AI readiness audit
An AI readiness audit answers a question that software demonstrations often avoid: is this business prepared to use AI well? The audit looks at the quality of information, the clarity of existing processes and the organisation's ability to review AI-assisted work.
Six areas examined
- Business goals: the outcomes that matter, such as faster response, lower rework or better lead quality.
- Workflow clarity: whether the current process is understood well enough to improve.
- Information quality: where authoritative documents live and how current they are.
- Technology fit: how new tools would connect with the website, email, forms or internal systems.
- People and skills: who will use, supervise and maintain the new approach.
- Risk boundaries: privacy, confidential information, accuracy and decisions requiring expert review.
The audit separates readiness from enthusiasm
A team may be excited about AI but still have inconsistent documents, unclear approval responsibilities or no way to measure quality. In that case, the best recommendation may be preparation rather than automation. Fixing the foundations first can prevent expensive rework.
A practical output
The final improvement map groups ideas into three categories:
- Quick wins: low-risk changes that can be tested promptly.
- Foundation work: information, process or website improvements needed before scaling.
- Longer-term projects: integrated systems requiring stronger governance or technical work.
Each recommendation should have an owner, a reason, a quality check and a way to judge whether it worked.
