When people talk about AI, the conversation almost always gravitates toward the model . Which algorithm? Which architecture? Which accuracy score? It’s understandable, the model is the most visible part. But it’s rarely the hardest part. The real challenge is everything around it. Setting up a model already demands significant expertise: feature engineering, hyperparameter tuning, validation strategy, performance monitoring… But the moment you move from experimentation to production, complexity multiplies. Now you’re dealing with request/response orchestration, latency constraints, failover strategies, and model versioning under real load. Inference must be reliable, fast, and traceable. That’s an engineering challenge, not just a data science exercise. If the model is the brain, data is the oxygen and getting clean, production-grade data is often the hardest part of the journey. In financial services, data flows from many sources, like core banking systems, transaction monitor...
In software, hundreds of start-ups consume millions of euros every year, while only a handful will ever become the next unicorn. Most of them don’t start with a bad idea. Quite the opposite. Many start with a very good idea , talented people and plenty of ambition. So what makes one company successful while another, with an equally promising idea, quietly disappears? Everything starts with an idea , of course. Unfortunately, the world is full of good ideas. Spend an hour browsing LinkedIn, Finextra or any technology forum and you will probably discover a dozen ideas that make you think: someone should build this . Having the idea is therefore rarely the real differentiator. This is why people often say that execution is everything . An idea is worth little if you cannot turn it into something that actually works. But even great execution isn’t enough. Technology history is full of companies that executed perfectly well and still lost. MySpace and Friendster created social ...