> ## Documentation Index
> Fetch the complete documentation index at: https://irisdocs.prescientlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Building ROI from Iris Evidence

> Use Iris evidence to create transparent expected-value assumptions without presenting diagnostic estimates as realised savings.

Iris evidence can strengthen the baseline of a business case by showing how the current process behaves.

## Build from the current state

Use evidence such as:

* activity volume;
* time spent;
* rework frequency;
* process delay;
* exception volume;
* stakeholder estimates;
* structured event data where available.

## Convert evidence into assumptions

For each opportunity estimate:

1. current baseline;
2. portion of the workflow affected;
3. expected improvement;
4. adoption;
5. implementation cost;
6. ongoing cost.

## Keep confidence visible

Label assumptions high, medium or low confidence and record their source.

## Avoid false precision

If a stakeholder says a task takes “about ten minutes,” do not model 10.00 minutes as exact truth. Use an appropriate range or sensitivity.

## Important boundary

The diagnostic supports an expected case. Realised ROI can only be established after implementation and measurement.

<Note>
  If Iris contains a dedicated editable ROI interface, the exact controls and fields are **\[PRODUCT DETAIL TO CONFIRM]**.
</Note>