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If your remit is to help your organisation add AI agents to accelerate its processes, you have to start at the foundation – and that means making your data available for AI consumption. Agentic AI scales on data strength, as Niels Zeilemaker, global CTO at Xebia, explains.
“If you don’t think about that, you can build the best agent, but it will never be able to find the correct data; maybe it will misinterpret the data, maybe it will join different fields together in your data which should never be connected,” explains Zeilemaker. “And these mistakes are not necessarily the fault of the agent. It’s the fault of your foundation, which is not ready for AI agents.”
One area to particularly consider, Zeilemaker notes, is data cataloguing. It’s not a new concept, but the game changes for agents. “If you’re setting up a data catalogue for an organisation only consisting of humans, there’s always a fallback,” he says. “If there’s something not really well documented, you can pick up the phone, walk to a colleague, and have a sort of back door, in ‘how should I work with this particular set of data?’
“Agents don’t have such a back door. They have to rely on the data catalogue, what’s written there, and if the description is wrong, the agents will not perform.”
Xebia’s focus is to help organisations turn AI strategy into production-ready solutions which drive real transformation faster. The company’s core values include being people first and quality without compromise, but perhaps the most important, as Zeilemaker sees it, is sharing knowledge – such as at events like TechEx Global North America, at which Xebia participated.
“I think sharing knowledge is very important for us, and it also allows us to be a bit ahead of the curve, adopt quickly to new changes in the market, because everybody has this eagerness to find out new things, and to share what works, what doesn’t work,” says Zeilemaker. “By pushing a lot into this sharing knowledge and innovation, we try to also pick a couple of domains where we want to be the authority.”
Data and AI is evidently one of those areas. At AI & Big Data Expo, Zeilemaker told attendees how to build this AI foundation and unify their fragmented data landscapes. It was an honest account of how combining purpose-built AI agents with expert engineering compresses a 12- to 24-month timeline into a fixed-price, milestone-bound engagement.
The overarching thread for this is what Xebia calls Agentic Data Foundation (ADF), which extends the data platform to host agents, and then make use of them both in customer-facing use cases and internal processes. While there has always been a big appetite in migrating from legacy to modern platforms, Xebia is seeing more customers asking for an approach to more quickly – and reliably – migrate into data platforms. Zeilemaker says this is where consultant and customer are co-developing the solution.