Prove your AI ROI.

Measure engineering before and after AI, and read the gain as capacity you did not have to hire. Then tie the speed to the roadmap, and the roadmap to revenue.

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NThe ROI statementLast 30 days · 60 engineers
Engineering capacity · one mark is one engineer
AI spend$124,880last 30 daysAcross 7 AI tools
Capacity added+56engineersNo new people
Capacity vs pre-AI1.93×per developerGraded per commit in ETV, not AI usage
On the payroll60 engineers
Ships like116 engineer-equivalents
Real peopleCapacity, not people
+56 engineers nobody had to hire

60 engineers on payroll. $124,880 bought 56 more. The same spend, split three ways:

Went to features$64,688· 51.8% of spend
New value
No stated outcome$35,841· 28.7% of spend
$60,691 (48.6%) went to a named objective
What it cost$2,230per added engineer

$124,880 ÷ +56 of capacity

Monthly, at today’s spend
01The pain

Everyone bought the tools. Nobody can show what they bought.

“That link is not there yet.”
Andrew Macdonald · COO, Uber
We build that link
Fortune headline: Uber burned through its entire 2026 AI budget in four months. Now its COO is questioning whether it’s worth it.
Fortune · May 2026
02The method

Four questions turn commits into an ROI statement.

The first three are the math. The fourth is the one teams skip.

Q1How much capacity did AI add?Engineers of capacity
Q2Faster at what?Work mix
Q3What did the capacity cost?Cost per engineer added
Q4Was any of it on the roadmap?The part everyone skips

Read the gain as capacity, not a percentage.

A percentage is hard to sign off. Capacity is not: the same gain, read as engineers nobody hired and nobody has to onboard. Per team is where the next decision lives.

The same +56 engineers, split by team
Real peopleCapacity, not people
Capacity vs pre-AI
  • Platform team2.39× per developer
    18+25
  • Mobile team1.79× per developer
    14+11
  • Web app team1.69× per developer
    16+11
  • Data & ML team1.75× per developer
    12+9

Split the performance, or the number lies.

A gain means nothing until you know the category. Bug fixes? Maintaining AI slop? Or actual features? Good adoption keeps bug and maintenance share flat. If those buckets balloon, you’re paying the tools to clean up after the tools.

51.8%$64,688
22.3%$27,848
Features 51.8%$64,688· New valueMaintenance 22.3%$27,848· Keeping it runningTests 11.9%$14,861· CoverageDocs 5.1%$6,369· Written downFixes 8.9%$11,114· Bugs & slop

Price the capacity the AI bought you.

Pull requests are the wrong denominator: activity is exactly what inflates when work gets cheaper to produce. Divide by the capacity added instead.

$124,880
spent on AI last month
7 AI tools, one month
+56
engineers of capacity it added
scored per commit in ETV
$2,230
per AI-added engineer
a month, at last month’s spend

Hold that against what hiring the same capacity costs, and the budget conversation answers itself.

Cut the number itself with smart model routing
03Roadmap alignment

The part everyone skips, in dollars.

Was any of this on the roadmap? Or did the team finally build the thing they always wanted, now that they can? Connect Navigara to Jira and the month prices itself out.

AI spend · last 30 days$124,880
Roadmap aligned $60,691· tied to a named objectiveAligned, off roadmap $28,348· a ticket, no objective above itUnaligned $35,841· no stated outcome
04The payoff

Put it all together and you get faster roadmap delivery.

Every roadmap initiative carries its AI cost and the months it landed early. Your AI bill stops being a number and starts being a feature list.

Roadmap initiative
Planned against shipped
shippedplanned
  • Revamp checkout experience
  • Self-serve onboarding
  • Platform reliability
  • Payments rework
MarAprMayJunJulAugSep
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The same measurement, run in public across 676 engineers in six big-tech orgs. See the index →