AI spend that doesn’t move EBITDA.
If your AI pilots look good in demos but invisible in the P&L, you don’t need more experiments. You need a plan that ties every dollar of AI to EBITDA.
In 2–3 weeks, you’ll know which AI projects to keep, which to cut, and where the real upside is.
Apply To Work With Us →What this looks like in your world
Right now you might have:
- +A few AI “experiments” in different departments.
- +Line items for tools and consultants that nobody can tie to profit.
- +Internal champions who are excited, and a CFO who’s skeptical.
Patterns we see:
- +Projects started because “the tech was cool,” not because EBITDA needed to move.
- +No success definition beyond “go live.”
- +No before/after numbers anyone trusts.
From the outside, it looks innovative. Inside the boardroom, it looks like Opex with unclear return.
Why the usual fixes don’t work
- Cut all AI spend. You protect short-term EBITDA, but you also freeze out long-term gains your competitors will capture.
- Double down on the loudest internal project. The most visible team wins budget, not the highest-return play.
- Hire an “AI lead” and hope. They spend six months mapping tools and vendors, not moving the P&L.
- Ask for more dashboards. You get more visibility into experiments, but not more impact.
The core problem: no one has connected AI work to a prioritized, quantified EBITDA plan.
Where AI actually increases EBITDA — and how to prove it
To make AI spend defensible, you need:
Clear selection criteria
Only fund projects that remove cost, protect revenue, or create it. Each one has a baseline and a target metric defined up front.
Defined payback windows
When does this project return its cost? And what happens if it doesn’t?
Simple, trusted measurement
Before/after metrics the CFO and board agree on. No vanity KPIs.
Without that, AI is just another IT line item.
How the EBITDA Impact Map turns “AI mess” into a plan
In the EBITDA Impact Map, we:
- +Inventory your existing AI tools, pilots, and vendors.
- +Quantify what each is costing and what it was supposed to improve.
- +Compare that against fresh opportunities across the operation.
You walk away with a rank-ordered list of 3–5 AI plays — each with an EBITDA impact range, a payback window, and an implementation effort — plus recommendations to:
- Kill low-impact or unprovable projects.
- Keep and tighten the ones that are close.
- Fund net-new plays with higher expected return.
This becomes the basis for an AI budget that’s actually defendable.
From AI mess to a defendable budget
- +Consolidate three overlapping tools into one
- +Shut down two pilots with unclear impact
- +Green-light one system in sales or retention with clear EBITDA upside
Net effect: lower AI Opex, higher ROI, and a story the CFO can tell the board without hand-waving.
You can’t scale AI without governance
Agents are scaling faster than the guardrails around them. Most teams are still early here, and the blockers are almost always the same: data privacy, compliance, and where the models actually run.
The EBITDA Impact Map builds this in. For every play, we:
- +Flag high-risk use cases before they reach production
- +Check data residency and where each vendor’s models run
- +Outline the minimal governance required to scale that play safely
So the plan you walk away with is one your CFO and your risk people can both sign off on.
If your AI spend isn’t showing up in EBITDA, start with the Map.
The EBITDA Impact Map turns your AI efforts into a prioritized, quantified plan: what to cut, what to keep, and what to fund next.
Apply To Work With Us →Five minutes to apply. If you’re a fit, you’ll book a call. If you’re not, we’ll tell you why — same day.