Choosing a project management methodology when doing AI projects is like choosing a dog breed, and 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.
From 74 competitive applications submitted across Europe, 14 SMEs were selected — one per challenge — as winners of the VR Health Champions Open Call 2026, highlighting the diversity, quality, and growing maturity of Europe’s XR health ecosystem.
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.
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.
Develop locally, run on remote machine. Easy! We will show how to go a step beyond, and develop, run, debug, on a docker, inside that remote machine, while using your favorite JetBrains IDE, PyCharm! Professional edition, but no problem if you are still in academia!