The Hidden Bill of AI

Welcome Back to XcessAI

What if the biggest risk to your AI strategy isn’t your competitors, or regulation — but the costs you didn’t see coming?

The 2025 Mavvrik AI Cost Governance Report surveyed 372 enterprise organizations across multiple industries and revenue tiers, uncovering a growing problem: while companies are racing to scale AI, few are controlling the financial side of the equation.

According to the report, 84% of companies say AI is already eating into gross margins — some by more than 16%.

This isn’t a “tech cost” story. It’s a structural one — and it’s already reshaping margins, forecasting, and governance across the corporate world.

What follows is not a set of prescriptions, but a range of options that executives may wish to weigh depending on their size, mission, and risk appetite.

What Kinds of Companies Were Surveyed

Mavvrik’s analysis draws from global enterprises across technology, finance, manufacturing, healthcare, and retail, primarily with revenues above $500M.
Most are either experimenting with or already deploying generative AI workloads in production.

In other words, this is a window into how serious adopters are grappling with the economics of scale.

Margin Erosion Isn’t Optional

According to Mavvrik’s findings, 84% of companies report that AI costs are reducing gross margins by more than 6%, and more than one in four say the impact exceeds 16%.
For AI-native or SaaS firms, that’s not an R&D overrun — it’s a hidden COGS leak.

Seen one way, this is the price of innovation: a temporary squeeze while AI creates long-term efficiency.
Seen another, it’s an early warning that scaling without financial discipline may turn “AI advantage” into a margin trap.

Forecasting: The Weak Link

Only 15% of companies can forecast AI spend within ±10% accuracy.
More than half miss by 11–25%, and nearly a quarter by over 50%.

Why? AI costs don’t behave linearly.
A single prompt change, model update, or workflow variation can multiply compute usage overnight.

Rather than seeking perfect predictability, finance leaders may consider working in probability bounds — budgeting for volatility rather than fighting it.

Visibility Gaps in Hybrid Environments

The report finds that 61% of organizations run hybrid AI stacks — combining cloud, private infrastructure, and third-party GPU providers.
Yet only 35% include on-prem AI costs in financial reporting, and nearly half of AI-centric firms don’t separately track LLM API spending.

Fragmentation creates blind spots.
When cost attribution is weak, optimization becomes guesswork — and accountability blurs across teams.

Some CIOs and CTOs are therefore experimenting with unified dashboards or showback models that tie costs directly to features or departments.

The Repatriation Wave

A striking 67% of respondents are planning to repatriate AI workloads — bringing inference and training back from the cloud to owned or co-located infrastructure. Another 19% are evaluating it.

This isn’t nostalgia for on-prem; it’s a hedge against volatility.
Owning compute means more predictable economics — though at the price of higher upfront investment and operational complexity.

For some CFOs, the calculus is shifting from “pay as you go” to “pay to control.”

Beyond Tokens: The Real Cost Surface

Many assume token fees or GPU hours dominate the AI bill. The report suggests otherwise.

• 56% cite data platform usage as their top surprise cost.

• 52% mention network and egress fees.

• 37% still list token consumption, but it ranks fifth overall.

In other words: compute is visible, but data movement and orchestration are often the silent profit drains.

Data pipelines, storage, logging, and telemetry all add up — and their cost curves are steeper than most models predict.

What Different Leaders Might Consider

The Mavvrik report closes with tailored suggestions for C-suite leaders.
Below are not recommendations, but alternative lenses through which each role might view the same challenge:

Each lens is partial — but together they form the cost governance mosaic that AI maturity will demand.

Closing Thoughts

AI isn’t free — and it isn’t experimental anymore.
As enterprises scale, AI spend is becoming a structural line item, one that can make or break gross margin if left ungoverned.

One truth seems unavoidable: the next wave of AI leaders won’t necessarily be those who deploy fastest, but those who deploy most efficiently.

The real question for every organization isn’t just “Can we build with AI?”
It’s “Can we afford what we build — every single day?”

Until next time,
Stay adaptive. Stay strategic.
And keep exploring the frontier of AI.

Fabio Lopes
XcessAI

P.S.: Sharing is caring - pass this knowledge on to a friend or colleague. Let’s build a community of AI aficionados at www.xcessai.com.

Read our previous episodes online!