Every AI tool vendor has a demo that looks flawless, because it’s running on their curated example data, in the one workflow they’ve optimized for. The gap between that demo and your actual documents, your actual edge cases, and your actual team’s tolerance for errors is where most tool evaluations go wrong.
A practical evaluation asks four questions before a single dollar is spent. First: what happens when it’s wrong, not just when it’s right — does a bad output get caught before it reaches a customer or a filing? Second: does it work on your messiest real data, not a cleaned-up sample? Third: who on your team is accountable for the output, and have they actually tried to break it? Fourth: what’s the cost of switching away from this vendor in eighteen months, once your process depends on it?
Tools that can’t survive those four questions tend to be the ones that get quietly abandoned six months after a confident rollout. Tools that can survive them are usually boring in the demo and reliable in production — which is exactly the trade worth making.