AI and Machine Learning: Practical Applications
Real-world applications of AI and ML technologies that are transforming industries today.
Start with a decision a person already makes. What information goes in, what decision comes out, and what the customer sees when it is wrong. Without that, a model is a demo.
Reading invoices, sorting support mail, and ranking a catalog are sensible first jobs, because a person can still check the answer.
Write down what the system must not guess. Keep a record of the inputs. Shipping fast still needs that record.
In Ghana, the quality of the data and the network matter as much as the model. A fallback when the phone is offline saves you from a confident wrong answer.
Give each job an owner, a number that says it worked, and a way to turn it off.
A first use that can be audited
Start where a person already makes a repetitive decision: sorting support mail, reading invoices, or ranking a catalog. Keep a human check, write down what the system must not infer, and store the inputs you would need to explain a bad answer. Do not put a model on a payment decision until the webhook path and the privacy notice are already true.
Leave payments and personal data on the existing path
A model can sort mail or read an invoice. It should not decide that a Paystack or Mobile Money order is paid, and it should not infer sensitive facts about a customer. The privacy notice has to name the tools that actually see the data. This is engineering guidance, not legal advice.
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