Enterprise Rollout Plan
A phased rollout de-risks enterprise Claude adoption by validating each layer before expanding to the next. Each phase has clear entry criteria, deliverables, and exit gates -- preventing the most common failure: expanding too fast before foundations are solid.
Rollout Timeline
KPI Framework
Productivity Metrics
Time saved per task type -- measure before and after. Tasks completed per hour. Time from request to first draft. These are the most convincing metrics for sceptical stakeholders at board level.
Financial Metrics
Total API cost vs value delivered. Cost per completed task. ROI as (value generated minus Claude cost) divided by Claude cost. Target a 3-10x ROI to justify expansion to the next phase.
Adoption Metrics
Monthly active users vs allocated seats. Session frequency per user. Feature adoption rate -- are users using advanced features or only basic chat? NPS from quarterly user surveys.
Quality and Safety
Human override rate -- what percentage of Claude outputs are edited before use, a proxy for quality. Compliance incidents. Hallucination rate on validated test sets. User-reported error rate.
RACI -- Roles and Responsibilities
| Activity | Exec Sponsor | CoE Lead | Engineering | Legal/Security | Dept Head |
|---|---|---|---|---|---|
| Strategy and vision | R/A | C | I | I | I |
| Use case approval | A | R | C | R | C |
| Policy authoring | A | R | C | R | I |
| Technical deployment | I | A | R | C | I |
| Employee training | I | R/A | C | I | R |
| Cost management | A | C | R | I | C |
| ROI reporting | A | R | C | I | R |
R = Responsible / A = Accountable / C = Consulted / I = Informed
You have completed all Claude modules including the full enterprise onboarding programme.
ShopMate -- KPI Dashboard
# scripts/shopmate_kpi_report.py -- Maya's monthly report for investors import json from pathlib import Path from collections import defaultdict logs = [json.loads(l) for l in Path("logs/shopmate_audit.jsonl").read_text().splitlines() if l] by_feature = defaultdict(lambda:{"calls":0,"cost":0}) for log in logs: f = log["feature"] by_feature[f]["calls"] += 1 by_feature[f]["cost"] += log["cost_usd"] total_cost = sum(v["cost"] for v in by_feature.values()) print("=== ShopMate Monthly KPI Report ===") print(f" {'Feature':<30} {'Calls':>7} {'Cost':>10} {'$/call':>8}") print("-" * 58) for feat, s in sorted(by_feature.items(), key=lambda x: -x[1]["cost"]): print(f"{feat:<30} {s['calls']:>7,} ${s['cost']:>9.2f} ${s['cost']/s['calls']:>7.4f}") print(f" Total AI cost this month: ${total_cost:.2f}") # Business impact estimates desc_calls = by_feature.get("product_description",{}).get("calls",0) chat_calls = by_feature.get("customer_chat",{}).get("calls",0) hours_saved = (desc_calls * 8 / 60) + (chat_calls * 5 / 60) print(f" Estimated staff hours saved: {hours_saved:.0f} hrs") print(f"Cost per hour saved: ${total_cost/max(hours_saved,1):.2f}")