What an AI Worker actually is
An AI Worker is a software component that takes over a defined task in your operation. Not as a standalone dashboard, but as an active participant in your existing process. The worker reads a document, queries your ERP, checks a condition, and then sets off an action: sending a message, creating an order, flagging a discrepancy. People review the exceptions. The everyday work carries on automatically. There's no self-learning model underneath inventing its own rules: the logic is transparent and signed off by the customer. That's also why it can run in a regulated setting such as customs or food.
Where people get stuck before they start
Most conversations open with a list of twelve processes that 'should really all be automated'. That's understandable, but it's also the quickest way to get nowhere. An AI Worker only works well when the input is reliable and repeatable. Anyone starting with a process where the input is structurally different every time, or where the decision logic can't be made explicit, ends up building something that generates too many exceptions to be useful within three weeks. Choosing the first process is therefore not a technical question. It's an operational one: what is the most predictable, high-volume work being done by hand right now?
Three patterns where it works well
First, status processing. Orders come in, get translated into a status in a system, a confirmation goes out. The same steps every time, high volume, little variation. Second, document-driven triggers. A waybill or packing slip comes in, the worker reads the relevant fields and sets an action off them. Works well alongside Dottle for the document processing. Third, watching threshold values. A delivery at risk of running late, stock below a minimum, a time window about to expire. The worker keeps track, so the planner doesn't have to monitor constantly. What these patterns share: the rules can be made explicit, the input is machine-readable or can be made so, and the exception is the exception, not the norm.
When an AI Worker is the wrong choice
If more than half the process consists of exceptions, it isn't really a process yet. A worker would just be an expensive way to wrap manual work. The same goes when the decision logic isn't clear-cut: if two experienced people reach different actions from the same input, the basis for automation is missing. A worker makes that problem visible, but it doesn't solve it. Finally: if your systems don't make the needed data structurally available, or the integration costs more than the process is worth, there's no point. We say that plainly in a first conversation too.
The question that settles it
Ask yourself one question before going further: could a new employee learn this process from a single-page instruction sheet? If the answer is yes, and the volume is high enough, this is a serious candidate for an AI Worker. If the answer is no, not because it's complicated but because it was never really written down, then the first step is to write the process down. That's not an obstacle. That's the work. And once it's done, the automation can move fast.