance and cross-functional strategic leadership, with the Chief Data and Analytics Officer at the center of AI strategy implementation. The rising share of CDAOs who lead AI initiatives, the increasing proportion who report directly to the CEO, and Gartner’s outlined paths for CDAOs—all point to a future where data assets and AI-enabled decision-making define competitive advantage. Organizations that recognize this shift early, invest in robust data foundations, and empower CDAOs to drive governance, ethics, and business value will be better positioned to navigate the complexities of AI at scale. The CDAO’s role—whether as the expert data steward, the connector who unites business and technology, or the pioneer driving transformation—will continue to evolve, but its strategic importance in shaping AI outcomes and enterprise resilience is now unequivocal.