DataTopics: All Things Data, AI & Tech

Send us a textIn our final episode of 2025, we sit down with Tim Van Erum to unpack what really stood out at Tech Expo Amsterdam.Together we revisit the most misunderstood AI statistic of the year, exploring why the “95% of AI projects fail” headline is misleading and how hype versus reality played out across the conference. Tim shares why failure and experimentation are not setbacks but essential drivers of innovation, and we highlight Reddit’s Scaling Safety strategy as a powerful example of machine learning in action.This candid conversation closes out the year with lessons on what AI truly delivered in 2025.

Send us a textWe go inside Mediahuis to see how a small GenAI team is transforming newsroom workflows without losing editorial judgment. From RAG search to headline suggestions and text‑to‑video assists, this episode shares what works, what doesn’t, and how adoption spreads across brands.You’ll hear about:Ten priority use cases shipped across the groupHeadline and summary suggestions that boost clarity and speedRAG‑powered search turning archives into instant contextText‑to‑video tools that free up local video teamsThe hurdles of adoption, quality, and scaling prototypes into productionTheir playbook blends engineering discipline with editorial empathy: use rules where you can, prompt carefully when you must, and always keep journalists in the loop. We also cover policies, guardrails, AI literacy, and how to survive model churn with reusable templates and grounded tests.The result: a practical path to AI in media — protecting judgment, raising quality, and scaling tools without losing each brand’s voice.🎧 If this sparks ideas for your newsroom or product team, follow the show, share with a colleague, and leave a quick review with your favorite takeaway.

Send us a textIn this episode of Data Topics, Ben speaks with Kim Smets, VP Data & AI at Telenet, about his journey from early machine learning work to leading enterprise-wide AI transformation at Telenet. Kim shares how he built a central data & AI team, shifted from fragmented reporting to product thinking, and embedded governance that actually works. They discuss the importance of simplicity, storytelling, and sustainable practices in making AI easy, relevant, and famous across the business. From GenAI exploration to real-world deployment, this episode is packed with practical insights on scaling AI with purpose.

Send us a textIn this episode, Gaelle Helsmoortel joins us to discuss how to make AI truly deliver business impact, not just proof of concept.With over 25 years of experience spanning L’Oréal, startup leadership, and her current role at Dataroots, Gaelle shares her approach to turning business challenges into measurable value. She breaks down her proven 5Ps framework (Purpose, People, Process, Platform, and Performance) and explains how companies can bridge the gap between strategy and execution to generate real results.🎧 You’ll learn:Why most AI projects fail (and how to prevent it)How to move from proof of concept to proof of valueHow to align business purpose, data, and people for maximum impactWhy “purpose before platform” is key to successful AI adoptionWhether you’re a business leader, strategist, or data professional, this episode will help you understand how to make AI work for business and deliver tangible results.🔗 Connect with Gaëlle:🌐 Website & Newsletter: Generative Booster – The Game Changer List▶️ YouTube Channel: @GenerativeBooster

Send us a textIn this episode, we're joined by Sam Debruyn and Dorian Van den Heede who reflect on their talks at SQL Bits 2025 and dive into the technical content they presented. Sam walks through how dbt integrates with Microsoft Fabric, explaining how it improves lakehouse and warehouse workflows by adding modularity, testing, and documentation to SQL development. He also touches on Fusion’s SQL optimization features and how it compares to tools like SQLMesh.Dorian shares his MLOps demo, which simulates beating football bookmakers using historical data,nshowing how to build a full pipeline with Azure ML, from feature engineering to model deployment. They discuss the role of Python modeling in dbt, orchestration with Azure ML, and the practical challenges of implementing MLOps in real-world scenarios.Toward the end, they explore how AI tools like Copilot are changing the way engineers learn and debug code, raising questions about explainability, skill development, and the future of junior roles in tech.It’s rich conversation covering dbt, MLOps, Python, Azure ML, and the evolving role of AI in engineering.