S1 E2 | What Not to Build with AI: Avoiding the New Technical Debt in Data-Driven Organizations
May 2, 2025
•
Kris Peeters & Pascal Brokmeier & Tim Schröder - The Data Playbook Podcast
Why AI acceleration can backfire: lessons on digital sprawl, governance trade-offs, and building what truly matters in data-driven teams.
What Not to Build with AI: Avoiding the New Technical Debt in Data-Driven Organizations In this episode of The Data Playbook, we explore a crucial and often overlooked question in the age of generative AI: not what to build—but what not to build. Host Kris Peeters (CEO of Dataminded) is joined by seasoned data leaders Pascal Brokmeier (Head of Engineering at Every Cure) and Tim Schröder (AI & Data Transformation Lead in Biopharma), to talk about the dark side of unlimited AI capabilities: technical debt, fragmented systems, and innovation chaos.
Topics we dive into:
Why generative AI lowers the barrier to building—but increases long-term complexity
The risks of treating LLMs as “magical oracles” without governance
How RAG systems became the default architecture—and why that’s dangerous
The zoo vs. factory dilemma: how to balance AI experimentation with structure Master data vs. knowledge graphs vs. embeddings – when and why each breaks down
What Klarna got right (and wrong) by replacing SaaS tools with AI-generated internal apps
The growing importance of AI literacy, data maps, and platform thinking
Real-world examples of AI agents autonomously debugging systems—and when that’s terrifying
We ask tough questions like: Are enterprises just building themselves into a new kind of mess, faster than ever before? Is the AI hype driving us toward “build now, regret later”? Should you really let every department build their own AI stack?
Whether you're a data engineer, data architect, AI product lead, or a data strategist, this episode is a must-listen. We’re cutting through the hype to figure out where the real value is—and where the future tech debt is quietly piling up.
🧠 Key quote: "If you can't tell me why you're building it, maybe you shouldn't be building it at all." 💡 Tune in to learn how to stay smart, intentional, and strategic when it comes to building with AI.
Latest
Data Science vs Data Engineering: Breaking the Wall
Breaking the wall between data science and data engineering with practical lessons on testing, notebooks, and production-ready data work.
A Structured Framework for Building Successful Data Solutions
In this episode we talk with Frederic Vanderveken about a practical framework to make sure you’re building the right data solutions.
Data Engineering Meets Excel: Building Explainable and Reliable Decision Models with River Solutions
How River Solutions turns messy Excel models into explainable, reliable decision tools for analysts and leaders.
