The Rise of Deep Personalization in AI: Beyond Recommender Systems

The future of artificial intelligence isn’t simply about creating agentic systems; it’s about achieving deep personalization. Rather than relying on traditional recommender systems that correlate user behavior to identify patterns, large language models (LLMs) and AI agents are now capable of directly analyzing users to create highly customized experiences. This level of aggressive customization is increasingly demanded by users, and enterprises that deliver it effectively will likely gain a significant competitive advantage.

The Demand for Personalized AI Experiences

As Lijuan Qin, head of product at Zoom AI, explains, users want AI to understand their individual needs rather than making assumptions. The ideal scenario is, “Don’t try to randomize, or guess who I am. I notify you, this is what I care about.” This shift necessitates a move beyond generalized AI solutions towards those that adapt and respond to individual user preferences and contexts.

Zoom’s Approach to AI Personalization

Zoom is actively incorporating this trend with its generative assistant, AI Companion. Beyond basic functionalities like summarization and action item creation, AI Companion tracks opinion divergence and user alignment during meetings. Users can customize meeting summaries based on their specific interests and generate targeted templates for follow-up communications tailored to different personas, such as sales or account executives. The AI assistant can then automatically populate these documents post-call.

Further personalization is achieved through Zoom AI Studio, which allows for the processing of unique enterprise terminology and vocabulary, ensuring more relevant AI outputs. A deep research mode provides comprehensive analyses based on both internal expertise and external insights.

Maintaining Control and Ensuring Safety

Control remains a critical aspect of this personalization. Users have specific permissions over agent actions, with clear controls on follow-up activities. For example, users can determine whether the agent automatically sends emails or triggers a verification step when sensitive information is detected in transcripts. As Qin emphasizes, “The most important thing is we do not assume AI is smart enough to get everything right.” Users can track agent behavior, enable or disable features, and control data access to prevent inaccurate or off-target outputs.

The “Land Grab for Context”

Sam Witteveen, co-founder of Red Dragon AI and host of the Beyond the Pilot podcast, describes the current AI landscape as a “land grab for context.” Companies are realizing that the more they know about their users – their applications, daily tasks, and workflows – the better the AI can customize and improve its performance.

Leading Platforms in Personalized AI

Several platforms are demonstrating success in this area. Claude Cowork and OpenClaw are highlighted as examples where models can make decisions for users and respond to requests like, “You know a bunch of things about me. You’ve got all this context. Go and generate the skills that are going to assist me do a better job.” OpenClaw allows for extensive customization, enabling users to schedule tasks and receive tailored assistance.

Challenges and Considerations

However, personalization isn’t without its challenges. Token usage and security are paramount concerns. OpenClaw, for instance, has faced security issues, leading some enterprises to uninstall or ban its use. Proper uninstallation procedures are crucial to avoid unintended consequences, such as deleting the entire enterprise stack. The cost of personalization, driven by increased token consumption, must be carefully monitored and managed. Tracking relevant metrics is key to optimizing costs and ensuring a return on investment.

The Future of Enterprise AI

The ability to experiment with AI skills is becoming increasingly vital for enterprises. Companies that fail to do so risk being left behind. The build vs. Buy decision is also becoming more urgent as the demand for customized AI solutions grows. “skills” – the specific capabilities an AI agent possesses – may prove more important than model parameters (MCP) in shaping the future of enterprise AI.