Act, Confirm, or Stop? Smarter behavior for AI assistants, wearables & robots

Official Schedule Context

Voice is our favorite way to command AI assistants and robots — and it is error-prone. The

industry's reflex is to chase accuracy, but accuracy is only one knob: we can control system

behavior in other ways to increase user satisfaction. This talk shifts the lens from accuracy to

user outcomes. Give the AI agent more than one move: besides acting, let it stop, reject, confirm,

clarify, or disambiguate. The question stops being "how often are we right?" and becomes "what does

each outcome cost the user?" Bad outcomes are not equally bad to users — so price them relatively,

then have the AI system minimize that user cost. Call it OUCH: Outcome User Cost Heuristic; we

optimize system behavior to minimize the OUCH. Same accuracy, lower user cost, greater user

adoption. We will walk through practical AI assistant examples illustrating this approach, then

show how the same framework extends across AI environments — smart speakers, TVs, glasses, embodied

AI, robots, wearables, and vehicles — by repricing outcomes and swapping the confirmation UI. Why

this matters now: the cost of voice-command errors is escalating as we move into AI assistants and

embodied AI, where wrong actions can be more expensive and dangerous. Mainstream voice adoption will

not come from chasing accuracy alone; we need systems to price in the cost of being wrong.

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