Everyone's Talking About What AI Costs the World. Who’s Talking About What It Costs Your Business?

We've spent the better part of the last two years debating what AI can do. The jobs it’ll replace. The industries it’ll transform or completely upend. When are we going to start really talking about what it costs us to let it run?

The license fee, as it stands today, is the easy part. The real exposure: AI (and data) consumption that scales unchecked across teams, tools, and product lines. Software development, marketing, digital commerce and media — who are adopting and consuming AI faster than almost anyone — are especially vulnerable here.

Here's the uncomfortable truth: AI companies are still testing and learning how to meter consumption, software companies are still struggling with how to price this in, and business leaders are struggling with how to budget for it long-term. 

The "Let It Ride" Trap

Some organizations are treating AI spend like a utility with no meter. Just enable access, let teams run, and sort out the bill later. We’ll get economies of scale, right? I call this the let it ride approach — and if you lived through the Texas electricity crisis of February 2021 like my family did, you know exactly how that story ends. 

When wholesale power prices spiked to the grid cap overnight, consumers on variable-rate plans got crushed because nobody had put a ceiling on what "unlimited" actually meant. And it feels like AI consumption is working the same way - at global scale. Uncapped API usage, open-ended agent workflows, and generative tools running day and night across large organizations can turn a manageable SaaS line item into a surprise six-figure quarterly bill. Fast.

The Monthly Budget Cap Model — And Where It Breaks Down

The obvious counter is to do what retail media taught us well: set a monthly budget, cap it, optimize within it. Any performance marketer understands this instinctively. You don't let a paid search campaign run without a daily cap. Why would AI be any different?

The problem is that not all AI use cases tolerate a hard stop equally.

A content generation workflow? Cap it. If the budget runs out mid-month, the team pivots, reprioritizes, waits. No catastrophic outcome.

A customer-facing AI agent embedded in your e-commerce experience? Capping that is a different conversation entirely. Hitting a budget ceiling mid-month could mean turning off a revenue-generating touchpoint or degrading a live customer experience. The business consequences are asymmetric.

This is the part most budget frameworks miss: use case risk profiling has to come before spend allocation and pricing discussions.

The Business Unit Allocation Problem

Here's where it gets politically messy. Once you've decided that AI spending needs governance, someone has to answer: who pays for what?

Do you centralize the budget under a technology or innovation team and let BUs draw from a shared pool? Do you allocate by headcount? By projected usage? By revenue contribution?

Media and marketing organizations tend to have fragmented structures — brand teams, agency relationships, regional leads, product marketing — all with their own priorities and, in some cases, separate P&Ls. Dropping a centralized AI budget on top of that without a clear chargeback model is a recipe for underinvestment in the places that need it most and overspend everywhere else.

The retail media parallel holds here, too. The best-performing media organizations treat budget allocation like portfolio management: core spend is protected, test-and-learn budgets are ring-fenced, and high-ROI channels get flex dollars. AI spend deserves the same rigor.

What Good Looks Like

It's early, but the organizations getting this right are doing a few things:

• Profiling use cases by business criticality before setting caps

• Creating tiered access models (high-trust, high-budget for revenue-critical; sandboxed for experimentation)

• Building AI spend into quarterly planning cycles versus an IT line item to be figured out later

• Establishing a clear owner: someone who sits at the intersection of marketing operations, finance, and technology

The AI adoption conversation is everywhere. The AI cost governance conversation is barely starting. That's the gap worth closing — before the bill arrives.