When an SME decides to automate, two acronyms that get mixed up soon appear: RPA and AI. Sometimes they are sold as if they were the same thing, and they are not. Choosing the wrong tool means overspending or falling short. Let’s explain it in plain English so you know what you really need.
RPA: a robot that repeats your steps
RPA stands for “robotic process automation”. In practice, it’s a program that mimics what a person would do using the computer: opening a website, copying a piece of data, pasting it into another application, downloading a file, filling in a form. It follows fixed rules, step by step, without deviating.
RPA is ideal when the task is:
- Repetitive and done the same way many times.
- Based on clear rules: if this happens, do that.
- Predictable: the data always arrives in the same format and place.
Typical examples: downloading statements from several portals every morning, copying orders from an email inbox into your system, or moving data between two programs that don’t talk to each other.
AI: judgment for what isn’t exact
Artificial intelligence comes into play when the task requires interpreting, not just repeating. AI detects patterns and handles ambiguity: it understands a text written in a thousand different ways, recognizes data on an invoice even though each supplier uses its own design, or classifies an email according to its content.
AI adds value when:
- The information arrives unstructured or in variable formats.
- It’s necessary to understand language (emails, documents, customer messages).
- There is no fixed rule; instead, you have to estimate or classify.
Simple rule: if you can write the exact step-by-step instructions, RPA is enough. If the task requires “understanding” or “interpreting”, that’s where AI adds value.
Why combining them is often the best option
In the real world, processes are rarely pure. The usual case is a flow where each tool does what it does best:
- RPA downloads the invoices from a mailbox and organizes them.
- AI reads each invoice and extracts supplier, amount, and date, even though each has a different format.
- RPA loads that now-structured data into your management system.
- A person reviews only the exceptions that the system flags as doubtful.
This way you don’t pay for AI on tasks a simple rule solves, nor do you try to force RPA to interpret something it wasn’t built for.
An illustrative example
A consultancy receives expense receipts from its clients by email, in a thousand formats: photos, PDFs, screenshots. RPA alone can’t do it, because no two documents are alike. AI alone isn’t enough either, because someone has to move the data into the accounting program. By combining the two, RPA collects and files the documents, AI reads their content, and RPA records it. The team stops typing and moves on to supervising.
How to choose without being sold smoke and mirrors
The most common trap is for them to want to sell you “AI” for everything, because it sounds modern and commands a high price. Before deciding, ask yourself: does the task follow fixed rules or require interpretation? Does the data always arrive the same way or does it vary? How much time does it consume me today? With those answers, you can quickly see whether you need RPA, AI, or a mix.
At Bravo IA we build these automations with open-source software, letting your data live on your server and without locking you into a vendor. And we start with the simplest thing that works, seeking returns in weeks, not the flashiest solution.
If you have a task that steals hours from you and you don’t know whether it calls for RPA, AI, or none of that, we offer you a free audit: we look at it together and tell you frankly which tool fits and what savings to expect. No commitment and no smoke and mirrors.