Agent Skills
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ai-roi-audit
GitHub针对企业AI支出进行ROI审计，替代供应商营销数据。通过基线对比、反事实分析和质量评估，生成每工具的保留/整合/削减建议及隐藏成本账本，为CFO提供可验证的决策依据。
触发场景
CFO询问AI工具投资回报
续订或谈判AI合同时
合并重叠的AI订阅时
制定下一年度AI支出测量计划前
安装
npx skills add mohitagw15856/pm-claude-skills --skill ai-roi-audit -g -y
SKILL.md
Frontmatter
{
"name": "ai-roi-audit",
"description": "Audit whether the organisation's AI spend actually paid — measured against baselines, not vendor math or vibes. Use when a CFO asks what the AI tools returned, when renewing AI contracts, when consolidating overlapping AI subscriptions, or to build the measurement plan before the next spend. Produces an ROI audit with per-tool verdicts (keep\/consolidate\/cut), the honest-measurement method behind each number, and a baseline plan for whatever can't be scored yet. To forecast ROI before an investment use roi-estimator; this skill measures what already happened."
}
AI ROI Audit Skill
Every org now spends real money on AI tools, and most justify it with adoption counts ("80% weekly active!") — which measure enthusiasm, not return. This skill audits what the spend returned, using methods that survive a sceptical CFO: baselines, counterfactuals, and quality deltas, with "we can't know yet" said out loud where it's true.
What This Skill Produces
- A per-tool verdict table: keep / consolidate / renegotiate / cut, each with its evidence
- The measurement behind each number — method, baseline, confidence — so the audit is checkable
- A hidden-cost ledger (the part vendor ROI decks omit)
- A baseline plan for every "unknown", so next year's audit has data
Required Inputs
Ask for (if not already provided):
- The AI tool inventory with costs: subscriptions, API spend, seats — and utilisation if known
- What each tool was bought to do (the promised outcome, from the original business case if it exists)
- Available evidence: usage data, before/after metrics, time studies, quality data, anecdotes (labelled as anecdotes)
- The decision at stake: renewal? consolidation? budget defence? (calibrates depth)
Audit Method
- Reconstruct the promise. Per tool: what outcome justified the purchase — time saved, quality improved, headcount avoided, revenue created? A tool without a stated outcome gets audited against the best-fit guess, flagged as retrofitted.
- Score with the strongest method the evidence allows, in descending order of credibility:
- Natural experiment — teams/periods with vs without the tool, same work (best available in most orgs)
- Before/after with baseline — the metric before adoption vs after, seasonality noted
- Task-level time study — 10-20 real tasks timed with/without (cheap to run during the audit — do it rather than skip to tier 4)
- Structured self-report — users estimating time saved, discounted (self-reported AI savings run ~2× actuals; say so) Never present a tier-4 number with tier-1 confidence. Every figure carries its method and a confidence label.
- Count the hidden costs. Verification time (humans checking AI output), rework from AI errors that shipped, licence sprawl (seats bought > seats active), integration/prompt-maintenance time, and training time. These come off the gross benefit — an ROI audit that skips them is a vendor deck.
- Convert honestly. Time saved → money only via a stated loaded rate and a stated assumption about what the time became (more output? earlier finishes? — different values). "Saved 400 hours" that nobody redeployed is capacity, not cash; label which one you're claiming.
- Verdict per tool. Keep (positive with tier ≤2 evidence) · Consolidate (positive but duplicative — name the overlap) · Renegotiate (positive but mispriced vs utilisation) · Cut (negative or unmeasurable after a fair baseline attempt). Ties break toward the tool with a measurement plan.
- Leave the audit better than you found it. Every "unknown" verdict gets a baseline plan: the metric, how it's instrumented, and the review date. The first audit is mostly this; that's a finding, not a failure.
Output Format
AI ROI Audit: [org/team] — [period]
Total AI spend: [sum] · Verdict summary: [n keep / n consolidate / n renegotiate / n cut / n unknown]
| Tool | Annual cost | Promised outcome | Measured return | Method (tier) | Confidence | Verdict |
|---|
Hidden-cost ledger: [verification, rework, sprawl, maintenance — quantified where possible, listed where not]
The math shown: [for each material number: baseline, method, conversion assumptions]
Baseline plan for the unknowns: [tool → metric → instrumentation → review date]
One-paragraph CFO summary: [net position, the two decisions to make, and what will be measurable by next audit]
Quality Checks
- Every figure carries its measurement method and confidence — no naked numbers
- Self-reported savings are discounted and labelled as self-reported
- Hidden costs appear as line items, not a caveat sentence
- Time→money conversions state the loaded rate and the capacity-vs-cash claim
- Every "unknown" has a baseline plan with a date — the audit compounds
Anti-Patterns
- Do not use adoption or engagement as return — usage is a cost signal until an outcome moves
- Do not accept vendor ROI calculators as evidence — reconstruct from your own data or score it unknown
- Do not average across tools into one triumphant number — the verdict is per-tool or it decides nothing
- Do not claim headcount avoidance without the counterfactual hiring plan that was actually cancelled
- Do not punish honest "unknowns" by cutting them reflexively — cut requires a failed measurement attempt, not a missing one
版本历史
- a38bc30 当前 2026-07-05 11:11
