Where AI actually returns money

Apply one rule: the highest-return AI removes recurring work from a recurring process where the data already exists and the process sits directly on a cost or revenue line. Judged that way the biggest unautomated cost in most companies is producing information, because preparing data, modeling it and rebuilding views happens every month, the data is already there, and it sits on a payroll line. Back-office transaction work ranks next. Customer-facing content ranks last, which is the opposite of where most AI budget has gone.

MIT studied 300 deployments in 2025 and found 95 percent produced no measurable profit-and-loss impact. Their conclusion was not that the models were weak but that the tools did not fit the work, so the same effort was still needed around them. That is the test to apply to any AI purchase: does it remove work, or does it add a faster way to do work you were already doing?

Price what you spend producing information today, then compare. The people side is the larger half and it is the one nobody totals: take your data headcount, multiply by a fully-loaded salary, and you have it. At the US median of $120,230 and a standard 1.3 multiplier, six people is about $937,800 a year before a single platform license. Then add the five or six platforms underneath. Compare that total to the platform price, and separately track one specific decision that changes, because a return you cannot point at is not a return.

Work that recurs monthly and already has its data. Reporting and data preparation qualify on both counts, which is why they pay back fastest and why they are usually last on the list. Anaconda has measured 37 to 45 percent of a data team's time going to data preparation four years running, and that share repeats every month rather than being paid once.

More than the license, and the gap is mostly invisible because it sits in payroll rather than in software. There are six layers to pay for: extraction, the warehouse, transformation and modeling, data quality, orchestration, and the business intelligence tool itself, which is usually the cheapest of the six. Add the people who operate them and a mid-market company is commonly over a million a year. Published aggregates for this vary widely, which is why the arithmetic on this page is built from a government salary figure and your own headcount instead.

The comparison to make is not software against salaries, it is the total cost of producing your information today against what it costs to have it produced. SQOR.ai is $48,960 a year for 25 users all-in with the data warehouse included, plus a nominal set-up fee that covers the proof of concept. What changes is that the production work stops being done by hand. What does not change is the need for people who know your business and can judge whether an answer is right.

Present it as a substitution rather than a new line, because that is what it is. Show the current fully-loaded cost of producing information including the payroll portion, show the platform cost against it, then name one specific leak you expect to close with a number attached and commit to reporting whether it closed. A board approves a substitution with a measurable outcome far more readily than a capability.

First findings in days, on your own data, read-only. A measurable return depends on what you point it at, which is why we would rather you pointed it at something specific and checked us than accepted a number in a deck. Onboarding is repaid inside a month purely on the difference against a conventional stack, before any finding.