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AI support readiness assessment

Most disappointing AI support rollouts are not model failures. They are input failures: nothing worth grounding in, a queue whose categories mean different things to different people, or no agreed boundary on what the AI may do. Eight questions, scored across those four dimensions, with the weakest one named.

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Grounding

How much resolved-ticket history could an AI read?

Grounding

Where do the answers to your common tickets actually live?

Queue consistency

If two people triaged the same ticket, would they pick the same category?

Queue consistency

How is priority set today?

Automation opportunity

What share of your tickets are repeats of something already answered?

Automation opportunity

Are your common fixes reversible and verifiable by a system?

Guardrails

Could you write down today which actions AI may take without approval?

Guardrails

Who would own the rollout — reviewing agreement and promoting categories?

Your readiness

0 of 8 answered. Nothing is sent anywhere — this runs entirely in your browser.

By dimension

Grounding
0/6
Queue consistency
0/6
Automation opportunity
0/6
Guardrails
0/6

Why these four dimensions

Grounding caps accuracy: an AI can only be as right as the best source it can read, which is why knowledge structure matters more than model choice. Queue consistency decides how much of what looks like AI error is really your own taxonomy disagreeing with itself.

Automation opportunity is the honest size of the prize — repeat rate times what is safely actionable, which the deflection estimator quantifies. Guardrails decide whether the rollout survives its first mistake; the guardrails playbook covers the allow-list and never-automate register, and the 30-day rollout plan is the sequence once you are ready.

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