Many people working within the data industry are saying the same thing: the role of the Chief Data Officer (CDO) has changed – but how?

CDO Magazine began an expert survey to find out. We recently asked eight data leaders across industries, geographies, and organizations a simple question: What exactly does it mean to be a Chief Data Officer in 2026?

Their answers offered a snapshot, frozen in time, of a role that hasn’t left its foundations behind but has built heavily on top of them.

The work of managing, governing, protecting, and making data useful is still there, but what has changed is how much more is expected of the person in the role. 

Data is feeding AI systems, products, workflows, customer experiences, and automated decisions. The consequences of getting it right – or wrong – now travel farther through the organization, and it’s the CDO’s responsibility to be on top of it.

Together, these eight leaders’ perspectives tell the story of what it looks like to be a Chief Data Officer in 2026.

What does a Chief Data Officer do in 2026?

We began by asking the respondents to finish a sentence: “In 2026, a Chief Data Officer is…” One called the CDO a transformation agent. Others chose architect, catalyst, trusted advisor, strategic partner, and even Chief Value Officer.

Their answers point to a role that extends beyond managing data – it’s now about being responsible for helping the organization put that data to work.

For example, Olimpia Nitti, Chief Data & AI Officer at Procter & Gamble, described the CDO as a “strategic architect,” connecting strong data foundations with AI capabilities. Carrie Cobb, Chief Data & AI Officer at Blackbaud, also chose “architect”, looking to proprietary data and contextual intelligence as sources of competitive advantage. 

The word architect is telling. It shows the evolution for the modern CDO: building something with the organization’s data rather than simply taking care of it. 

Others see it more from a business perspective – how the CDO connects their work with the rest of the business (and board) is becoming one of the most crucial metrics for success.

Jim Merrifield, Chief Data Officer at Robinson+Cole, described the CDO as “the executive who turns data, AI, and information into measurable business value.” For Merrifield, the best CDOs are “strategic partners to the business,” helping the organization make better decisions faster. 

What Merrifield adds is a sense of where the work is supposed to lead. Data can be well governed, trustworthy, and ready to use, but now it needs to clearly help people across the organization make decisions, solve problems, and get work done. 

Obviously, the CDO hasn’t stopped being a data leader. However, these responses suggest the job is now defined just as much by where that data needs to go as by how well it’s obtained. 

How has the CDO role changed?

Day-to-day priorities for CDOs have shifted over the past two years, constantly adding layers of responsibility to the role.

Yazhene Krishnaraj, Chief Information & Digital Officer at McKesson’s Sarah Cannon Research Institute (SCRI), summed up what has changed through the two most popular letters in the last 24 months for CDOs: “The conversation has shifted from ‘How do we govern and manage data?’ to ‘How do we use trusted data to safely operationalize AI at scale?’” 

Nitti’s own experience shows what has been added to the job. Two years ago, she shared that her priorities centered more heavily on:

• Data quality
• Data warehousing
• Reporting
• Basic analytics

Today, priorities include:

• Preparing agent-ready data
• Developing responsible AI frameworks 
• Working with semantic layers
• Enabling data for AI at scale

Even the responsibilities CDOs have had for years carry different stakes now. Merrifield pointed to data quality as one example. “Poor data quality used to be a reporting problem; now it is an AI, client-service, compliance, and reputational-risk problem.” 

The data-quality problem didn’t disappear – instead, its consequences just spread, and the impact was felt more widely. 

The expectations around value have changed as well. Anthony Morra, Chief Data Officer at BDO Canada, described a move from primarily building data foundations and improving reporting toward “embedding data and AI directly into decisions and day-to-day workflows.” 

“Governance, quality, and architecture remain essential,” Morra said, but highlighted that CDOs now spend more time on “enterprise prioritization, executive alignment, responsible AI, organizational adoption, and demonstrating the value of investments.”

Put in more immediate terms: data, analytics, and AI are expected to “actually show up in the business in ways people notice.” 

The foundations of good data management are still very much a part of the role – but they’re now being expected to support new AI scaffolding for businesses on top.

The role of the CDO is still misunderstood.

The rapid evolution of the role of the CDO has created a peculiar problem: organizations are misunderstanding the CDO job description in wildly different ways. Some expect the CDO to own every data problem, while others treat the role as a narrow technical or reporting function. 

The first puts too much responsibility on the CDO. “One of the biggest misconceptions,” shares Krishnaraj, “is that the Chief Data Officer is solely responsible for data governance and compliance.”

Merrifield offers a clarification of the misconception that the CDO “owns all the data.” He states, “Business units, practice groups, functions, and system owners still own the meaning, quality, and use of their data; the CDO sets standards, governance, and accountability.” 

Many of the problems that affect data also live in business processes and behavior. Merrifield described data transformation as “an enterprise operating change, not a single-person rescue mission.”

On the other end of the spectrum, another misconception makes the role too narrow.

One respondent, who wished to remain anonymous to allow them to speak more freely, described this misconception using an analogy with a newspaper. The old expectation, they said, is that the CDO will “drop off the newspaper”: deliver the dashboard, KPI, or report after the important questions have already been decided.

But the CDO’s role in 2026 begins much earlier. The respondent described today’s CDO as being “in the newsroom,” talking with people across the business, investigating what’s happening, and helping determine which questions need to be answered before the report or dashboard is ever produced.

Paul Ballew, Chief Data and Analytics Officer at the National Football League (NFL), has encountered another version of the same misconception: that CDOs are “running science projects.” In reality, he said, the work they do and the data they manage correctly “are core to the business.” 

Whether an organization sees the CDO as a technical function or a business leader can shape the role before a CDO is even hired – or how they’re interviewed. 

In a recent CDO Magazine article on Chief Data Officer interview questions, Dr. Elena Alikhachkina noted that a CDO reporting to the CIO may be hired to modernize infrastructure and improve data quality, while a business-aligned CDO may be expected to drive commercial growth. 

A CEO-reporting CDO could be brought in to lead enterprise transformation. Interview questions change accordingly, revealing very different expectations behind the same title.

How an organization defines the role has consequences beyond the hiring process. Cobb noted that misconceptions about the CDO can show up in reporting lines, funding, authority, and ultimately what the leader is measured against.

How an organization defines the CDO role ultimately shapes the job that person is able to do. 

What are the CDO’s priorities in 2026?

Given everything being asked of the role, what should CDOs actually be working on now?

AI appeared throughout the responses. But the answers themselves covered more than simply “do more AI.”

For example, Scott Richardson, Chief Data & Analytics Officer at First Citizens Bank, pointed to the condition of enterprise data. Large companies, he noted, still have enormous amounts of data that aren’t well organized, deduplicated, or consistently high quality. AI gives that longstanding problem a new urgency because more systems and decisions depend on the data being ready for use.

And “ready” now means more than “clean.” Nitti pointed to context-rich unstructured data and semantic layers as part of preparing enterprise data for AI at scale. She is also using AI itself through automated metadata management, data labeling, and agentic data pipelines to help prepare and maintain those foundations. 

Cobb also pointed to the safeguards needed as AI moves into production. Her approach to an AI governance framework includes measures such as fairness testing, explainability, and model monitoring. Responsible AI, she said, should function as “infrastructure, not a policy document.” 

Ballew gave a much simpler answer when asked about his top priority for CDOs: “Talent.” People. Putting AI into production is one challenge; having the people who can put it to work is another.  

Another respondent also emphasized investing in front-line analysts and data literacy while staying close to the “real workflows, constraints, and pain points” of the people doing the work.

For Krishnaraj, the larger goal for CDOs is moving AI “from experimentation to execution.” Eventually, she said, successful AI may become “invisible”– so embedded in everyday processes and technology that people simply use it as part of their work.

AI may be driving many of these priorities, but putting it to work in the business requires more than the technology itself. The data has to be ready, the right safeguards have to be in place, and people have to be able to use it responsibly and effectively. 

How is AI changing what success looks like for CDOs?

Generative and agentic AI are changing what boards and executive teams expect from CDOs. They want to see business impact, and they want to see it as soon as possible. 

Ballew described the pressure as “newer, fast, better,” with “lots of interest in business impact quickly.” 

Richardson also sees pressure behind some of that urgency, specifically with regard to competitors. “The strongest companies are leveraging AI to move faster and more nimbly, and to deliver better products, that is a competitive risk.” 

When companies use AI to operate more efficiently, move faster, or improve products, they can change what everyone else in the market is expected to deliver.

Speed isn’t the only measure of how AI is changing the world of the CDO. Morra pointed to the expectation that results also be obvious: “GenAI and agentic AI have raised executive expectations for both speed and visible business impact.” 

And business impact isn’t the only consideration. Cobb pointed to trust and explainability as board-level concerns when autonomous systems can act on a customer’s behalf – the need to have a strong governance loop. 

A system that can take action introduces a different set of questions than one that simply produces an answer.

Leaders want to know what data and AI are producing for the business. They also need confidence in what the systems are doing to produce it.

What do CEOs need to understand about the CDO role?

A CDO being asked to influence business outcomes, AI adoption, and enterprise transformation can’t do that work in isolation. That requires support from the CEO and enough influence across the organization to do the job. 

As Cobb stated, “The CEOs who get the most from this function are the ones treating the CDO as a peer in shaping where the business is going, not just a custodian of what it already has.” 

Ballew put the sentiment more succinctly: “To be effective, we need a seat at the table.” Earning board-level influence matters because of what the CDO is being asked to accomplish. 

Merrifield warned that if the CEO treats the CDO as only a technical or compliance role, “the organization will likely underuse one of its most important levers for responsible growth and innovation.” 

Across the responses, the same needs came up repeatedly: a clear mandate, executive sponsorship, funding, and authority. 

Richardson raised a related concern about making data and AI one of many responsibilities within a technology function, where the work can lose the dedicated focus and business connection it needs.

Krishnaraj also emphasized that a CDO’s greatest value may emerge beyond the early pilots, through “long-term organizational changes, new ways of working, and business outcomes” that develop as AI becomes part of the enterprise. A CDO may need to show that the work is moving while some of its most meaningful effects are still developing.

The role these respondents describe extends beyond where the CDO sits on an organizational chart. It requires understanding the business well enough to know which problems matter and enough organizational support to work across the functions involved in solving them.

What should a new CDO prioritize in the first 90 days?

When we asked these leaders what a newly appointed CDO should prioritize in the first three months, few of them started with the technology they needed for the data. They started with the business.

Richardson recommended understanding the company’s revenue and expense drivers. Another respondent advised spending time in the field with business leaders to see “real workflows, constraints, and pain points” firsthand. Krishnaraj and Morra also put relationships and business understanding near the beginning.

Only then did the advice turn toward assessing what the new CDO has inherited.

Cobb offered a direct, targeted action: “Audit the actual data moat, not the org chart.” Her point was to understand which proprietary data genuinely gives the company an advantage before deciding where to invest.

From there, the respondents generally moved toward choosing a small number of meaningful opportunities where progress is possible.

Cobb sees an early result as a way to establish credibility. A credible win, she said, “earns the room to pursue the longer-term strategic agenda.”

Krishnaraj placed the emphasis slightly differently. She described the first 90 days as a CDO as “less about delivering immediate results and more about establishing trust, alignment, and a credible strategy for long-term value creation.”

Taken together, the advice isn’t really a 90-day sprint. These leaders are telling a new CDO to understand what matters before deciding what to change – and to use the first few months to show the organization how they intend to work.

What is non-negotiable for the CDO in 2026? 

For our final question, we asked each leader to name just one responsibility that has become non-negotiable for the CDO in 2026.

They didn’t give us the same answer.

• Morra chose accountability for measurable business outcomes. 
• Richardson focused on trusted data. 
• Cobb pointed to responsible AI as a commercial trust function. 
• Ballew said responsibility for data “end to end.” 
• Others chose AI-ready data, enterprise data, and AI strategy, value realization, or operationalizing AI safely and effectively.

Interestingly, when they explained their choices, the responsibilities were less neatly separated.

Morra’s focus on business outcomes led him back through the things required to produce them, including trusted data, responsible AI, process redesign, and adoption. The respondent who chose value realization also talked about cost, security, ethics, data foundations, and how technology fits into actual work.

Merrifield began with trusted data. But as he explained what that requires when data is being used for analytics, AI, and automated decisions, his answer expanded into how the data is governed, secured, accessed, and overseen. 

One respondent said the CDO must be able to show “not only what was built,” but what changed because of it: productivity, decisions, growth, risk management, or the digital experience. Delivery isn’t necessarily the end of the job. CDOs are also being held accountable for whether the organization gets something from the work. 

The CDOs in our survey aren’t claiming ownership of everything data and AI touch. Several explicitly said the opposite. But their answers show why developing a clear definition around the responsibilities of a CDO has become so difficult. 

At the beginning of our survey, we asked eight data leaders to finish the sentence, “In 2026, a Chief Data Officer is…” They gave us architects, catalysts, transformation agents, trusted advisors, strategic partners, and a Chief Value Officer.

But the story that emerged from their answers was less about finding the right title for the CDO than understanding what has happened to the job itself.

The foundations are still there. CDOs still have to make sure the organization’s data can be trusted and used responsibly. What’s different is how much happens next. The data is being put to work in more places and asked to do more for the business. The CDO’s work doesn’t end (any longer) once the foundations are in place. 

Perhaps that is what makes the Chief Data Officer in 2026 so difficult to define. The story of the role is still being written by what organizations are learning to do with their data.