AI tools are creating the same story, just faster. An employee signs up for an AI writing assistant with a company card. A developer spins up an API key for a coding assistant. A marketing team adds an AI image generator to its stack. Each purchase looks small. None of it goes through the usual review. Unlike a forgotten SaaS subscription that quietly renews at a flat rate, AI spend is usage-based. It can climb without warning while nobody is watching.
This is an emerging challenge for IT and finance teams: AI-related costs are spreading across departments, payment methods, software agreements and cloud environments, while few organizations have a complete view of what they are spending. This is where AI spend management comes in.
Why AI Spend Hides in Plain Sight
AI spending has become one of the fastest-growing and least visible categories of technology cost. Gartner forecasts worldwide AI spending to grow 47 percent in 2026. Separately, industry analyses, including a 2026 compilation by AI platform provider Airia, suggest that a meaningful portion of AI adoption is occurring outside formal IT oversight. While estimates vary, the broader concern is clear: many organizations still lack a reliable inventory of the AI tools and services being used across the enterprise.
Part of the problem is that AI tools rarely show up as their own line item anymore. Gartner made a prediction back in 2023. By 2026, up to 70 percent of employee interactions with AI would happen through features already embedded in approved tools. That prediction has aged well. It’s exactly why it’s gotten harder for IT and finance to tell approved spend from unapproved spend.
None of this happens because employees are trying to hide anything. AI tools are simply easy to adopt. Signing up takes minutes, sometimes even with a personal or department credit card. The value is immediate enough that nobody stops to loop in procurement first. By the time finance notices the charge, the tool has already been in daily use for months. And even if procurement goes through proper channels and you deploy the tool to the appropriately designated employees or department, you’re up against adoption. The old 80/20 rules still apply.
This Looks Familiar, and That Should Worry You
IntraTEM has spent years helping enterprises get in front of SaaS management problems. That means duplicate subscriptions, unused licenses, and auto-renewing contracts nobody remembers signing. AI spend management is shaping up to be the same problem, arriving faster and harder to track.
The response is scaling accordingly. Gartner forecasts that spending on AI governance platforms will reach $492 million in 2026. That figure is expected to surpass $1 billion by 2030. Organizations are racing to build the visibility and oversight they don’t currently have. That kind of growth curve usually signals something. A cost category has moved from “nice to monitor” to “actively out of control.”
The pattern is the same one that built SaaS spend management into a category in the first place. Fast, decentralized adoption keeps outpacing the systems built to track it.
What Makes AI Spend Harder to Manage Than SaaS Spend
AI spend shares the same root cause as SaaS sprawl. But it comes with a few problems SaaS spend management wasn’t originally built to solve.
Usage-based pricing means the same tool can cost a fraction of last month’s bill or several times more. It all depends on how heavily teams use it. Seat-based SaaS contracts are predictable by comparison. AI tools also tend to arrive through personal accounts or embedded features inside software a company already owns. That makes them harder to separate from spend that’s already approved. Adoption happens tool by tool, department by department. The totals are rarely visible anywhere until someone goes looking for them.
None of this means AI tools are the problem. Employees are adopting them because they genuinely help. The problem is that spend without visibility eventually becomes spend without control.
What Real AI Spend Management Looks Like
Getting ahead of AI spend takes the same discipline IntraTEM has always applied to SaaS and telecom spend. It’s just adjusted for how differently AI tools get adopted and billed.
That starts with a full inventory of every AI tool in use across the organization. Who’s using it, and how is it being paid for, including subscriptions that never went through procurement? From there, it means mapping actual usage and cost against what each tool is delivering. Renewal decisions should be based on value instead of habit. It also means building contract and vendor oversight into the process from the start. AI vendors change pricing models faster than traditional software vendors ever did. And it means treating this as ongoing management, not a one-time audit. The AI tool landscape is still changing month to month.
Done well, this doesn’t slow AI adoption down. It just makes sure the organization knows what it’s paying for and why.
Where IntraTEM Fits In
IntraTEM takes full responsibility for managing technology spend across Telecom, Mobility, and Cloud and SaaS. Internal teams don’t have to chase it themselves. AI tools are quickly becoming part of that same picture. Most of them are delivered and billed the same way SaaS always has been. IntraTEM already provides visibility, reporting, and vendor management for SaaS spend. That same discipline extends naturally to the AI tools showing up alongside it.
The organizations that get ahead of this now will ask the right questions instead of catching up later. Waiting only lets AI spend management become its own emergency.
Looking to get an audit of your AI spend? Contact us.
Frequently Asked Questions
What is AI spend management?
AI spend management is the practice of tracking, controlling, and optimizing what an organization spends on AI tools and services. That includes subscriptions, API usage, and embedded AI features inside existing software. It covers discovery of tools already in use, cost tracking, contract oversight, and ongoing optimization.
How is AI spend different from typical SaaS spend?
AI spend is typically usage-based rather than seat-based, which makes costs harder to predict. AI tools are also often adopted through personal accounts or embedded inside tools a company already owns. That makes them harder to separate out and track compared to a standalone SaaS subscription.
How much are companies actually spending on AI tools they don’t know about?
Estimates vary. But industry research consistently points to a meaningful share of enterprise AI spend happening outside formal IT and finance visibility. That often includes duplicate tools the organization is already paying for elsewhere.
How do I start reducing AI spend at my company?
Start with visibility. Build a full inventory of the AI tools currently in use, including ones adopted outside procurement. Then decide which to keep, consolidate, or cut. This is something that IntraTEM handles for you.