Exclusive: Teradata CTO Louis Landry on AI and data strategy

Louis Landry has spent more than two decades working at the intersection of data and technology. Today, as Chief Technology Officer at Teradata, he is steering the company's innovation strategy at a moment when enterprises are rethinking how they manage data in the AI era.

"I am a technologist that has spanned years in the web technology stack as well as the data technology stack," he explained to TechDay, during a recent interview.

"I have been with the company around 11 years, and almost always focused on generating value out of data and emerging technology."

Having been a customer, partner and now leader within Teradata, Landry believes this range of experience gives him "an edge."

"It absolutely does help with perspective," he explained. "It really helps me communicate with people on the inside, but also with customers, around what the challenges are, what the opportunities are, and how to best get value out of the platform."

Before becoming CTO, Landry served as Vice President of Technology & Innovation, where he led advanced research projects that pushed the company into new areas such as AI-powered analytics and large-scale vector processing for retrieval augmented generation.

Earlier roles at Teradata focused on platform engineering and data innovation, building the foundations that support its current AI strategy.

Opportunities in Australia

Visiting Sydney for the first time in his role as CTO, Landry said he came to connect directly with customers and partners. "We've got a great team here," he noted. "Australia represents a pretty sophisticated market that's navigating some interesting challenges."

Among these, he highlighted cloud cost pressures, sovereignty rules and the rush to adopt AI.

"I see a more practical approach here," he said. "Things feel more grounded and ROI is much more of a focus, rather than speculative push into AI."

He added that this approach "resonates well with how we see value generation and how we see the world. Our philosophy is really around meeting customers where they are, and that seems to really resonate here."

Cloud, sovereignty and compliance

Landry argued that cloud costs, compliance and sovereignty are inseparable issues shaping enterprise architecture.

"We're seeing enterprises move from a cloud first, almost blindly, to a cloud smart model, which means rebalancing workloads," he said. "It's not about repatriation. It's about optimisation."

With financial services and government organisations in Australia facing strong sovereignty demands, he stressed the importance of enabling AI without forcing sensitive data into the cloud. "That drives an interesting demand for a capability that we offer around bringing AI to your data, rather than forcing people to move data to the cloud in all cases."

AI Factory and beyond

This thinking underpins AI Factory, a new Teradata offering that brings its advanced AI and machine learning capabilities into secure on-premises environments. "Some data can't move. Some data shouldn't move," Landry said. "AI Factory enables trusted, scalable AI infrastructure with complete control over the infrastructure, the data, the costs. You don't have to choose between AI innovation and maintaining that control. We bring it to you."

Alongside AI Factory, Landry pointed to Teradata's Enterprise Vector Store, which bridges structured and unstructured data, and prebuilt GPU-accelerated AI microservices.

"Think of them as Lego bricks for AI use cases," he explained. "Altogether, it accelerates time to value."

The company's new MCP server is another piece of the puzzle. "It provides AI agents with the context and tools they need from the complete data estate of one of our customers," he said. "It really connects those two different worlds together."

Advice for technology leaders

Asked what advice he would give CIOs and CTOs considering hybrid or private AI strategies, Landry stressed pragmatism. "Start where you are," he said. "The perfect architecture on paper doesn't mean anything if it's going to take two years to implement."

He urged leaders to ground AI in trusted data, embrace hybrid as an end state rather than a temporary phase, and integrate human oversight. "If people can't trust these systems, they're not going to leverage them, and then it's just a wasted expense," he said.

What is the future looking like?

For Landry, three trends stand out as the future unfolds: agentic AI, the shift from model-centric to data-centric approaches, and model sharing without data sharing.

"Agents that are going to reason through ambiguity, collaborate with each other and humans and become teammates – I think I'm really excited about that," he said.

He also sees opportunity in industries like banking and healthcare exchanging intelligence through trained models while keeping customer or patient data private.

"I think there's a lot of opportunity and exciting stuff ahead of us on that front as well."