A one-page diagnostic

The AI readiness checklist for a business pilot.

The model is rarely the hard part. Ownership, acceptance criteria, failure handling and adoption decide whether a pilot becomes a dependable system or quietly dies. Sixteen practical questions to expose gaps before a build.

Tick every box that is honestly true today, not the ones you intend to fix. Empty boxes are your work list. The count updates as you go, and the checklist prints cleanly. This is a discussion aid, not a validated readiness score, certification or assurance that a system is safe.

Start here

What should be ready before an AI pilot?

Choose one process, name its owner and gather examples of normal work and exceptions. Record current volume, time, errors and review effort. Decide what the system may prepare, what needs approval and what happens when the data is missing. The sixteen checks below help organise that conversation.

If you have not chosen a process, use the first-workflow guide. If the process is clear, compare its build and running costs before commissioning a pilot.

0 / 16 true

Start tickingBe honest. Intentions do not count.

Ownership

Who is accountable when it is wrong?

One named person is accountable for this workflow's output. Not a committee, not "the AI team".
That person can change or switch it off without booking a meeting.
Someone owns the data it depends on and is answerable for its quality.
When the output is wrong, it is clear whose job it is to notice.

Acceptance criteria

How do you know it is good enough?

You wrote down what "good enough" means before you built it.
There is a test set of real examples with known-right answers.
You measure against that set on every meaningful change, not by feel.
The bar is a number a non-technical stakeholder agreed to.

Failure handling

What happens when it breaks?

You know its top three failure modes and roughly what each one costs.
When confidence is low it has a defined fallback: hand to a human, apply a rule, or refuse.
Failures are logged somewhere a human actually reads.
A bad output cannot silently reach a customer.

Adoption

Will people actually use it?

The people who must use it were involved before launch, not handed it after.
Using it is easier than the old way, not just better in theory.
There is a plan for the week trust breaks, because at some point it will.
Keeping it alive after the launch buzz is explicitly someone's job.

13–16

Most foundations recorded.

Review the evidence behind each answer and test the workflow before expanding its scope.

8–12

Gaps to close.

Use the empty boxes to agree what needs attention before committing to a pilot.

0–7

Early preparation.

Start with the process, its owner and usable examples. An assessment can help define the next step.

From experiment to operating system

Most pilots stall on two or three of these boxes.

That gap is the work: defining ownership, evidence, failure controls and adoption around one valuable workflow until it is dependable. That is what I help leadership teams do.

Explore an automation pilot Send a project brief Explore a readiness assessment

Based on the essay Why AI experiments fail to become dependable systems.