AI audit - how to find processes to automate

The easiest way to find processes worth automating is to observe real work briefly and supplement it with team conversations. Record where time goes, group activities into processes, and compare today’s cost with delivery effort, risk, and plausible benefit.

Step 1: measure a week, not an hour of conversation

For five working days, participants can record the activity, time, and number of repetitions in one shared sheet. Compare this with system logs where available. The aim is not employee surveillance; it is to make recurring work visible and to understand the process load rather than an individual.

Step 2: turn activities into processes

“Entering invoice data”, “checking it against an order”, and “putting it into a system” are one invoice-handling process, not three separate opportunities. For each process, record its start, end, owner, systems, inputs, decisions, exceptions, volume, time, and consequence of error.

Step 3: compare candidates consistently

Rate expected impact, implementation effort, data readiness, error cost, and ease of measurement. A frequent process with stable rules and reversible mistakes is usually a stronger first pilot than the task people merely find most annoying. A complex, high-risk process may still be valuable, but it needs a different starting scope.

Step 4: decide whether rules or AI are needed

Use deterministic automation where data is structured and rules are stable. Add an AI layer when unstructured text or documents require interpretation. An AI agent is appropriate only where later steps genuinely depend on earlier outcomes. Do not add a model where a rule is cheaper and more predictable.

Step 5: pilot, measure, and assign an owner

Run first on historical data or beside the live process. Compare the outcome with human work, then use an approval mode before granting autonomous actions. Add logs, alerts, and a named owner for exceptions. The first measure should include both time or cost and a quality safeguard, such as error or return rate.

An audit should produce a decision document: priorities, current-process description, estimated benefit and effort, risks, sequence, pricing ranges, and initiatives not recommended yet. The document should remain useful with any delivery team.

Our AI audit starts at 2,000–6,000 PLN net and usually takes one to two weeks. If you already know the problematic process, describe it.