Why the technology usually isn't the problem
When an AI initiative fails to deliver value, the cause is often not that the model was inaccurate, it's that the people who were supposed to use it never adopted it. A well-built tool that nobody trusts, understands, or bothers to open is worth nothing, regardless of how good its underlying model is. This is a people problem wearing a technology costume, and it needs to be treated as one from the start of a project, not patched on afterward.
Resistance usually comes from a specific, reasonable place: people worry the tool will make their job harder before it makes it easier, they don't trust outputs they can't explain, or they were never given a real reason the change benefits them rather than just the organization. None of these are irrational objections, and none of them get resolved by better model accuracy alone.