# AI Age Readiness Checklist

Use this checklist when the business wants to prepare for the AI era without confusing hype with operational readiness.

## Workflow Pressure Points

Score where the business is already under strain:

- inbound calls
- form and text response
- estimate or consult follow-up
- review response
- knowledge handoff between people
- repetitive admin work

If the current workflow is chaotic, AI adoption should start there instead of in novelty experiments.

## Knowledge Capture

The business is more AI-ready when it can clearly document:

- service categories
- qualification rules
- escalation rules
- pricing or quoting logic
- FAQs and objection handling
- handoff expectations

If that knowledge lives only in the owner’s head, the AI layer will stay weak.

## Tooling Readiness

Before adding more AI systems, confirm:

- lead data lands somewhere visible
- intake has ownership rules
- the website has clear next actions
- the team already tracks basic response and conversion signals
- there is at least one person responsible for workflow quality

AI does not fix missing ownership.

## Adoption Sequence

Adopt in this order:

1. diagnosis
2. workflow clarity
3. knowledge capture
4. narrow automation
5. measurement and iteration

That sequence prevents the team from buying tools before it understands what the tool is supposed to improve.

## Risk Controls

- define where humans must stay in the loop
- document escalation triggers
- protect sensitive or high-value situations from generic handling
- review outputs for brand and compliance risk

AI readiness is partly about judgment boundaries.

## Team Readiness

The team should know:

- what AI is being used for
- what still requires human judgment
- where they report issues or weak outputs
- which workflows are being improved first

Fear and confusion slow adoption more than the tools themselves.

## Monthly Maturity Review

- Which workflow improved?
- Which workflow is still messy enough to block adoption?
- Where is the owner still acting as the hidden fallback?
- What knowledge still needs to be written down?
- Which AI use case actually created measurable relief or lift?

## Failure Modes

- buying AI tools before documenting the process
- automating low-value tasks while high-value workflows stay broken
- treating prompts as strategy
- skipping measurement because the tool “feels advanced”