YOU’VE BEEN ASKED TO DEFEND THE AI BUDGET
Priced by the token, budgeted by the seat, and used in every browser. Nobody can say what it cost last month, and the benefit case is counted in hours saved. We bring the discipline that governs cloud to AI: what it costs, what it's for, and whether it's paying off. First working view in four weeks.
WHAT YOU’RE FACING
Where it breaks down:
- An AI number nobody can produce, let alone split by team
- A premium on renewals you already own, approved by people who don't know it's AI spend
- Seat licenses and metered usage in one bucket, under one owner, though they're different problems
- Cost that scales with autonomy, not headcount, inside a budget built on seats
- Benefits counted in hours saved, with no path to a P&L line
Four systems, never wired together
No link
No link
No link
Nobody is failing at their job. The systems were never wired to each other.
What AI value management builds:
- A monthly AI number, by team, by use case, and by seat versus usage
- A cost per unit of work: per ticket, per document, per active seat, per agent run
- AI inside the cost model you already run, shown back before anyone is charged back
- Guardrails in the platform, with alerts that fire mid-month, not at invoice
- Stage gates that have actually retired something, and benefits finance will book
THIS HAPPENED ONCE ALREADY
Cloud arrived priced by consumption, bought by engineers, and explained by finance months later. The fix wasn't a tool. It was a unit cost and an owner on every workload, read every month. AI arrives the same way, one layer up, with one twist cloud never had: unit prices fall while total spend rises. Forecast rate and volume together, and you'll be wrong in both directions.
| Spend | How it arrived | What fixed it |
|---|---|---|
| Cloud | How it arrivedPriced by consumption. Impossible to attribute. On the bill months after the decision. | What fixed itA unit cost and an owner on every workload, read monthly. |
| AI | How it arrivedPriced by the token. Used in every browser. Paid for centrally. | What fixed itThe same discipline, one layer up, inside the TBM model you already run. |
A SEPARATE AI LEDGER IS A SECOND SET OF NUMBERS.
THE CFO WILL NOTICE
HOW WE HELP
You don't need all of this, and you almost certainly don't need it at once. Most organizations start at one of three points.
ASSESS
Four weeks to a number you can defend
Every AI dollar found and named: the premium inside renewals, metered usage, the infrastructure spun up to serve it, and the subscriptions on expense reports. Mapped to the capabilities it supports.
Four weeks, fixed fee.
AI SPEND DIAGNOSTIC
What AI is for in each capability, the unit it will be measured by, and a benefit case your CFO can book, with cost out, cost avoided, and capacity counted separately.
AI INVESTMENT CASE
BUILD
The Practice, not the policy memo
AI placed in the cost model you already run, with an account structure set before spend scales and show back defensible enough to become chargeback. A cost per unit of work for every major workload.
AI COST ALLOCATION & UNIT COSTS
The right-sized model for the task, caching and batch where the work allows, a spend ceiling on every agent, and data rules that pick the tool. Set as platform defaults, not written in a document.
policy, tiering & guardrails
run
Govern AI like the rest of your technology spend
Experiment, pilot, production, each with its own funding, a named owner, and a kill date that gets used. Forecasts that separate rate from volume, re-baselined when the models change.
STAGED INVESTMENT PLAN
AI brought into the reviews you already run: TBM, FinOps, vendor management, and renewals. One governance model with clear decision rights, not a parallel one.
AI IN YOUR IT SPEND GOVERNANCE
W H Y F A L C O N B R I D G E
WE’VE DONE THIS ONCE ALREADY.
We built the cloud version of this
When cloud arrived priced by consumption, our practitioners put unit costs and owners on spend nobody could attribute, inside Fortune 100 finance and IT teams. AI is the same shape, one layer up.
We helped write the standards
Seven years on the TBM Council Standards Committee, and FinOps Foundation committee service alongside it. When we apply both disciplines to AI, we're working from the source.
We hold a view where the field hasn't settled
Where AI sits in the TBM taxonomy, its own tower, a sub-tower, or a cost pool, is still an open question in the TBM community. We'll recommend one and show you why, before your reporting locks it in.
We'll tell you what's proven
Nobody has this finished yet, including the organizations publishing their numbers. We'll tell you which practices keep turning up, and which are still experiments.
models change. the unit cost is what you keep.
bring the ai budget you have to defend.
Whether AI is a line nobody can find yet, a budget you're defending next quarter, or a program that needs governance that holds — thirty minutes, no pitch deck. We'll work out where the spend actually sits and who should own it. If you're not ready for us yet, we'll say so.