The AI Subscription Explosion

Between 2023 and 2025, AI tool spending at mid-market companies grew from roughly $15,000/year to over $65,000/year. The tools arrived fast - ChatGPT Teams, GitHub Copilot, Jasper, Midjourney, Claude, Perplexity, Notion AI, Grammarly Business, and dozens of vertical-specific AI tools for sales, marketing, customer support, and development.

Most of these subscriptions were bought bottom-up - individual contributors signing up with a company credit card because they saw a demo or heard about it from a colleague. Finance didn't know. IT didn't know. And nobody tracked whether the tools were being used.

The problem: A recent survey of 500 mid-market companies found that 67% had at least 3 AI subscriptions where usage had dropped below 20% of licensed seats within 90 days of purchase. The "shiny new AI tool" problem is real - and expensive.

How to Measure AI Tool ROI

ROI measurement for AI tools is harder than for traditional software because the value is often diffuse - time saved here, quality improved there, fewer errors on tasks nobody was tracking. But that doesn't mean it can't be measured. Here's a framework:

1. Usage-Based Minimum Bar

Before you can measure value, you need to establish whether the tool is being used at all. Pull usage data from the vendor (most enterprise AI tools provide this) or look at login frequency. Set a minimum bar: if fewer than 40% of licensed seats are active weekly, the tool is underperforming regardless of the ROI story.

2. Time-Savings Calculation

For productivity tools (writing assistants, code completers, meeting summarizers), the ROI is in hours saved. Survey the active users: "How many hours per week does this tool save you?" Multiply by average hourly cost (salary + benefits / working hours). Compare to monthly license cost.

Example: GitHub Copilot at $19/developer/month. If a developer saves 2 hours per week and costs $80/hour fully loaded, monthly savings = $640. Monthly cost = $19. ROI = 33:1. Easy to justify.

3. Output Quality Tracking

For AI tools that affect deliverable quality (content generators, code reviewers, data analyzers), track output metrics before and after deployment. Fewer bugs, higher content engagement, faster proposal turnaround - these are quantifiable if you have baseline data.

Practical tip: When evaluating a new AI tool, establish baseline metrics BEFORE deployment. It's much harder to measure improvement if you don't know where you started. Even a simple pre-deployment survey of the team takes 15 minutes and makes ROI measurement dramatically easier.

Common AI Subscription Waste Patterns

  • The trial-to-paid drift: A free trial converts to a paid plan without anyone noticing or intending it
  • The champion departure: The person who championed an AI tool leaves the company; usage drops to zero but the subscription continues
  • The API cost surprise: Usage-based AI API costs scale faster than expected; nobody is watching the meter
  • The feature overlap: You bought a standalone AI writing tool before your existing writing platform launched their own AI feature; now you have both
  • The department silo: Marketing, sales, and product each bought different AI tools for the same function because nobody coordinated

Building an AI Spend Governance Framework

AI tools need the same governance discipline as any other SaaS spend - but the fast-moving nature of the AI market makes it harder. A lightweight framework:

  1. Centralized registry: Every AI subscription goes into a shared tracker the moment it's purchased - vendor, cost, owner, use case, users
  2. 90-day reviews: Every AI tool gets a mandatory 90-day review: Is usage meeting the minimum bar? Is there measurable ROI? Keep, downgrade, or cancel
  3. Overlap checks: Before buying any new AI tool, check whether existing tools in your stack already cover the function
  4. API cost monitoring: For usage-based AI APIs, set budget alerts. Don't let a misconfigured batch job run up a $5,000 bill before anyone notices

SubScrub flags AI subscriptions automatically from your financial data and tracks their usage trends. Join the waitlist to see the AI spend dashboard.