Turn an idea into a first usable workflow
Many good ideas stay abstract too long. I help turn a problem or AI use case into a small, usable prototype, workflow, or MVP. The goal is a first test in real work, not a project that runs for months.
What it is about
Implementation pays off when an idea is tangible and should be tested in practice. That can be an AI-supported flow, an internal assistant, a form process, an automation, or a small web app. The first step stays small enough to learn fast.
Typical starting situations
An idea exists but is not yet testable.
A manual process should become simpler.
AI should be built into a repeatable routine.
An Excel or SharePoint setup hits its limits.
An MVP (Minimum Viable Product) is intended to serve as a provisional version for functional testing, to determine whether a larger-scale implementation is worthwhile.
How it works
Clarify the problem. What should be solved, and what marks a good first test?
Scope the MVP. I reduce the idea to the smallest sensible scope.
Build the prototype. A first usable version as workflow, assistant, form, or web app.
Test and plan the next version. Check with realistic material, then extend, simplify, or hand over cleanly.
Outcome
A usable first prototype (depending on the environment and where possible)
A clear description of the workflow
Findings from the test
Notes on value, limits, and risks
A recommendation for further development or rollout
Suitable formats
Mini prototype: for a clear idea with limited scope. Duration: a few days to two weeks.
Workflow MVP: for recurring tasks with several steps or people. Duration: two to four weeks.
AI assistant concept: for internal assistants, research, or quality checks. Duration: one to three weeks.
Method
A real task, visible proof, clear anchoring. I start with a concrete task, check the difference in everyday work, and secure the result so it can keep being used.
Safe start
For prototypes I prefer anonymised, synthetic, or approved material. Sensitive data is only used once data protection, IT framework, and responsibilities are clear.
Next steps
A good fit before or after: Process analysis, AI Reality Lab, or Safe AI use.
Services / Formats
How we can start
Orientation and strategy
Turn many options into a reliable direction. Priorities, risks, and a clear first step.
AI training
Teams learn on their own tasks how to use AI safely, clearly, and practically.
Process analysis
Make visible where time and quality are lost, and where relief can start.
Implementation and prototyping
Turn an idea into a small, usable workflow, assistant, or MVP.
Safe AI use
Clear guardrails for data, approvals, and responsibility, before uncertainty grows.
Research and decision templates
Sort complex topics neutrally and turn them into a clear basis for decisions.