Where the AI Budget Actually Goes

When leaders start talking about AI budgets, the conversation usually sounds the same: How much should we spend on tools? The image in their head is a line item for a shiny new platform or a chatbot subscription. But here’s the truth: software is the cheap part. The real budget—where things get complicated, political, and occasionally funny—is everywhere else.

Companies that make progress with AI don’t just spend money on tech. They spend money on people, on processes, and on the messy business of change. The budget isn’t just about buying AI—it’s about making AI stick.

The Obvious Spend: Tools

Let’s get this out of the way: yes, you’ll spend money on platforms. You’ll buy product licenses, analytics models, maybe some domain-specific software for contracts or customer service. You’ll beef up your data storage, security, and APIs. This part is easy to explain to the CFO because it feels tangible: We bought a thing.

But here’s the problem. If you stop here, congratulations—you’ve just bought yourself a very expensive pilot project. The real work (and spending) is only beginning.

The Hidden Spend: People

AI doesn’t transform work by itself. People do. Which means you need to budget for them.

Training is the first big-ticket item. Giving employees access to AI without training is like buying everyone a fancy BBQ/smoker combo and not explaining how to use it. The result? Burnt brisket, over-cooked chicken, and resentment. People need time, practice, and support to build AI fluency.

Then come the new roles you didn’t have five years ago: AI product managers, prompt engineers, data stewards, ethicists. These aren’t luxuries—they’re the glue that makes AI usable and safe.

And don’t forget the champions. Every company has that one person who eagerly tests the new tool before anyone else dares. If you don’t give them resources and recognition, they burn out—or worse, they quit and take their energy somewhere else.

The Overlooked Spend: Processes

AI isn’t plug-and-play. If your workflows are broken, AI will just help you break them faster. Fixing that requires redesign, and redesign costs time and money.

You’ll need teams to map current processes, identify where AI should step in, and test the new flow. You’ll need governance frameworks so no one accidentally unleashes an AI tool in a way that terrifies your compliance team. You’ll need integration so tools don’t sit abandoned in a corner like that treadmill you swore you’d use every day.

The Sneaky Spend: Change Management

This is the budget line item most leaders pretend doesn’t exist. After all, how do you quantify convincing people to try the new thing? But ignore it, and you’ll pay anyway—in stalled adoption and frustrated employees.

Change management isn’t about glossy slide decks. It’s about communication, coaching, and creating spaces where employees can ask: Is this thing really safe to use? What happens if it makes a mistake? It’s about measuring not just whether the software runs, but whether people actually trust it enough to use it.

Thinking About ROI

Everyone wants to know: does this investment pay off? It does, but not always in the way you expect. Some returns are obvious: fewer hours spent on repetitive tasks, faster analysis. Others are subtler: quicker decision-making, happier employees, and eventually, entirely new products and services you couldn’t have built before.

Think of ROI like a time horizon:

• In the short term, you save time.
• In the medium term, you move faster.
• In the long term, you grow.

If your budget only accounts for the short term, you’re missing the real prize.

The Budget Reality

If I had to sketch a “typical” AI budget, it wouldn’t be 90% software and 10% everything else. It would look more like: tools at the center, surrounded by a bigger ring of people, processes, and change. Because that’s what actually makes adoption work. The companies pulling ahead in AI aren’t the ones spending the most on platforms. They’re the ones treating their budget like a strategy: balancing technology with the human and organizational shifts that make technology stick.

And yes, that means less money for shiny demos—and more for the decidedly unsexy line items like training programs, process redesigns, and coaching sessions. But those “boring” investments? They’re the difference between AI as a toy and AI as a transformation.