In short. Artificial intelligence pays off where repetitive, high-volume work is done by hand today and takes hours every week. You start from a small, measurable case, see how many hours it really frees and widen it only if the number is there. Where volume is low or the process changes every time, it often is not needed.
Where do you start?
With the number, not the technology. The right question is not "which model do we use" but how much time, or how many errors, we take off people's plates. If someone today spends hours sorting identical requests, reading documents or copying data from one system to another, that is where automation pays for itself fast.
What can you automate with AI?
The requests we see most often are four:
- Assistants and agents that answer on the company's documents and data, not on a generic internet search.
- Repetitive processes, from sorting requests to everyday operations.
- Data extraction from documents: contracts, invoices, requests, emails. The AI reads, understands and sorts, with automatic classification.
- Internal copilots that take the boring work off the team, without replacing anyone.
How does it work in practice?
The method is always the same, in three steps:
- You pick one process and measure how many hours it costs today.
- You automate it on a small scale, inside the flow the company already uses.
- You see how many hours it really frees. If the number is there, you widen it. If it is not, you do not push to make the numbers add up.
To answer on company data we use RAG: the model answers on the company's documents and not on the world's, and the documents do not end up training other people's models.
When is AI not needed?
Often. A good if beats a model costing thousands a month if the problem is simple, and a well-made spreadsheet solves more than an agent if the data is scarce. AI is one tool among others: you use it when it is the right one, and if it is not you say so before writing a line of code.
Do you need a lot of data to start?
For an assistant on documents, the documents you already have are enough. For a custom model you need more data. If it is not there, better to know before than after.
Two real examples
For Vernice Italia we built a neural network that, given an RGB colour, says how to mix it. For the Fondazione Fashion Research Italy, an AI tool that puts historical textile archives back into circulation without selling them short: this article on the FFRI project tells the story.
Want to know if it pays off in your case?
Tell us about the repetitive job that takes up most of your hours: we will see whether it can be automated, and if it is not worth it we will tell you. You can see how we work on the AI and automation page.
