The AI line in your budget
should be priced per finished task.

The pricing page of Intercom shows two ways of paying for software on one screen. The help desk on the Advanced plan costs $85 per seat a month, billed annually. Right below it, the company’s AI agent, Fin, costs $0.99 per outcome. An outcome is counted when a customer confirms the issue is resolved, stops asking for help, or Fin completes a workflow.

The first price buys a person access to a tool. The second buys a piece of work that got done. Most finance teams I talk to still budget AI the first way, as licenses on the software line, while the vendors have started to bill the second way. That mismatch is where most of the confusion about AI return on investment comes from.

In April, Alex Lieberman, founder of the AI transformation firm Tenex and co-founder of Morning Brew, co-led an AI training for 20 executives from what he called one of the largest financial institutions in the world. The first “aha moment” on his list was about money: “Historically, leaders had to think about allocating budget for headcount and software. Now, token budget must be a serious consideration.” His questions for that budget are the ones I would hand any CFO: “Do you set limits per employee? How do you measure the ROI on your token budget?”

Box’s CEO says to watch the I in ROI

The clearest argument for why this budget line will grow came from Aaron Levie, CEO of Box, in an essay he called “Jevons Paradox for Knowledge Work.” William Stanley Jevons noticed in the nineteenth century that more efficient use of coal led to more coal being burned, because cheaper coal found new uses. Levie applies that to the work agents now do, from reviewing contracts to running market research.

The mistake that people make when thinking about ROI is making the “R” the core variable, when the real point of leverage is bringing down the cost of “I”.

His point is that a small company has always had to choose between a better website, a support desk and a cleaner finance process, because each one cost people it did not have. Once the cost of doing a task drops far enough, those trade-offs loosen. In his words, “every business in the world has access to the talent and resources of a Fortune 500 company 10 years ago.” He expects demand for many kinds of work to go up “10X or 100X,” and he expects most future tokens to be spent on “things we don’t even do today as workers.”

For a finance team, that has an awkward consequence. If Levie is right, total AI spend should rise, and a rising bill tells you very little about whether the money is working. The useful number is the unit cost: what one finished piece of work costs now, compared with what it cost before.

Podium sells against the staffing budget

Eric Rea, co-founder and CEO of Podium, described in January how his company went from selling software to local businesses to selling them a worker that runs it. Podium already helped more than 60,000 local businesses handle leads and customer messages. Then, he wrote, “one reality became impossible to ignore: Our customers’ biggest constraint isn’t software. It’s staffing.”

75%

Annual staff turnover among Podium’s customers, as stated by Podium.

30%

Share of their leads that arrive after hours, per the same post.

10,000+

AI agents Podium says it has live in production.

These are the company’s own numbers, from a CEO announcing “$100M+ in AI agent ARR” in under two years, so I treat them as a claim. The logic behind them still matters for anyone holding a budget. Podium’s agent, which it calls Jerry, qualifies and schedules leads around the clock inside Podium’s own software. A business owner judges that against what a person on the front desk costs. The money comes out of the staffing line, and the owner measures it the way he would measure a hire: are the leads answered, and do they turn into appointments.

Counting licenses hides where the money goes

Inside large companies the pattern runs the other way. Vas, CEO of Varick Agents, wrote in August about an executive who had just signed “an eight-figure commitment for the year” in enterprise licenses. The result: “Roughly 10% of people burn 90% of the tokens.”

Share of people10%

Share of tokens90%

He then ran the sum most budgets skip. If everyone else used AI the way the top tenth does, “spend goes up roughly 10x. $10M of commitment becomes $100M, and your best-case scenario is now your worst.” The license count says the rollout succeeded. It says nothing about which of those tokens produced anything. Vas names the measure he would want instead: “what is their ROI per token used?”

Optimized model routing is the new salary banding. You wouldn’t pay executive salary for data entryGarrett Lord, CEO of Handshake, June 2026

Lord’s comparison is the one that made the problem click for me, because finance already knows how to do salary bands. You match what you pay to the difficulty of the work, and you check the output. He adds the condition that keeps it honest: routing to a cheaper model “only works if you have the evals to know whether a cheaper model can actually deliver,” and companies that over-optimize for cost “pay for it in quality.”

Gartner put the same warning to CFOs in May: they “must not mistake activity for impact.” I covered that survey in the note on why our AI team hires from HR and finance.

Three numbers I would ask finance to own

If the budget is moving from seats to tokens and outcomes, the measures should move with it. These are the three I would put in a monthly report, for each workflow where AI does part of the work.

1. Cost per completed task. Take everything the task now costs: tokens, the licenses it uses, the hours people spend reviewing and fixing the agent’s output, and the rework that comes back later. Divide by the number of tasks finished to an agreed standard, and compare it with the same task before. Intercom’s definition of an outcome is a good template, because it says exactly when a piece of work counts as done and charges nothing when it is not.
2. Share of the work that is manual, hybrid or automated. Vas suggests boards should see this split, and I argued in the note on adoption and diffusion that it beats any login count. It shows whether the work itself is changing, one workflow at a time.
3. Quality against the human baseline. Lieberman’s working model for AI ROI makes this a hard condition. In his resume-screening experiment, AI-picked and human-picked candidates go blind to the hiring manager, and the experiment only counts as a success if time saved is above zero and the “AI Average Quality Score ≥ Human Average Quality Score.” A falling cost per task with falling quality is a loss that shows up later.

With those three in hand, the decision rule gets simple. Vas, quoted by Annelies Gamble of Zetta Venture Partners, put it as a gate: “A workflow with a 10x ROI should get prioritized. A workflow with a 2x ROI maybe, maybe not. And a workflow with a sub 1x ROI should be left alone.” You can only apply that gate if you know what the task costs.

Small spend can stay loose

The strongest objection is that this is too much accounting for something this cheap and this new. Lieberman himself argues that below a certain level of spend, ROI “can be vibes-based through conversations with users,” because the goal at that stage is to “remove friction & empower people to play with the technology.” He is right about that. A finance team that demands a cost-per-task study before anyone may try an agent will get a tidy report and no learning.

Jevons also cuts against tight caps. If the cheap tokens are the ones that start the contract review nobody used to do, a hard spend limit per employee can kill the work with the best return before it shows up.

So I would split it the way Lieberman does. Let small spend stay loose, and keep the three numbers for the workflows where the money is real: the ones on the eight-figure contract, the ones that replace a hire you would otherwise make, and the ones a board will ask about. For those, the license count can stay in the appendix.

If you run finance, pick the single workflow with the largest AI bill this quarter and write down what one finished task cost last month, what it cost a year ago, and who checked the quality both times.