How to Automate Repetitive Tasks With AI

Start with a task ledger rather than a tool. For two weeks, record every repetitive task a team performs: what triggers it, what it produces, how long it takes and how often it happens. Price each row in hours per month. The ledger almost always shows that three or four tasks account for the majority of the load, which turns an open-ended AI project into a short list. For each of those rows, decide the mechanism honestly. If the task follows fixed rules and structured data, use integration and rules — it will be cheaper, faster and more reliable than a model. If it requires reading unstructured language, classifying intent, summarising or drafting, use an AI model. If the output is never used, stop doing the task. Then design the failure path before the happy path: what confidence threshold sends a case to a human, who receives it, and what the rollback is. Finally, instrument it. An automation without a before-and-after measurement is an opinion, and opinions get cancelled at the next budget review.