The AI model can be fine-tuned by adjusting various hyperparameters such as learning rate, number of epochs, batch size, number and size of layers, activation functions, and optimizers. The hyperparameters can be adjusted based on the performance of the model on a validation set during the training process. Grid search, random search, and Bayesian optimization are some techniques that can be used to find the best hyperparameters for a given task. Additionally, transfer learning can be used to fine-tune a pre-trained model on a new dataset with similar characteristics.