Private AI, with a clear business case.

Executive presentation structure. Replace the marked fields with verified facts from the assessment.

The workflow worth changing

[Workflow and accountable owner]

What slows the team down today?
Which measured baseline shows the impact?
Why is the information sensitive?

The value hypothesis

[Expected improvement, baseline, and measurement method]

Present projected benefits as a hypothesis. Separate staff time, quality, and risk reduction; do not turn unmeasured estimates into ROI claims.

What private AI changes

Keep the agreed processing boundary under customer control. Answer from approved sources, apply permissions before retrieval, and expose citations for human review.

The architecture and its limits

[Selected deployment model and data flow]

Show identity, retrieval, inference, logging, and maintenance boundaries. Name external dependencies and who can access them. Do not imply an air gap without verified isolation.

The pilot scorecard

[Accuracy, citation, denied-access, latency, cost, and workflow targets]

Explain the evaluation set, domain reviewers, sample size, and conditions that would stop the project.

The investment and responsibilities

[Commercial scope, hardware, operations, owners, and assumptions]

Separate one-time deployment from recurring operation. Include review effort, updates, backup, recovery, and support.

The decision requested

[Approve assessment / approve bounded pilot / revise scope / stop]

Confirm the owner, budget, data authorization, acceptance criteria, review date, and next action.