The demo is the easiest room
A good AI demo is supposed to feel unfair. The model answers fast, the screen looks clean, and nobody has had time to ask how the thing will survive a normal Monday.
That is why I do not trust the demo as the main unit of progress. The useful question is what changes when the person leaves the meeting, opens their real inbox, finds the messy spreadsheet, and has to decide whether this new workflow is worth interrupting the old one.
Adoption is a trust problem
Most enterprise AI work is not blocked by imagination. People can imagine summaries, agents, copilots, contract review, reporting help, and workflow automation very quickly. The harder part is whether they trust the system enough to move a piece of real work through it.
That means the product work is half technical and half social. You need the model behavior, the data boundaries, the handoff, the escalation path, and the person in the business who can say, yes, this saves time without making us reckless.
What I look for
I care about use cases where the before-and-after is boring enough to be real: a report arrives faster, a contract abstraction gets reviewed with clearer evidence, a process map stops living in someone's head, a team uses the same language for the same workflow.
The best signal is not applause in the demo. It is repeat usage after the novelty is gone.