For two decades, I've been the strategist and the designer, product teams usually split in two—shipping complex financial workflows in commercial banking, treasury, payments, KYC, entity management, and wealth management. I led digital transformation at Lincoln Financial, unifying advisor-facing platforms across Group Insurance, Retirement, and Annuities into a single coherent experience.

That dual fluency is now the baseline for AI-native experience architecture. AI products are probabilistic—they fail differently than deterministic systems, and they require design thinking that accounts for confidence, uncertainty, and model drift, not just flows and states. Most teams still separate the person who asks "what should this do?" from the person who asks "how will a human trust it?" That separation is why AI deployments in regulated environments stall after the pilot.

I work in that gap.

I'm the author of Semantic Interaction Design: A Practitioner's Framework for Designing AI Systems at the Intelligence Layer—the practitioner framework for designing intent architecture, knowledge structure, confidence boundaries, and context packaging in high-stakes AI systems. I write about the operational reality of building AI-native design practices in regulated environments on Medium and in my forthcoming book, Designing Intelligence: A Practitioner's Handbook for the AI Era.

I also founded Visayan Labs, an AI and financial risk intelligence venture in early product development.