KI Radar

This is the single most important deadline for European companies deploying AI. If you have AI making decisions about people — hiring, lending, medical diagnoses — you need to be compliant by August. The companies who started 6 months ago are in good shape. The companies who haven't started yet are in trouble. My recommendation: begin with an AI system inventory. You can't comply with what you can't see.

This matters for European companies who need data sovereignty. Until now, the 'use the best model' and 'keep data in Europe' goals conflicted. Mistral Large 2 closes that gap. For regulated industries — banking, healthcare, government — this changes the calculus. You can now get GPT-4-class performance while keeping every byte of data under EU jurisdiction.

o3 is a paradigm shift — not faster, but smarter. For enterprise use cases like legal analysis, financial modeling, or engineering design, the extra reasoning time is a worthwhile trade-off. My advice: don't default to o3 for everything. Use fast models for simple tasks, reasoning models for complex ones. The real innovation is knowing when to use which.

2025 was the year of AI agent hype. 2026 is the year they actually work in production. The key difference? Guardrails. The frameworks that won aren't the most autonomous — they're the ones with the best human-in-the-loop patterns. If you're building AI agents, invest in evaluation and monitoring before you invest in capability. An agent that's 80% accurate but you can't monitor is worse than one that's 70% accurate with full observability.

The money is flowing, but the execution gap is widening. 65% with stuck pilots tells you everything — the bottleneck isn't budget, it's production ML engineering. Companies hire data scientists but not MLOps engineers. They build great models that never leave the notebook. If you're in that 65%, the fix isn't more R&D spend — it's an engineer who's shipped AI to production before.

Finally. The biggest risk with RAG systems isn't bad retrieval — it's confidently wrong answers. These evaluation frameworks let you catch hallucinations before users do. If you're running RAG in production, integrate automated eval into your CI/CD pipeline. Test every prompt template change against a golden dataset. This is the RAG equivalent of unit testing — nobody does it until something breaks in production.

This is a game-changer for data sovereignty. Running Llama 3 or Mistral on-premise with Blackwell means you get cloud-class inference without cloud-class data risk. For European companies under GDPR and the AI Act, on-premise AI just became viable. The cost is high upfront but the TCO calculation works for companies processing sensitive data at volume. If you're spending over €50K/month on API calls, do the math on self-hosting.

Europe is finally building its AI ecosystem, not just consuming American models. The rise of compliance-focused AI startups (Credo AI, Holistic AI) is uniquely European — we're turning regulation into innovation. For enterprise buyers: this means more choice and more European options. For founders: vertical AI with built-in compliance is the European advantage.