AI is ill-defined so the premise of your comment makes it difficult to answer. For small well-known tasks (image classification, object detection, sentiment detection) that is train-once on a single dataset and deploy-once what you are saying is true, but for more complex products there is a lot of arcane knowledge that can go in training/deploying/maintaining a model.
On the training side, you need to be able to define the correct metrics, identify bottlenecks in your dataloader, scale to multiple nodes (which is itself a sub-field because distributing a model is not simple) and run evaluation. Throughout the whole thing you have to implement proper dataset versioning (otherwise your evaluation results won't be comparable) and store it in a way that has enough throughput to not bottleneck your training without bankrupting the company (images and videos are not small).
Finally you have a trained model that needs to be deployed, GPU time is expensive so you need to know about compilation techniques/operator fusing, quantization and you need to be able to scale. The requirements to do that are complex because the input data is not always just text.
So yes all the above (and a lot more) require specific expertise.