Startup founders must manage the high compliance costs of the EU AI Act and navigate 27 different national healthcare systems. From fragmented reimbursement pathways to the difficulty of integrating with legacy hospital workflows, this post maps the structural barriers that prevent medical AI from achieving commercial scale in Europe.

This document maps the recurring ways robotics companies fail to cross from prototype to production. The patterns come from my professional network and hands-on experience, which means a bias toward Computer Vision heavy systems, NVIDIA robotics, and the organizational dynamics typical of venture-backed teams.

Start ML with about €200 and 200 hours: pick one course (fast.ai), use Mamba and PyTorch on Kaggle or Colab, build a safe but meaningful project, then win through data, honest evaluation, and steady iteration. Business sense multiplies impact.

Traditional project management fails in AI development due to high uncertainty, leading to wasted resources. Based on my 17 years in tech and research, I’ll show how Lean Startup principles can transform AI projects through rapid experimentation and validated learning, helping you deliver real business value faster.

Choosing a project management methodology when doing AI projects is like choosing a dog breed—there’s no perfect one, but you need to pick one that fits your needs. From the Waterfall’s rigidity to the chaotic YOLO runs, each approach has its quirks. Understanding these trade-offs is key to turning challenges into success.

This article presents a battle-tested framework that helps you identify winning AI projects before investing significant resources. By following these five criteria, you’ll be able to focus on technical execution with confidence, knowing you’re building something valuable.