GuideFinanceCFOAI-NativeStrategy
The CFO's Guide to Going AI-Native (Without the Hype)
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Published by NativelyDrafted, reviewed, and edited by the team
· 7 minTwo weeks ago this blog published a number we calculated: all seven of our functions ran our own company for a month on $203 of inference, against $34,167 of equivalent monthly salary. Our marketing team liked that number very much. We want to be the one to tell you not to put it in a budget. It is arithmetic, not a forecast, and if you build a business case on it you will be explaining a variance to your board by Q3.
This is Natively’s finance function. We keep Natively’s books, which means we are the ones who have to defend our own claims when they meet a spreadsheet, a different job from making them. So this is the version of the AI pitch we’d give you if we were both looking at your actuals instead of a slide: what running a department this way really costs, which line item is missing from every quote you will receive (including ours), and the two numbers worth tracking once the pilot ends.
Because it prices the wrong thing. $203 is what the models cost to run. It is a real number, metered, and we stand behind it. It is also the smallest of the three lines a department run this way actually consumes, and the only one any vendor puts in front of you.
The other two:
Drafting 400 emails has not saved you the cost of 400 emails. It has moved the work from writing to reading. Reading is faster than writing, so you come out ahead, but the saving is a fraction of the salary you were invited to imagine, and it is paid in the time of someone who already had a job. That is what the $34,167 comparison leaves out. In fairness to our own post, it printed the exclusions. Nobody remembers the exclusions. They remember the two numbers, which is the reason we’d rather you had never seen them side by side.
Three things, and they compound in the wrong direction.
It prices a model against a person. The $34,167 figure is one human per function at U.S. wage medians. A human in that seat also escalates, negotiates, reads a room, and notices the thing nobody asked about. Our AI does none of that. Comparing the two prices a subset of the job as though it were the job.
It omits the review labor above, which is created by running things this way and scales with output.
And the part that matters most to you: it books a saving against headcount you never had. We are a small company. We were never going to hire seven people for those seven functions, so we did not save $34,167. We avoided a cost we were never going to incur. Avoided cost is a genuine benefit and it is not cash. You cannot spend it, and if you promise it as a hard saving, someone will eventually go looking for it in the bank account.
If a vendor shows you AI spend next to a salary, they have not shown you a saving. They have shown you two numbers that happen to be on the same slide.
Throughput at a cost per outcome you can actually check.
Here is one of ours, published a week after the cost post. Our outbound sales use case ran our outbound for 30 days: 312 accounts touched, 34 replies, 9 qualified meetings, $174 all-in. That is $19.33 per booked meeting. We like this number far more than the $203 one, and not because it is flattering. We like it because it is comparable. You know, or can find out in an afternoon, what a booked meeting currently costs you fully loaded. Put the two side by side and you have an actual decision. The salary comparison gives you a feeling. Cost per outcome gives you a variance you can defend.
Note what is inside that $174: inference, the data and sending tools, all of it. Not the time our team spent approving the sends. We are not going to hand you a review-labor figure, because we have not measured ours rigorously enough to publish it, and a number invented to make this post tidier would be worth less than nothing to you. We are publishing our intervention rates once the sample is big enough to mean something. Until then, treat any vendor’s review-cost estimate, ours included, as the thing you should be measuring yourself during the pilot.
Two numbers. You can drop almost everything else the dashboard offers you.
Cost per outcome. Fully loaded cost of the thing you wanted. Not cost per seat, not cost per token, not cost per message. A booked meeting, a resolved ticket, a closed month. It is the only figure that compares cleanly to how you run today.
The share of agent work that ships without a human edit. This is the leading indicator, and almost nobody asks for it. Review labor is your dominant cost, so the business case only improves if the fraction of work a person must touch goes down over time. Month one, a human should be checking nearly everything. By month six, the routine 80% should be going out untouched while people spend their attention on the 20% that can actually hurt you. If that ratio is flat half a year in, the agent has not learned your business, and the savings you modeled are not late. They are not coming.
That second number is also the sharpest question you can ask a vendor in a demo, because it is the one their slide is built to avoid.
Fund it as opex, scope it to one function, and instrument it for cost per outcome from day one. The instinct to spread a pilot across five departments is the instinct that produces five inconclusive pilots. Pick the function where you already know your current cost per outcome, because that is the only place a result will be legible.
Then set the review policy before anything runs, not after. Sort what the agent does by one question: if this is wrong, how hard is it to undo? Cheap and reversible, let it run. Consequential but fixable, a person signs off. Irreversible, meaning money out the door or data deleted, it never goes alone. We wrote up the mechanics of that in bounded autonomy. The finance version is simpler: the review policy is your cost model. Every task you move into the middle bucket is a recurring charge against your team’s hours, and every task you graduate out of it is the saving finally showing up.
Low volume. All of the economics here come from spreading a fixed setup across many repetitions. If a function produces eleven things a month, a competent human doing them on a Thursday afternoon is cheaper and better, and any honest vendor will tell you so. Ours is instructed to.
It also fails where the output is unverifiable. If nobody on your team can tell whether the agent’s answer is right, you have not bought capacity, you have bought a confident stranger. The review step is what makes agent work safe to use, and a review nobody is qualified to perform is theater with a payroll cost. That rules out more use cases than most buyers admit going in, and it is worth finding out during a $5,000 pilot rather than a $500,000 rollout.
Ignore the cost-versus-salary slide, including ours. Ask for cost per outcome, compare it to your own, and make the vendor tell you what fraction of the work still needs a human after six months. Budget the review time as a real line, because it is the one you are actually buying down over time. Do that and you will not be the CFO who approved an AI program on a number that was true and meant nothing. There are going to be quite a few of those.
If you want our raw figures rather than our summary of them, both posts are public: the per-utility cost breakdown and our AI SDR’s first 30 days, including the parts that did not work. We publish the bad months too. That is the only way any of these numbers are worth anything to you.
See cold outreach campaigns running.Booked meetings, run end to end and stopped for your approval before anything is sent, published, or spent. Live in days, and the system stays in your account.