I think about human AI interaction through the lens of product systems, not isolated features. In assistant interface design, 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 assuming chat is the right interface for every task, then discover that users need timing, control, and interaction patterns that fit the work. 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. The interface should match the job, not the model has to show up in the actual workflow, not just in a launch post or a demo script.
Fit the job
Chat is flexible, but it is also an easy default is where the product starts to prove itself. Many tasks are better served by forms, previews, inline edits, or one click approvals. 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 start with how the user already thinks about the task, then select the AI surface. 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, the interface should match the job, not the model should still guide the next step. The best AI experiences often feel less like a new ritual and more like a better way to do the same work.
Preserve agency
Users adopt assistants that make them feel more capable is where the product starts to prove itself. They need previews, easy edits, reversible actions, and permission to ignore suggestions. 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 make dismissal, correction, and undo feel normal instead of treating them as edge cases. 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, the interface should match the job, not the model should still guide the next step. The best AI experiences often feel less like a new ritual and more like a better way to do the same work.
Design for interruption
Real users are busy and context switching is where the product starts to prove itself. Long conversational rituals break down when someone has partial attention. 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 favor short confirmations, visible state, and progress cues that survive interrupted work. 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, the interface should match the job, not the model should still guide the next step. The best AI experiences often feel less like a new ritual and more like a better way to do the same work.