Teradata wants to remove one of the biggest roadblocks keeping companies from getting AI from pilot to production: their data.

In the pilot phase, enterprises can curate data in very specific ways that don’t always translate to the live data used in production-grade AI, Steve McMillan, President and Chief Executive Officer of Teradata, told The Deep View. This was the driving force behind the company’s Autonomous Knowledge platform, unveiled Thursday, designed to serve as a hub for customers' data and AI tools, with a focus on supporting agentic solutions.

The product addresses a gap Teradata identified through more than 150 proof-of-concept runs in 2025 and a study of more than 1,000 customers.

“Once you are dealing with your live enterprise data, it can be very messy, and so we help our customers curate that data in such a way that we can take the messiness away, structure it in the right way, so that you can have a trusted data platform feeding your agentic solutions,” McMillan told me at the company’s launch event.

Though enterprises are aware that data is the foundation of AI, the problem has evolved. Now, companies recognize the value of their data, but aren’t structuring and governing it well enough to actually deploy AI at scale.

Here are a few things that enterprises can do to help themselves:

• Trusting your tooling is key. “We have an approach in Teradata that says, keep your enterprise data together, have it well governed, so that you can trust it, and then deploy tools on that data so that you're not replicating it, you don’t have to take security risks,” said McMillan.
• Cost efficiency is all about context. “In the realm of tokenomics, context is important, because when you have the right context, the right experience, you will (add) nuance to your questions, your queries, which basically means you will use fewer tokens, right? Because a lot of the stuff is already provided,” said CPO Sumeet Arora.
• AI systems are smart, but not wise. “I'm often found describing them as like a fifth grader with a PhD, they [AI models, LLMs] are incredibly intelligent in the sense that they have all of these frameworks trained into them…but they know nothing about your company…so you have to understand how the data relates to how you work and choices you make, because that is the context that you must give to the fifth grader in order to have them wield all of the frameworks and tools and and generalized knowledge to make choices and to guide decisions,” said CTO Louis Landry.

Our Deeper View

It's interesting that nearly four years after AI’s explosion, data remains one of the biggest bottlenecks for companies. I remember hearing the refrain "garbage in, garbage out" from the outset, and Landry mentioned he first encountered it in college. Given that he's been in the industry for twenty-plus years, this has long been a guiding principle. Teradata joins a myriad of companies focusing on that and building products specifically designed for the agentic era. It gives customers more options to tackle the problem before increased autonomy arrives, which will only further add pressure on enterprises to shore up the quality and governance of their data.