AI Spend Audit

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:

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
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%
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
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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