01Strategy
AI readiness and use-case selection
Where AI realistically helps in your stack, where it would just be expensive theatre, what GDPR and the EU AI Act mean for it, which architecture you actually want to operate.
Stance · Long read
AI is neither the salvation of humanity nor the end of every profession. And no, a Claude Code prompt alone usually does not replace stable software, a thought-through process, or years of experience with real systems.
But anyone who completely ignores what AI can do today will run into problems before long.
That is why I take a pragmatic view of AI. Where it is sensible, economical, and technically defensible, it can be extremely helpful:
Processes, data, reporting
Automation, data analysis, reporting, data prep, and ETL-style work.
Knowledge and internal systems
Knowledge bases, internal search, internal tools, and structured workflows.
Routine, code, text
Routine tasks, structuring, code support, and documentation.
But that is often where the real work only begins.
"It works on my machine" is not yet a productive business process.
An AI agent is quick to build. Then what?
Today Claude Code, ChatGPT, or Cursor can, in remarkably little time, build small tools, analyze data, create workflows, write scripts, build automations, connect APIs, and even generate complete mini systems.
And honestly: that is impressive.
But after the first wow moment, the questions that follow are usually far less viral on LinkedIn:
And suddenly you notice: the prompt was the easy part.
AI is not a substitute for understanding
I am not into panic. But I am also not into "AI will replace all developers tomorrow". Reality sits somewhere in between.
AI is a tool. An extremely powerful tool. But a tool is only as good as the person who uses it well.
An AI agent working unchecked on production data is not innovation. It is often just a new failure mode with better marketing.
- personal data and internal company data
- reporting, permissions, and sensitive documents
- customer information
- public institutions
- medical or financial contexts
Cloud or on-prem?
It depends. There is no single best answer for every case.
Cloud (e.g. ChatGPT, Claude, Azure OpenAI, Gemini)
- Fast to start, strong models, little own infrastructure, straightforward integration.
- Watch closely: privacy, data transfer, running costs, vendor lock-in, dependencies, and sometimes unclear data flows.
On-prem / local (e.g. Ollama, Open WebUI, local LLMs, Qdrant, RAG)
- More data control, can run locally, often more GDPR-friendly, fewer external API dependencies.
- But significantly more complex: maintenance, hardware, updates, security, monitoring, user management, and performance.
Many small teams underestimate how much infrastructure suddenly sits behind we host it locally.
Public sector and sensitive environments
That is where AI is often far harder than in YouTube demos. Topics like these suddenly matter:
Law and governance
Data protection impact assessment, GDPR, EU AI Act, internal policies, and documentation duties.
Infrastructure and operations
Hosting location, network separation, VPN, logging, and audit-grade records.
Organization
Role models, permissions, and works councils when it comes to rollout and use.
And that is appropriate. A flashy tool is not automatically sensible or permitted in every environment.
Where AI is genuinely useful, in my view
Not everywhere. But often where repetitive work piles up. Typical areas:
Reporting and data flow
Reporting, data preparation, ETL-style processes, and SQL support.
Knowledge and search
Knowledge systems, internal search, support knowledge bases, and sensible summaries.
Office and workflows
Office automation, structured workflows, and small internal tools.
Assistants
Intelligent assistants embedded in existing processes.
Combined with real processes, AI can save a lot of time. But only if:
- data quality is right
- processes are understood
- security is considered
- people stay in control
- systems stay maintainable
What I deliberately do not promise
No magical fully automated companies.
No AI replaces every employee.
No miracle fixes after two prompts.
No agent systems that supposedly do everything alone.
Because reality usually looks different.
My approach
I treat AI as a tool, not a religion. Where it really helps, can be integrated sensibly, and delivers a real economic upside, I am glad to support ideas, evaluation, automation, on-prem AI setups, reporting, workflows, data analysis, internal tools, and office or process automation.
Related topics
Discuss an AI use case?
Briefly describe the scenario, data context, and constraints (cloud, on-prem, public sector). I will reply with an honest view.