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Teams seem to be spending more time talking to Claude, ChatGPT, or some other AI-powered agent, and less time thinking deeply or talking to one another. But we’re all getting more done, right?
Meanwhile, your finance team is scrambling to keep up. So your chart of accounts looks more like an over-decorated tree than a system for reporting performance.
Take a breath, I’ve got you. The following should help you regain control over your ledger. First, let’s define “AI spend.”
Separate pure-play AI from AI-flavored SaaS
Let’s talk about inference, a word that tends to get thrown around liberally. But it has a real definition.
Inference is the doing part. It is when a trained model takes what it has learned from training to turn data into findings, predictions, classifications, or decisions in real time.
Training comes first. You can think of it as showing your kid how to tie their shoes, or giving an agent a flux analysis skill. Inference is the application of that skill. Your kid sees the laces are undone, you remind them, and they remember how to tie their laces. Getting them to tie their laces on their own is another story.
Once trained, chatbots are probably the cleanest example of inference in action. Like when you ask your month-end close agent to run the /me-flux-analysis skill and it jumps to action, writing out a summary of the accounts that exceeded your thresholds with links to the associated transactions.
Alternatively, when you log into your accounting automation tool and click the “start close” button and it generates an in-app flux analysis report, that report could be generated based on a series of rules or it could be powered by a trained agent. Depending on how the flux gets generated, there could be some inference involved. But is it worth pulling out and tracking separately? Nope, that’s just a software tool powered by AI on the back-end.
Every account needs a job
Generally speaking, when it comes to managing a chart of accounts, every account should clear one of four hurdles:
Volume: Ten or more transactions in a period
Size: More than 2% of its statement section
Decision: It informs a strategic or budgetary decision
Key Metric: It feeds a number or input to a KPI or other tracked figure
Inference spend clears three of those with room to spare. And even when it might still be small today, it is a topic of conversation deserving of a presence in your chart of accounts.
But how many accounts should you be using for your AI spend? At least four, and up to six depending on the size of your organization.
Embedded in your product — COGS
Improving operations — Opex
Prepaid commitments — Asset
Usage billed in arrears — Liability
Two more worth considering. If you include software spend in your opex family for GTM, give sales and marketing a dedicated account there. And larger teams will want to consider one for their engineering team, but only if you already have a family of opex accounts for R&D.
Let your departments do the splitting
Speaking of departments, I’d like to use this as an opportunity to remind you to let your departments split the bill, not your accounts.
Some companies will likely have a dedicated family of opex for the sales and marketing team, others will go so far as to have one for the product and engineering team. That’s great! But every team shouldn’t have their own set of accounts. You eventually need to draw the line.
When you do, please just bear in mind that headcount allocation works fine for seat-based software, but then quickly falls apart for consumption pricing. In some cases, engineering can burn ten times what finance does. Allocate all of your costs by heads and you get a materially wrong P&L and a gross margin that is effectively useless.
Pro tip: Track your balance sheet by department.
This is one I learned trying to cobble together an answer for a CMO. He asked me how much he had sitting in prepaid commitments that had not yet hit his budget, because the event had not happened yet. Great question, I said. As I quickly ran off to pull some magic out of a hat.
If only I’d been tracking our prepaids and accrued expenses by department. Something I have never had to wonder about since. Now and forever, my balance sheet will use departments.
Watch the ratio, not just the total
Now that you have the what, time for the why.
At some point your board is going to ask what you are spending on the frontier models and you want that number before they ask. This is the fastest-growing cost line at most companies right now, and anything growing that fast that you cannot see clearly is exactly where things go off the rails.
But a total only tells you the size. It does not tell you whether the money is working. That’s where being able to compare it relative to traditional software and people costs becomes a strategic unlock.
That relationship is what tells you whether you are seeing real impact in productivity and revenue growth. Or whether you are just paying to play in a company sandbox, handing the kids more sand to push around.
Make the tens mean something
What does that even mean?
This is something I picked up—might have invented, not sure—but one thing I have learned remixing too many CoAs over the years. Step back and take the time to truly think about and look at your numbers. Creating a numbering convention that means something can quickly become a strategic unlock for both your finance and accounting teams (potentially others).
Pick a tens slot, give it a meaning, and hold that meaning everywhere it shows up. I’m referring to the tens digit. This is the “10” in a 4010 revenue account. And make sure its deferred revenue twin matches at 2410.
Now do the same with your product costs, whether they’re sitting in COGS or Opex. Your in-product inference and your engineering inference should share a ten. And again with people costs sitting above or below the line.
Final Thoughts
You do not need more accounts to see your AI spend. You need four to six, carefully designed. Each with a purpose.
Do that and your chart of accounts stops looking like a tree somebody kept hanging ornaments on, and starts looking like a system you can actually report from.
If you got this far and want more, I wrote a much longer (some would say long-winded) piece on the art of the chart of accounts.
Tracking AI spend with intention is the right instinct, and the trap is tracking the invoice instead of the outcome. Most finance teams can tell you the monthly model bill to the dollar and cannot tell you which workflow it replaced or what that workflow used to cost in hours. The spend is visible, the saving is not, so the ROI conversation defaults to cutting tokens. The number worth instrumenting is cost per completed task, not cost per API call.