Imagine that, in a Libreville bank, an advisor uses artificial intelligence (AI) to prepare investment recommendations. The tool compares client profiles, reads financial reports, and suggests trades. One morning, it recommends the same operation to several hundred clients, almost at the same time.
Now imagine a small and medium-sized enterprise entrusting a software with the automatic payment of its suppliers. Everything speeds up, until the day a questionable invoice is paid without review. The question is no longer just: is the tool efficient? It becomes: who stays in charge when the AI acts?
What is it, concretely?
The subject is not about an AI that simply answers a question, but about a tool capable of acting after receiving an objective. It can search for information, compare data, propose a decision, call another software, trigger a payment, or help buy and sell financial assets. For a bank, insurer, or asset manager, the gain can be real: faster processing of files, anomaly detection, better decision-making preparation. The main risk comes from poorly controlled autonomy. An error can be repeated very quickly and affect many clients. The good news: with clear rules, audit trails, and a possible shutdown mechanism, usage can remain controlled.
Concrete case: what to do and what not to do
Questions to Ask Before Acting
Does AI propose an action or can it execute it on its own?
Which decisions must always be validated by a human: payment, credit, insurance, investment, account blocking?
Can the client understand why a decision was made and challenge it if necessary?
Is there a verifiable record of every recommendation, validation, and modification?
Are the data used necessary, authorized, protected, and limited to proper use?
Who can stop the tool in an emergency, and in how much time?
Does the vendor meet your security, privacy, and compliance requirements?
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