The 10-Point AI Operator Audit Checklist (Free)

Write them down from memory. Then pull your actual statements for the last 90 days and compare. The gap between what you think you're paying for and what you're actually paying for is often your first source of savings.

Flag anything you couldn't name from memory — it almost certainly means you're not using it regularly enough to justify the cost.

This is the single most clarifying question in a stack audit. For every tool, name the workflow or output that depends on it. If you can't name one, that's your answer.

Be specific. "It's useful for writing" isn't a workflow. "It drafts our weekly newsletter every Tuesday via a Make automation" is a workflow. Only the specific ones count.

Common duplicates we find: multiple LLM chat interfaces, two automation platforms, two AI writing assistants, two transcription tools, two AI image generators. List every tool and mark its primary function. When two tools share a primary function, you have redundancy.

Note: "one is better for X, the other for Y" is often rationalization. Ask whether the difference is meaningful to your actual daily workflow, not theoretical capability.

Pull the login history if you can. If you can't, be honest: when did you actually open each tool? Log in and check. Most tools have "last active" data in account settings.

30 days is a generous window. If a tool hasn't been touched in 30 days, the default assumption should be "cancel" unless there's a specific reason to keep it.

Many founders upgrade to Pro or Business tiers for a feature they needed once, then forget to downgrade. For each paid tool, look at your usage stats and compare against what each tier provides.

Common scenarios: on ChatGPT Team when you're a solo user; on Zapier Professional when you have 3 active Zaps; on an image tool's Pro tier when free covers your volume.

This is the fastest growing category of duplicate spending. Notion, HubSpot, Canva, Slack, Linear, Figma, and dozens of other tools now include AI features in their base or paid plans. If you're paying for these platforms AND separate AI tools for the same functions, you have overlap.

Check: Does your project management tool have AI? Does your design tool? Your CRM? Your email platform?

This one goes beyond subscriptions into operational waste. Broken automations — Zaps and Make scenarios that fire with errors, skip steps, or produce wrong outputs — are often worse than no automation at all because they create the false sense that something is being handled.

Log into Zapier or Make. Look at the error logs. Look at the task history. Count how many automations ran successfully last week vs. how many failed or produced no output.

If you have a team, unclear AI tool usage leads to parallel spending (everyone buys what they prefer), inconsistent outputs, and training gaps. Ask your team: "What AI tools do you use regularly, and for what?" The answers will often surprise you.

A simple team-facing AI stack doc — one page, three columns: tool, purpose, who uses it — eliminates most of this waste.

Most AI stacks grew organically. Tools got added when they were trending, when someone recommended them, when a specific problem appeared. Nobody designed the stack from first principles.

Take 10 minutes and sketch what you'd build if you were starting from zero today, knowing what your business needs. One LLM. One automation platform. One tool per specialized function. Compare that to what you have.

Audits aren't just about cutting — they're about reallocating. After you've identified the waste, ask: where would that money generate 10x more value?

Common answers: better prompt engineering training for the team, a higher-quality LLM plan for the primary user, an automation build that eliminates a manual process someone does 5 hours a week.