Baseline: definition, measurement and value in AI projects
Why the Term Matters
Many AI initiatives fail not at the build but at the missing point of comparison. Without a starting value you can only guess after go-live whether anything improved. The baseline gives the later assessment an anchor and guards against flattering numbers. In an approach with fixed scope and a fast prototype it is captured early, so the prototype competes against real values rather than a gut feeling.
When It Is Relevant
- You want to prove after go-live what concretely changed.
- A process should be optimised whose current numbers no one knows cleanly.
- A prototype is due and needs a real value to measure itself against.
- A budget is only released when the benefit is measurable later.
When Not
When a workflow is brand new and there is no before, no classic baseline can be captured. Then you better define a target range and observe the first weeks. And when a decision is made for strategic reasons anyway, an elaborate measurement up front is not an end in itself but only effort without effect on the outcome.
Industries That Use It
- Logistics and freight forwarding: capture lead times and error rates in shipment handling before the build, so an assistant competes against real values.
- Insurance: record processing time and query rate for claims as a starting value before automation kicks in.
- Human resources: measure the time from an application arriving to a reply, to make later improvement provable.
- Manufacturing: document scrap and rework rates per shift before an AI-supported inspection step is introduced.
Typical Use Cases
- Capturing current lead times from existing log data before the workshop.
- A short measurement window that logs error rate and effort per case.
- Setting two to three metrics that describe the process honestly.
- A comparison run in which human and prototype handle the same cases.
- Documenting cost per case as the basis for the later ROI calculation.
Decision Guide
| Signal | Baseline fits | Alternative |
|---|---|---|
| Benefit should be provable later | capture baseline before the build | no clean comparison without a starting value |
| Process is entirely new | rather not | define a target range and observe |
| Numbers exist in logs | derive baseline from data | manual sample over a short window |
| Decision is politically set | measurement optional | save effort, check the effect |
Example
A freight forwarding team with long response times on shipment queries first measures the average handling time and the number of follow-up questions over two weeks. This starting value goes into the workshop where a prototype is built. The prototype then handles the same query types, and only the comparison with the baseline shows whether it pays off. This describes the method, not an asserted client metric.
Service Connection
The starting value usually emerges in the AI workshop, before an AI automation or AI implementation is measured against it.
Sources
This framing describes common practice in measuring success in AI projects. Which metrics make sense and how they are captured depends on the concrete process and the available data and should be checked there.