AI Readiness Assessment
A structured diagnostic, not a tooling audit: interviews, a review of your delivery data, and a look at how changes actually get made. You leave with a ranked list of what is really constraining output, and a written thesis on where the AI spend is paying off.
Who it is for CTOs and VPs of Engineering who are planning, scaling or rescuing an AI rollout
what usually brings people here
You are investing in AI across engineering and you cannot tell whether it is improving anything. Licence costs are visible, adoption looks healthy in the dashboards, and none of it shows up in what the business receives. Somebody is going to ask what the budget returned, and the honest answer today is that nobody can tell. The uncomfortable possibility is that AI is not the problem: it is amplifying a delivery system that was already the constraint.
- Engineers report writing far more code, and cycle time has not moved.
- Review queues and unclear requirements absorb whatever coding time was saved.
- Nobody can say which part of delivery got faster, because nothing was measured before the rollout.
- The board wants an AI strategy and engineering wants the release date back.
what I do about it
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Interviews across the whole chain
8 to 12 conversations of 45 minutes, from product to platform, reported without attribution. The constraint is almost never where the org chart says it is.
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Delivery data, read as it is
Your issue tracker and version control, read for where work waits rather than for how busy anybody looks. No new tooling and no instrumentation project.
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A review of how a change gets made
How work is defined, reviewed, released and owned. AI amplifies whatever this already is, which is why it is the part worth looking at.
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A written report and a leadership session
The findings in writing before we meet, so the session is spent arguing with the conclusion rather than hearing it for the first time.
what you leave with
- A ranked list of bottlenecks, with the evidence for each and the ones that turned out not to matter.
- A 90-day roadmap sequenced by what unblocks what, with an owner against every item.
- A realistic thesis on where the return on AI spend is, and where it is not.
- A metric set your team can keep reading after I have gone.
format, timing and price
- Format
- 2 to 3 weeks. 8 to 12 interviews, delivery data review, SDLC review, written report, session with the leadership team.
- Outcome
- A ranked list of bottlenecks, a 90-day roadmap, and a realistic thesis on where the ROI actually is.
- Price
- from €9,000
Fixed scope and fixed price once we agree the scope on the diagnostic call. Remote, with the closing session on site if you want it.
Free. No pitch. You'll leave with at least one named bottleneck.
questions people ask about this engagement
Do you need access to our code?
No. I need read access to your issue tracker and to version control history. I read where work waits, not what it says.
Is this a tooling comparison?
No. Which assistant you bought is usually the least interesting variable. If the answer really is the tooling, the report says so in one paragraph and you have spent a small budget to find out.
Who needs to be in the interviews?
Engineers, tech leads, product, and whoever signs off a release. The value is in the disagreement between those groups, so a curated list of confident people is the one way to waste the engagement.
What if the finding is that we are fine?
Then you get that in writing, with the evidence, which is worth having when the next person proposes a transformation programme.