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
- Estimate current average time.
- Measure AI-assisted average time after review.
- Calculate time saved per instance.
- Multiply by realistic volume.
- 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?