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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 engagement