Not everything has to become a prompt — the four use-case classes
Most companies think of a chat when they think of AI. Bitkom 2025 shows how early the field still is: only 2 % of companies use AI in IT-supported production, and only 26 % give their employees access to AI at all. Anyone who just hands out a chat window leaves the largest part on the table. In our AI adoption work, we see four use-case classes that keep appearing.
The four classes — your portfolio
In sheer usage, chat dominates — at one mid-sized company we measured, it was over 70 % of all interactions. The economic value per use case, however, sits in workflows and AI apps. A single workflow often delivers more in a month than hundreds of chat requests.
Building in only one class means losing a large part of the potential.
Assistant or workflow — the most important decision
Teams confuse the two middle classes most often. The dividing line is simple: an assistant is flexible — the model decides depending on the request, calls tools itself and returns Markdown or artifacts. A workflow has fixed steps, typed variables and its own model per step. File output in PowerPoint, Excel or Word goes exclusively through a workflow.
- Same flow, structured inputs → workflow.
- Open question, the model uses tools on its own → assistant.
Building workflows that do not drift
Badly built workflows deliver a good result on the first test and fail silently on the tenth run, when a different person enters different inputs. Two rules prevent that.
Manually first, then model it. Run the process through the chat three times — with three different inputs — before you save it as a workflow. You notice in the process which variables really vary and whether the model delivers what you need.
Three components per step. Every good step names at least one variable, one reference and one output schema. Three to four steps at most — beyond that the model loses the original task.
Anti-pattern: output without a schema
The most common mistake in the workflow builder: the prompt says what should be
produced, but not in what form. Models then vary from run to run in length,
order and bullet style — and a colleague gets a different format a week later.
Write the output schema into the prompt explicitly, as a list of sections. Where
possible, enable document output — a .docx or .xlsx enforces a fixed
structure.
Connector or data pool — connecting knowledge
For AI to take on real work, it has to reach your systems. Two routes, deliberately separated by risk:
- Connector: a live API with actions — the assistant reads current data and writes back (send emails, create tickets). Permissions match those of the individual user in the source system.
- Data pool (RAG): read-only knowledge search across large document sets, maintained centrally by admins.
Careful with data-pool permissions
A data pool does not automatically inherit the permissions of the source. Unlike a connector, SharePoint permissions for instance do not apply by themselves — the admin configures access manually. Settle that before sensitive knowledge is indexed.
What this means in hours
The numbers from real introductions show the difference between a toy and a production system — all anonymised.
| Task | Before | With AI | |
|---|---|---|---|
| Job ad (workflow) | 20 min | 2 min | |
| HVAC quote (assistant) | 45 min | 5 min | |
| Sales dashboard (AI app) | weeks | 30 min | |
| Requirement spec, first version | 3–5 days | 60 % in one afternoon | |
| Saving on the HVAC quote | 80–90 % | ||
The sales dashboard was built by an employee with no programming skills — the classic route would have needed a business analyst and several weeks. This is exactly where AI tips from a helper into a process.
Where to read on
Start read-only: search and read first, then write and act. How to govern write permissions and data access cleanly is in governance & security. Which tasks in your team offer the greatest leverage is shown by the use-case map — and what breadth across all four classes is really worth is calculated by measuring impact.
Start with AI in your company
Together we find the right use cases, connect your systems, and bring AI into daily work in line with your business.