#ai-interface

#ai-interface

Users can describe their restaurant needs, such as 'find a table for two at a dog-friendly Italian restaurant in Shoreditch for Saturday at 7PM.'

"We're not saying that AI use is good or bad, just like we wouldn't say, substance use is necessarily good or bad, or consulting with a friend about something is good or bad."

For decades in SAAS, products reduced ambiguity. Users supplied constrained inputs, and the system handled the output. It's never been Minority Report cinematic, but it was predictable. By providing predictable environments for manipulating data, users learned by moving things, adjusting variables - and the outcome emerged through interaction.

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.

LLMs have made AI assistants a standard feature across SaaS. AI assistants allow users to instantly retrieve information and interact with a system through text-based prompts. Mathias Biilmann, in his article " Introducing AX: Why Agent Experience Matters," discusses two distinct approaches to building AI assistants. The Closed Approach involves a conversational assistant embedded directly within a single SaaS product. Examples include Zoom's AI Companion, Salesforce CRM's Einstein, and Microsoft's Copilot. The Open Approach involves external conversational assistants, such as Claude, ChatGPT, and Gemini,