Disconnect between business leaders and IT practitioners
Let’s start with the facts: 87% of business leaders are confident they’ve got the data infrastructure to deploy AI at scale. On the surface, that’s a bold, optimistic perspective. But, only 41% of those same organizations have successfully scaled AI-based solutions. What’s going on here? It’s a classic case of confidence outpacing capability.
From the IT practitioners’ side, the picture looks very different. Seventy percent of them are spending up to four hours a day troubleshooting data problems (fixing errors, checking quality, and untangling inconsistencies). That’s a huge time sink, one that highlights the gap between leadership perception and technical reality. These professionals are essentially firefighters, putting out the same data blazes repeatedly instead of building systems that stop the fires in the first place.
This disconnect reveals a fundamental misalignment. Leadership sees AI as a finished product, ready to be deployed at scale. On the other hand, the practitioners tasked with making it work are struggling to fix the foundation. Here’s a clear message: you can’t run before you learn to walk, and right now, many organizations are stumbling on the basics of data management.
Data-focused roles and AI strategies are more important now
AI is a massive lever for transformation, but there’s a catch: it doesn’t work without clean, reliable, and accessible data. That’s why we’re seeing a rise in specialized roles like Chief Data Officer, SVP of AI Product, and Head of Enterprise AI. These roles are the result of strategic shifts that recognize the complexity of making AI work.
Terren Peterson, VP of Data Engineering at Capital One, points out that without proper investments in tools and skilled personnel, AI initiatives will fall flat.
Capital One has leaned hard into this idea, democratizing access to machine learning tools, creating systems that aren’t just for data scientists but for everyone in the organization. This approach has paid off—Capital One ranks second in AI adoption among banks, just behind JPMorgan Chase, according to Evident.
It’s also worth noting the impact of these new leadership roles. They bring focus and accountability to AI initiatives. It’s one thing to have an AI strategy on paper; it’s another to have dedicated leaders who wake up every day thinking about how to make it a reality. These positions are steering organizations away from piecemeal AI projects and toward integrated systems that drive measurable outcomes.
You need to build a competent data culture
Culture eats strategy for breakfast, especially when it comes to data. It’s one thing to have the tools and systems in place; it’s another to have a workforce that knows how to use them well. The numbers here are telling: only 35% of business leaders believe their organizations provide sufficient support for building up a strong data culture, yet 80% of these same leaders say they can easily find and use the data they need.
What’s the issue then? It boils down to gaps in governance and readiness. Peterson points out just how important data hygiene and governance are, stating that organizations need to “get it right the first time.” Without this foundation, IT teams end up wasting time fixing problems that could have been prevented.
Leadership plays a big role here too. A Chief Data Officer, or someone in a similar position, is key to driving alignment and making sure data is treated as more than just an afterthought. In addition to leadership, it’s also important to focus on training, support, and embedding data literacy throughout the organization.
Then there’s the skill gap to consider. Only 36% of IT practitioners and 47% of business leaders believe their teams have the expertise to tackle complex AI projects. That’s a major challenge and a clear signal that organizations need to double down on training and development if they want to stay competitive in the AI era.
Data issues have a major impact on IT efficiency
Data issues are one of the biggest bottlenecks in IT today. According to the survey findings, IT professionals spend up to half their workday (nearly four hours) fixing data-related problems. Think about that for a moment. Half of their time is spent on repetitive tasks like correcting errors, running quality checks, and patching inconsistencies. That’s a staggering drain on resources and energy.
The real problem? This is both inefficiency and opportunity cost. Every hour spent cleaning up messy data is an hour not spent building the systems and strategies that could move the business forward.
Terren Peterson reiterates that these inefficiencies are the result of overlooked basics in data management. Companies are jumping into advanced AI projects without first laying a strong foundation for data quality and governance.
The solution is straightforward. You need to address data quality issues at their root. Build systems and processes that prevent these problems from happening in the first place. Not only does this reduce the daily burden on IT teams, but it’ll also free them up to focus on the more high-value, strategic work.
Organizations that tackle this issue head-on will see a domino effect. When data systems are clean and reliable, IT teams become more productive, AI initiatives become more effective, and the entire business operates more efficiently. It’s not glamorous work, but it’s the kind of behind-the-scenes effort that can make or break your data strategy.
Use cloud modernization and platform strategies to scale AI
Scaling AI is heavily dependent on having the right infrastructure in place to support. Capital One offers a textbook example of how to do this effectively. They’ve taken a cloud-first approach, modernizing their systems to create an enterprise-wide platform that supports AI initiatives across the organization.
Here’s why that matters. A modern cloud infrastructure is able to store data, and make it accessible, usable, and scalable. It lets teams collaborate seamlessly, whether they’re building machine learning models or deploying AI-driven solutions in real time. Democratizing access to machine learning tools, Capital One has made sure these capabilities aren’t limited to a small group of data scientists. Instead, they’ve put the power of AI into the hands of employees at every level.
AI at scale isn’t a distant goal, and is practically achievable today—when you pair advanced technology with the right infrastructure. Capital One’s success shows that with the right investments and strategic focus, businesses can create systems that support AI and accelerate innovation across the board.
Final thoughts
Is your data working for you, or are you working for your data? Success in AI and innovation demands more than ambition, requiring alignment, precision, and a culture that treats data as your most valuable asset.
