AI Model Quality Is Table Stakes—UX Now Decides Adoption

The strongest signal is that AI features suffer from a trust crisis, not a capability crisis. Users, given poorly designed interfaces, get subpar results and disengage. Model improvements alone won't fix this; the real lever is UX patterns that build confidence—citations, visible reasoning, and human-in-the-loop approval turn skepticism into repeated use.

Comparing to the Macintosh UI evolution, she argues we are in an early phase where abstraction and guidance must be layered over raw AI output. Leaders, therefore, must treat UX design as a core requirement, not an afterthought, because users will abandon a tool after only a few failed attempts.

The talk structures the problem around five pillars—trust, clarity, control, transparency, meaningful benefit—mapped to patterns. For trust: source citations, agent action plans. For control: stop buttons, version history, granular undo. The hidden implication: these patterns are non-negotiable if teams want to avoid user rejection.

A counterintuitive insight: AI cannot yet design good AI interfaces because needed patterns are still being invented. The design burden falls on humans; leaders must resist deferring to AI-generated templates. The ultimate bet: UX, not model capability, will determine tool winners. This shifts focus from model selection to interaction design.