Ai Spend Audit

Audit and optimize your company's AI spending by identifying waste, measuring ROI, right-sizing tool tiers, and consolidating vendors for cost savings.

Security Scan

This is an instruction-only audit framework for AI spend that is internally consistent with its stated purpose and contains no code, installs, or built-in credential requests.

Purpose & Capability

The name and description promise an AI spend audit; the SKILL.md provides a detailed, plausible framework (inventory, scoring, model optimization, vendor consolidation, reporting) that matches that purpose. There are no unrelated dependencies, binaries, or config requirements declared.

Instruction Scope

The instructions are largely advisory and procedural. They do recommend actions that, in practice, require access to billing data, API-based models, and production queries (e.g., 'run 100 production queries through a cheaper model' and mapping API-based tools). The skill itself does not include code to perform these operations nor does it request credentials — implementers will need to supply data and keys. This is scope-appropriate but important to note: carrying out the recommendations will require sensitive inputs from the user.

Install Mechanism

No install spec and no code files are included; nothing is written to disk and there are no downloaded binaries. This is the lowest-risk installation profile.

Credentials

The skill declares no required environment variables or credentials, which is appropriate for an instruction-only framework. However, several suggested checks implicitly require access to API keys, billing exports, or production queries. Users must provide those credentials or data to perform the audit; the skill does not attempt to obtain them automatically.

Persistence & Privilege

The skill does not request persistent presence (always:false), does not modify other skills, and contains no install steps that would alter agent/system configuration. Normal autonomous invocation remains possible but is not elevated by the skill itself.

Assessment

This skill is an advisory playbook (no code), so installing it itself is low-risk. Before executing an audit guided by this skill: 1) Do not paste long-lived API keys, passwords, or raw billing exports into chats—use read-only or scoped/ephemeral keys where possible. 2) Run any model-comparison tests on anonymized or synthetic data or in a staging environment to avoid leaking PII. 3) Provide the agent only the minimum data needed (e.g., aggregated billing exports, usage reports) rather than full credentials when possible. 4) Verify the AfrexAI links and the publisher if you need provenance or commercial support. 5) If you delegate execution to the agent, explicitly approve any concrete actions that would cancel subscriptions, change tiers, or share vendor credentials. Following these precautions will let you use the framework without exposing unnecessary secrets or production data.

Audit your company's AI spending — find waste, measure ROI, and right-size your tool stack.

When to Use

• Quarterly AI budget reviews
• Before renewing AI tool subscriptions
• When AI spend exceeds 3% of revenue without clear ROI
• Evaluating build vs buy decisions for AI capabilities

The Framework

Step 1: Inventory Every AI Line Item

Map all AI spending across these categories:

Category	Examples	Typical Waste
Foundation Models	OpenAI, Anthropic, Google API keys	40-60% (unused capacity, wrong model tier)
SaaS with AI	Salesforce Einstein, HubSpot AI, Notion AI	30-50% (features enabled but unused)
Custom Development	Internal ML teams, fine-tuning, RAG pipelines	25-45% (duplicate efforts, over-engineering)
Infrastructure	GPU instances, vector DBs, embedding compute	35-55% (over-provisioned, always-on dev instances)
Data & Training	Labeling services, training data, synthetic data	20-40% (one-time costs recurring unnecessarily)

Step 2: Score Each Tool (0-100)

Usage Score (0-30)

• 0: Nobody uses it
• 10: <25% of licensed users active
• 20: 25-75% active
• 30: >75% active, daily use

ROI Score (0-40)

• 0: No measurable business impact
• 10: Saves time but no revenue/cost link
• 20: Measurable cost reduction (<2x spend)
• 30: Clear ROI (2-5x spend)
• 40: High ROI (>5x spend)

Replaceability Score (0-30)

• 0: Commodity (10+ alternatives at lower cost)
• 10: Some alternatives exist
• 20: Few alternatives, moderate switching cost
• 30: Irreplaceable, deep integration

Action Thresholds:

• Score 0-30: CUT — cancel immediately
• Score 31-50: REVIEW — renegotiate or find alternative
• Score 51-70: OPTIMIZE — right-size tier/usage
• Score 71-100: KEEP — monitor quarterly

Step 3: Model Cost Optimization

For every API-based AI tool, check:

1. Model Selection: Are you using GPT-4 where GPT-3.5 suffices? Claude Opus where Sonnet works?

   • Rule: Use the cheapest model that meets quality threshold
   • Test: Run 100 production queries through cheaper model, measure quality delta

2. Caching: Are you re-processing identical or similar queries?

   • Semantic cache can cut 20-40% of API calls
   • Exact-match cache catches another 5-15%

3. Batch vs Real-time: Which requests actually need sub-second response?

   • Batch processing is 50% cheaper on most providers
   • Queue non-urgent requests for batch windows

4. Token Optimization:

   • Trim system prompts (every token costs money at scale)
   • Use structured output to reduce response tokens
   • Implement max_tokens limits per use case

Step 4: Vendor Consolidation

Map overlapping capabilities:

Current State → Target State
─────────────────────────────────────────
ChatGPT Teams + Claude Pro + Gemini → Pick ONE primary + ONE backup
Jasper + Copy.ai + ChatGPT for content → Single content tool
3 different vector databases → Consolidate to 1
Internal embeddings + OpenAI embeddings → Standardize on one

Consolidation savings: Typically 25-40% of total AI spend.

Step 5: Build the Audit Report

AI SPEND AUDIT — [Company Name] — [Quarter/Year]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Total AI Spend: $___/month ($___/year)
AI Spend as % Revenue: ___%
Industry Benchmark: 2-5% (early adopter) / 0.5-2% (mainstream)

WASTE IDENTIFIED
├── Unused licenses: $___/month
├── Over-provisioned infra: $___/month
├── Model tier downgrades: $___/month
├── Vendor consolidation: $___/month
└── TOTAL RECOVERABLE: $___/month ($___/year)

ACTIONS
┌─ CUT (Score 0-30): [list tools]
├─ REVIEW (Score 31-50): [list tools]
├─ OPTIMIZE (Score 51-70): [list tools]
└─ KEEP (Score 71-100): [list tools]

90-DAY PLAN
Week 1-2: Cancel CUT items, begin REVIEW negotiations
Week 3-4: Implement model downgrades and caching
Week 5-8: Vendor consolidation migration
Week 9-12: Measure savings, establish ongoing monitoring

Company Size Benchmarks (2026)

Company Size	Typical AI Spend	Typical Waste	Recoverable
10-25 employees	$2K-$8K/mo	35-50%	$700-$4K/mo
25-50 employees	$8K-$25K/mo	30-45%	$2.4K-$11K/mo
50-200 employees	$25K-$80K/mo	25-40%	$6K-$32K/mo
200-500 employees	$80K-$300K/mo	20-35%	$16K-$105K/mo
500+ employees	$300K-$1M+/mo	15-30%	$45K-$300K/mo

Red Flags

• AI spend growing faster than revenue (unsustainable)
• More than 3 overlapping tools in same category
• No usage tracking on AI SaaS licenses
• GPU instances running 24/7 for dev/test workloads
• Paying for enterprise tiers with startup-level usage
• No A/B testing between model tiers
• "Innovation budget" with no success metrics

Industry Adjustments

• SaaS/Tech: Higher AI spend acceptable (5-8%) if it's in the product
• Professional Services: Focus on billable hour impact — $1 AI spend should save $5+ in labor
• Manufacturing: AI spend should tie to defect reduction or throughput gains
• Healthcare: Compliance costs inflate spend 20-30% — factor in before judging waste
• Financial Services: Model risk management adds 15-25% overhead — legitimate cost
• Ecommerce: Measure AI spend per order — should decrease as volume scales

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