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We 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 group
• Headline and summary suggestions that boost clarity and speed
• RAG‑powered search turning archives into instant context
• Text‑to‑video tools that free up local video teams
• The hurdles of adoption, quality, and scaling prototypes into production

Their 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.

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In 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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In 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 value
• How to align business purpose, data, and people for maximum impact
• Why “purpose before platform” is key to successful AI adoption

Whether 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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In 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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In 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 scale
• Building a modular, privacy-conscious analytics stack
• Metadata governance across fragmented content systems
• Recommendation systems for discovery, not just engagement
• The circular relationship between data quality and AI performance
• Applying machine learning in service of cultural and civic missions

Whether 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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What 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.

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Welcome to the cozy corner of the tech world! Datatopics is your go-to spot for relaxed discussions around tech, news, data, and society.

In this episode of Data Topics, we sit down with Nick Schouten — data engineer at dataroots — for a full recap of KubeCon Europe 2025 and a deep dive into the current and future state of Kubernetes.

We talk through what’s actually happening in the Kubernetes ecosystem — from platform engineering trends to AI infra challenges — and why some teams are doubling down while others are stepping away.

Here’s what we cover:

• What Kubernetes actually is, and how to explain it beyond the buzzword
• When Kubernetes is the right choice (e.g., hybrid environments, GPU-heavy workloads) — and when it’s overkill
• How teams are trying to host LLMs and AI models on Kubernetes, and the blockers they’re hitting (GPUs, complexity, cost)
• GitOps innovations spotted at KubeCon — like tools that convert UI clicks into Git commits for infrastructure-as-code
• Why observability is still one of Kubernetes’ biggest weaknesses, and how a wave of new startups are trying to solve it
• The push to improve developer experience for ML and data teams (no more YAML overload)
• The debate around abstraction vs control — and how some teams are turning away from Kubernetes entirely in favor of simpler tools
• What “vibe coding” means in an LLM-driven world, and how voice-to-code workflows are changing how we write infrastructure
• Whether the future of Kubernetes is more “visible and accessible,” or further under the hood

If you're a data engineer, MLOps practitioner, platform lead, or simply trying to stay ahead of the curve in infrastructure and AI — this episode is packed with relevant insights from someone who's hands-on with both the tools and the teaching.

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Welcome to the cozy corner of the tech world! Datatopics is your go-to spot for relaxed discussions around tech, news, data, and society.

This week, co-host Ben is joined by Jackie Janssen, former Chief Data Officer at CM, author of AI: De Hype Voorbij, and an evangelist for pragmatic, human-centered AI. Together, they trace the winding path from early tech roles to enterprise transformation, exploring how AI can actually serve humans (and not just the hype machine).

In this episode:

• Leadership in AI transformation: From KBC to CM, lessons on creating cultural buy-in.
• Building effective data teams: Why the first hire isn’t always a data engineer.
• AI governance: What makes a strong AI Council and why CEOs should care.
• Product and process thinking: How MLOps, data factories, and product mindsets intersect.
• Agents and autonomy: The future of work with AI teammates, not just tools.
• The human edge in a machine world: A preview of Jackie’s next book on rediscovering humanity in the age of AI.

Curious about Jackie’s take on AI agents, cultural inertia, or what really makes a great data strategy tick? Tune in, you might just find a new way to think about your tech stack and your team.

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Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unpluggedis 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!

In this episode, we dig deep into a concept everyone pretends to understand: metadata. Joined by our guest Corentin, we unpack what it really means, why it’s more than just “data about data,” and how to make metadata management less of a formality and more of a value driver.

Expect hot takes, real-world metaphors, and zero tolerance for shelfware strategies as we cover:

• Defining metadata: Beyond the buzzphrase, into systems thinking
• Metadata vs. data governance: Why this split often misses the point
• Shop-floor pragmatism: What lean thinking brings to metadata workflows
• Common traps: Like starting with tools instead of actual pain points
• Driving value: From tribal knowledge to structured, sustainable processes

Whether you're managing a data platform or just wondering why your data catalog feels like a graveyard, this one’s for you.

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Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged 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!

In this episode, host Murilo is joined by returning guest Paolo, Data Management Team Lead at dataroots, for a deep dive into the often-overlooked but rapidly evolving domain of unstructured data quality. Tune in for a field guide to navigating documents, images, and embeddings without losing your sanity.

What we unpack:

• Data management basics: Metadata, ownership, and why Excel isn’t everything.
• Structured vs unstructured data: How the wild west of PDFs, images, and audio is redefining quality.
• Data quality challenges for LLMs: From apples and pears to rogue chatbots with “legally binding” hallucinations.
• Practical checks for document hygiene: Versioning, ownership, embedding similarity, and tagging strategies.
• Retrieval-Augmented Generation (RAG): When ChatGPT meets your HR policies and things get weird.
• Monitoring and governance: Building systems that flag rot before your chatbot gives out 2017 vacation rules.
• Tooling and gaps: Where open source is doing well—and where we’re still duct-taping workflows.
• Real-world inspirations: A look at how QuantumBlack (McKinsey) is tackling similar issues with their AI for DQ framework.