Your engineering
AI transformation
The AI transformation report your board keeps asking for, built from commits, pull requests, and tickets.
Trusted by engineering teams
no new people
676 needed before AI · 1.9× per developer
- AI transformation report, board view.
- Engineering AI assessment by work type.
- Engineering AI assessment by team.
- So you open one team and ask why.
- And this is where the work actually is.
AI transformation report, board view.
350 engineers now deliver what 676 would have before AI, for $226,900 a month. Three numbers say whether that works: capacity added, $696 for each added engineer, and 48.6% of it on the roadmap. All correct. None of them says what to do next.
Engineering AI assessment by work type.
A gain means nothing until you know the category. Here 51.8% of the spend built features and 31.2% kept old code running or fixed it. Only 48.6% answers to a named objective, and 28.7% to no stated outcome at all.
Engineering AI assessment by team.
The Platform team pays $541 a month for each added engineer. The Data & ML team pays $1,082. Nobody decided this, and one company-wide program will not fix it.
So you open one team and ask why.
Good at everything except one thing: nobody tells them what to build. So the fastest team in the company keeps building things nobody asked for. Capacity and the scores are measured separately: this is what sits behind the number, not what caused it.
0 = not started · 5 = fully there
And this is where the work actually is.
When writing code gets cheap, the hard part is knowing what to build. For the Web app team, the row holding it down is objective context.
- Ticket clarity1.8
- Epic decomposition1.4
- Roadmap coverage2.2
- Objective context1.2
NextWrite down why each objective exists, not only what it delivers
- Spec readiness1.6
AI transformation report, board view.
350 engineers now deliver what 676 would have before AI, for $226,900 a month. Three numbers say whether that works: capacity added, $696 for each added engineer, and 48.6% of it on the roadmap. All correct. None of them says what to do next.
no new people
676 needed before AI · 1.9× per developer
Engineering AI assessment by work type.
A gain means nothing until you know the category. Here 51.8% of the spend built features and 31.2% kept old code running or fixed it. Only 48.6% answers to a named objective, and 28.7% to no stated outcome at all.
- FeaturesNew value51.8%$117,534
- MaintenanceKeeping it running22.3%$50,599
- TestsCoverage11.9%$27,001
- DocsWritten down5.1%$11,572
- FixesBugs & slop8.9%$20,194
Engineering AI assessment by team.
The Platform team pays $541 a month for each added engineer. The Data & ML team pays $1,082. Nobody decided this, and one company-wide program will not fix it.
- Platform team2.4×$541 each105 people, delivering like 251
- Mobile team1.8×$641 each82 people, delivering like 146
- Web app team1.7×$787 each93 people, delivering like 156
- Data & ML team1.8×$1,082 each70 people, delivering like 123
So you open one team and ask why.
Good at everything except one thing: nobody tells them what to build. So the fastest team in the company keeps building things nobody asked for. Capacity and the scores are measured separately: this is what sits behind the number, not what caused it.
0 = not started · 5 = fully there
- Capacity
- 1.7×
- AI spend
- $49,550
- Cost per engineer
- $787
And this is where the work actually is.
When writing code gets cheap, the hard part is knowing what to build. For the Web app team, the row holding it down is objective context.
- Ticket clarity1.8
- Epic decomposition1.4
- Roadmap coverage2.2
- Objective context1.2
NextWrite down why each objective exists, not only what it delivers
- Spec readiness1.6
Objective context at 1.2 is the row holding this down. The other four are between 1.4 and 2.2.
An AI transformation report for the board.
An eight-page AI transformation report on capacity, adoption, process bottlenecks, and cost. You choose the repositories, teams, and period the engineering AI assessment covers. The example AI transformation report below uses an invented company; yours comes from your own systems.
The engineering AI assessment covers five areas and 25 checks, with evidence for boards and technical due diligence.
The state of your AI transformation.
Five questions, 25 checks, scored per team from the work itself, never from a survey.
- Capacity
- +326none hired
- Cost
- $696each
- Roadmap
- 48.6%of the work
350 engineers · 4 teams · 27 repos
Last 30 days
An invented company, so you can see the shape of the answer before you send us a single repository.
Inspect the engineering AI assessment.
25 checks across 4 teams. Read a row for one team, a column for what is weak everywhere, and open any cell for the evidence behind the score and the next step that raises it.
Swipe for every question · tap a cell to open itFive bars in every cell · click a cell to open it
No number here is an average. Each question scores as its weakest check, and the overall score comes from how much each team improves, reported as capacity, cost and roadmap alignment. The evidence behind all 25 checks is in the method page of the example report. Every cell is a check you can run nightly: see all 25 →
Read AI transformation report results in context.
The AI transformation report first compares your throughput with your pre-AI baseline. The engineering AI assessment then places you on the public benchmark below, measured the same way, for context.
What 100 engineers at each organization now deliver, on their public repositories. The company in this report is measured the same way, and ranked with them.
- #1OpenAI15.8× since Apr 2025+1,484per 100 real
- #2Microsoft7.3× since Apr 2025+630per 100 real
- #3Vercel5.1× since Apr 2025+409per 100 real
- #4Cloudflare4.9× since Apr 2025+394per 100 real
- #5Meta3.7× since Apr 2025+268per 100 real
- #6Google3.5× since Apr 2025+253per 100 real
- #7This report’s company1.9× · 350 engineers+93per 100 real
- #8UiPath1.4× since Apr 2025+44per 100 real
Same period, same measurement, 11× between top and bottom. The median company reached 4.94×. The difference is not talent, and it is not budget. Your own company is placed on this scale from your own repositories, public or private.
The same measurement on your own repositories, priced per engineer added, is AI ROI. What that capacity costs, and whether anyone asked for it, is token spend intelligence.
An engineering AI assessment for a buyer.
For a company you are buying, the engineering AI assessment links 25 checks to commits and tickets, and the AI transformation report proves the work behind the deck’s claims for due diligence.
- 45%from one team
Is the gain real, or is it a few people?
Platform team carries 45% of the added capacity with 105 of the 350 engineers. A strong result and a concentrated one. The report shows the split per team and per repository, before you price it.
- 51.4%off the roadmap
What happens if the AI budget is cut?
$226,900 a month is buying 326 engineers of capacity, and 51.4% of the work it funds is not on the roadmap. The report separates the spend that is working from the spend nobody asked for.
- +161already on payroll
What is the upside still inside the company?
Every team at Platform team’s rate would be 837 engineers of capacity instead of 676. That is 161 engineers you are already paying for and not yet getting.
AI transformation report questions.
What does an AI transformation report include?
Capacity added, cost per added engineer, roadmap share, developer adoption, and process readiness, team by team. Every finding in the engineering AI assessment links to the commits, pull requests, or tickets behind it.
How is an engineering AI assessment different from a survey?
A survey asks engineers how much they use AI. The engineering AI assessment reads what they shipped. The AI transformation report scores each team from repository and tracker evidence, and a low score comes with the examples behind it.
Does an AI transformation report prove a financial return?
No. The AI transformation report converts throughput changes into equivalent engineering capacity and compares them to cost. The engineering AI assessment adds work mix and roadmap share. Staffing and scope changes can move the result, and estimated capacity belongs on a different line from profit.
Can we use an engineering AI assessment for due diligence?
Yes. Agree on the company, repositories, teams, and period the engineering AI assessment covers. The AI transformation report provides traceable technical due diligence findings and sits alongside the financial model, never in place of it.
How should we read team differences in an AI transformation report?
Against each team’s own baseline first. A company average in an AI transformation report can hide that one team of 105 carries 45% of the gain. The engineering AI assessment shows the split, so one policy does not land on four different problems.
What happens after the engineering AI assessment?
Walk through the AI transformation report with the people who own the work. Pick the weakest check, give it an owner, and run the engineering AI assessment again next month. The second AI transformation report shows whether the fix moved anything.
Everyone says they use AI.
Almost nobody can show it.
Scope an engineering AI assessment for your company, or one you are buying. The AI transformation report comes from your existing history.