Artificial intelligence in business: 5 concrete use cases (and 3 to avoid)

Talking about artificial intelligence is easy; using it well in a business is a little harder. The most common risk is not that it "does not work": it is that it gets introduced without a clear purpose, because "everyone is doing it now", and then saves nobody anything. The right starting point is not the technology but the work: which activities waste your time and have a regular enough shape to be helped by a machine?

Here are five concrete use cases and three mistakes worth avoiding.

1. An assistant that answers repetitive questions

If your team answers the same questions every day (opening hours, price lists, procedures, terms), an assistant can take care of them for you. The difference from a classic rule-based chatbot is that it understands freely written questions and answers using only the company's content, not what it "knows" in general.

The advantage: immediate answers at any hour, and people can focus on the cases that really need their time. I go into more detail in AI chatbot for your website.

2. Search across documents

Contracts, manuals, minutes, historical archives: finding the right information can cost a lot of time. An intelligent search understands the question in plain language ("what is the termination clause with supplier X?") and answers pointing to the exact source, with a link to the original paragraph. Whoever reads can verify, and that is essential.

3. Reading and routing incoming requests

Emails, forms, reports: a person reads them, understands what they are about and routes them. AI can do this first reading, assign a category or urgency, enter the data into the management system and prepare a draft reply for approval. The last word stays with a person.

4. Content drafts and descriptions

Product descriptions for a catalogue, summaries, reports, versions in several languages: AI is good at preparing a first draft that is consistent in style and terminology. Human work becomes review, which is faster than writing from scratch. It is especially useful when there is a lot of content and it is similar.

5. Automations between the programs you already use

Time is often lost in the steps between one tool and another: copying data from an email to the management system, updating a sheet, notifying a colleague. AI, combined with connections between software, can carry out these steps automatically. If your problem is mostly data exchange between programs, start from How to integrate your management system, e-commerce and invoicing.

3 mistakes to avoid

Mistake 1: starting from the technology and not from the problem

"We must use AI" is not a goal. "We want to halve the time it takes to answer quote requests" is. Without a measurable problem, you cannot know whether the intervention helped.

Mistake 2: leaving AI unsupervised

AI can get things wrong, sometimes with great confidence. For delicate decisions (contracts, payments, replies to important customers) it should propose and a person should approve. A serious system provides verifiable sources, limits on what it can do and a way to report errors.

Mistake 3: neglecting data, privacy and costs

Before putting company documents into a service, ask yourself:

• is the data used to train the models?
• who can see what, and with which permissions?
• how much will it cost to use when requests grow?

Good design takes privacy and GDPR into account from the start and sets spending limits and monitoring, so there are no surprises.

Where to start

A sensible path is this.

1. List your team's repetitive activities and estimate the time they take.
2. Pick one or two with a clear saving and a result that is easy to check.
3. Build a prototype on real data, in a few days, before deciding anything else.
4. Have the people who will use it try it and gather feedback.
5. Measure quality, costs and time saved, and improve.

If after the prototype you find AI is not needed for that process, you have spent little and learned something. That is a legitimate result.

When AI is not the answer

Sometimes the problem is better solved without artificial intelligence: a well-made form, a rule-based automation, a management system instead of a spreadsheet. An honest consultant tells you so. If your problem is a process run with scattered sheets and files, also read Custom management system or Excel?.

How to measure whether it works

An AI project without measures is an act of faith. Before starting, choose two or three simple indicators and compare them over time.

• Time saved per activity: how long it took before, how long it takes now.
• Quality of answers or results: a sample is checked by hand and errors are noted.
• Share of requests solved without human intervention, in the case of an assistant.
• Cost of use: how much each conversation or each processing run costs.
• Satisfaction of those who use it, collected with a few direct questions.

If after a few weeks the indicators do not improve, you correct or change course.

Who in the team should be involved

AI works when the people who do the work take part in the design. Involve:

• whoever does the activity every day, because they know the odd cases and the exceptions;
• whoever answers for the result, who decides when an output is acceptable;
• whoever looks after privacy and data, for the limits on what can be shared.

Explaining early that AI is a support tool, not a replacement, reduces a lot of resistance.

Questions to ask whoever proposes an AI project

1. Which precise problem does it solve, and how will we measure it?
2. What data does it work with, and where is it processed?
3. What happens when it gets it wrong? Is there human oversight?
4. How much will it cost to use when requests grow?
5. Can I try a prototype on my data before committing?
6. How can I change supplier or model in the future, if needed?

Be wary of anyone who promises extraordinary results without having seen your processes.

Why entrust it to me

I have been developing software since 1999, with more than 300 projects delivered and about 30 agencies as an external developer. Today I also work on integrating AI and automation, connected to the company's data: the chat you find on this site is a real example, and it answers only what is written in its knowledge base.

My method: together we choose one or two activities where the saving is clear, I prepare a prototype in a few days on your real data and only then do you decide whether to go on. If AI is not needed, I tell you so. I work from Rome and remotely. To understand whether it makes sense in your case, write to me: a chat of about 20 minutes, no strings attached. The details are in AI integration and automation.