Neuronify
Free tool

AI ROI Calculator

You've paid for AI tools. If your team isn't using them, that's money on the table. See what stalled adoption is costing you — and what closing the gap is worth.

Your numbers

A conservative model: value per person = their cost × the share of a 40-hour week AI gives back. We only count people actually using AI well. Adjust every number to your reality.

Value unlocked by reaching 70% adoption

$531K / yr

about $44,271 every month

$850K

Cost of stalled adoption today (vs. full adoption / yr)

$10,625

Value per proficient user / yr

Get a tailored breakdown

We'll email your numbers and show how we'd move your team from 20% to 70% adoption.

How the math works

The model is deliberately simple and conservative, so the number is easy to defend to your CFO:

  1. Value per proficient user = their fully-loaded annual cost × the share of a 40-hour week AI gives back.
  2. Total potential = that value × everyone who could use AI.
  3. We only count the value from people actually using AI well — so the current-adoption slider is what really moves the number.
  4. Value unlocked = the gain from moving from today's adoption to your target.

It deliberately ignores softer gains (quality, speed-to-market, retention) and one-time costs — it's a floor, not a forecast. The point isn't a precise figure; it's that the gap between buying AI and adopting it is worth real money.

What the inputs actually cost, as of September 9, 2026

Every AI ROI model divides by the cost of the people doing the work, and most of them guess at that number. We don’t have to. New York’s pay transparency law requires employers to publish a good-faith salary range on roles performed in the city, so we read those ranges off the job boards of 47 New York AI companies each morning. Today 92% of the city’s open AI roles carry one.

Across 154 engineering postings, the midpoint of the posted range sits at $215,000, with the middle half of the market falling between $180,500 and $250,650. That is base salary, not total compensation.

LevelPosted medianFully loadedCost per working hour
Mid-level$175,000$228,000$110
Senior$185,000$241,000$116
Staff / principal$240,000$312,000$150

Fully loaded applies a 1.3× multiplier for payroll taxes, benefits, equipment and software — the middle of the usual 25–40% range. Equity is excluded because it is not a cash cost in the year. Hourly assumes 2,080 working hours.

That last column is the one that matters here. If a fully loaded senior engineer costs roughly $116 an hour, then five hours a week returned to that person is worth about $27,808 a year — before you count a single new thing they build with the time. Multiply by headcount and the reason adoption matters more than licence spend becomes obvious.

Three ROI models, and when each one is honest

1. Time returned

The model this calculator uses. Hours saved × loaded hourly cost × adoption rate. It is the easiest to defend and the easiest to overstate, because saved time only becomes money if it is redeployed onto something that matters. Honest when your team is capacity-constrained and has a visible backlog. Dishonest when the reclaimed hour just becomes a longer lunch, and everyone in the room knows which situation they are in.

2. Throughput

Units of work completed per period, before and after. Tickets closed, claims processed, contracts reviewed, candidates screened. Harder to game than time saved because the denominator is a real count you were already tracking. This is the model to use when you have a queue with a measurable length — and the one auditors and boards find most credible.

3. Revenue or loss avoided

Deals closed, churn prevented, fraud caught, penalties dodged. The largest numbers and the weakest attribution: revenue moves for many reasons, and anyone can claim credit after the fact. Only use it when you can run a holdout — a comparable group that did not get the tool — and be sceptical of any vendor case study built on this model without one.

Pick one and stay with it. Most AI business cases fall apart because they silently switch models between the pitch and the review, quoting time saved to get funded and revenue to declare victory.

Why most AI ROI numbers don’t survive a CFO

  • Base salary used instead of loaded cost. Understates the value of time returned by roughly 30%, which is the one error that works in your favour and still gets the model thrown out when it is found.
  • Licence cost mistaken for total cost. Seats are the small number. The real spend is the engineering time to integrate, the data work to make outputs trustworthy, and the review time a human still spends checking the machine.
  • Assuming adoption. A tool bought for 400 people and used well by 60 returns 15% of the modelled value. Adoption is not a rollout detail; in this model it is the multiplier on everything.
  • No baseline. If you did not measure throughput before the pilot, you cannot claim an improvement afterwards. Capture the baseline in the two weeks before anyone touches the tool, because you will never be able to reconstruct it.
  • Counting the pilot as the cost. Pilots are cheap and production is not. The step from a working demo to something the business depends on is where budgets go, and it is routinely left out of the business case entirely.

What to measure in the first 90 days

A number produced by this calculator is a hypothesis. These four measurements turn it into evidence, and all of them are cheap.

  1. Weekly active use, per person, by team. Not seats issued. The gap between the two is the whole story, and it usually shows up within three weeks.
  2. Throughput on one queue. Pick the single process with the clearest count and track it before, during and after. One well-measured workflow beats five anecdotes.
  3. Rework rate. How often AI output gets rejected or heavily edited. Rising throughput with rising rework is not a win, and this is the number vendors never volunteer.
  4. Time-to-first-value for new users. Days from access to first useful output. If it exceeds two weeks, the constraint is enablement, not the model.

If those four move in the right direction on one workflow, you have something worth scaling. If they do not, you have saved yourself an expensive rollout — which is also a return, just not the kind anyone puts in a deck.

Questions

How does this AI ROI calculator work?

It estimates the annual value of AI for your team using a simple, conservative model: each proficient AI user gives back a share of their working week, worth that fraction of their fully-loaded cost. We only count people actually using AI well, so raising adoption raises the value. You control every input.

What is a realistic time savings from AI?

Studies of knowledge workers using AI well on suitable tasks commonly show 1-2 hours saved per day, though it varies widely by role and task. The calculator defaults to a cautious 5 hours per week — adjust it up or down for your reality.

How is this different from the AI Readiness Assessment?

The calculator quantifies the prize. The AI Readiness Assessment diagnoses where your team stands across five dimensions and what is blocking adoption. Together they tell you what the gap is worth and how to close it.

Should I use base salary or fully loaded cost?

Fully loaded. Payroll taxes, benefits, equipment and software typically add 25-40% on top of base pay, so using base understates the value of time returned by roughly a third. We apply a 1.3x multiplier throughout, and exclude equity because it is not a cash cost in the year.

What does an AI engineer actually cost in New York?

From salary ranges posted on New York job listings — which the city’s pay transparency law requires employers to publish — the midpoint of engineering postings currently sits around $217,500 base, with the middle half of the market between $185,000 and $253,500. Staff and principal roles run materially higher. We re-read those ranges daily, so the figures on this page track the market rather than a survey from last year.

Why does adoption matter more than the cost of the tools?

Because adoption is a multiplier and licences are a constant. A tool bought for 400 people and used well by 60 returns about 15% of the modelled value, no matter what the seats cost. In almost every business case we see, the seat spend is a rounding error next to the gap between issued and actually used.

How long before AI investment shows a return?

On a single well-chosen workflow, expect a readable signal within 90 days: weekly active use, throughput on one queue, rework rate, and time-to-first-value for new users. Enterprise-wide returns take considerably longer and are much harder to attribute, which is why we recommend proving one workflow before scaling.

Is this calculator biased toward buying something?

It is deliberately built to produce a floor rather than a forecast. It ignores softer gains like quality, speed-to-market and retention, and it counts value only from people who are actually using AI well. If the number it produces is small for your team, that is a real answer and worth acting on.

Now close the gap.

The calculator shows the prize. The free AI Readiness Assessment shows exactly where your team stands and what's blocking adoption — the first step to capturing it.

Take the free AI Readiness Assessment

Explore More

AI Enablement

AI enablement via vetted training partners.