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The AI Tool Audit Checklist: How to Find What Your Team Actually Uses

You think your team uses 5 or 6 AI tools. Maybe 7 if someone added a new one recently. The reality: the average knowledge worker at a growing startup uses 12-15 AI-powered tools, and finance only knows about half of them.

This isn't negligence. It's a structural blind spot. AI tools are easy to adopt — free tiers, credit card signups, browser extensions — and hard to track. Nobody tells finance when a product manager signs up for Jasper on a personal card. Nobody flags the $400/month Claude Pro seat that's been running on a company card nobody reconciled.

Result: shadow AI subscriptions bleeding budget, overlap between tools nobody's connecting, and no visibility when it's time to cut costs.

This checklist fixes that. 5 steps, 20-30 minutes total, and you'll know exactly what your team is paying for.

12–15
AI tools used by the average startup team
5–7
AI tools the average company tracks
$800/mo
Average hidden AI spend at companies with 20-50 employees

Step 1: Check Expense Reports (5 minutes)

Start where money actually moves. Pull credit card statements from the last 3 months and scan for anything AI-adjacent.

What to look for:

Quick Win

If you're doing this manually, export 3 months of statements to a spreadsheet and search for "AI", "OpenAI", "Anthropic", "Jasper", "Midjourney", and "Copilot". Flag anything you can't explain. That's your first audit list.

Step 2: Survey Your Teams (5 minutes)

Finance found half the picture. Now ask the humans. Send a Slack message or a simple form to every team — engineering, marketing, sales, ops:

"Quick question: List every AI tool you've used in the last month — anything that helped you write, code, design, research, or automate. No wrong answers. We just want to know what tools are in use."

You'd be surprised what comes back. Most people will list 3-5 tools they use regularly but didn't think to mention because "everyone probably knows about it."

Pro tips for the survey:

Step 3: Scan SSO and Identity Logs (5 minutes)

Your SSO provider (Okta, Google Workspace, Azure AD) logs every app your team authenticates to. This is often the single fastest path to discovering tools nobody mentioned.

How to run this:

Look for apps with "AI", "GPT", "Copilot", "Assistant", or similar keywords in the name. Also flag anything with a large number of active users that wasn't on your original list.

⚠ Watch out for

Browser Extensions and Personal Accounts

SSO only catches enterprise-authenticated apps. AI tools added as browser extensions (Monica, Monica AI, Sider) or personal subscriptions paid outside company systems won't show up here. Survey data fills this gap.

Step 4: Review API Keys and Integrations (5 minutes)

Engineering teams often spin up AI API keys for internal projects that never make it to finance. Check:

API spend is particularly easy to miss because it often hits a corporate card as "platform services" rather than "AI tool subscription."

Step 5: Calculate the Total (5 minutes)

You now have three data sources. Cross-reference them and build your audit list.

Tool Name Where Found Est. Monthly Cost Known Seats Notes
ChatGPT Team Expense report + Survey $240/mo 20 All confirmed users
Claude API API logs $180/mo 5 devs 3 internal tools consuming this
Jasper Expense report $125/mo Unknown 2 survey responses mention it
Monica AI (extension) Survey $0 (free tier) 3 Flag for review

Add up the monthly total. Compare to what finance was tracking. The gap is your hidden AI spend.

What a Typical Audit Finds

What to Do With the Results

The audit is the hard part. Now you have options:

Set a recurring audit cadence. Do this once a quarter. AI tool sprawl grows fast — a tool adopted in January that's paid annually and forgotten by March can run for 9 months before anyone notices.

Track Every AI Tool Automatically

StackPilot discovers AI tools across your expense reports, SSO logs, and API keys — so you stop finding surprises at the end of the quarter. Get a full audit in minutes, not hours.

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Practical frameworks for tracking AI spend, finding overlap, and cutting waste. No fluff.