Three concrete sample deliverables show how a vague AI idea becomes a sound basis for decisions — with clear processes, assessed data, governed access and explicit ownership.
Many AI initiatives start with the tool. My work starts earlier: with the business problem, the actual process and the question of which data can be used reliably.
These examples show the professional core of my work. Technical implementation can build on it — but it is an optional next step, not the starting point.
From a vague AI idea to a robust foundation for decisions, data and implementation.
A realistic example of what is available after ten working days: data inventory, quality assessment, ownership, target design and a prioritised 90-day roadmap.
Tasks, costs and risks become a consistent management report.
The case study shows data mapping, quality rules, interface logic and management-ready output. The decisive factor is not the workflow alone, but the traceable consolidation of the data.
A local knowledge assistant as technical proof — focused on sources, quality, access and ownership.
The demonstrator makes visible what sits behind a dependable knowledge system: structured data intake, semantic knowledge units, source citations and an honest boundary between prototype and production readiness.
I create the professional and organisational foundation on which automation and AI can operate reliably.
The business problem, current process, target state and measurable value are clearly defined.
Sources, quality, validity, permissions and technical accessibility are assessed.
Responsibilities, priorities, risks and next steps become decision-ready.
If the data and process are viable, I can add a limited technical prototype or implementation support. This is scoped separately according to the findings.
In a personal call, we clarify whether an AI & Data Readiness Sprint makes sense for your specific situation.
