A common pattern in companies exploring AI: a tool is purchased or a proof of concept is built first, and the business case is written afterwards to justify it. This produces software that works technically but does not solve a problem anyone had prioritised.
An audit reverses the order. Before any implementation, it answers three questions.
What is actually happening today?
Most operational processes are only understood in outline by the people who do not perform them daily. A short set of structured workshops with the people who actually do the work — not just their managers — usually surfaces details that change the scope entirely: an extra manual step that exists because of a one-off exception two years ago, a system that is "the source of truth" on paper but not in practice, or a handoff that takes three days because of a single unavailable approver.
Where does the work concentrate?
Once the process is mapped, effort concentrates in a small number of places: a slow manual review step, a repeated data transfer between two systems that do not talk to each other, or a report that takes a full day to assemble every month. These points are where automation or better tooling produce disproportionate returns, and they are rarely the part of the process people mention first when asked "what should we automate?"
What is the realistic path to a result?
Not every finding leads to an AI system. Some lead to a straightforward integration between two existing tools. Some lead to a shared dashboard. Some lead to a policy change that removes a step entirely, which is often cheaper and faster than automating it. An audit should recommend whichever of these actually solves the problem, not default to the most technically interesting option.
A useful test for any audit recommendation: could you explain, in one sentence, the specific time, cost, or error currently being lost, and how the recommendation removes it? If not, the recommendation needs more work.
The output that matters
The result of a properly scoped audit is not a slide deck of possibilities. It is a short, ordered list: the two or three opportunities worth pursuing, a rough estimate of effort and impact for each, and a definition of what a safe first pilot looks like — including who approves exceptions, what data is involved, and how success will be measured before anything scales beyond that first pilot.