There's a debate in enterprise automation circles about whether RPA is dead now that AI can do "everything." Short answer: no. Longer answer: they're different tools for different problems, and the real power is in combining them correctly.

RPA is great at predictable, rule-based tasks. Click this button, copy that field, paste it here, repeat 500 times. It doesn't understand what it's doing — it just follows instructions. That's fine for processes that don't change.

AI is great at tasks that require judgment. Read this email and figure out if it's a complaint or a request. Look at this document and extract the relevant data even though every vendor formats it differently. That requires understanding, not just button-clicking.

Where companies get it wrong

The most common mistake we see is using AI where RPA would be simpler, cheaper, and more reliable. If your process is: "every morning, download a report from System A, reformat it, and upload it to System B" — that's RPA. You don't need a language model for that. You need a bot that clicks buttons.

The second most common mistake is using RPA where AI is needed. If the process involves reading unstructured text, making judgment calls, or handling variations — a rule-based bot will break constantly. That's a job for AI document processing.

The sweet spot: hybrid systems

Our best-performing systems use both. Here's a real example:

An insurance company processes claims. The RPA bot handles the mechanical parts: pulling the claim from the email system, creating a case in their claims management software, routing it to the right department. That's all rule-based — RPA handles it perfectly.

But determining the claim category, extracting damage amounts from free-text descriptions, and flagging potentially fraudulent patterns? That's where the AI model comes in. It reads the claim, classifies it, extracts the numbers, and adds a risk score. Then the RPA bot takes over again to update the case and notify the adjuster.

RPA does the clicking. AI does the thinking. Together they handle about 70% of routine claims without human intervention.

How to decide

Here's our rule of thumb:

  • If you can write an exact flowchart for the process → RPA
  • If the process involves "it depends" decisions → AI
  • If it's a mix of both → hybrid (which is most real-world processes)

The cost difference matters too. A rule-based RPA bot is usually significantly cheaper to build and maintain than a bespoke AI system. So if RPA can solve 80% of your problem, start there. Add AI only where it creates measurable value.

We've talked clients out of AI projects and built them RPA solutions instead. Saved them money, shipped faster, worked better. That's not a bad outcome.