Skill: 1kalin/afrexai-ai-spend-audit (version 1.0.0)

# 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: 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 --- *Built by [AfrexAI](https://afrexai-cto.github.io/context-packs/) — AI operations context packs for business teams. Run the [AI Revenue Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/) to find your biggest automation opportunities.*


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