The future of AI isn’t just agentic; it’s deep personalization. Rather than simple recommender systems that correlate user behavior to identify patterns and apply those to individual workflows, large language models (LLMs) and AI agents can analyze users directly to create deeply personalized experiences. It’s this kind of aggressive customization users are increasingly demanding — and the savviest enterprises who provide it (and soon) will win. The goal is: “Don’t try to randomize, or guess who I am. I tell you, this is what I care about,” Lijuan Qin, head of product, at Zoom AI, explains in a new Beyond the Pilot podcast. How Zoom is incorporating personalizationZoom is one company that has adapted to this trend: Its generative assistant, AI Companion, goes beyond basic summarization, smart recordings, and after-meeting action items to opinion divergence and user alignment tracking. Users can customize meeting summaries based on their specific interests, and create targeted templates for follow-up emails to different personas (whether it be a salesperson or account executive). The AI assistant can then automatically populate these documents post-call. Meanwhile, a custom dictionary in Zoom AI Studio can process unique enterprise terminology and vocabulary for more relevant AI outputs, and a deep research mode can quickly deliver comprehensive analyses based on “internal expertise and external insights.”Control is key here; the human can be “very specific [and] nail down” agent permissioning, Qin explained. They have “very clear controls” on follow-up actions, such as: Can the agent automatically send emails to specific recipients? Or will it trigger a verification step when it recognizes transcripts contain sensitive information (as dictated by the user)? Knowing that AI can go off the rails at t …
