CDAOs’ crucial role in GenAI success and risk of C-Suite elimination
We’re at an inflection point with generative AI. The companies that scale it right will unlock serious value. But the path starts with leadership, specifically, with Chief Data & Analytics Officers. Right now, CDAOs are central to unlocking generative AI at the enterprise level. They control the data pipelines, the models, the ethics protocols, everything that glues GenAI into real business outcomes.
But let’s be clear: this influence isn’t guaranteed to last. Gartner projects that by 2027, 75% of CDAOs who don’t prove their value in AI leadership will be out of the C-suite. That’s not just about performance; it’s about being indispensable. If you’re a CDAO today and you’re not framing your actions around driving AI at scale, you’re not leading, you’re reacting.
The bar has moved. It’s no longer just about managing data assets. It’s about becoming the driving force behind business transformation through AI. For CEOs, this is a fresh opportunity. CDAOs can serve as your internal engine for AI-based growth. Give them space and the accountability to deliver. If they can’t lead the charge on GenAI, they shouldn’t be in that chair.
Sarah James, Senior Director Analyst at Gartner, put it clearly: “This year is a critical one for CDAOs, as AI presents a new opportunity for them to establish their rank in AI leadership.” If your CDAO isn’t already pushing AI strategy across functions, they’re missing the plot.
The imperative of clean and standardized data
Let’s make something obvious: if your data is a mess, your genAI project isn’t going anywhere. This isn’t about edge cases or rare errors, it’s foundational. If your generative models are trained on noisy, duplicated, or incomplete data, what you get is bloated systems producing second-rate outcomes.
Gartner’s insights highlight a key issue: over the past few years, one in three AI projects has failed to reach completion or generate real ROI. The common thread? Unclean data. That’s not a minor operational glitch. That’s strategy-level failure. Every C-suite executive should understand that no AI initiative pays off unless the underlying data is trained, cleaned, and scaled correctly.
This is where CDAOs should be earning their stripes. Their job, before they launch the next AI model, is to make sure the infrastructure under it is rock-solid. Preprocessing needs to be systematic: identify outliers, eliminate redundancy, standardize everything. If you’re cutting corners here, you’re burning cash later.
So, if you’re serious about AI, start where the problem usually begins, with raw data. Focus less on the flashy use cases, and more on the foundational work that actually drives outcomes. That’s the only way to maximize investments in generative AI.
CDAO responsibilities and executive influence on AI strategy
The CDAO title has moved into real business power, because AI isn’t just another tool, it’s now shaping strategy and operations at scale. Right now, 70% of CDAOs hold primary responsibility for AI strategy across their companies, according to Gartner. This isn’t symbolic. It means they’re designing the operating models that will define how companies compete.
We’re seeing a shift in reporting lines too. In 2024, only 21% of CDAOs reported directly to the CEO. That number has now risen to 36%. That signals increased visibility and influence, and it’s deserved, at least for those who’ve stepped up to lead in AI, not just in theory, but in execution.
For senior leadership, the signal is clear: your CDAO isn’t just back office anymore. They’re a core driver of product innovation, customer insights, and operational efficiency, especially through generative tools that run on data engines they oversee. The AI mandate means strategic decisions and model design are happening simultaneously. That only works when there’s tight alignment between C-level strategy and the data science function.
Put simply, CDAOs who lead with both data credibility and innovation confidence are now among your top few decision-makers. Make sure they’re at the table, and make sure they’re worth having there.
The challenges of demonstrating measurable business value
Even with the title and the responsibilities, a lot of CDAOs are still falling short on turning influence into measurable impact. Gartner’s latest CDAO Agenda Survey makes this very clear. While 74% of executives say they trust their data and analytics functions, only 49% can track those efforts using business-outcome-driven metrics. That gap doesn’t just weaken the function, it weakens the position.
Right now, the average CDAO is juggling 14 core responsibilities, up 56% from last year. Too often this leads to a scattershot approach, lots of operational work, but minimal strategic focus. That becomes a problem when it’s time to justify budgets or link AI initiatives back to bottom-line results.
For boards and CEOs, trust in your CDAO and their team isn’t enough. You need clarity. If you’re investing in AI, data platforms, and analytics tools, you need to know what’s moving the needle and what’s just creating load. That means fewer vanity dashboards, more impact tracking.
Sarah James from Gartner put it plainly: “There is clearly work to do for CDAOs to establish themselves, their function, and their value to the organization.” If decision-makers want to get real returns on AI and analytics, expect more than execution, demand strategy, ownership, and proof.
Diversification of the CDAO role into distinct pathways
The CDAO role is no longer a single-track position. As companies mature in their data capabilities, the expectations placed on CDAOs are evolving in three very different directions. Gartner outlines this shift clearly: some CDAOs are becoming Expert D&A Leaders, others are transitioning into Connector CDAOs, and a smaller group is pushing forward as Pioneer CDAx executives.
Expert D&A Leaders focus on enabling business intelligence, reporting, and technical infrastructure. They often report into IT and support operational execution. Then there’s the Connector CDAO, someone who works across functions, aligning data insights to product and customer strategies. These individuals drive AI integration within products and services. Finally, the Pioneer CDAx role combines data, AI, and innovation leadership. This version is highly strategic and oversees data ethics, AI governance, and enterprise-wide transformation.
Why does this matter? Because clarity of role increases effectiveness. CEOs and boards need to know which version they have, or which version they need. Hiring a CTO-style CDAO when your business needs a product-integrated vision won’t deliver results. These paths aren’t interchangeable. The function your CDAO fills must align tightly with your broader business goals and AI ambitions.
As business reliance on data and AI expands, this differentiation becomes more important. Growth-stage companies may need the connector. Large, regulated enterprises might need the expert or pioneer. Misalignment in role definition can delay impact and stall AI deployment across the business.
Limited enterprise-scale adoption of generative AI
Many executives now understand the importance of generative AI, but understanding and execution are two different things. The issue is in the scale. A report from Accenture shows that while most companies acknowledge AI’s strategic value, only 8% of AI leaders are actually scaling it across the enterprise. That’s a weak showing. Even fewer, just 15%—are considered “AI reinvention-ready,” meaning they’ve built the infrastructure and workflows needed to support AI as a core capability.
This gap between recognition and readiness is where most companies are stuck. Leadership sees the potential, budgets are increasing, and teams are experimenting, but the supporting systems, governance, and talent aren’t in place. Ownership of proprietary data is common, yet few organizations are activating it at the level needed to drive anything meaningful with GenAI.
C-suite executives need to stop relying solely on pilot programs and task force outputs. To make AI part of your business model, it has to be embedded into operations, guided by clear objectives, powered by clean data, and run on scalable systems. If you’re not solving those prerequisites, you’re not in a position to win with AI.
Accenture’s research points to a critical factor: scaling starts with data. That means organizations must do more than collect data, they need to structure, govern, and optimize it to move AI out of isolated projects and into the core of the enterprise.
The potential phasing out of centralized data roles
The rise of generative AI tools is changing how organizations think about leadership in data and analytics. What we’re seeing now is a slow shift from centralized executive authority toward distributed AI capabilities embedded within different parts of the business. According to a Gartner survey of 400 software engineering leaders, nearly half of all development teams are using genAI tools to enhance workflows. This decentralization means AI is increasingly being owned and operated across departments, not just from the CDAO’s office.
This raises a hard question for enterprise leadership: how long do we need a centralized CDAO role? For many companies, especially those with overlapping titles like CIO, CTO, Chief Digital Officer, and CAIO, the value of one additional executive in charge of the data stack becomes less clear, especially if there’s limited differentiation in what each role delivers.
Tom Davenport, Professor of IT and Management at Babson College, has pointed out the issue. He notes that many CDOs focus too heavily on governance, which makes it difficult to show concrete value. It’s the CDAOs who’re also leading on AI and analytics who have a better shot at sustaining executive relevance.
From a board-level perspective, the key is role clarity and output. It’s no longer enough to say someone “leads AI.” If various teams already execute AI models through genAI tools and cloud platforms, leadership needs to clarify who is delivering cross-functional oversight, who’s responsible for risk, and who’s accountable for performance. Without that, roles like CDAO risk being seen as transitory.
The need for aspiring CDAOs
As genAI becomes a defining force for competitive differentiation, the expectations for CDAOs are changing fast. It’s not just about data fluency anymore. Leaders in this space need to combine AI literacy with business strategy, ethical technology governance, and strong operational management. If someone’s working toward a CDAO or similar role, acquiring these cross-domain skills isn’t optional, it’s essential.
Sarah James of Gartner made the point directly: those entering the field should focus on developing capabilities in AI readiness, data governance, and strategic leadership. That combination will allow new leaders to navigate the complexity of scaling AI initiatives and tie those efforts to actual business outcomes.
The demands here aren’t theoretical. According to an IBM survey of CEOs, only 25% of companies have realized expected returns on their AI investments. That sets a high bar for future CDAO candidates. They must be able to close the ROI gap, not just by implementing the right technologies, but by driving adoption, aligning with business goals, and managing risks through structured governance.
For C-suite leaders, this is a practical call to build leadership pipelines with AI-literate operators. Whether recruiting externally or developing talent internally, the goal now is to ensure future CDAOs know how to scale AI responsibly, move from experimentation to execution, and deliver enterprise-wide value. That’s the profile the next generation of successful data executives will need.
In conclusion
The real value of generative AI won’t come from isolated pilots or flashy demos. It’ll come from execution, led by people who know how to turn raw data into real impact. That’s where CDAOs earn their place or lose it.
Today’s CDAO is no longer just a technical function buried in data governance. The position is shaping up to be a strategic cornerstone, if handled right. But titles alone don’t carry weight. Business leaders need to push for clarity: What outcomes are being delivered? What value is measurable? Who’s accountable when results don’t land?
The window to lead is open. The tools are here. The risk is letting structure and expectations lag behind the pace of change. For executives ready to drive with intent, this is your moment to align data, AI, and leadership into something that moves markets, not just dashboards.
