// glossary
AI Readiness Audit plain-English.
Operator-grade definition. Plain words, plus where the term shows up in real work.
What is a AI Readiness Audit?
An AI readiness audit is a focused assessment of a company's data, codebase, team, risk surface, and existing tooling against a specific AI bet — answering whether the company is actually ready to ship that bet, and what needs fixing before it can.
The audit is not a generic AI maturity report. It is anchored to a specific bet: 'we want to ship a customer-support copilot' or 'we want to put agents on our internal ops.' Against that bet, we audit data access, identity and auth, integration surface, observability, regulatory posture, and the team's ability to operate the system once it ships.
A good readiness audit comes back with a short verdict and a checklist: green, yellow, or red on each dimension, with the cheapest remediation for each yellow or red. The point is to surface the blockers before the pilot, not to fail at the production wall because nobody checked.
The 14-point checklist we actually run covers: data quality, data access, identity, integration auth, vendor lock-in risk, latency budget, cost budget, eval feasibility, observability coverage, kill-switch authority, regulatory surface (EU AI Act, sector rules), customer-comms surface, operator runbooks, and the hand-off plan.
Related questions
How long does an AI readiness audit take?
Five to ten working days for a focused bet; longer for cross-BU enterprise scope. Anything shorter is a vibe check; anything longer is scope creep.
What is the most common blocker an audit surfaces?
Either missing observability (you cannot operate what you cannot see) or missing identity/auth (your model cannot act on behalf of a specific user safely). Both are infrastructure debts, not AI problems.
Can we audit ourselves?
You can run the checklist; the value of an external audit is the outside perspective on which yellows are actually reds in disguise. Self-audits tend to under-rate familiar problems.
Related work
Need someone to actually run this in production? book a call.