Conversations about data and AI as they appear in practice. No hype stories, just what works, what goes wrong and why. From our headquarters in Herentals, we sit together with colleagues, clients and experts around the table. About projects that make it into production and about pilots that are stalled. About the role of business, IT, and especially of the people who ultimately have to work with it. For those who make decisions in an organisation about data, AI and digitisation: managing directors, IT and data managers, team leads and executives. New episodes are released regularly. Subscribe to the channel.
Peppol is sinds 1 januari 2026 verplicht in België. Toch wordt er in veel finance-afdelingen nog altijd overgetypt, gematcht en in Excel uitgezocht. Waar kan generatieve AI vandaag echt helpen, en waar niet? In de tweede JUVO-podcast praat Lodewijk Babi met Bart Nulens, Head of AI bij JUVO, over generatieve AI in de financiële afdeling. Ze hebben het over waarom klassieke OCR vastloopt bij een nieuwe factuurlayout, wat Peppol níet voor je oplost, en wie er beslist wanneer een AI-agent een a...
11 Sept 2026
1. Waarom AI-projecten falen (en wat wél werkt)
95% van de AI-projecten haalt de eindstreep niet. Niet omdat de technologie tekortschiet, maar omdat er iets misloopt rond het project. In de allereerste JUVO-podcast praat Lodewijk Babi met Dimitri Frederickx, Head of Studio bij JUVO, over waarom AI-trajecten stranden en wat je concreet anders moet doen. Opgenomen in de JUVO headquarters in Herentals. Aan bod komt onder meer waarom een AI-project zelden in IT hoort te starten, waarom een proof of concept maximaal zes weken mag duren, en wa...
26 Mar 2026
Why Data Quality Fails (And What to Do About it) | Juvo Webinar
Most organisations don't have a data problem. They have a trust problem. In this webinar, Bert Maes (Juvo) breaks down why data quality keeps failing — and what you can do about it today. No frameworks, no tooling pitches. Just real examples, real pains, and a concrete plan to move forward. 📌 WHAT YOU'LL LEARN Why data quality is not a technical problem (it's an alignment problem)Three common data pains most companies shareWhy "perfect data" is the wrong goalHow to define "good enough" for yo...
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