Chief Data Officers Now Hold the Keys to Enterprise AI Success as Governance Gaps Threaten Scale

The chief data officer has undergone a radical transformation from a compliance-focused back-office role into one of the most pivotal positions in modern enterprise AI strategy. These executives now sit at the critical nexus of data governance, artificial intelligence deployment, and workforce readiness—essentially determining whether companies successfully transition from experimental AI pilots to enterprise-wide production systems or remain trapped in perpetual testing mode.

That’s precisely why Informatica’s third annual survey—the most comprehensive examination yet of CDO perspectives on AI readiness, encompassing 600 executives across the globe—carries such significant weight in today’s rapidly evolving technology landscape. The findings expose a dangerous disconnect that explains why so many organizations struggle to scale AI beyond initial pilots: While 69% of enterprises have already deployed generative AI and 47% are actively running agentic AI systems that autonomously execute tasks, a staggering 76% admit their governance frameworks simply cannot keep pace with how employees actually use these transformative technologies in real-world scenarios.

The survey reveals what Informatica terms a “trust paradox”—and explains why data leaders may be dangerously overconfident about their organizations’ AI readiness. Companies have deployed AI systems at breakneck speed, far outpacing their ability to build the necessary governance structures and training infrastructure to support responsible implementation. The result is a workforce that generally trusts the data powering AI systems but lacks the literacy to critically evaluate that data or use AI responsibly. A full 75% of data leaders say employees desperately need upskilling in data literacy, while 74% require comprehensive AI literacy training for day-to-day operations.

“The gap now is simply, 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 fundamental gap we’re facing.”

Why Infrastructure Isn’t the Bottleneck for Data and AI Success

Generative AI adoption has surged dramatically, jumping from 48% just a year ago to 69% today. Nearly half of organizations (47%) now run agentic AI—systems that autonomously take actions rather than merely generating content. This rapid expansion has triggered a frantic race to acquire vector databases, upgrade data pipelines, and expand compute infrastructure across the enterprise.

However, Thompson dismisses infrastructure gaps as the primary obstacle to AI success. The technology exists, it works, and it’s proven. The limitation is fundamentally organizational rather than technical.

“The technology that we have available at the moment, the infrastructure, is more than sufficient—it’s not the problem yet,” Thompson stated emphatically. He drew an apt comparison to amateur athletes who blame their equipment for poor performance. “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 strongly supports this perspective. When asked about their 2026 investment priorities, the top three responses all relate to people and process issues rather than technology: data privacy and security (43%), AI governance (41%), and workforce upskilling (39%). This clearly indicates that the technology gap is actually a people gap.

Five Hard Lessons for Enterprise CDOs Moving Forward

The survey data, combined with Thompson’s extensive implementation experience, reveals specific, actionable lessons for data leaders trying to move from pilots to production at scale.

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 fundamentally 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 explained. “And also the AI people are super expensive, just like data scientists are super expensive. You’re competing in a very tight labor market.”

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 removed from day-to-day operations.

“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 because they lack organizational alignment and shared accountability.

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, demonstrating the power of cross-functional collaboration.

“You need that literacy across your business teams as well as in your technology teams,” Thompson emphasized. He noted that the marketing operations team understands both the technology and data. They know that the answer to “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 by simply adding more people and increasing Google ad spend.

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. This bottom-up demand is far more powerful than top-down mandates.

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 and toward strategic growth.

“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, his frustration evident. “What a waste of an opportunity. We’re thinking too small.”

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 or resource-intensive.

“It’s not about saving money,” Thompson reiterated. “And if that’s mainly the approach that you have, then your company’s not going to win in the long run. You’re thinking too small.”

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. You prove the concept, demonstrate value, and build momentum.

“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 warned. “You have to show progress, show wins, and build from there.”

The path forward for enterprise AI success isn’t about waiting for perfect infrastructure or comprehensive governance frameworks. It’s about making deliberate choices about organizational structure, focusing relentlessly on people and literacy, and demonstrating strategic value through targeted, high-impact implementations.

CDOs who master these lessons will transform from compliance officers into strategic leaders who determine whether their organizations capture the full potential of AI or remain stuck in perpetual pilot purgatory.

Tags: AI governance, data literacy, chief data officer, enterprise AI, generative AI, agentic AI, workforce upskilling, data strategy, technology adoption, organizational transformation, digital transformation, AI implementation, data governance frameworks, enterprise technology, CDO role, AI readiness, data quality, IT leadership, strategic AI, production AI

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