Agents
Workflow Automation
Recurring processes — sorting an inbox, transferring data, producing reports — run without human involvement, with AI as the flexible part of the chain. The difference from classic automation: AI tolerates variation that used to break on special cases. The basic rule stands: first understand and declutter the process, then automate it. Otherwise you automate the chaos.
An example
An order arrives by email, gets entered into the ERP, checked, confirmed, scheduled. Five steps, four systems, three rounds of copy and paste. Classic automation failed here at step one: the emails are too varied to capture with rules. Language models close exactly this gap — they turn unstructured text into structured fields. The four steps after that remain ordinary process automation.
A closer look
What gets automated is a process that has been described: trigger, steps, decision points, exceptions, responsibility. Where that description is missing, you automate habits and only find out in operation which of them were special cases.
Why it matters
The place where AI actually earns money in companies is rarely the spectacular one and almost always this one: the spot where a person previously had to translate because something arrived as text, image, or PDF. Anyone looking for worthwhile cases is therefore not looking for “AI use cases” but for handoff points involving manual transfer. They are easy to find, easy to measure, and deliver a number before the investment decision, unlike most pilots.
Commonly misunderstood
A poorly cut process does not get better through automation, only faster at being wrong. The question of whether the step is needed at all comes first.