The AI Reliability
Audit

What the audit is.

AI fails to deliver for one of two reasons, or both: the content it reads is unreliable, or your people have no defined way of using it. The audit examines both, not the tool itself. Most organizations have both problems. Before anything is fixed, you need to know which one is the bigger problem and what to fix first. I trace each wrong or unreliable answer to its source in your content, and I read whether low usage is really an adoption problem, so the diagnosis rests on evidence rather than instinct.

Where this sits.

When this is the right place to start.

You have rolled out an AI tool and it is not delivering. It may look like any of these:

You suspect the problem is the content, or the way people use it, but you cannot yet point to where.

Six questions give you a reliability score and your single biggest risk. Take the 2-minute Reliability Quiz →

Which of your sources it actually reads, and what it is set up to draw on. Often the tool is pointed at the wrong content, or at nothing dependable at all.

Every reliability problem traces back to four gaps in what your AI reads: Discovery (the right content cannot be found), Authority (competing versions with no clear source of truth), Freshness (out-of-date content the AI repeats anyway), and Transfer (knowledge that left with the people who held it).

Your platforms are already logging why the AI is failing: usage reports, search and zero-result logs, the questions your assistant could not answer. Almost no one opens them, and fewer can read them. I do, and I turn them into a clear list of what is actually going wrong.

What your people actually ask, set against what your content can answer.

I run genuine queries against your AI and trace each poor answer to its cause, so you see the problem in concrete terms, not in the abstract.

Whether each function has a defined way of working with the AI, or whether it sits unused. Sometimes the content is sound and the real gap is adoption, which points to a different fix.

Repair the worst of it in weeks — and prove it.

Where the audit uncovers urgent failures, a short sprint can repair the worst of them within weeks, re-running the same questions that failed to show the difference, measured before and after. If you already know you want the fixes and not just the diagnosis, the audit and the sprint can be booked together.

You stop guessing.

You know exactly why the AI is failing, you have evidence you can show your team, and you have a clear order of operations. The tool stops being a mystery, and the next steps become obvious. You can act on the roadmap with your own team, or continue with me.