3 Things I Learned from Rabobank’s Data Journey at Informatica World

AI doesn’t fail because the technology isn’t capable. It fails when the business isn’t ready to support it.

That was one of the big takeaways for me at Informatica World, listening to Rabobank’s Edmond Tarée share the bank’s approach to building the foundations for AI success.

So many organisations hit the same barrier: AI pilots perform well in controlled environments, but struggle to scale in the real world. More often than not because the right data and organisational processes aren’t in place to support AI production.

Here are 3 lessons I took away from Rabobank’s session, insights every leader should consider when moving AI from pilot to production.

 1. Build a Trusted Data Foundation

Rabobank’s approach reinforces a hard truth: AI is only as good as the data it’s built on. As Edmond put it:

"Data is not a technical thing: it represents your business. You need to make sure it can be trusted as a foundation for everything else you're going to do, including AI. If you think about what a data analyst does, AI can do a lot of that really fast, but in the end, it has the same needs as a human. An analyst would have to ask, 'What kind of data do we need? What sort? Where can I find it? How do I check its quality?' So you need to make sure your data management gets priority and is in order."

The findings of our CDO Insights 2025 report back that up: tech leaders cited reliability of results (43%) and lack of trust in data quality (38%) as key barriers to demonstrating the business value of AI. If you don’t feed AI the right data, it won’t perform.

2. Empower Autonomous Teams

AI success doesn’t happen in a vacuum. It takes the right organisational processes to enable delivery at scale. Rabobank has made deliberate choices to empower teams:

"You have to make sure you're not having to go through all kinds of hierarchical channels — that gets messy. You need to have more autonomous teams, which is quite scary to a lot of people. But when they start taking ownership, they’re quite capable of doing the right thing with their expertise."

The key lesson here for me is one of decentralised ownership and removing friction. Scaling AI requires teams to act quickly and confidently, without being bottlenecked by outdated processes.

3. Set Clear Direction and Foster Collaboration

Empowerment alone isn’t enough. Teams also need clear direction and strong cross-functional collaboration to succeed.

As Edmond explained:

" Management need to be really clear about direction setting, so people have a reference to work from. It’s also about cooperation. People can't stay on their own island. If you get that done — while you can't predict everything — as long as your team is skilled and working together, you can solve basically anything you come across quickly."

The combination of trusted data, empowered teams, and clear strategic direction is what enables AI to deliver business value — not just technology outcomes.

These lessons from Rabobank reinforced something we see time and again: AI success starts with getting the foundations right - data and people first, AI second. That’s the approach leading organisations are taking. The ones that don’t will continue to see promising pilots stall before delivering real impact.

A huge thank you to Edmond for sharing his story on stage, along with Kathy Chou, from Nutanix and Sachin Sontakke, from Gilead Sciences. I’ll be sharing more stories and conversations over the next few months.