#91 How Kim Smets, VP Data & AI at Telenet, Scales Enterprise AI with Strategy, People, and Purpose
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.
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#90 How To Make AI Deliver Business Impact: Gaelle Helsmoortel on Turning Strategy Into Tangible Results
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
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#89 SQLBits Unfiltered: dbt in Fabric, MLOps in Action & Copilot in Question
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.
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#88 “Data Shapes AI, and AI Shapes Data,” Emilie Nenquin on VRT’s Digital Transformation
Send us a textIn this episode, we explore how public media can build scalable, transparent, and mission-driven data infrastructure - with Emilie Nenquin, Head of Data & Intelligence at VRT, and Stijn Dolphen, Team Lead & Analytics Engineer at Dataroots.Emilie shares how she architected VRT’s data transformation from the ground up: evolving from basic analytics to a full-stack data organization with 45+ specialists across engineering, analytics, AI, and user management. We dive into the strategic shift from Adobe Analytics to Snowplow, and what it means to own your data pipeline in a public service context.Stijn joins to unpack the technical decisions behind VRT’s current architecture, including real-time event tracking, metadata modeling, and integrating 70+ digital platforms into a unified ecosystem.💡 Topics include:Designing data infrastructure for transparency and scaleBuilding a modular, privacy-conscious analytics stackMetadata governance across fragmented content systemsRecommendation systems for discovery, not just engagementThe circular relationship between data quality and AI performanceApplying machine learning in service of cultural and civic missionsWhether you're leading a data team, rethinking your stack, or exploring ethical AI in media, this episode offers practical insights into how data strategy can align with public value.
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#87 How to Successfully Integrate AI into Your Business, with Tim Leers (Global Generative & Agentic AI Lead)
Send us a textWhat happens when AI hype collides with enterprise reality? Tim Leers, Global Generative & Agentic AI Lead at Dataroots, pulls back the curtain on what's actually working—and what's not—in enterprise AI deployment today.We begin by examining why companies like Klarna publicly announced replacing customer service teams with AI, only to quietly backtrack months later when quality suffered. This pattern of inflated expectations followed by reality checks has become common, creating what Tim calls "AI theater" – impressive demos with minimal business impact.The conversation tackles the often misunderstood concept of "agentic AI." Rather than viewing it as a specific technology, Tim frames agency as fundamentally about delegated authority – the ability to trust AI systems with meaningful responsibilities. However, this delegation requires contextual intelligence—providing the right data at the right time—which most organizations struggle to implement effectively."Models are commodities, data is your moat," Tim explains, arguing that proprietary business context will remain the key differentiator even as AI models continue advancing. This perspective challenges the conventional wisdom that focuses primarily on model capabilities rather than data infrastructure.Perhaps most valuably, Tim outlines three pillars for successful enterprise AI: contextual intelligence, continuous improvement (designing systems that evolve with changing business contexts), and human-AI collaboration. This framework shifts focus from technology deployment to sustainable business value creation.The discussion concludes with eight practical lessons for organizations implementing generative AI, from avoiding the temptation to build proprietary models to recognizing that teaching employees to prompt effectively isn't sufficient for enterprise-wide adoption. Each lesson reinforces a central theme: successful AI implementation requires designing for change rather than building rigid systems that quickly become obsolete.Whether you're a technical leader evaluating vendor claims or a business executive trying to separate AI reality from fantasy, this episode provides the practical guidance needed to move beyond the hype cycle toward meaningful implementation.
Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics is your go-to spot for relaxed discussions around tech, news, data, and society.Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style!
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