Those are reasonable opening questions. In my experience, they are rarely the questions that determine whether AI scales safely inside an enterprise. They are just the entry point.
More than once, I have watched a meeting begin with a simple request to approve an AI coding assistant and end twenty minutes later in a debate about repository access, model approvals, prompt retention, audit trails and whether an agent should be allowed anywhere near a deployment workflow. That is the pattern that matters.
What I have seen instead is a predictable progression. First comes enthusiasm around copilots and coding assistants. Teams want faster code completion, quicker debugging, better documentation and help writing tests. Then the conversation shifts. Leaders start asking what these tools can see, where prompts go, which models are approved, whether responses are retained and how generated output should be reviewed. Then the issue gets bigger again. Once AI starts interacting with repositories, tickets, pipelines, internal knowledge, APIs and systems of record, the problem is no longer the assistant itself. It is the control plane around it.