AiThority Interview with Leo Brunnick, Chief Product Officer at Cloudera

Leo Brunnick, Chief Product Officer at Cloudera chats about the evolving enterprise data ecosystem in this catch-up with AiThority.com:

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What would you tell technology teams to enable them with better AI adoption while reducing operational complexity? 

“Something I hear consistently from customers is that AI adoption isn’t being held back by a lack of ambition, but rather by complexity. Organizations are eager to move from experimentation to real business impact, but many teams are still spending too much time navigating disconnected systems, data silos, and operational hurdles.

The companies making the most progress are focused on simplifying the experience for both technical and business users. That means making it easier to find, access, and work with trusted data without adding more layers of infrastructure or process. When teams can spend less time preparing data and more time applying insights, AI becomes much easier to scale across the organization.”

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Why are open data architectures becoming critical for enterprise AI success? What should tech teams do to fix data architectures across the board? 

“One of the biggest changes we’ve seen in the last few years is that organizations’ enterprise data no longer lives in just one place. Organizations are operating across multiple clouds, on-premises environments, and increasingly at the edge, and they need the flexibility to use AI wherever their data exists.

That’s why open architectures have become so important. They give organizations the freedom to access and work with data across diverse environments without creating new silos or introducing unnecessary complexity. From a product perspective, the most successful strategies are focused on creating consistency—consistent access, consistent governance, and a consistent user experience—regardless of where the underlying data resides.”

What trends will shape the future of enterprise data ecosystems? How should technology heads and business heads maintain a healthy balance between use of AI, privacy and integrity here? 

“A notable shift happening right now is that organizations are no longer treating AI and governance as separate conversations. Early on, the focus was largely on how quickly companies could adopt AI. Today, the conversation is much more focused on how to scale it responsibly while maintaining trust in the data and the outcomes it produces.

At the same time, data ecosystems are becoming more distributed, which makes visibility and accountability even more important. Business leaders need confidence in the information they’re using to make decisions, while technology teams need to ensure data remains secure, well-governed, and accessible. The organizations making the most progress are building those considerations into their AI strategies from the start rather than treating them as an afterthought. When trust is built in from day one, it becomes much easier to innovate with confidence.”

Some thoughts on the future of AI before we wrap up

“I think one of the biggest changes we’ll see over the next few years is a growing recognition that AI success is a data challenge, not a model . Organizations have spent years investing in collecting and storing data, but the real opportunity now is making that data accessible and usable wherever it lives.

What’s interesting is that we’re already seeing evidence of this. While AI adoption continues to accelerate, nearly 80% of enterprises say their AI initiatives are being held back by challenges from accessing data across different environments. That tells us the next phase of AI won’t be defined by who has access to the latest model, it will be defined by who can most effectively connect AI to trusted, governed data across the enterprise.

Looking ahead, I think the organizations that stand out will be the ones that make it easier for people to get value from the data they already have, whether that is on premise, in the cloud, or at the edge. The amount of data businesses generate isn’t slowing down, but employees shouldn’t have to think about where that data lives in order to use it. The companies that remove those barriers will be in the best position to move faster, make better decisions, and adapt to whatever comes next.”

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