I think about the AI definition of done through the lens of product systems, not isolated features. In production readiness, the impressive part is rarely a single model response. The real work is the chain of context, choices, permissions, interface details, and follow through that turns an idea into something people can depend on.
The pattern I keep seeing is simple: teams get excited by a happy path demo that makes the feature feel finished, then discover that users need reliable behavior after launch, not just an impressive review. That gap is where product judgment matters. It is also where a lot of AI work becomes either genuinely useful or quietly fragile.
My default question is not whether the system sounds smart. It is whether the system helps someone make progress with less confusion, less rework, and more confidence. Done means observable, recoverable, and owned has to show up in the actual workflow, not just in a launch post or a demo script.
Make done measurable
A useful done checklist has evidence attached to it is where the product starts to prove itself. Target scenarios, known failure modes, evaluation coverage, and ownership should be named before release. Hidden tradeoffs become support tickets, abandoned flows, and a slow loss of trust that is hard to win back later.
In practice, that means designing the surrounding system with the same care as the model behavior. I ask how the team will know the feature got worse next month, then make sure that signal exists. I look for places where a user could be surprised, blocked, or silently misled before those moments become production problems.
The test I use is whether the product still feels dependable when conditions are messy. If the data is incomplete or the system is uncertain, done means observable, recoverable, and owned should still guide the next step. A stronger definition of done makes launch pressure less likely to erase safety work.
Cover the ugly paths
Empty states, tool failures, conflicting instructions, and low confidence moments are part of the product is where the product starts to prove itself. Users rarely lose trust on the perfect path. They lose it when the system gets awkward and the interface goes silent. Hidden tradeoffs become support tickets, abandoned flows, and a slow loss of trust that is hard to win back later.
In practice, that means designing the surrounding system with the same care as the model behavior. I write the awkward cases into acceptance criteria so they do not disappear during implementation. I look for places where a user could be surprised, blocked, or silently misled before those moments become production problems.
The test I use is whether the product still feels dependable when conditions are messy. If the data is incomplete or the system is uncertain, done means observable, recoverable, and owned should still guide the next step. A stronger definition of done makes launch pressure less likely to erase safety work.
Treat polish as correctness
Uncertainty messaging, permission copy, and recovery UX are not cosmetic is where the product starts to prove itself. They are how users decide whether the system deserves more trust after it surprises them. Hidden tradeoffs become support tickets, abandoned flows, and a slow loss of trust that is hard to win back later.
In practice, that means designing the surrounding system with the same care as the model behavior. I review those surfaces with the same seriousness as data quality and tool behavior. I look for places where a user could be surprised, blocked, or silently misled before those moments become production problems.
The test I use is whether the product still feels dependable when conditions are messy. If the data is incomplete or the system is uncertain, done means observable, recoverable, and owned should still guide the next step. A stronger definition of done makes launch pressure less likely to erase safety work.