The executive case for Claude
A board-ready, one-page case for a governed, company-wide Claude rollout: the opportunity, the controls, the ROI math, and a 90-day plan.
3 min read · Current as of
This is the one-page executive pitch. Fill in [bracketed] values with your numbers. Current as of July 2026. Verify model details and pricing at platform.claude.com / claude.com/pricing before formal use.
The opportunity
Claude (by Anthropic) is a frontier AI assistant that helps every team work faster and better: drafting, analyzing, summarizing, extracting data, researching, and writing and maintaining code. The technology is mature, the controls are enterprise-grade, and our competitors are already compounding these gains. A governed, company-wide rollout lets us capture the productivity upside while keeping our data and compliance posture firmly in our control.
Three high-value use cases at [Company]
- [Function A (e.g., Legal/Procurement)]: [e.g., contract & document review: summarize, extract obligations, draft redlines]. Est. [X] hrs/week saved per reviewer.
- [Function B (e.g., Sales/Marketing)]: [e.g., proposals, RFPs, and content drafted from our own templates]. Faster cycle time, more consistent quality.
- [Function C (e.g., Engineering/Support)]: [e.g., Claude Code for bug fixes, tests, and reviews; faster ticket resolution]. Measurable throughput gains.
What we’d deploy
| Surface | Who | Value |
|---|---|---|
| claude.ai (web/desktop/mobile) | Everyone, no coding | Chat + Projects for everyday knowledge work |
| Claude Code | Engineering | Agent that edits code, runs tests, opens PRs |
| Developer Platform / API | Builders | Claude embedded in our own internal tools |
Delivered on a Team or Enterprise plan with central admin, SSO, and spend controls.
The numbers (illustrative, replace with our data)
| Item | Assumption |
|---|---|
| Pilot size | [50] users |
| Cost | ~$[20 to 30]/user/month seat + metered usage (capped) |
| Time saved | [3] hrs/user/week × $[60] loaded hourly cost |
| Est. monthly value | ≈ $[36,000] vs. est. cost ≈ $[3,000 to 5,000] → [~8×] return |
Cost is controllable by design, and largely an engineering choice, not a fixed rate. The levers we’ll pull, in order of impact:
- Send fewer tokens. Retrieve the relevant slice instead of pasting whole documents or repositories into every request: the biggest lever at scale, because input volume drives the bill.
- Right-size the model. Cheaper tiers for routine work, the frontier model only for hard reasoning; tune reasoning depth to the task.
- Cache and batch. Prompt caching cuts repeated context to a fraction of full price, and the Batch API (−50%) halves cost on bulk, non-urgent jobs, and the two stack.
- Cap and attribute. Per-seat + metered usage with hard spend caps and per-team usage reporting, so spend is bounded and traceable, not a surprise invoice.
Governed by design (why this is safe)
- No training on our content by default (Team & Enterprise).
- SSO, domain capture, JIT provisioning, role-based access, and org/user spend limits.
- Enterprise adds: audit logs, Compliance API, customer-managed encryption keys, US-only inference, and Zero Data Retention options for sensitive workloads.
90-day plan
| Phase | Weeks | What happens |
|---|---|---|
| Pitch | 1 to 2 | Sponsor + budget secured; Security approves permitted data classes |
| Pilot | 3 to 6 | Train 1 to 2 departments; capture before/after metrics |
| Prove | 7 to 10 | Expand; stand up champions + support |
| Scale | 11 to 13 | Report measured impact; decide on company-wide rollout |
The ask
A named executive sponsor, a [50]-seat pilot budget for [one quarter], and Security’s approval of permitted data classes. I will own delivery, training, and impact reporting.
Prepared by [Name] · [Title] · [Date] · [email]