AI readiness checklist for mid-market teams

AI readiness is not a buzzword quiz. It is a concrete view of whether your organization can support useful AI workflows without breaking operations.

Start with systems of record

List where truth lives: CRM, ERP, ticketing, data warehouse, document stores. If a model cannot reliably read (and, when appropriate, write) those systems, you are buying theatre.

Check API and permission reality

Ask: can we access the fields we need under real auth? Are there rate limits, brittle exports, or tribal knowledge only humans hold? Integration debt is the most common AI blocker.

Name an owner for each workflow

AI without process ownership becomes a pilot nobody adopts. Assign who owns exceptions, quality, and the decision to stop or scale.

Prefer leverage over novelty

Fund use cases that remove handoffs or compress cycle time. Defer chat demos that do not touch a system of record.

Use a baseline, then go deeper

A public AI readiness scan is a fast external signal. Pair it with internal architecture work when the stakes are operational—that is where AI consulting earns its keep.