AI use case: definition, selection and limits
AI use case Updated
Definition
An AI use case is a clearly bounded application in which AI or automation measurably improves a specific business flow. It describes the trigger, the inputs, the expected value and the systems involved. In the mid-market a good use case is small enough for a provable ROI and large enough that the effort for build and operation pays off.
Why the Term Matters
Many automation efforts fail not at the technology but at the selection. Who takes on everything at once loses focus and budget. A cleanly described AI use case makes value, data situation and integration need visible before anything is built. That allows prioritising by ROI instead of by gut feeling, and the first flow is the one that pays off provably fastest.
When It Is Relevant
- A flow is frequent, costs noticeable time and is today manual.
- The value can be put in numbers against a baseline.
- The needed data and system access (SAP, CRM, DATEV) exist.
- The case is narrow enough to show a pilot in weeks.
When Not
When a case is only vaguely phrased, such as "we want to do something with AI", the basis for a decision is missing. And when data is not accessible or the flow constantly changes, a use case is more a wish than a candidate. Then process clarification comes first, not the build.
Industries That Use It
- Mechanical engineering: pick a single quoting or order step as the first candidate instead of the whole process chain.
- Insurance: describe claims intake as a clearly bounded case with defined inputs and approvals.
- Logistics: read freight documents as a tightly scoped use case with measurable turnaround.
- Staffing: pre-sort application intake as a small, recurring flow with a clear baseline.
Typical Use Cases
- Collect manual standard flows as a candidate list and sort by value.
- Capture trigger, inputs, exceptions and target system for one case in a brief.
- Check the data situation before a build is decided.
- Implement a single case as a pilot and measure against the baseline.
- Decide from the pilot result whether the case goes into rollout.
Decision Guide
| Signal | AI use case fits | Alternative |
|---|---|---|
| Flow clearly boundable | describe and prioritise now | clarify the process first |
| Value expressible in numbers | brief plus baseline | shelve the idea |
| Data and access present | plan a pilot | solve integration first |
| Case too large | cut into sub-cases | no big-bang project |
Example
A mid-market company lists ten manual flows, estimates value and data situation for each and describes the strongest case as a brief with trigger, inputs and target system. Only this one case goes into a pilot, with RPA for the flow and an AI step for unstructured inputs. Value is measured against the baseline. This describes the method, not an asserted client metric.
Service Connection
Selection usually belongs in process automation consulting, and the build in an RPA build or process automation with AI.
Sources
This framing describes common mid-market practice for selecting automation cases. Concrete prioritisation, data situation and integration depend on the case, the systems and the tools and must be checked case by case.