How to audit AI tools before spend gets messy

Start with a full tool inventory

The audit should include paid tools, free tools, built-in AI features, browser extensions, vendor add-ons, personal accounts used for work, and tools being tested by individual staff.

For each tool, record the owner, cost, users, business purpose, data access, renewal date, and whether the tool is approved.

Match each tool to a workflow

A tool should not be kept just because it sounds useful. It should support a specific workflow, decision, customer moment, internal process, or productivity need.

Which work does this tool improve?
Who uses it and how often?
What changed because the tool exists?
Does another approved tool already do the same job?
What data or customer information does it touch?

Decide what to keep, consolidate, or stop

After the review, most tools should fall into one of four categories: keep, consolidate, pilot with clearer measures, or remove. The audit should also identify tools that need security review before continued use.

Prevent the same problem from returning

The business needs a simple approval path for new AI tools. Staff should know who can approve access, what information is needed before a subscription starts, and when a tool needs governance review.

Practical checklist

Use this before you move forward.

List every AI tool, add-on, extension, and embedded AI feature in use.
Record owner, users, cost, renewal date, data access, and business purpose.
Identify duplicate tools and tools without clear workflow value.
Flag tools that handle sensitive data or customer-facing output.
Set an approval process for new AI spend.