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What is AI actually worth to your engineering org?
“The tools feel faster” doesn’t survive a budget meeting. This calculator models the capacity your team unlocks by maturing AI across the software lifecycle — and translates it into the units a board actually funds: hours, engineer-equivalents, and dollars.
What you walk away with
A number you can defend.
Five inputs, grounded in industry benchmarks for where engineering time actually goes and how AI changes it, phase by phase.
- Nº 01
Your capacity gain
The projected lift across your whole portfolio — weighted by the work you actually do, not a generic benchmark average.
- Nº 02
The dollar translation
Hours returned per year, engineer-equivalents freed, and capacity value on your team size and cost basis.
- Nº 03
Where the gain comes from
A phase-by-phase breakdown across the lifecycle, so you know which investments move the number and which don't.
- Nº 04
The caveats that keep it honest
What the model assumes, where it breaks, and why capacity is not a headcount cut — so the number survives scrutiny.
How it works
- i.
Shape your portfolio
Start from a preset — legacy SaaS product, startup, modernization push — and drag until the mix looks like your year.
- ii.
Set today and the target
Pick the AI usage level your teams actually work at in each lifecycle phase, then the level you intend to reach in 12–18 months.
- iii.
Add your team's numbers
Engineer count and loaded cost turn percentages into a business case.
Built for
CTOs and VPs of Engineering who have to defend an AI line item — to a board, a PE sponsor, or a CFO — and want a starting number that’s better than a vendor slide. It’s the model we open on the whiteboard in a first working session.
Benchmarks adapted from industry research on engineering time allocation and measured AI impact across the lifecycle. Estimates, not guarantees — the honest version of the math, caveats included.
Part of the AI Value Proof engagementQuestions, answered
About this model.
Where the numbers come from, what they mean, and what they deliberately don't promise.
- What is AI actually worth to my engineering team, in dollars?
- This calculator gives you a defensible first answer. You describe your portfolio, how deeply your team uses AI in each lifecycle phase, and your cost basis; the model translates that into capacity — hours given back, engineer-equivalents, and dollars per year. Three minutes, and the result renders on screen.
- Where do the calculator's numbers come from?
- The time-allocation benchmarks and per-phase AI-impact curves are adapted from Gartner's AI Productivity Calculator for Software Engineering, applied to the inputs you provide. Cross-phase effects — like AI-generated code adding rework in testing — are already folded in, which is why the model doesn't just multiply your coding time by a discount.
- Is this a guarantee of savings?
- No, and we say so on the results page. It's a capacity model: what maturing AI across your lifecycle is worth if the delivery system around it lets the gains land. The caveats ship with the number — and the honest next step is measuring one real initiative, which is exactly what our AI Value Proof engagement does.
- Will this number survive my CFO?
- It's built for that conversation: the PDF shows the assumptions, the phase-by-phase breakdown, and the caveats alongside the headline, so it reads as a model to interrogate rather than a promise to doubt. A defensible directional number beats a precise-looking one nobody can explain.