AI-Driven Navigation and Personal Assistance Tools: Designing for Seamless User Experiences

In the rapidly evolving world of artificial intelligence, one of the most important questions is no longer what AI can do, but how it can work alongside people. For product leader and AI researcher Chris Wunan, the answer lies in building systems that keep humans firmly in the decision-making loop, especially in high-stakes domains where accuracy, safety, and trust are paramount.

Today, Wunan is focusing his research and prototyping efforts on two verticals where AI can make a noticeable difference: healthcare for small or underserved clinics, and tools for novice or retail investors. Both areas share a common challenge, professionals and everyday users often face resource constraints, information overload, and the risk of costly mistakes. Wunan's work aims to design AI systems that support, rather than replace, the people making these decisions.

"The next phase of vertical AI isn't just smarter models, it's smarter collaboration between people and models," Wunan explains. His approach emphasizes "human-in-the-loop" (HITL) design, where algorithms offer guidance, highlight anomalies, or suggest possible actions, but final decisions remain in human hands. This model ensures that domain expertise, context, and ethical considerations remain central.

In healthcare, Wunan is exploring concept demos for AI-assisted triage and decision-support tools tailored for smaller clinics that often lack access to advanced infrastructure. Instead of handing over diagnostic authority to an algorithm, his prototypes focus on assisting medical professionals, surfacing relevant patient data, cross-referencing symptoms with medical guidelines, and flagging potential issues for review. The goal is to help clinicians make faster, more informed decisions without compromising care quality.

In parallel, Wunan is developing proof-of-concept tools for novice investors, people who may be managing their own portfolios for the first time. Here, the HITL approach prioritizes clarity over automation. Rather than executing trades automatically, AI could help users interpret market signals, run scenario analyses, and understand potential risks in plain language. "In healthcare or investing, safety and clarity matter more than speed," Wunan notes. "This is where thoughtful AI design is critical."

These projects are firmly in the research and exploration phase, with Wunan actively publishing papers that examine how domain-specific AI can be deployed responsibly. His current writing delves into frameworks for integrating expert oversight into AI pipelines, as well as the potential for multimodal AI, combining text, voice, and visual data to enhance usability for non-technical users.

By keeping the emphasis on human oversight and domain collaboration, Wunan's work is part of a broader shift in the AI field: moving away from "black box" automation and toward transparent, adaptive systems that professionals can trust.

While his earlier career saw him delivering large-scale AI solutions for global platforms, Wunan's current efforts are more exploratory, grounded in the belief that the future of AI will be defined not just by computational power, but by the quality of interaction between humans and machines. For healthcare providers and retail investors alike, that could mean tools that are not only intelligent but also collaborative, designed to augment human judgment not sideline it.