The 20 pressing AI questions in business
These are the questions I see on boardroom tables every day — with extra weight on data sovereignty, local hosting and European regulation. With 20+ years of engineering, working daily with Claude, Gemini, Codex and Grok (xAI), I translate them into answers that run.
I. ROI & strategy
What will AI demonstrably deliver — and when?
I start with the business case: which processes cost time or money today, what the savings are and when you break even. Measured from week one.
Which ecosystem do we choose — Microsoft 365 Copilot or open source?
Switch on Copilot or build a flexible open-source layer? I benchmark both on your use case and prevent vendor lock-in.
Build, buy or wait for our software vendor?
Your own AI layer, or wait until AFAS, SAP or your PIM ships AI? I run the numbers on both scenarios — including lock-in and exit costs.
II. From prototype to production
Why do 80% of AI projects fail — and ours won't?
Pilots fail on data, integration and ownership, rarely on the model. I build from day one with evals, monitoring and an owner per process.
How does our ChatGPT/Claude prototype become a production system?
From sandbox to hundreds of users: LLMOps, scalability, monitoring and SLAs.
How do we safely connect AI to ERP and CRM?
Reliable APIs and RAG pipelines with live access to your existing systems — with permissions and logging.
Is our data infrastructure ready for AI?
Garbage in, garbage out. I help make folders, sheets and mailboxes AI-ready before we build.
How do we test whether the AI is good enough for customers?
Eval sets with real cases and a clear bar per release: only live when the numbers say so.
III. Sovereignty, data & EU regulation
How do we guarantee our data never leaves the EU?
Sovereign cloud guarantees and EU data centers — locked down technically and contractually.
Can we run AI fully on-premise or in a private cloud?
Llama or Mistral on your own servers, even without an internet connection. This is my specialty.
Does our AI application count as 'high-risk' under the EU AI Act?
I assess your system against the AI Act and set up compliance and transparency pragmatically — before the regulator asks.
How do we keep foreign governments (CLOUD Act) out of our data?
US cloud in the EU isn't enough. I arrange legal-technical shielding.
IV. Quality, costs & risks
Who is liable when the AI makes a critical mistake?
I design processes with human-in-the-loop and clear responsibility per decision.
How do we prevent hallucinations reaching customers?
Automated guardrails, evals and observability — measuring instead of hoping.
What does this cost us monthly (FinOps)?
Token budgets, caching and the right model per task keep costs predictable — accounted per use case.
Who owns the intellectual property of AI output?
Design, code or contract: I advise on protecting and licensing output.
V. People, organisation & adoption
How do we deal with 'shadow AI' on the work floor?
Employees already use free ChatGPT. I channel that into a safe business environment.
How do we get employees to actually use it?
Training, prompt skills and taking away fear — adoption is change management.
Can our team manage it themselves afterwards?
Documentation, runbooks and handover are part of the delivery. You don't depend on me — unless you want to.
How do we prevent our solution being outdated in two years?
Model-agnostic architecture: swap LLMs tomorrow without rebuilding.