Summary: Most AI agent business cases price two things: the labor removed and the technology added. The third axis usually gets a hand wave, and it is often the largest number in the room: cycle time. This post shows how to price a day of turnaround in real currency, why architecture determines who captures that value, and how to model it with the speed dividend in our ROI calculator.
The lending example that makes it concrete
A commercial loan review takes three days of elapsed time in a manual operation. Not three days of work; perhaps an hour of work, spread across queues, handoffs, and reviewer availability. The borrower and the lender pay for the elapsed time anyway.
One day of interest carry on a $2 million loan at 8% is about $440. If the review clears in one day instead of three, each loan banks roughly $880 of carry. At 1,000 reviews a month, faster turnaround is worth on the order of $880K per month, before counting a single hour of labor saved. In construction lending the same math applies to draw disbursements: at Built Technologies, MightyBot cut time on task by roughly 95% and accelerated draws to borrowers by up to 60%, and the value of that acceleration lands directly in working capital.
Insurance runs the same equation with different names: SLA penalties avoided, claim cycle targets met, reserves released sooner. Payments operations count it in float and exception aging. Anywhere a regulated team manages an SLA, elapsed time is money with a rate attached.
Why slow paths stay slow
Two architectural choices quietly cap how much speed value a system can capture.
Residual human review is a queue, not just a cost. If your agent’s accuracy leaves 25% of cases returning to humans, those cases inherit human turnaround: they wait for capacity. The labor line shows the cost; the queue shows up in cycle time, and the speed dividend on that volume is forfeited.
Redundancy multiplies compute time. Self-consistency voting buys accuracy by running 2 or 3 full passes plus verification. The token bill multiplies, and so does the clock: a measured multi-document evaluation workload completed in 50 to 105 seconds per single compiled cycle, while multi-pass configurations run minutes to tens of minutes per case. Production cycle time scales with case complexity on any architecture; the multiplier is what you choose.
Compiled execution keeps both hands on the dividend: high first-pass accuracy keeps cases out of the human queue (MightyBot runs 99%+ decision accuracy in production, with roughly 2% residual review), and deterministic plans clear cases in minutes rather than review-queue days.
Pricing it: the speed dividend
Our ROI calculator models this as an optional speed dividend, so the core comparison stays cost-only until you opt in:
- Current turnaround per case, in days. Elapsed time, queue included.
- Value of one day faster, per case. Interest carry, SLA exposure, working capital rate, or revenue pulled forward.
The dividend accrues per path, only on cases that clear without returning to the human queue, and only from the month a path is live. That last clause matters: a 15-month build forfeits the dividend on its entire volume for 15 months, which is a cost the build-versus-buy debate rarely prices.
The takeaway for the business case
Put three lines in the model, not two. Labor savings are real. Technology costs are real. And elapsed time has a rate: in lending it is literally the interest rate, in insurance it is the SLA schedule, in operations it is the working-capital line. When someone says the speed does not matter, ask what a day costs on your highest-value workflow, and multiply.
