Make Your AI Opportunity Audit Traceable to Real Work

An AI opportunity audit is more useful when another person can trace each recommendation to work the business actually does. Instead of ending with a list of tools, give the owner a short record of the observed task, the evidence, and the next question to test.

The following structure is an editorial example you can adapt. It is not a claim about the contents of a particular paid template.

Follow one recent piece of work

Ask the person who performs the task to walk through a recent, representative example. An incoming inquiry, a document filing task, or a routine report can provide a useful starting point. Follow it from arrival to completion.

Record where information enters, who handles it, which systems are used, and what counts as finished. Ask what usually interrupts the process. Use authorized, appropriately redacted examples when taking notes; you do not need to copy an entire customer record into an audit.

Separate observation from recollection

“This takes about an hour” is useful context, but it is not the same as a timed sample. Label each figure as observed, estimated, or unknown. Keep waiting time separate from active work.

If someone spends ten minutes preparing a response but waits until the next day for approval, an assistant that drafts faster addresses only part of the delay. That distinction changes the recommendation.

Write one evidence card per candidate

• Task and owner: who does the work and when it occurs.
• Example reviewed: a safe reference to the case, with its date.
• Current steps: the path from input to a usable result.
• Observed problem: the repeated work, delay, or correction you found.
• Unknowns: missing information that could change the recommendation.
• Small next test: one way to learn whether a proposed change helps.

Keep a possible non-AI solution on the card too. A clearer form, an updated response template, or an assigned owner may address the issue without adding another tool.

Present a testable recommendation

Avoid promising an 80% improvement or a five-minute response target without a baseline and an agreed measurement method. Instead, propose measuring preparation time, correction work, and completion time on a small set of comparable cases.

Agree with the owner on the sample and what result would justify continuing. Include unsuccessful cases in the review. A demonstration with one carefully chosen input does not establish how the process will behave throughout a normal week.

Close the audit with a decision

For each candidate, record whether to test it, gather more evidence, improve the existing process, or leave it alone. Name the next owner and review date. This gives the client a practical handoff even when the right decision is to postpone automation.

AI Consultant Launchpad lists AI opportunity audits in its curriculum and an audit template among its resources. The program provides education and support, without guaranteeing income, clients, or business results. Get the Starter Kit to explore its introductory resources.