Imagine a hospital center in Libreville waiting for medical supplies stuck at the port. The teams know that some containers are critical, but they must also comply with customs regulations, traceability, and priority order. Can artificial intelligence (AI) help without creating favoritism or gray areas?
Another scenario: a small and medium-sized enterprise (SME) importer receives spare parts via Owendo. They no longer want to call their freight forwarder ten times a day. They dream of a dashboard that signals: this container is at risk of missing the delivery truck.
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What is it, concretely?
The subject is simple to understand: AI is no longer just used to predict a delay on a dashboard. It can also help organize physical movements: which truck leaves first, which container goes to which dock, which warehouse should prepare a receiving area, or which delivery needs to be monitored. Westwell presents this approach as a combination of an AI that acts on the ground, with autonomous electric vehicles, and an AI that organizes daily decisions. The real challenge is therefore not the magic of the software, but control: who decides, with what data, and according to what rules. When used correctly, this technology remains a tool for assistance, not an autopilot without human involvement.
Concrete case: what to do and what not to do
Questions to Ask Before Acting
- What decisions will the AI make: inform, recommend, or trigger an action?
- Are transport data clean, complete, and updated at the right time?
- Who validates a decision when it affects a client, an urgent delivery, or a customs procedure?
- Where is the data stored: in Gabon, in Africa, or outside the continent?
- Does the provider know how to explain why the system recommends a priority or a change in planning?
- Have the field teams been trained before the actual project start?
- What backup plan exists if the tool breaks down or gives a wrong priority?
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