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

“Can’t we just build this with AI?” This question comes up in almost every data sprint these days. AI builds a script or a pipeline in no time, but as soon as a data source quietly changes its schema right before the monthly closing, the automated output fails. At that exact moment, the questions pile up, leaving problems an AI model cannot answer upfront.

Prompt engineering is not data engineering

A well-crafted prompt creates the illusion that the result is equivalent to a properly designed data platform. In practice, this process often accelerates the spread of uncertain data across the organization. Prompt engineering delivers unpredictable, fluctuating output. Sound data engineering, on the other hand, creates systems that remain predictable, testable, and maintainable.

AI works excellently as a temporary solution in the initial phase to quickly get a prototype on the table. In a production environment, different laws apply; there, the exact same input must always yield the exact same result.

The synergy between engineering and AI

Structure and automation reinforce one another. AI accelerates the building phase, while data engineering ensures that the output remains verifiable and explainable. Once this foundation is in place, AI becomes the accelerator the business expects. Think of automated documentation and semantic search through your data landscape, built on our team’s expertise in Rotterdam.

Webinar key takeaways:

• Architectural choices: Learn where AI adds genuine value within a data platform and where it introduces risks or unforeseen costs.
• Stability and maintenance: Gain concrete tools to embed data quality, central definitions, and transferability directly into production environments.

Practical details:

• Date & Time: Thursday, June 18, 2026 | 10:00 – 10:30 CEST
• Location & Language: Online via live stream, English spoken