An AI workflow audit is a structured look at how work happens today and what would have to change for AI to help. It should produce a decision and a scoped next step, not simply a list of tools.
1. Map the real process
- What triggers the work, and how often?
- Who handles each step, including informal handoffs?
- Which systems and documents are involved?
- Where do waiting, repeated entry, and corrections happen?
- How is completion recorded?
2. Inspect the information
List the inputs and their owners. Check whether examples are available, whether formats vary, and whether the information is up to date. Identify confidential or restricted data before choosing a tool. A process that relies on missing information may need operational repair before automation.
3. Define the human checkpoints
- Which decisions require judgment or approval?
- What can the system prepare, and what can it actually change?
- Who checks outputs, and what source do they compare against?
- What happens when information is missing or contradictory?
- Can the original process be resumed if the pilot fails?
4. Account for the full cost
Include setup, software, usage, integrations, training, maintenance, and review time. Ask whether existing subscriptions already support part of the workflow. Establish who will own recurring expenses and improvements after launch.
5. Choose a decision
Every candidate should end in one of three outcomes: pilot now, fix the process first, or leave it as it is. A task with a stable template and clear review may be a pilot candidate. A task with ambiguous ownership may need process work. A rare, judgment-heavy decision may not be worth automating.
What the audit should leave behind
A concise process map, a prioritized opportunity list, the proposed pilot scope, known data and access requirements, baseline measures, and the person responsible for the result. That is enough to have a concrete conversation about effort and value.