Hands-on Research and Design Leadership
I treat human-automation trust and collaboration as a design problem with decades of research behind it. My PhD dissertation studied how pilots and air traffic controllers calibrate trust in flight deck and ATC automation. The same questions now sit at the center of every enterprise AI product I've worked on, from Evisort's contract intelligence application to Vendr's SaaS marketplace. The cost of getting this wrong is a feature that ships and quietly fails. The cost of getting it right is measurable: at Evisort, I drove a 91% increase in AI-powered feature adoption through iterative design and validation.
I move roadmap conversations from HiPPO-driven to evidence-driven. Product, design, and engineering leaders stop debating what users want and start working from what users actually do. Research stops being a checkpoint and becomes the shared language the team uses to argue about priorities. At Vendr, I drove research for the AI-powered SaaS marketplace that helped double conversions.
I build insight-to-action pipelines. Research success should measured by how often a finding drove a decision. These days, that requires AI-augmented synthesis to keep pace with product velocity. At Vendr, I built supervised LLM workflows that cut research analysis cycle time in half, plus an AI-powered, self-serve research exploration artifact that democratized access to insights across the company.
Case studies from recent product research and strategy work.
I designed and validated a new-to-world AI-powered clause library for the Evisort contract management platform, resulting in a successful feature launch and a massive increase in user adoption within three months post-launch.
My research and recommendations improved product-market fit, increased visitor understanding and trust, and led to a 100% increase in conversions.
I built a repeatable three-method evaluation practice and applied it across multiple organizations, leading to better user experiences for my clients' customers and conformance with accessibility guidelines and standards.
Using rapid discovery, facilitation, and design techniques, I designed and validated a predictive analytics application for workers at one of the world's largest copper mines.
I identified key personas and unmet user needs for a next-generation navigation application, leading to a safer and useful in-car navigation experience.
I'm currently exploring principal, staff, and director-level roles at companies building complex products. Open to strategic consulting roles as well. Reach out if your team is working on wicked problems where rapid and valid research needs to be wired into how you make decisions.
I used AI to help me create the content on this site. Did I write every word? No. Did I review, brainstorm, and iterate on the content with some AIs? Of course. Why wouldn't I? Web copy has a distinct rhythm and tone. It's punchy and effortlessly authoritative. That's not how I write. Or think. So of course I used AI for some of the site copy.
Our oldest kid tells me that I should've written all this myself without relying on the clankers. I get it. But I also like to work fast and ship. So I jammed on the content with Opus 4.6, had it build an easily-maintainable site, and stood up v1 within about 24 hours from the first prompt. It's a tool. One I found helpful.
Again, I get it. I understand that current AI offerings are just uncomprehending stochastic parrots that we grant a sometimes-concerning amount of agency. And data centers are the next socio-ecological disaster-in-progress. I feel like I'm making typical Gen-X excuses here, and maybe I am. But what am I gonna do? Not use AI? In this economy? You first. ;-)
I wrote that disclosure. And this sentence. And now I'm trying to avoid an infinite loop.