Are Your Developers Actually More Productive With AI?
A scored profile measuring real AI-augmented developer productivity vs. tool-usage theatre.
- A scored profile across 6 dimensions — see exactly where you're strong and where the gaps are.
- Your biggest opportunities, mapped to specific next moves.
- A personalized video walkthrough from Shawn (optional) — a real read on your results.
92% of US developers use AI coding tools daily. Only 29% trust the output. That gap — between how often AI gets typed and how often it actually ships — is the question most engineering leaders haven't honestly answered yet. Adoption is easy to measure and easy to celebrate. Productivity isn't. The orgs getting real lift from AI aren't the ones with the highest tool-usage rate; they're the ones where AI-generated code makes it through review, AI-generated tests catch real defects, AI-assisted debugging shortens MTTR, and seniors use AI to reason about tradeoffs, not just to type faster.
This free assessment scores your engineering org across six dimensions that separate real AI-augmented productivity from tool-usage theatre — in about six minutes. It's built from 27 years of technology leadership across Fortune 500 and growth-stage companies — the same lens a fractional CTO would bring to your first conversation about why the AI adoption numbers look great and the throughput numbers don't.
What the developer AI productivity scorecard measures
Productivity is a profile, not a single number. The scorecard evaluates six dimensions independently so you can see exactly where AI is delivering real lift and where it's mostly theatre: Code Generation & Acceptance (how often AI-suggested code actually ships without rewrites), Code Review & Quality Discipline (whether your review process catches AI-specific failure modes), Testing Velocity & Coverage (whether AI is moving behavior coverage up or just generating more lines), Debugging & Investigation (whether AI accelerates root-cause analysis or generates plausible wrong answers), Architecture & Design Use (whether engineers use AI to reason about tradeoffs), and Tooling Maturity & Integration (whether your IDE, CI, and agent infrastructure make AI native or sidebar). The final question maps the specific investment areas where targeted work would most move productivity in the next two quarters.
Why measuring AI usage isn't measuring AI productivity
Engineering leaders who measure AI tool adoption end up reporting numbers that go up while the metrics that matter — merged-PR throughput, change failure rate, MTTR, behavior coverage — don't move. The reason is that AI is easy to use and hard to ship. Suggestions look right and are subtly wrong. Generated tests pass against the code as written and don't exercise the behavior. AI-proposed fixes sound confident and are pointed at the wrong cause. The orgs capturing real productivity treat AI as an operating-discipline problem first: what gets measured, what gets reviewed, how seniors apply it, and where the tooling actually carries repo context. A productivity profile turns a fuzzy 'engineers say it's helpful' instinct into a measured read, and it tells you whether your real constraint is acceptance, review, testing, debugging, design use, or tooling.
What you get at the end
You'll see an overall developer AI productivity score, a band that describes where you stand (from Tool-Usage Theatre through AI-Native Engineering), a per-dimension breakdown across all six pillars, and a map of your highest-value investment areas across acceptance, review, testing, debugging, architecture, and tooling. From there you can request a personalized video walkthrough — a short, recorded read on your specific results and what a fractional CTO engagement would do for your org. No generic sales deck.
Frequently asked questions
What is a developer AI productivity assessment?
It's a structured evaluation of whether AI tooling is actually moving the metrics that matter in your engineering org — merged-PR throughput, change failure rate, behavior test coverage, MTTR, and senior-engineer leverage — versus simply being typed into IDEs every day. Rather than measuring AI tool adoption, it measures AI-augmented output.
How long does the assessment take?
About six minutes. It's 17 scored questions across six dimensions — acceptance, review, testing, debugging, architecture, and tooling — plus a final investment-mapping question covering where targeted improvement would most move the needle. Your progress auto-saves, so you can leave and come back without losing answers.
Is the assessment free?
Yes. The assessment and your scored results are completely free. You can optionally request a personalized video walkthrough of your results, which is also free.
Who is this assessment for?
It's built for CTOs, VPs of engineering, heads of platform, and engineering directors who have rolled out AI tooling — Copilot, Cursor, Claude Code, internal agents — and want a clear-eyed read on whether the org is getting real productivity lift or just high tool-usage rates. It's general-purpose, not specific to a stage or stack.
Why the 92% / 29% framing?
Recent industry surveys put daily AI coding tool usage at roughly 92% of US developers, while trust in the generated output sits near 29%. The gap captures the core question this assessment is built to answer: is AI shipping work in your org, or is it being typed and rewritten? Adoption is easy. Productivity is the hard part.