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    "sections": [
      {
        "title": "AI Spend Audit",
        "body": "Audit your company's AI spending — find waste, measure ROI, and right-size your tool stack."
      },
      {
        "title": "When to Use",
        "body": "Quarterly AI budget reviews\nBefore renewing AI tool subscriptions\nWhen AI spend exceeds 3% of revenue without clear ROI\nEvaluating build vs buy decisions for AI capabilities"
      },
      {
        "title": "Step 1: Inventory Every AI Line Item",
        "body": "Map all AI spending across these categories:\n\nCategoryExamplesTypical WasteFoundation ModelsOpenAI, Anthropic, Google API keys40-60% (unused capacity, wrong model tier)SaaS with AISalesforce Einstein, HubSpot AI, Notion AI30-50% (features enabled but unused)Custom DevelopmentInternal ML teams, fine-tuning, RAG pipelines25-45% (duplicate efforts, over-engineering)InfrastructureGPU instances, vector DBs, embedding compute35-55% (over-provisioned, always-on dev instances)Data & TrainingLabeling services, training data, synthetic data20-40% (one-time costs recurring unnecessarily)"
      },
      {
        "title": "Step 2: Score Each Tool (0-100)",
        "body": "Usage Score (0-30)\n\n0: Nobody uses it\n10: <25% of licensed users active\n20: 25-75% active\n30: >75% active, daily use\n\nROI Score (0-40)\n\n0: No measurable business impact\n10: Saves time but no revenue/cost link\n20: Measurable cost reduction (<2x spend)\n30: Clear ROI (2-5x spend)\n40: High ROI (>5x spend)\n\nReplaceability Score (0-30)\n\n0: Commodity (10+ alternatives at lower cost)\n10: Some alternatives exist\n20: Few alternatives, moderate switching cost\n30: Irreplaceable, deep integration\n\nAction Thresholds:\n\nScore 0-30: CUT — cancel immediately\nScore 31-50: REVIEW — renegotiate or find alternative\nScore 51-70: OPTIMIZE — right-size tier/usage\nScore 71-100: KEEP — monitor quarterly"
      },
      {
        "title": "Step 3: Model Cost Optimization",
        "body": "For every API-based AI tool, check:\n\nModel Selection: Are you using GPT-4 where GPT-3.5 suffices? Claude Opus where Sonnet works?\n\nRule: Use the cheapest model that meets quality threshold\nTest: Run 100 production queries through cheaper model, measure quality delta\n\n\n\nCaching: Are you re-processing identical or similar queries?\n\nSemantic cache can cut 20-40% of API calls\nExact-match cache catches another 5-15%\n\n\n\nBatch vs Real-time: Which requests actually need sub-second response?\n\nBatch processing is 50% cheaper on most providers\nQueue non-urgent requests for batch windows\n\n\n\nToken Optimization:\n\nTrim system prompts (every token costs money at scale)\nUse structured output to reduce response tokens\nImplement max_tokens limits per use case"
      },
      {
        "title": "Step 4: Vendor Consolidation",
        "body": "Map overlapping capabilities:\n\nCurrent State → Target State\n─────────────────────────────────────────\nChatGPT Teams + Claude Pro + Gemini → Pick ONE primary + ONE backup\nJasper + Copy.ai + ChatGPT for content → Single content tool\n3 different vector databases → Consolidate to 1\nInternal embeddings + OpenAI embeddings → Standardize on one\n\nConsolidation savings: Typically 25-40% of total AI spend."
      },
      {
        "title": "Step 5: Build the Audit Report",
        "body": "AI SPEND AUDIT — [Company Name] — [Quarter/Year]\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\nTotal AI Spend: $___/month ($___/year)\nAI Spend as % Revenue: ___%\nIndustry Benchmark: 2-5% (early adopter) / 0.5-2% (mainstream)\n\nWASTE IDENTIFIED\n├── Unused licenses: $___/month\n├── Over-provisioned infra: $___/month\n├── Model tier downgrades: $___/month\n├── Vendor consolidation: $___/month\n└── TOTAL RECOVERABLE: $___/month ($___/year)\n\nACTIONS\n┌─ CUT (Score 0-30): [list tools]\n├─ REVIEW (Score 31-50): [list tools]\n├─ OPTIMIZE (Score 51-70): [list tools]\n└─ KEEP (Score 71-100): [list tools]\n\n90-DAY PLAN\nWeek 1-2: Cancel CUT items, begin REVIEW negotiations\nWeek 3-4: Implement model downgrades and caching\nWeek 5-8: Vendor consolidation migration\nWeek 9-12: Measure savings, establish ongoing monitoring"
      },
      {
        "title": "Company Size Benchmarks (2026)",
        "body": "Company SizeTypical AI SpendTypical WasteRecoverable10-25 employees$2K-$8K/mo35-50%$700-$4K/mo25-50 employees$8K-$25K/mo30-45%$2.4K-$11K/mo50-200 employees$25K-$80K/mo25-40%$6K-$32K/mo200-500 employees$80K-$300K/mo20-35%$16K-$105K/mo500+ employees$300K-$1M+/mo15-30%$45K-$300K/mo"
      },
      {
        "title": "Red Flags",
        "body": "AI spend growing faster than revenue (unsustainable)\nMore than 3 overlapping tools in same category\nNo usage tracking on AI SaaS licenses\nGPU instances running 24/7 for dev/test workloads\nPaying for enterprise tiers with startup-level usage\nNo A/B testing between model tiers\n\"Innovation budget\" with no success metrics"
      },
      {
        "title": "Industry Adjustments",
        "body": "SaaS/Tech: Higher AI spend acceptable (5-8%) if it's in the product\nProfessional Services: Focus on billable hour impact — $1 AI spend should save $5+ in labor\nManufacturing: AI spend should tie to defect reduction or throughput gains\nHealthcare: Compliance costs inflate spend 20-30% — factor in before judging waste\nFinancial Services: Model risk management adds 15-25% overhead — legitimate cost\nEcommerce: Measure AI spend per order — should decrease as volume scales\n\nBuilt by AfrexAI — AI operations context packs for business teams. Run the AI Revenue Calculator to find your biggest automation opportunities."
      }
    ],
    "body": "AI Spend Audit\n\nAudit your company's AI spending — find waste, measure ROI, and right-size your tool stack.\n\nWhen to Use\nQuarterly AI budget reviews\nBefore renewing AI tool subscriptions\nWhen AI spend exceeds 3% of revenue without clear ROI\nEvaluating build vs buy decisions for AI capabilities\nThe Framework\nStep 1: Inventory Every AI Line Item\n\nMap all AI spending across these categories:\n\nCategory\tExamples\tTypical Waste\nFoundation Models\tOpenAI, Anthropic, Google API keys\t40-60% (unused capacity, wrong model tier)\nSaaS with AI\tSalesforce Einstein, HubSpot AI, Notion AI\t30-50% (features enabled but unused)\nCustom Development\tInternal ML teams, fine-tuning, RAG pipelines\t25-45% (duplicate efforts, over-engineering)\nInfrastructure\tGPU instances, vector DBs, embedding compute\t35-55% (over-provisioned, always-on dev instances)\nData & Training\tLabeling services, training data, synthetic data\t20-40% (one-time costs recurring unnecessarily)\nStep 2: Score Each Tool (0-100)\n\nUsage Score (0-30)\n\n0: Nobody uses it\n10: <25% of licensed users active\n20: 25-75% active\n30: >75% active, daily use\n\nROI Score (0-40)\n\n0: No measurable business impact\n10: Saves time but no revenue/cost link\n20: Measurable cost reduction (<2x spend)\n30: Clear ROI (2-5x spend)\n40: High ROI (>5x spend)\n\nReplaceability Score (0-30)\n\n0: Commodity (10+ alternatives at lower cost)\n10: Some alternatives exist\n20: Few alternatives, moderate switching cost\n30: Irreplaceable, deep integration\n\nAction Thresholds:\n\nScore 0-30: CUT — cancel immediately\nScore 31-50: REVIEW — renegotiate or find alternative\nScore 51-70: OPTIMIZE — right-size tier/usage\nScore 71-100: KEEP — monitor quarterly\nStep 3: Model Cost Optimization\n\nFor every API-based AI tool, check:\n\nModel Selection: Are you using GPT-4 where GPT-3.5 suffices? Claude Opus where Sonnet works?\n\nRule: Use the cheapest model that meets quality threshold\nTest: Run 100 production queries through cheaper model, measure quality delta\n\nCaching: Are you re-processing identical or similar queries?\n\nSemantic cache can cut 20-40% of API calls\nExact-match cache catches another 5-15%\n\nBatch vs Real-time: Which requests actually need sub-second response?\n\nBatch processing is 50% cheaper on most providers\nQueue non-urgent requests for batch windows\n\nToken Optimization:\n\nTrim system prompts (every token costs money at scale)\nUse structured output to reduce response tokens\nImplement max_tokens limits per use case\nStep 4: Vendor Consolidation\n\nMap overlapping capabilities:\n\nCurrent State → Target State\n─────────────────────────────────────────\nChatGPT Teams + Claude Pro + Gemini → Pick ONE primary + ONE backup\nJasper + Copy.ai + ChatGPT for content → Single content tool\n3 different vector databases → Consolidate to 1\nInternal embeddings + OpenAI embeddings → Standardize on one\n\n\nConsolidation savings: Typically 25-40% of total AI spend.\n\nStep 5: Build the Audit Report\nAI SPEND AUDIT — [Company Name] — [Quarter/Year]\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\nTotal AI Spend: $___/month ($___/year)\nAI Spend as % Revenue: ___%\nIndustry Benchmark: 2-5% (early adopter) / 0.5-2% (mainstream)\n\nWASTE IDENTIFIED\n├── Unused licenses: $___/month\n├── Over-provisioned infra: $___/month\n├── Model tier downgrades: $___/month\n├── Vendor consolidation: $___/month\n└── TOTAL RECOVERABLE: $___/month ($___/year)\n\nACTIONS\n┌─ CUT (Score 0-30): [list tools]\n├─ REVIEW (Score 31-50): [list tools]\n├─ OPTIMIZE (Score 51-70): [list tools]\n└─ KEEP (Score 71-100): [list tools]\n\n90-DAY PLAN\nWeek 1-2: Cancel CUT items, begin REVIEW negotiations\nWeek 3-4: Implement model downgrades and caching\nWeek 5-8: Vendor consolidation migration\nWeek 9-12: Measure savings, establish ongoing monitoring\n\nCompany Size Benchmarks (2026)\nCompany Size\tTypical AI Spend\tTypical Waste\tRecoverable\n10-25 employees\t$2K-$8K/mo\t35-50%\t$700-$4K/mo\n25-50 employees\t$8K-$25K/mo\t30-45%\t$2.4K-$11K/mo\n50-200 employees\t$25K-$80K/mo\t25-40%\t$6K-$32K/mo\n200-500 employees\t$80K-$300K/mo\t20-35%\t$16K-$105K/mo\n500+ employees\t$300K-$1M+/mo\t15-30%\t$45K-$300K/mo\nRed Flags\nAI spend growing faster than revenue (unsustainable)\nMore than 3 overlapping tools in same category\nNo usage tracking on AI SaaS licenses\nGPU instances running 24/7 for dev/test workloads\nPaying for enterprise tiers with startup-level usage\nNo A/B testing between model tiers\n\"Innovation budget\" with no success metrics\nIndustry Adjustments\nSaaS/Tech: Higher AI spend acceptable (5-8%) if it's in the product\nProfessional Services: Focus on billable hour impact — $1 AI spend should save $5+ in labor\nManufacturing: AI spend should tie to defect reduction or throughput gains\nHealthcare: Compliance costs inflate spend 20-30% — factor in before judging waste\nFinancial Services: Model risk management adds 15-25% overhead — legitimate cost\nEcommerce: Measure AI spend per order — should decrease as volume scales\n\nBuilt by AfrexAI — AI operations context packs for business teams. Run the AI Revenue Calculator to find your biggest automation opportunities."
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