Episode 65: Scaling AI in Engineering with Alexander Krumm and Prof. Dr. Thomas Meenken

Feb 21, 2026

Guest

Alexander Krumm

Guest

Thomas Meenken

Alexander Krumm, founder of VPATH AI and former systems architect, and Prof. Dr. Thomas Meenken, university professor and AI-in-engineering advisor with industry leadership experience. They discuss scaling AI beyond pilots into integrated engineering intelligence. Short takes cover starting with imperfect data, agent-based workflows, structuring legacy knowledge, and how AI augments engineering teams.

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INSIGHT

Deployment And People Drive AI Success

AI adoption fails more from deployment, process, and people issues than from model capability.
Thomas Meenken: unclear processes, missing written steps, and legal/compliance gaps cause promising proofs to stall and be labeled failures.

ADVICE

Start From The Problem Not The Tool

Avoid starting from the tool; start from the problem and pick AI where it solves a clear pain point.
Alexander Krumm: prioritize high-impact engineering problems, then scale step by step rather than copying ChatGPT into silos.

ADVICE

Learn Chat Workflows Before Big Projects

Begin individually with chat windows and simple prompts, then graduate to frameworks like Claude or OpenAI when limits are reached.
Thomas Meenken: learn prompting and tweak prompts before assuming AI won't help; training on usage prevents early abandonment.

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Broadcast date: February 24th, 2026, 18:30 CET

Artificial Intelligence is everywhere — but how do engineering organizations move beyond experimentation and isolated pilots toward real, scalable impact?

In this episode of the MBSE Podcast, we welcome Prof. Dr. Thomas Meenken and Alexander Krumm, two experts who combine deep methodological expertise with hands-on industrial scaling experience.

Thomas brings academic rigor and industry leadership experience from BHS, Roche, and Zeiss.
Alexander, a seasoned system architect, contributes large-scale implementation expertise from Continental, CARIAD, and CLAAS.

Together, they share practical insights from real-world projects conducted through their companies VPATH AI and AI Powered Engineering — spanning the journey from first pragmatic AI agents to agentic workflows and ultimately to integrated Engineering Intelligence platforms.

What you will learn:

Scaling AI without frontloading perfection
You don’t need perfect data to start. AI can actively help structure and clean information from the very first use case. Learn how companies move from isolated quick wins to a scalable, integrated platform approach.

From isolated agents to Engineering Intelligence
True scaling requires context — understanding technical decisions, organizational constraints, resource bottlenecks, and skills gaps. We discuss what it really takes to industrialize AI in engineering environments.

Lessons learned from real implementations
AI should act as a relevance filter — protecting overloaded teams from data overflow and overspecification. One key benefit: drastically reduced onboarding time for complex software systems, even when legacy documentation is incomplete or missing.

Strategic advice from the field
AI adoption is an organizational learning journey — not a one-time software purchase. Why expert-led implementation projects with tool vendors matter far more than simply buying licenses, and how to shape an AI strategy that truly fits your company.

Watch the livestream on YouTube or catch it later on YouTube, Spotify, iTunes, or Amazon Music.

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#MBSE #AI #ModelBasedSystemsEngineering #SystemsEngineering #MBSEPodcast

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