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SaaS management

Managing AI Tool Subscriptions Across the Business

How to bring AI tool subscriptions under control: inventory AI apps and add-ons, measure value, govern data use and keep AI spend from outrunning results.

By the MI Solutions SAM team10 min read1 exhibit

AI tools have spread faster than almost any software category before them. Zylo reported that spending on AI-native applications grew 75.2% year over year in its 2025 index. Teams subscribe to assistants, writing tools, coding copilots and meeting summarizers, while existing vendors add AI features as paid add-ons or consumption-based credits. The result is a new layer of spend, much of it overlapping, much of it unmeasured.

Three ways AI spend arrives

+75.2%year-over-year growth in spend on AI-native applicationsZylo, 2025
66.5%of IT leaders report unexpected charges from consumption or AI pricingZylo, 2025
$30per user per month list price for the Microsoft 365 Copilot add-on, annualMicrosoft
01Standalone AI apps

Assistants, writing, coding and meeting tools bought by teams, often on cards.

02AI add-ons

Paid features on products you already own, such as Microsoft 365 Copilot. See Copilot licensing.

03Consumption credits

AI features billed by usage: tokens, credits, summaries or actions. See usage-based pricing.

Each channel needs a different control. Standalone apps are found in finance and identity data; add-ons are found on existing contracts; consumption credits are found only by watching the meter.

Exhibit 1
AI spend is spread across three channelsAI-related software spend by channel, illustrative 2,000-person companyStandalone AI appsAI add-onsConsumption creditsYear 158%30%12%Year 236%44%20%Illustrative. As vendors build AI into existing products, more spend moves to add-ons and credits, which areeasier to overlook.

A governance approach

  1. Inventory

    AI apps from expense data, SSO sign-ins and OAuth grants, plus add-ons and credits on existing contracts.

  2. Assign owners and use cases

    Each AI tool has an owner and an approved purpose.

  3. Review data access

    What each tool can see, and where data goes.

  4. Pilot before scaling

    With clear measures of value per role.

  5. Reclaim quarterly

    Unused AI licenses go back to the pool.

  6. Consolidate

    Overlapping assistants once a standard emerges.

AI tool approval questions

How MI One helps

Frequently asked questions

Should we ban unapproved AI tools?

Block clear data risks; otherwise offer approved alternatives quickly so teams do not go around the process.

How do we measure AI value?

Agree simple measures per use case, such as time saved or output produced, before rolling out.

Should AI spend have its own budget line?

Yes, at least for the first years. It makes growth visible and keeps value conversations honest.


Sources

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