Somewhere in your company right now, an employee is probably running an AI agent on a task that used to take five minutes — and it’s quietly racking up a bill that will land on your desk weeks from now. That’s not a hypothetical. It’s already happened at some of the biggest companies in the world, and it’s happening at growing companies too, just with fewer headlines.
If you’ve been putting off an AI strategy because you weren’t sure where to start, that instinct wasn’t wrong. What’s wrong is doing it without a plan. Here’s what’s actually going on, why so many AI initiatives are failing before they ever deliver value, and what smart, growing companies are doing differently.
The Token Bill Nobody Saw Coming
AI tools don’t charge a flat monthly fee the way most software does. Behind the scenes, every prompt, every document an AI reads, and every step an AI agent takes gets broken into “tokens” — and companies pay for every one of them. The more autonomous and “agentic” these tools become, the faster tokens burn, often with no ceiling in sight unless someone sets one.
That gap between how AI tools are marketed and how they’re actually billed has caught major companies off guard. One large company reportedly ran up a bill in the hundreds of millions of dollars in a single month after forgetting to activate basic spending limits. A major ride-hailing company burned through its entire annual AI coding budget in about four months after adoption among its engineering team jumped from roughly a third to over 80% in a matter of months. Another large tech company pulled AI coding licenses from an entire division after per-employee costs climbed into the thousands of dollars a month.
These aren’t reckless companies. They’re sophisticated, well-resourced organizations with finance teams and IT governance. The problem wasn’t the technology — it was that the tools were deployed before anyone modeled what real-world usage would actually cost, or what business outcome it was supposed to produce.
For a growing company without a Fortune 500 finance department behind it, that same mistake can be a lot more painful, a lot faster.
Why 80% of AI Projects Fail to Deliver Value
Runaway costs are only half the story. The other half is even more sobering: research from RAND Corporation, MIT, and other analysts tracking thousands of enterprise AI initiatives consistently finds that roughly 80% of AI projects fail to deliver their intended business value. Some are abandoned before they ever reach production. Others technically launch but never move the needle on revenue, efficiency, or customer experience. A large share deliver some benefit — just not enough to justify what was spent.
The research points to the same root causes over and over, and none of them are really about the AI itself:
- No clear, measurable outcome defined up front. Teams start “doing AI” before anyone agrees on what success looks like or how it will be measured.
- The wrong tool for the job. Companies chase the flashiest or most talked-about platform instead of the tool that actually fits their workflow, data, and team.
- Weak data and process foundations. AI built on top of messy data or undefined processes just automates the mess faster.
- Fading leadership sponsorship. Without an executive owner and a budget tied to real ROI, projects quietly stall.
Put simply: most AI failures are planning failures, not technology failures. And planning failures compound — a project without a defined outcome is also a project without a defined budget, which is exactly how a token bill spirals out of control.
Why This Hits Growing Companies Especially Hard
Enterprise AI headlines get attention because the dollar figures are eye-popping, but the underlying pattern is just as risky — arguably riskier — for mid-sized and growing businesses. A large enterprise can absorb a budget overrun as a bad quarter. A 50- to 500-employee company usually can’t. One over-ambitious pilot, one unmonitored AI subscription, or one tool that doesn’t fit the actual workflow can eat a disproportionate share of the year’s technology budget with nothing to show for it.
At the same time, the pressure to “do something with AI” is real. Competitors are talking about it. Vendors are pitching it. Boards and leadership teams are asking about it. That pressure is exactly what pushes companies to buy a tool first and figure out the use case later — the single biggest predictor of AI projects that fail.
We’ve seen this play out firsthand. One company we worked with got hit with an $85,000 bill from AI token overages — a direct result of rolling out a tool without usage limits or a clear plan for how it would be used. It’s a smaller number than the enterprise horror stories making headlines, but for a growing business, an unplanned $85K hit lands just as hard, and it’s completely avoidable with the right guardrails in place from day one.
The Fix Isn’t Slowing Down — It’s Getting the Right Guide
None of this means AI isn’t worth pursuing. The 20% of AI projects that do succeed tend to share the opposite traits of the failures: a clearly defined outcome, the right tool matched to that outcome, and disciplined oversight of cost and usage from day one. The difference between the companies burning through budgets and the ones seeing real ROI usually isn’t the technology they chose — it’s the process they followed to choose it.
That’s where a vendor-agnostic technology advisor earns its keep. Because a firm like My Resource Partners isn’t selling a specific AI platform, our job is to start with your outcome, not a product. We work backward from the business problem you’re actually trying to solve, then evaluate the AI-powered tools — across an ecosystem of hundreds of vetted providers — that genuinely fit your team, your data, and your budget. That’s a fundamentally different starting point than a vendor demo, and it’s the starting point that shows up in the success data.
A technology advisor can help your team:
- Define the outcome before the tool. Get specific about what success looks like and how you’ll measure it, before a single contract gets signed.
- Avoid tool-first mistakes. Compare AI options objectively instead of getting pulled toward whichever platform has the loudest marketing.
- Build cost guardrails from the start. Model realistic usage and set spending controls before deployment, not after the invoice arrives.
- Create a phased AI roadmap. Sequence initiatives so early wins fund and inform the next phase, instead of betting the whole budget on one big launch.
- Protect ROI. Keep projects accountable to the business outcome they were built for, not just whether they technically shipped.
Start With a Free AI Assessment
Before you commit budget to another AI tool or pilot project, it’s worth getting an outside, unbiased read on where AI genuinely fits in your business — and where it doesn’t yet. My Resource Partners offers a FREE AI Assessment to help your team cut through the noise, avoid the tool-first trap, and build a roadmap that’s designed to reach completion and deliver measurable ROI, not just another line item on next year’s token bill.
