Lab · diagnostic

Know if AI is ready to ship.

Ten questions across data, infrastructure, process, talent, and ROI clarity. The output tells you whether to pilot, scale, or fix the foundation first.

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questions

10

dimensions

5

email gate

none

data kept

browser

honest score

Answer from where the business is today.

No inflated maturity language. No signup wall. The diagnostic only works if the answers are blunt.

0 / 10 answered0%
01 · Data foundation

How would you describe the state of your operational data today?

02 · Data foundation

Can a developer pull customer or product data via API today?

03 · Infrastructure & shipping

Where does your application infrastructure live?

04 · Infrastructure & shipping

How fast can a small change reach production?

05 · Process & evaluation

How is institutional knowledge captured?

06 · Process & evaluation

When something AI-related ships, how do you measure if it works?

07 · Talent & vendors

Who on your team is comfortable shipping production AI features?

08 · Talent & vendors

Have you worked with an external AI consultancy or contractor before?

09 · ROI clarity

What budget have you set aside for AI / automation in the next 6 months?

10 · ROI clarity

Do you have a specific business outcome you want AI to move?

how to use it

The score becomes a sequence.

Most AI failures are sequencing failures. This frames the first move before budget gets burned.

  1. 01

    Measure readiness

    Score the five areas that determine if AI will survive real use.

  2. 02

    Find the bottleneck

    Identify whether data, workflow, ownership, or ROI clarity is the constraint.

  3. 03

    Choose the first build

    Route to the right audit, pilot, agent, or operating-system fix.

  4. 04

    Ship with proof

    Move only when the system has acceptance criteria, evals, and a human owner.

livebuild 5d6c8652026-08-05 06:00Z
// solo studio// no analytics resold// every commit human-reviewed