An AI readiness audit evaluates whether a marketing team has the goals, data access, workflows, skills, governance, and measurement needed to use AI responsibly. The output should be a prioritized operating plan, not a score with no path forward.
Six areas, each with a concrete question and a concrete output.
| Area | Questions | Output |
|---|---|---|
| Goals | Which marketing outcomes matter now? | Use cases tied to business priorities |
| Workflows | Where do time, quality, or handoffs break? | Current-state workflow map |
| Data and access | What information can safely enter each system? | Access and data constraints |
| People and skills | Who owns the work and what do they need to learn? | Role and enablement plan |
| Governance | What needs human review, disclosure, or approval? | Working rules and escalation points |
| Measurement | How will the team know a pilot worked? | Baseline and success measures |
An AI readiness audit is a structured review of a team's goals, workflows, data, skills, governance, and measurement. Its purpose is to identify a small number of useful, feasible AI changes and define how to test them safely.
No. Tools matter, but readiness depends on the work around them: ownership, data, review, skills, and measures. A long tool list is not an operating plan.
The audit should include the executive sponsor, marketing operations, people who do the selected work, and any legal, security, data, or IT partner whose approval is needed.
The team should choose one or two pilots, establish a baseline, assign an owner, and schedule a decision point. The decision can be to adopt, revise, or stop.
The method: interviews, workflow review, evidence collection, prioritization, and a leadership readout.
Contact David BerkowitzWritten by David Berkowitz, founder and CEO of High Caliber AI, author of The Non-Obvious Guide to Using AI for Marketing. Reviewed September 9, 2026.