About
This is the autumn edition of the AI in Manufacturing course — the same programme as the spring run in Kortrijk, now at KU Leuven Campus De Nayer in Sint-Katelijne-Waver. Four in-person sessions between October and December 2026, taught by Pieter De Buysser (NXTGN) and Mathias Verbeke (KU Leuven), with a pre-course online AI maturity scan and e-learning module included.
The course is designed for SME decision-makers in Flemish manufacturing: CEOs, CTOs, COOs, and directors responsible for strategy, innovation, and digitalisation. It is not a technology course. It is a management course about how to use AI as a structural strategic tool rather than a reactive response to vendor pressure or conference hype.
Missed the spring edition? This is your second chance. The programme is identical — same instructors, same methodology, different location and cohort. Financial support from VLAIO; an initiative of KU Leuven M-Group, PUC-KU Leuven Continue, NXTGN, Flanders Make, and VAIA.
Session Dates
In-Person Sessions — Campus De Nayer, Sint-Katelijne-Waver
- 8 October 2026 — 9:00 – 12:30
- 29 October 2026 — 13:00 – 16:30
- 18 November 2026 — 9:00 – 12:30
- 3 December 2026 — 9:00 – 12:30
Pre-course online AI Maturity Scan + 1-hour e-learning module on AI risks and legislation included. Each in-person session includes lunch. Final session closes with a networking drink. All sessions in Dutch. Registration deadline: 1 October 2026.
Programme
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Module 0
Online AI Maturity Scan (pre-course, async)
A structured diagnostic completed before the first session. Maps where your organisation currently stands on AI, data, governance, and organisational readiness. Results are aggregated and shared with the instructors to tailor session focus.
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Module 1
AI & Strategy in Manufacturing — Introduction (3h, 8 October)
Opens with a business case from a manufacturing company that has deployed AI at scale. Followed by an accessible introduction to AI types (predictive, generative, agentic) with manufacturing examples. Introduces the “AI-ready organisation” framework covering strategy, data, technology, governance, organisation, process, and change management.
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Module 2
E-Learning: AI Risks and Legislation (1h, online)
Developed by UMANIQ. EU AI Act, GDPR, intellectual property, and practical AI risk categories (bias, deepfakes, unreliable outputs). Designed for non-lawyers: actionable guidance, not legal theory.
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Module 3
Technical Deep Dive: AI in Manufacturing (3h, in-person)
Taught by Mathias Verbeke. ML pipeline fundamentals (data to trained model), MLOps and production deployment, generative AI and foundation models in industrial contexts, edge vs. cloud architecture, IT/OT integration. Grounded in manufacturing use cases: predictive maintenance, computer vision quality inspection, production optimisation, robotics, digital twins.
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Module 4
Workshop: AI Strategy (3h, in-person)
Applied workshop. Participants define an AI ambition for their own company, map opportunity domains, and work through data, governance, talent, and change management dependencies using structured methodology. Taught by Pieter De Buysser.
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Module 5
Workshop: AI Procurement Guide (3h, in-person — 3 December)
How to evaluate AI vendors without being sold something you can’t assess. Structured scoring methodology across strategy fit, technology, data requirements, legal aspects, cost models, and organisational impact. Small-group exercises with realistic vendor scenarios. Closes with a synthesis session and networking drink.
Instructors
Pieter De Buysser
Founder, NXTGN — AI Strategy, Transformation & Adoption
Computer scientist and entrepreneur with over ten years in AI strategy and digital transformation. Founded NXTGN to help organisations move beyond AI as a trend and towards AI as a structural competitive tool. His focus is on the strategic and organisational challenge of AI adoption: how leadership teams build an AI vision that is realistic, how they develop internal capability, and how they evaluate the market without being captured by vendor narratives.
Leads Modules 1, 4, and 5 — the strategy, methodology, and procurement components of the course.
Mathias Verbeke
Professor AI — KU Leuven Faculty of Engineering Technology (IIW)
Professor of Artificial Intelligence at KU Leuven’s Faculty of Industrial Engineering Sciences, based at the Bruges campus where he is part of the Mechatronics Group (M-Group). His research focuses on the specific difficulties of deploying machine learning in industrial environments — where data quality varies, safety requirements are real, and models need to function reliably in conditions that differ from the training set.
Also affiliated with Flanders Make (Belgium’s strategic manufacturing research centre) and Leuven.AI. Leads Module 3 — the technical foundations session covering ML pipelines, MLOps, generative AI architectures, and manufacturing-specific AI applications.
Context
Why a Second Edition?
The spring edition (Kortrijk, starting 6 May) is designed for manufacturers in West Flanders. The autumn edition at Campus De Nayer — the KU Leuven campus in Sint-Katelijne-Waver (Mechelen region) — serves manufacturers in Antwerp province and greater Brussels. The same programme runs at a different location with a different cohort, so the peer group in the workshops will reflect your region’s industrial context.
Campus De Nayer is KU Leuven’s engineering campus specialising in industrial engineering, electromechanics, and electronics. Running an AI-in-manufacturing programme there locates the learning inside an industrial engineering environment, which shapes the case studies and the conversations in the workshops.
What “AI-Ready Organisation” Means
The central framework of this course is the “AI-ready organisation” model, which is introduced in Module 1 and revisited throughout. It identifies seven domains that must be developed together for AI to deliver structural value: strategy (why are you doing this?), data (does your data support the use case?), technology (what infrastructure do you have or need?), governance (who owns AI decisions and risks?), organisation (how is the work structured?), processes (where does AI touch workflows?), and change management (how do people adapt?).
Most AI projects in SMEs fail at governance and data. Strategy and technology get attention; the rest is assumed to sort itself out. The AI-ready organisation framework treats all seven domains as design variables, not implementation details. The workshops ask participants to honestly assess all seven dimensions for their own context, which is often the most productive part of the course.
Register
Organised by
PUC — KU Leuven Continue
The continuing education branch of KU Leuven. This course is a joint initiative of KU Leuven M-Group, PUC-KU Leuven Continue, NXTGN, Flanders Make, and VAIA, with financial support from VLAIO.
puc.kuleuven.be ›