Webinar | From Fast AI Output to Trusted Data: Where Engineering Still Matters

“Can’t we just build this with AI?” The question comes up in nearly every data sprint, often with the best intentions. When pipelines run smoothly and solutions work as they should, nobody asks questions. Until the output gets reused by another team. Until finance and sales turn out to rely on different definitions. Until a source changes its schema right before quarter-end. That is when the questions AI did not answer start to stack up.

Prompt engineering is not engineering

AI makes it tempting to believe a well-formulated prompt can produce the same result as a well-engineered data platform. You don’t feel the difference right away. Until you realize you’re not accelerating value, you’re accelerating the spread of uncertainty inside your organization. Prompt engineering produces plausible output. Engineering creates systems that stay predictable, testable, explainable, and maintainable – even when the original builder has moved.

AI as duct tape is fine in the prototype phase, where putting an idea on the table matters more than maintainability. In production, the story changes. There, you expect the same input to always produce the same result and someone to be able to explain why a number is what it is.

Data engineering as the foundation under AI

Good engineering and AI complement each other: AI accelerates the build, engineering makes sure what comes out stays predictable, testable, and explainable. In practice, that means definitions shared between teams, automated tests on pipelines, data quality checks that break instead of letting errors through, monitoring, and ownership that survives when the original builder moves on.

Once that foundation is in place, AI becomes the accelerator the hype promised. Faster documentation, semantic search across your data landscape, and quicker analysis on data you actually trust. That is how we look at AI at Rockfeather: not as a replacement for data engineering, but as an accelerator on top of it. We know this sounds easy coming from engineers. But in practice, this shows that it actually works that way.

What you take away

• Where AI does and doesn’t belong inside a data platform, and what that means for reliability and cost.
• What keeps a data platform reliable: definitions, tests, modelling, ownership.
• Where AI creates immediate value for your data foundation

Who this is for

Managers, data leads, IT decision-makers, and analysts actively working with AI and data. Especially relevant if you are responsible for the scalability or reliability of your data platform, or if you build yourself and want to know how your work stays handover-ready once it starts to matter.

Practical info

• Date: June 18, 2026
• Time: 10:00-10:30
• Location: Online
• Language: Dutch