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The AI-nativity diagnostic

An evidence-grounded read of where your organization actually stands on the path to AI-native — and the one next move you can defend. Fixed-scope: not a transformation engagement, not a lead-gen funnel. The read is the deliverable.

What you get

  • A graded position, not a label. L0–L5 placement per capability area, graded against the 38 technologies of an AI-native organization (13 deployed & measured), with your coverage gaps named.
  • One defensible next move — tied to your most consequential gap, with what it changes and how you'd know it worked.
  • Both languages. Capability gaps for engineering; learning, role, and trust gaps for people leadership. One read, one map.

How it runs

  1. Self-check first (when it ships — waitlist open): a preliminary L0–L5 placement in about five minutes, no call.
  2. The full read: structured evidence collection with your teams — technical and people side.
  3. The verdict: your position on the map, named gaps with evidence classes, one next move — presented so you can defend it to your board, or to yourself and your team.
  4. Re-measure when you've moved (and if the gap is a learning gap — Agents University is the teaching arm built on the same discipline).

How the read is built

  1. The technology map. A graded tree of the 38 technologies that make an organization AI-native — each tagged with its evidence class. The backbone the read is derived from, not a checklist invented for the pitch.
  2. The coverage schema. An interactive map of what an AI-native organization has to cover — including where today's 24 published standards leave gaps open.
  3. Your diagnostic. Your L0–L5 placement and named gaps are derived from that map and schema — reproducible and inspectable, not a consultant's slide.

What it costs

The read is a fixed-scope diagnostic — you know the scope before you commit to anything. No build contract attached.

Why the usual options leave you guessing

  • Generic maturity models. The big transformation practices — Accenture, BCG X, McKinsey QuantumBlack — read your AI maturity off a model built for everyone. It gives you a level, not a measured position — and a level can't tell you why your agents fail. Our read works the other way around: the breakage points to the missing capability, and the read names which one sits behind it.
  • The 40-item roadmap. A transformation engagement hands you forty things to do and a build contract attached. You wanted to know where you stand and what to do next — you got a backlog and an invoice.
  • Reading and guessing. Infer your own level from reports, approve spend on instinct. No measurement, no named gaps, no position you'd stake a quarter on.

What you're probably worried about

"This is the front-end of a big contract."

The self-check runs with no call, and the read is the deliverable — not a lead-gen funnel. The map, the coverage schema, the datasets, our own score — all public before you ever talk to us.

"This is priced like a transformation engagement."

It isn't one. The read is a fixed-scope diagnostic — you know the scope before you commit to anything.

"The assessment will be generic and I'll learn nothing."

The read is graded against 38 named technologies with named gaps and evidence classes. You leave knowing which capabilities are missing and which one to move on.

"A polished site, not a discipline."

So we hold ourselves to the same scale — and the scale is strict. By it, even we score L0.9 / 5 today (self-assessment) — including the case where we measured our way to disproving our own hypothesis. Strict scales are the point: an easy scale would just tell you you're fine. Want to see where you land?

What it looks like once you can measure it

For the enterprise leader

Monday's board meeting: you open with a measured position, one named gap, and one move — not forty. Every question the room asks, the read has already answered. You're not defending a guess; you're presenting evidence — and you can see, on the map, whether the next quarter compounds.

For the builder

Six weeks after the read: the failure that kept burning you has a named cause, the fix shipped, and your team switched the automation back on — because they trust it again. You know what to build next, and why.

Honest notes

  • The diagnostic offering is in validation — this page is part of that validation. Early reads get more of our senior attention, not less.
  • We hold ourselves to the same scale: L0.9/5 today — a self-assessment, published reasoning included. Strict scales are the point.

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