Artificial intelligence takes up so much space in public debate that it has become hard to assess. Between promises of total transformation and the scepticism of those who see only a passing fashion, SME leaders are left without practical bearings.
Our position is simple: AI is only worth it if it solves a problem you already have. Here are the three uses where we see real return on investment in the Ivorian context.
1. Answering repetitive questions
By far the most profitable use, and the simplest to put in place.
Look at the WhatsApp messages your business received this week. How many are about your opening hours, your address, your prices, whether a product is in stock or where an order is? In most of the shops we work with, the answer is above 70%.
An assistant connected to your catalogue and your procedures absorbs these enquiries in seconds, at any hour. Your teams then focus on the exchanges that genuinely require human judgement.
What it requires: that your information is up to date somewhere. An assistant plugged into wrong data answers wrongly, faster.
2. Extracting data from your documents
Supplier invoices, delivery notes, bank statements, purchase orders. These arrive as PDFs or photos, and someone retypes them by hand.
Current models read these documents and extract the information with reliability well above manual entry at the end of a long day. The gain is measured in hours per week, and above all in errors avoided.
What it requires: a verification step. Automatic extraction is not perfect; it must be checked — but checking is far faster than typing.
3. Drafting your commercial documents
Quotes, proposals, meeting notes, activity reports. If you regularly produce documents whose structure repeats, AI divides drafting time by three or four.
The nature of the gain matters: the assistant produces a solid first version that you correct, not a finished document. Going from blank page to draft is the expensive part — and that is exactly what it saves you.
The three mistakes we see most often
Starting from the technology. "We need AI" is not an objective. "We lose two hours a day answering the same questions" is.
Aiming too big at once. Automate one precise task first and measure the result. A modest early success convinces your teams more than an ambitious project that drags.
Neglecting the data question. Your customers entrust you with information. Check where it travels, who has access, how long it is kept. This is not a legal detail, it is a matter of trust.
Where to start concretely
Take a sheet of paper. Write down the five tasks your teams repeat most this week. Estimate the time each one consumes.
The one that combines the most time and the least human judgement is your starting point. In nine cases out of ten, it is answering customer enquiries.
The rest is a matter of implementation — and that is our trade.