Related question
Can we trust the AI features already built into the software we own?
Short answer
Judge the fit, not the label. An embedded AI feature does not make a tool suitable for a job it was never designed to do. We have seen a platform forced into a use case it was not built for deliver results that were roughly a quarter useful, with the rest defended as commitment to a decision.
The situation is common enough to be worth naming. A widely used platform had been mandated as the system for everything, including a use case it was never designed to handle. The person responsible for enforcing adoption held the line with a simple argument: the platform has AI built in, so we can do this.
The executive's assessment was blunt. The tool was delivering results that were about 25 percent useful for the purpose it had been forced to serve. Seventy-five percent waste, defended as commitment to a decision.
Built-in AI is often genuinely useful, and it has real advantages: your data may already be protected at an enterprise level, and your people already know the interface. Those are not small things.
What it does not do is change what the underlying product was designed for. The test is the same one you would apply to any tool. Was this system built for the job I am asking it to do? If the honest answer is no, an AI feature will not close that gap. It will just make the mismatch harder to see.