The chief data officer (CDO) has evolved from a niche compliance role into one of the most critical positions for AI deployment. These executives now sit at the intersection of data governance, AI strategy, and workforce readiness. Their decisions determine whether enterprises move from AI pilots to production scale or remain stuck in experimentation mode.
That's why Informatica's third annual survey — the largest survey yet of CDOs specifically on AI readiness, spanning 600 executives globally — carries particular weight. The findings expose a dangerous disconnect that explains why so many organizations struggle to scale AI beyond pilots: While 69% of enterprises have deployed generative AI and 47% are running agentic AI systems, 76% admit their governance frameworks can't keep pace with how employees actually use these technologies.
The survey reveals what Informatica calls a "trust paradox" — and explains why data leaders are dangerously overconfident about AI readiness. Organizations deployed generative AI systems faster than they built the governance and training infrastructure to support them. The result: Employees generally trust the data powering AI systems, but organizations acknowledge their workforces lack the literacy to question that data or use AI responsibly. Seventy-five percent of data leaders say employees need upskilling in data literacy. Seventy-four percent require AI literacy training for day-to-day operations.
"The gap now is just, can you trust the data to set an agent loose on it?" Graeme Thompson, CIO at Informatica, told VentureBeat. "The agents do what they're supposed to do if you give them the right information. There's just such a lack of trust in the data that I think that's the gap."
Why infrastructure isn't the bottleneck for data and AI
GenAI adoption jumped from 48% a year ago to 69% today. Nearly half of organizations (47%) now run agentic AI — systems that autonomously take actions rather than just generate content. This rapid expansion has created a race to acquire vector databases, upgrade data pipelines, and expand compute infrastructure.
But Thompson dismisses infrastructure gaps as the primary problem. The technology exists and works. The limitation is organizational, not technical.
"The technology that we have available at the moment, the infrastructure, is more than — it's not the problem yet," Thompson said. He compared the situation to amateur athletes blaming their equipment. "There's a long way to go before the equipment is the problem in the room. People chase equipment like golfers. Those golfers are a sucker for a new driver, a new putter that's going to cure their physical inability to hit a golf ball straight."
The survey data supports this. When asked about 2026 investment priorities, the top three are all people and process issues: data privacy and security (43%), AI governance (41%), and workforce upskilling (39%).
Five hard lessons for enterprise CDOs
The survey data combined with Thompson's implementation experience reveals specific lessons for data leaders trying to move from pilots to production.
Stop chasing infrastructure, fix the people problem
The trust paradox exists because organizations can deploy AI technology faster than they can train people to use it responsibly. Seventy-five percent need data literacy upskilling. Seventy-four percent need AI literacy training. The technology gap is a people gap.
"It's much easier to get your people that know your company and know your data and know your processes to learn AI than it is to bring an AI person in that doesn't know anything about those things and teach them about your company," Thompson said. "And also the AI people are super expensive, just like data scientists are super expensive."
Make the CDO an execution function, not an ivory tower
Thompson structures Informatica so the CDO reports directly to him as CIO. This makes data governance an execution function rather than a separate strategic layer.
"That is a deliberate decision based on that function being a get things done function instead of an ivory tower function," Thompson said. The structure ensures data teams and application owners share common priorities through a common boss. "If they have a common boss, their priorities should be aligned. And if not, it's because the boss isn't doing his job, not because the two functions aren't working off the same priority list."
If 76% of organizations can't govern AI usage effectively, reporting structure may be part of the problem. Siloed data and IT functions create the conditions for pilots that never scale.
Build literacy outside IT teams
The breakthrough insight is that AI literacy programs must extend beyond technology teams into business functions. At Informatica, the chief marketing officer is one of Thompson's strongest AI partners.
"You need that literacy across your business teams as well as in your technology teams," Thompson said.
He noted that the marketing operations team understands the technology and data. It knows that the answer to the "How do I get more value out of my limited marketing program dollars each year?" is by automating and adding AI to how that job is done, not adding people and more Google ad dollars.
Business-side literacy creates pull rather than push for AI adoption. Marketing, sales and operations teams start demanding AI capabilities because they see strategic value, not just efficiency gains.
Pitch AI as strategic expansion, not cost reduction
Data leaders have spent decades fighting perceptions that IT is just a cost center. AI offers the opportunity to change that narrative, but only if CDOs reframe the value proposition away from productivity savings.
"I am very disappointed that, given this new technology capability on a plate, as IT people and as data people, we immediately turn around and talk about productivity savings," Thompson said. "What a waste of an opportunity."
The tactical shift: Pitch AI's ability to remove headcount constraints entirely rather than reduce existing headcount. This reframes AI from operational efficiency to strategic capability. Organizations can expand market reach, enter new geographies and test initiatives that were previously cost-prohibitive.
"It's not about saving money," Thompson said. "And if that's mainly the approach that you have, then your company's not going to win."
Go vertical first, scale the pattern
Don't wait for perfect horizontal data governance layers before delivering production value. Pick one high-value use case. Build the complete governance, data quality and literacy stack for that specific workflow. Validate results. Then replicate the pattern to adjacent use cases.
This delivers production value while building organizational capability incrementally.
“I think this space is moving so quickly that if you try and solve 100% your governance problem before you get to your semantic layer problem, before you get to your glossary of terms problem, then you're never going to generate any outcome and people are going to lose patience," Thompson said.
