"The majority of AI products remain tethered to a single, monolithic UI pattern: the chat box. While conversational interfaces are effective for exploration and managing ambiguity, they frequently become suboptimal when applied to structured professional workflows. To move beyond "bolted-on" chat, product teams must shift from asking where AI can be added to identifying the specific user intent and the interface best suited to deliver it."
"Know/Learn - "I want to make sense of this."Objective: Reducing uncertainty through sense-making and explanation. Create - "I want to create or change this."Objective: Generating or transforming artifacts without losing authorship or control. Delegate - "I want this done for me."Objective: Delegating multi-step workflows to an AI operator. Oversee - "Let me step in and stay in control."Objective: Providing high-stakes review and correction of AI-proposed actions."
Most AI products use a single, monolithic chat-box interface that fits exploration and ambiguity management but often underperforms in structured professional workflows. Product teams should prioritize identifying specific user intents and matching each intent to the most suitable interface instead of bolting chat onto workflows. A taxonomy of eight core user intents—Know/Learn, Create, Delegate, Oversee, Monitor, Find/Explore, Play, and Connect—captures distinct goals like sense-making, artifact generation, delegated multi-step work, high-stakes review, continuous updates, option comparison, entertainment, and emotional presence. Meta-intent axes act as tunable variables that adjust system behavior, including personalization.
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