A neural network is a type of computer model made of many small connected units arranged in layers. Each unit does a simple calculation and passes its result on. Together they can learn complicated patterns such as what a face, a sentence or a sales trend looks like.
It learns by example. During training, the network makes a prediction, compares it with the correct answer, and adjusts the strength of its many connections slightly to improve. Repeating this across a vast number of examples tunes it to the task. The strengths it ends up with are often called parameters or weights.
Neural networks sit behind most modern AI that retailers meet: voice recognition at a drive-through, product recommendations, demand forecasting, image recognition of shelves or people counting, and the large language models behind chat assistants.
They are powerful but not transparent. It can be hard to explain exactly why a network gave a particular answer, and they reflect the data they were trained on, including its gaps and biases. Check how a vendor tests accuracy on conditions like yours, such as your lighting, your products and your customers.
Knowing what a neural network is demystifies AI: it is a pattern-finder trained on examples, not a thinking mind, which helps set realistic expectations.