Agentic AI in Production: What Data Leaders Need to Know Before Scaling AI

Agentic AI in Production: What Data Leaders Need to Know Before Scaling AI

Agentic AI is everywhere. Production-ready AI is not. In this episode, Kris Peeters talks with Dataminded Data Engineer Jesus Garcia about the gap between AI hype and enterprise reality. Drawing on lessons from building a large-scale Agentic AI platform, they discuss security, governance, user adoption, change management, AI skills, and the future role of engineers in an AI-first world. For data leaders, CIOs, CDOs and technology executives, this conversation provides practical insights into what it takes to deploy AI systems that people trust and actually use. 🌐 More at https://www.dataminded.com/ [https://www.dataminded.com/] Topics covered: Agentic AI, Enterprise AI, RAG, AI Security, AI Governance, Change Management, Data Leadership, AI Adoption, Data Engineering. Chapters: 00:00 Agentic AI in Production: Introduction 01:34 Enterprise Knowledge Management with AI Agents 05:19 RAG, Vector Search and Agentic AI Explained 07:36 Deploying Agentic AI at Enterprise Scale 12:14 AI Governance, Security and Access Control 17:06 What Data Leaders Can Learn from AI Security Incidents 25:34 Will AI Replace Software Engineers? 40:20 Why Coding Is Easier for AI Than Business Decisions 43:36 Real Productivity Gains from AI Skills and Automation 49:35 The Limits of Large Language Models 56:44 Change Management: The Real Challenge of AI Adoption

Can We Outsource Thinking? AI, Education, and the Future of Knowledge Work

Agentic AI is changing how we build software, manage data, conduct research, and learn new skills. But as AI takes over more cognitive tasks, a fundamental question emerges: what capabilities do humans still need to develop themselves? In this episode, Kris Peeters sits down with Frank Neven, Professor of Computer Science at Hasselt University and Vice Director of the Data Science Institute, to discuss the future of data engineering, AI-assisted learning, database systems, and human-AI collaboration. The conversation explores: * Why understanding remains essential in an AI-driven world * How universities are adapting to AI-powered education * What the latest database research tells us about the future of data platforms * The rise of agentic coding and AI-native software development * How AI is transforming scientific research * Why structured knowledge and semantic data are becoming more valuable This episode is particularly relevant for data leaders, CIOs, CDOs, architects, and engineering teams navigating the rapid evolution of AI. 🌐 More at https://www.dataminded.com/ [https://www.dataminded.com/] #DataEngineering #AgenticAI #DataLeadership #ArtificialIntelligence Chapters: 00:00 From Theory to Data Engineering 03:20 AI and Healthcare Data Integration 06:00 The Data Science Institute in Practice 19:00 Why Computer Science Education Matters 25:40 AI in Education: Opportunity and Risk 35:00 You Can Outsource Reasoning, Not Understanding 38:45 Agentic AI and the Future of Databases 45:50 How AI Is Changing Research 51:45 Personal AI Systems and Knowledge Management 59:30 Staying Open-Minded in Technology

The Data Challenge behind the Einstein Telescope - The Data Playbook Podcast with Kris Peeters & Tjonnie Li

What does it take to listen to the universe? In this episode of The Data Playbook, Kris Peeters talks with Tjonnie Li, Professor at KU Leuven, about gravitational waves, black hole collisions, and the massive data challenge behind the Einstein Telescope. They explore how modern science is becoming deeply data-driven, why the next generation of research infrastructure will need to operate like a science factory, and how AI, automation, and large-scale compute could become essential for turning petabytes of raw data into scientific discovery. This episode covers: * what gravitational waves are and why they matter * how black hole collisions are measured * why the Einstein Telescope could transform European science * the data, compute, and storage challenge behind next-gen physics * what academia can learn from industry about automation and orchestration * how AI agents could support future scientific discovery 👉 Subscribe for more episodes: https://www.youtube.com/@Dataminded [https://www.youtube.com/@Dataminded] 👉 Watch on YouTube: https://youtu.be/aBbykwnsmpI [https://youtu.be/aBbykwnsmpI] 👉 Explore more content & insights: https://dataminded.com [https://dataminded.com] 👉 Follow Dataminded on LinkedIn: https://www.linkedin.com/company/dataminded [https://www.linkedin.com/company/dataminded] #DataEngineering #AI #GravitationalWaves #EinsteinTelescope #BigData #ScientificComputing #ResearchInfrastructure #DataPlaybook Chapters: 00:00 Intro: Tjonnie Li joins The Data Playbook 02:08 What gravitational waves are - in plain English 05:19 Why science is becoming data-driven 11:28 How we measure black hole collisions today 15:50 The Einstein Telescope: ambition, timeline, and European bid 19:44 The data infrastructure challenge: from terabytes to petabytes 30:56 AI, automation, and the idea of a “science factory” 38:50 Why this matters for Europe, innovation, and society

Scaling Data in Aviation: Inside Brussels Airlines’ Data Strategy - The Data Playbook Podcast with Kris Peeters & Tom Holsteens

How do you transform a broken data landscape into a scalable, self-service data platform? In this episode of The Data Playbook, Kris Peeters sits down with Tom Holsteens to unpack how Brussels Airlines rebuilt their data foundation from the ground up. Coming out of the pandemic, the organisation faced a classic problem: 👉 A “spaghetti” data warehouse 👉 No ownership of data assets 👉 A central team becoming the bottleneck What followed was a multi-year transformation focused on: * Building a modern cloud data platform * Moving to a data product architecture * Enabling self-service analytics across teams * Balancing central governance with decentral ownership * Leveraging AI tools to empower non-technical users 💡 You’ll learn: * Why most data platforms fail (and how to fix them) * How to introduce data ownership in business teams * The real difference between controlling vs. BI * How to reduce bottlenecks with hub-and-spoke models * A real use case: cutting food waste by 30% with data * Why perfect data quality is a myth This is a must-watch for data leaders, engineers, and anyone scaling data in complex organisations. 👉 Subscribe for more episodes: https://www.youtube.com/@Dataminded [https://www.youtube.com/@Dataminded] 👉 Listen on Spotify: https://open.spotify.com/show/your-podcast-link [https://open.spotify.com/show/your-podcast-link] 👉 Explore more content & insights: https://dataminded.com [https://dataminded.com] Struggling with data bottlenecks, unclear ownership, or slow delivery? 👉 Explore our Data Product Workshop: https://www.dataminded.com/what-we-do/data-product-workshop [https://www.dataminded.com/what-we-do/data-product-workshop] Turn your data landscape into a business accelerator with a shared framework, clear ownership, and hands-on guidance in just one day. Chapters 00:00 Introduction & Brussels Airlines context 02:30 What is controlling vs. business intelligence? 06:00 The problem: “spaghetti” data warehouse & bottlenecks 12:30 The transformation: platform, operating model & group strategy 19:00 Hub-and-spoke model & self-service analytics 27:30 Data products & the “restaurant” analogy 35:30 AI, data analysts & scaling data adoption 43:30 Real impact: reducing waste & driving business value

Machine Learning in Energy: Forecasting, MLOps, and Business Impact - The Data Playbook Podcast with Kris Peeters & Jean-Michel Begon

How do you move machine learning from notebook experiments to production in a real business environment? In this episode of The Data Playbook Podcast, Kris Peeters sits down with Jean-Michel Begon, Senior Machine Learning Engineer at Luminus, to explore how machine learning models are built and operationalized inside an energy company. They discuss electricity demand forecasting, the machine learning lifecycle, model experimentation, industrialisation, monitoring, collaboration with IT, and the role of GenAI and LLMs in modern ML teams. You’ll hear practical lessons on: * production machine learning * ML team structure * forecasting model development * data pipelines and platform support * model monitoring and performance review * balancing business value with technical rigor Explore the full podcast series: The Data Playbook Playlist [https://youtube.com/playlist?list=PLJ_da7qdfL83BFGdNF0CIFB9ygsYPH2Ai&si=4IKtFqBcZhUSg77r ]Discover more podcasts, blogs, and webinars: Dataminded Resources [https://www.dataminded.com/resources ]Visit the Dataminded website: https://www.dataminded.com/ [https://www.dataminded.com/]