# 90-Day Proof Phase Model

This is an example model for evaluating AI adoption in a healthcare operations setting. It is educational and must be reviewed locally before use.

## Goal

Demonstrate whether a deliberate AI enablement program can produce measurable learning, productivity, quality, and risk-awareness improvements within one quarter.

## Operating Principles

- **Time-boxed:** 13 weeks.
- **Bounded:** two or three generic pilot examples.
- **Measured:** baseline before pilot use and compare after adoption.
- **Low-risk:** use synthetic or de-identified material in reusable examples.
- **Human-reviewed:** AI outputs are drafts, not final decisions.

## Workstreams

| Workstream | Example owner role | Primary deliverable |
|---|---|---|
| Governance model | Program Lead + local reviewers | Decision model and review checklist |
| Literacy | Program Lead | Tier 1 and Tier 2 learning materials |
| Ticket drafting example | Operations champion | Reviewed assistant pattern and usage data |
| Training content example | Training champion | Reviewed assistant pattern and usage data |
| Measurement | Program Lead | Weekly KPI summary |

## Phase 0: Stand-Up

- Select a small voluntary cohort.
- Define data boundaries and example-only use cases.
- Capture baseline time, quality, and confidence metrics.
- Confirm local review requirements.
- Schedule lightweight feedback sessions.

## Phase 1: Build And Teach

- Deliver introductory AI literacy content.
- Build or simulate the example assistants.
- Test against synthetic examples.
- Teach review habits and output verification.
- Collect early feedback.

## Phase 2: Measure And Iterate

- Compare output time and quality against baseline.
- Record adoption barriers and support needs.
- Refine examples and guidance.
- Identify where local review would be required for broader use.

## Phase 3: Decide

At the end of 90 days, leadership can decide whether to expand, pause, or redesign the program based on evidence.

Example success criteria:

- Two example assistant patterns tested with synthetic or de-identified inputs.
- One cohort completes basic AI literacy training.
- A practical data-handling checklist is understood by participants.
- Measurable improvement appears in at least one workflow.
- No public claims are made about production readiness or institutional endorsement.