Ep 2. What Not to Build with AI: Avoiding the New Technical Debt in Data-Driven Organizations - The Data Playbook podcast: Kris Peeters, Tim Schröder, Pascal Brokmeier
The Data Playbook Podcast
<p><strong>What Not to Build with AI: Avoiding the New Technical Debt in Data-Driven Organizations</strong></p>
<p>In this episode of <em>The Data Playbook</em>, we explore a crucial and often overlooked question in the age of generative AI: <strong>not what to build—but what </strong><em><strong>not</strong></em><strong> to build.</strong></p>
<p>Host <strong>Kris Peeters</strong> (CEO of Dataminded) is joined by seasoned data leaders <strong>Pascal Brokmeier</strong> (Head of Engineering at Every Cure) and <strong>Tim Schröder</strong> (AI & Data Transformation Lead in Biopharma), to talk about the dark side of unlimited AI capabilities: technical debt, fragmented systems, and innovation chaos.</p>
<p><strong>Topics we dive into:</strong></p>
<ul>
<li><p>Why <strong>generative AI lowers the barrier to building</strong>—but increases long-term complexity</p></li>
<li><p>The risks of treating LLMs as “magical oracles” without governance</p></li>
<li><p>How <strong>RAG systems</strong> became the default architecture—and why that’s dangerous</p></li>
<li><p>The <strong>zoo vs. factory</strong> dilemma: how to balance AI experimentation with structure</p></li>
<li><p><strong>Master data vs. knowledge graphs vs. embeddings</strong> – when and why each breaks down</p></li>
<li><p>What Klarna got <em>right</em> (and wrong) by replacing SaaS tools with AI-generated internal apps</p></li>
<li><p>The growing importance of <strong>AI literacy, data maps, and platform thinking</strong></p></li>
<li><p>Real-world examples of AI agents autonomously debugging systems—and when that’s terrifying</p></li>
</ul>
<p>We ask tough questions like:</p>
<ul>
<li><p>Are enterprises just building themselves into a new kind of mess, faster than ever before?</p></li>
<li><p>Is the AI hype driving us toward <strong>“build now, regret later”?</strong></p></li>
<li><p>Should you <em>really</em> let every department build their own AI stack?</p></li>
</ul>
<p>Whether you're a <strong>data engineer</strong>, <strong>data architect</strong>, <strong>AI product lead</strong>, or a <strong>data strategist</strong>, 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.</p>
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<p>🧠 <em>Key quote:"If you can't tell me why you're building it, maybe you shouldn't be building it at all."</em></p>
<p>💡 <strong>Tune in to learn how to stay smart, intentional, and strategic when it comes to building with AI.</strong></p>
<p>#TheDataPlaybook #DataEngineering #AIinBusiness #TechnicalDebt #RAG #LLMs #DataStrategy #EnterpriseAI #DataGovernance #DataLeadership #KnowledgeGraphs #GenerativeAI #AIinHealthcare #AIProduct #Dataminded</p>