Tencent SkillHub · Productivity

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.

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

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: README.md, SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: 1kalin
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 10 sections Open source page

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

Step 1: Inventory Every AI Line Item

Map all AI spending across these categories: CategoryExamplesTypical 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)

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 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

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 — AI operations context packs for business teams. Run the AI Revenue Calculator to find your biggest automation opportunities.

Category context

Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.

Source: Tencent SkillHub

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Package contents

Included in package

2 Docs

SKILL.md Primary doc
README.md Docs