Before you approve the AI budget, ask yourself one question: did you budget for everything around the AI?
Almost every AI proposal I review leads with the same number: the cost of the model or the tool. That might be a per-seat license, a price per million tokens, or a subscription tier.
That number is real, but it’s usually the smallest one in the project.
The real cost of AI is everything your business has to build around the model so it works reliably and securely, and so it still runs on a Tuesday afternoon when the person who set it up is on vacation. Most budgets I see don’t have a line for that, and some don’t account for any of it.
The Wrong Number Gets All the Attention
The model price dominates the conversation for good reasons. Vendors put it on the slide, it’s easy to compare, and it keeps falling. Per-token pricing has dropped as much as 280-fold since 2022, which I wrote about in July.
Those falling prices have convinced many executives that AI is getting cheap. The model is getting cheap. Running AI inside a real business is not.
Gartner has predicted that organizations will abandon 60% of AI projects that aren’t backed by AI-ready data. Those projects won’t die because the model cost too much. They’ll die because the money went to the visible part and the foundation never got funded.
The Real Bill Has Three Parts
When I walk a client through a full AI budget, I sort the costs into three buckets. Executives already think in these terms for every other system they run. They just don’t apply them to AI, because AI arrives looking like software you buy rather than a capability you build.
Part One: Getting the Data to the AI
A model is only as useful as the data it can see. In the demo, someone picked clean data by hand and dropped it in. In production, the AI needs live access to your ERP, your CRM, your ticketing system, your file shares, and whatever spreadsheet finance actually runs on.
That means cleaning records that have been inconsistent for a decade. It means building pipelines that keep data current instead of copying it once. And it means integration work for every system the AI has to read from or write to. Each of those connections has to be built, tested, secured, and maintained.
Budgets usually break here first. The sentence I’ve learned to worry about in a kickoff meeting is “our data is pretty clean.” People say it with complete sincerity, and they’re almost always wrong.
Part Two: Running It Safely
Once the AI touches real data and real workflows, it becomes infrastructure, and it needs what every production system needs.
It needs compute and separate environments for development, test, and production, not one shared sandbox. It needs identity and access controls, so the AI can reach only what it should and you can prove that. It needs logging and monitoring, so that when an answer is wrong or a cost spikes, your team sees it before a customer does. And depending on your industry, it needs compliance work that nobody quoted you.
None of this is exotic. Any IT leader would apply the same discipline to a new ERP module. The problem is that AI rarely goes through that discipline, because it was bought as a tool.
Part Three: Owning It
This bucket almost never shows up in the budget, and I’d argue it matters most.
Someone has to own the system after launch. That person or team supports it, handles incidents, approves changes, retrains users when the workflow shifts, and measures whether it delivers what the business case promised. Those are people costs, and they come back every year.
Then there’s adoption. A tool nobody uses has an ROI of zero, however well it was built. Training, change management, and redesigning the workflow around the AI take real time from real people. If that time isn’t in the budget, it comes out of somewhere else, usually out of the people the AI was supposed to help.
Where the Value Actually Came From
At Kyndryl, I led an engagement for a Fortune 500 client where we improved incident detection by 38% and documented more than $2 million in savings. That’s the result people remember.
What they forget is where the work went. Very little of it was clever technology. It was instrumentation, monitoring, and operational discipline, which is the “running it safely” bucket. We didn’t get much better at fixing things. We got better at seeing them. The value came from the layer most AI budgets leave out.
I saw the same pattern when I designed a 24-month Azure modernization roadmap for a large industrial manufacturer. AI capability was the destination, but most of the roadmap was the road to it: data platform, integration, security, and operating-model work that had to land first.
That isn’t a failure of AI. That’s what building an AI capability looks like.
The Budget Review I’d Run Before Approving Anything
If there’s an AI proposal on your desk right now, here’s how I’d pressure-test it. Look for a funded answer to each of these questions.
Where does the data come from, and who pays to clean and connect it? If the answer is “the vendor handles that,” ask which systems, and get the answer in writing.
Where will this run, and who secures and monitors it? A production system needs production-grade environments, access controls, and alerting, and someone has to build them.
Who owns it in year two? Name the person or team, then find their time in the budget.
How will people actually start using it? Look for training and workflow redesign. If there’s no line for either, the proposal assumes adoption instead of planning for it.
What does it cost to run every year, not just to launch? The first-year number is the one that gets approved. The recurring number is the one you’ll live with.
If the only clear number in the proposal is the license cost, you aren’t looking at an AI budget. You’re looking at a down payment.
Buying AI Is Not Building a Capability
This is the distinction I wish every executive carried into budget season.
Buying AI is a technology decision. You compare vendors, negotiate price, and sign.
Building an AI capability is an infrastructure and operating-model decision. It touches your data, your systems, your security posture, and your people. It needs a budget that reflects all of that, sequenced so the foundation is funded before the expensive mistakes get made.
The companies I see succeed with AI didn’t find a cheaper model. They paid for the parts nobody puts on the slide.
Know What Your AI Initiative Will Actually Require
Get an honest picture before you commit budget. Summit AI’s AI Infrastructure Assessment identifies the data, infrastructure, security, integration, and operational capabilities your initiative needs, along with what’s missing, before you start spending heavily. Start the conversation →
See how infrastructure-first AI works end to end. Our services run from assessment and strategy through implementation and adoption, sequenced so the foundation comes first. Explore our services →
Read the other half of the cost story. If model pricing is the number you’ve been watching, start with why AI got 280x cheaper and the bill didn’t. Visit the blog →
Russell Love is the Founder & CEO of Summit AI Business Solutions, based in Browns Summit, NC. With 20+ years of enterprise transformation experience at IBM and Kyndryl, Russell helps businesses build the foundations that make AI actually work.