My honest take on AI in mid-market business.

1. Most AI projects fail because of execution, not technology.

I see it constantly: companies have a consultant's report, a roadmap, sometimes even a prototype. Then reality hits - nobody builds the actual system because the consultant delivers advice but doesn't build anything. This isn't a weakness in these businesses. It's the gap where I work.

2. Just using AI tools creates more mediocrity - only faster.

Copy that sounds like nothing, analysis nobody questions, presentations that could be anyone's. The problem isn't AI. The problem is most people never learned how to direct it - so the output never gets better than the question you ask.

3. AI workshops without working results are money down the drain.

The usual format: two days of inspiration, lots of applause, then back to business as usual - with a manual gathering dust. That's why my workshops end with a system that actually works the Monday after. Not just good vibes.

4. GDPR doesn't kill AI - it's a quality benchmark.

Companies that blanket-reject US cloud tools and then avoid AI for years haven't looked closely enough. Powerful, compliant systems exist - self-hosted, on German servers, with proper data processing agreements. Using GDPR as an excuse usually means you don't want to start anyway.

5. AI won't save mid-market companies - it amplifies who's already delivering.

Efficiency magnifies what's there: good processes get faster, bad processes create more bad work faster. AI isn't a turnaround tool. That's why I always start audits with: What's working well? Only then do we look at what should speed up.