Corporate finance teams want to know what they gain from the millions spent on artificial intelligence.
Most chief information officers lack clear answers. The issue isn’t that AI fails to deliver results, but that existing accounting systems weren’t built to track it as a distinct type of work.
The enterprise now has four sources of labor
The modern enterprise relies on four sources of work: humans, humans assisted by AI, humans collaborating with AI, and humans overseeing AI. The latter three represent different levels of supervised machine labor—none of them have a line item, a manager, or an hourly rate.
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In our 2026 AI Labor Report, 78% of leaders view AI as both software and a labor force. The org chart has not caught up. Neither has the P&L. Most enterprises are stuck at an early stage of adoption with no accounting for any of it, while quietly sliding into the next stage. The job descriptions have not caught up. The budget has not caught up. You cannot upskill into a role that has not been named. AI is the only category of work the modern enterprise has ever bought without a system of record for what it produced.
Supervised machine labor, unsupervised accounting
Every AI-generated task requires human review before completion. One hundred percent of leaders surveyed said AI work requires human review before it ships; 34% said substantial editing. That is a workforce with no manager, no hourly rate, and no line on the income statement.
Accounting systems fail in three key areas. Under generally accepted accounting principles, costs fall into cost of goods sold if they contribute to a product, or operating expenses if they support internal operations. The same workflow can hit all three buckets at once. A tier-one support resolution involves the human’s salary (OpEx), the AI’s tokens (COGS if support is a delivered service), and the supervisor’s review time (OpEx). Three buckets. One piece of work. No reconciliation. The token invoice arrives from Anthropic or OpenAI and gets coded to OpEx-software because that is what the bill looks like. Audit partners will be asking about this by next year.
When you call AI a tool, you book it like software. When you call it labor, you have to ask which kind and what it is producing.
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The confusion produces what is described as AI Labor Orphaning. AI does the work. The output gets credited to the human who approved it. The token bill lands in OpEx-software. The supervision time absorbs into salaried hours nobody is auditing. Eighty-seven percent of leaders admitted AI output is sometimes or always credited entirely to the human employee. This is the last-click attribution problem of the AI era, running in reverse.
What fills the vacuum? Belief. Forty-three percent assume that if AI was involved, it contributed. Only twelve percent have a clear methodology. Seventy-nine percent are worried AI budgets will be cut because they cannot demonstrate value.
The wrong unit of measurement
Per-employee AI spend collapses a workforce into a per-head average. It hides the only number that matters: what AI is producing inside each workflow.
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Lanai measured two teams inside the same finance organization. Same monthly prep and variance analysis. AI took the same amount of time to produce outputs of similar quality. The only variable was the model each team reached for by default—a choice nobody had made deliberately and nobody had seen until it was measured. The gap existed for months before anyone saw it. Faith-based budgeting is what made it invisible.
The CIOs who craft a credible AI narrative will be those who redefined the work. The technology didn’t change—what changed was their ability to account for it.
The true expense of AI isn’t the model itself. It’s the necessary redesign to track, measure, and manage it as a new form of labor that doesn’t fit existing categories.
