Automation with artificial intelligence is no longer the preserve of large corporations. Today any company (including yours) can hand repetitive tasks to intelligent systems that work on their own, without pause and without errors. In this article we explain what it actually is, how it works and why it can make a difference to your business.
“AI automation” is used today for such different things that the term has lost almost all meaning. Let us narrow it down: what it actually is, how it differs from automation as we have always known it, what it can do today and what it cannot.
The short definition
Automating with AI means making a system carry out, on its own, tasks that require interpreting information, not just following fixed instructions.
That word (interpreting) is the whole difference. A classic program does what you tell it, step by step. An AI system can understand a text it has never seen, work out what it is about and decide what to do with it.
The difference from traditional automation
Automation has existed for decades: a rule that moves emails to a folder, a macro that formats a spreadsheet. It works very well, but it has a hard limit: it only works if reality matches exactly what was anticipated.
An example. You want to sort incoming emails by urgency.
With classic automation you define rules: if the subject contains “urgent”, flag it as a priority. It works until an email arrives saying “I need this by first thing tomorrow” without using the word urgent. And it always arrives.
With AI the system reads the whole email and judges whether it conveys urgency, even if it is written in a way nobody anticipated.
Put another way: classic automation carries out decisions you made in advance. AI automation makes decisions within the limits you set for it.
What it can do today, specifically
- Read and understand text. Sorting emails, extracting data from invoices or contracts, summarising long documents, detecting personal data in a PDF.
- Write within a given context. Draft replies, proposals from templates, reports summarising what has changed.
- Hold a conversation. Answering repeat enquiries, understanding the question even when it does not use the expected words.
- Classify and prioritise. Sorting sales contacts by likelihood of closing, separating the urgent from what can wait.
- Connect systems that do not talk to each other. Here AI usually contributes little: the automation layer does it. But it is half the real work of any project.
What it cannot do, whatever anyone says
- Run unsupervised on tasks with consequences. It can draft; whoever sends should be a person when a mistake is costly.
- Understand a process nobody has managed to explain. If the knowledge only lives in someone's head, it has to come out of there first.
- Be right 100% of the time. No automatic system does. The right design is not the one that never fails, it is the one that knows when it is unsure and asks.
- Fix a badly designed process. Automating a mess produces a faster mess.
What a real project looks like inside
Almost nobody buys “AI” on its own. What gets built usually has three layers:
1. The connection. Que tus herramientas se hablen: el formulario con el CRM, el CRM con el correo, el correo con la facturación. Es la parte menos vistosa y la que más trabajo lleva.
2. The interpretation. Where the language model comes in: reading, classifying, drafting, deciding.
3. The rules and the limits. What the system does alone, what needs approval, what happens when something fails and who gets notified. It is the layer that decides whether the project is reliable or an experiment.
People buy thinking about the second layer. Success depends mostly on the first and the third.
When it is worth it
A task is a good candidate when it meets three conditions at once: it repeats a lot, it has a recognisable pattern and someone can explain the rules.
If volume is missing, it does not pay off however tedious it is. If the pattern is missing, there is nothing to encode. And if nobody can explain the rules, the first job is not automating: it is understanding the process.
An example with figures
Santa Coloma de Gramenet City Council had to anonymise around 5,000 documents a year before publishing them, removing names, ID numbers, addresses and bank accounts. Each took about 20 minutes by hand: over 1,600 hours a year.
It is a case where classic automation falls short: personal data does not always appear written the same way and a literal search misses half of it. The document had to be interpreted. With RelevX Redactor, the time per document went from 20 minutes to 2-3 seconds.
Where to begin
With one contained process with high volume and a clear pattern. You build it, measure it and, if the result is there, move to the next. Starting with everything at once stretches the time to the first result and makes it impossible to know what worked.
If you want specific candidates, here are the processes that automate best. To know what determines the price, what it costs and what it depends on. And if you would rather put numbers to your case, the savings calculator does it in thirty seconds.
Want to automate your company with AI?
We help you identify which processes you can automate and how. The first session is free and with no obligation.
Request your free diagnosis