After years of AI projects for SMEs, I've learned to recognise patterns. Here are the five mistakes I encounter most often.
1. Starting Too Big
The most common mistake: a company wants a fully AI-driven system immediately. Better to start with a specific problem and prove the value.
2. Underestimating Data Quality
AI is only as good as the data you feed it. I always spend the first weeks understanding and cleaning existing data.
3. No Clear KPIs
If you don't know what success looks like, you can't measure it. Define upfront: what should improve and by how much?
4. Not Bringing the Team Along
AI adoption fails when employees don't understand it or fear it. Training and communication are at least as important as the technology.
5. No Post-Launch Plan
An AI system needs maintenance. Models degrade, data changes, users have new needs. Plan for this from day one.
Implementatie
Fouten
Best Practices
MKB