With every back-office automation conversation now defaulting to "can we just use AI for that," it's worth saying plainly: for a large share of real business processes, traditional RPA is still the better tool. The two aren't competitors so much as different instruments for different jobs, and picking the wrong one costs either reliability or flexibility.
Where RPA wins
RPA excels at high-volume, rule-based, structured processes where the steps are well defined and don't change often — data entry between systems that don't natively integrate, reconciliation, standard reporting. It's deterministic: the same input produces the same output every time, which makes it easy to audit, easy to test, and easy to explain to a compliance team. For processes like this, an LLM-based approach adds cost, latency and a non-zero error rate to a problem that doesn't need any of them.
Where AI wins
AI earns its place when the input is unstructured — free-text emails, scanned documents with inconsistent formats, customer messages — or when the task genuinely requires judgement rather than a fixed rule. Classifying an ambiguous support ticket, summarising a long document, or extracting information from a form that has no fixed layout are all places where rigid RPA rules break constantly and an AI-based approach adapts.
The best systems combine both
In practice, the most robust automations we build aren't purely one or the other. A common pattern: AI handles the unstructured front end — reading a document, classifying an inbound request — and hands a clean, structured result to a deterministic RPA workflow that executes the rest of the process reliably. Human-in-the-loop review handles the cases where either system isn't confident, so exceptions get caught instead of silently mishandled.
A simple way to decide
Ask whether the process has a fixed, well-defined set of rules or genuinely requires interpretation. Ask how much it matters that the same input always produces the same output. Ask whether the inputs are structured or messy. The answers usually point clearly toward RPA, AI, or — more often than either camp likes to admit — a combination of both.