# Business Case Model

This model helps estimate whether an AI adoption program is worth expanding. It is educational and not a financial claim.

## Framing

The primary investment is not the AI tool itself. It is the program effort required to turn available tools into reliable, governed, and measurable team practices.

## Proof Phase Cost Categories

| Cost category | Example estimate approach |
|---|---|
| Program lead time | Percentage of one staff member's quarter |
| Participant time | Cohort size multiplied by training and pilot time |
| Reviewer time | Local legal, privacy, security, compliance, or platform review as needed |
| Measurement time | Baseline capture, weekly reporting, and final summary |
| Tooling | Existing enterprise tools where available; no assumption of new procurement |

## Benefit Categories

### Productivity

Measure time saved in repetitive drafting or synthesis tasks. Use conservative baselines and require human review.

### Quality

Measure completeness, consistency, missing information, and rework.

### Learning

Measure confidence, appropriate use, verification behavior, and ability to identify uncertain data scenarios.

### Risk Awareness

Measure whether participants can recognize when a workflow needs local review before AI use.

## Example Return Model

For each workflow:

1. Estimate current average time.
2. Measure AI-assisted average time after review.
3. Calculate time saved per instance.
4. Multiply by realistic volume.
5. Discount for review, training, and maintenance effort.

This framework should produce a directional case for further evaluation, not a guarantee of return.

## Expansion Decision

A proof phase is useful if it creates enough evidence to answer:

- Which workflows are good candidates?
- Which workflows are not appropriate?
- What review steps are required?
- What training gaps remain?
- What support model would expansion require?