The AI Tool Sprawl Problem
In 2024, the average mid-market company used 12 AI tools. By 2026, that number has doubled. Teams adopted tools independently — engineering grabbed Cursor, marketing signed up for Jasper, sales started using Clay, and nobody told ops about any of it.
The result: overlapping functionality, untracked spend, and zero visibility into what's actually driving value.
An AI stack audit fixes that. Here's how to do one in an afternoon.
Step 1: Inventory Every AI Tool
Start by collecting every AI tool your company pays for. Check these sources:
- Finance records — credit card statements, expense reports, vendor invoices
- IT admin panels — Google Workspace, Okta, or SSO dashboards show connected apps
- Team surveys — Ask each department lead: "What AI tools does your team use daily?"
- Browser extensions — Don't forget Chrome extensions and desktop apps with AI features
You'll likely find 30-50% more tools than anyone expected. That's normal. The goal is complete visibility before making any decisions.
Step 2: Classify Each Tool
Not all AI tools are created equal. Classify each one into three categories:
- AI-Native — The product couldn't exist without AI. The model is the product. Examples: ChatGPT, Perplexity, Cursor, Granola.
- AI-Enabled — A traditional product that added AI features. Examples: Notion AI, Linear's AI triage, Figma's AI tools.
- AI-Adjacent — Tools that work with AI outputs but aren't AI themselves. Examples: Zapier connecting AI tools, dashboards displaying AI-generated data.
This classification matters because AI-native tools tend to improve faster (they ship model upgrades weekly), while AI-enabled tools may have bolted-on features that don't justify their price premium.
Trackr scores every tool on "AI Nativeness" automatically — giving you an objective measure of how deeply AI is integrated into the product versus how much is marketing.
Step 3: Score Against Your Criteria
For each tool, score it on a 1-10 scale across these dimensions:
- Usage frequency — Is it used daily, weekly, or did someone sign up and forget?
- Unique value — Does this tool do something no other tool in your stack does?
- Integration depth — Does it connect to your existing workflows or is it a standalone silo?
- Cost efficiency — What's the per-seat or per-usage cost relative to the value delivered?
- Security posture — Does it meet your compliance requirements? SOC 2? GDPR?
Tools scoring below 5 across multiple dimensions are immediate candidates for review. Tools scoring above 8 are your keepers.
Step 4: Identify Redundancies
Map your tools by function. You'll likely find clusters:
- 2-3 tools that summarize meetings
- 2-3 tools that generate or edit text
- Multiple tools that do "AI search" in different ways
For each cluster, pick one winner. Migrate the holdouts. This alone typically saves 20-30% of AI tool spend.
Step 5: Calculate ROI and Set Review Cadence
For your remaining tools, estimate monthly ROI:
ROI = (Hours saved x Hourly rate) - Monthly tool cost
If a $30/month tool saves 4 hours of work at $50/hour, that's $170/month in net value. Keep it. If a $200/month tool saves 1 hour, that's -$150/month. Cut it.
Set a quarterly review cadence. AI tools evolve fast — a tool that was best-in-class 6 months ago may have been leapfrogged by a new entrant. Your audit isn't a one-time project; it's an ongoing discipline.
Make It Repeatable
The hardest part of an AI stack audit is doing it the first time. The second hardest part is doing it consistently. Trackr helps by giving every tool in your stack a scored, comparable research report — so when renewal time comes, you have data instead of opinions.
Start your AI stack audit with Trackr — submit any tool and get a scored report in under 2 minutes.