Afrexai Ai Spend Audit

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)

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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