Finetuning would never add any new knowledge. For internal corporate data, use RAG or train a model from scratch. Finetuning would not help anyone answer a question from that data.
In a strict sense, finetuning can add new knowledge but for that you need millions of tokens and multiple runs without using LoRA or Peft. For practical purposes, it does not.
But chatbots are only one single use-case. And broadly I think this pattern of LLM-as-store-of-knowledge is a bad one (of course, until ASI, and then it isn't).
That said, you absolutely can impart new knowledge through fine-tuning. Millions of tokens is a rather small hurdle to overcome. And if you're not retraining with original/general data, then your model will become very specialized and possibly overfit...which is not an issue in many instances, and may even be desirable.
(not mine)