About
A researcher who learned to think in systems, then in strategy.
I’ve always been curious about people—how they interact with the world, what shapes their behavior, and why they make the choices they do. That curiosity has followed me through every stage of my career. As an educator, I became especially interested in learning, assistive technology, and the ways thoughtfully designed digital experiences can make complex tasks more accessible.
My path into research and AI has taken me through both startup and enterprise environments. In the startup world, I worked alongside engineers to identify user needs and help define requirements for a new AI-enabled system. In enterprise settings, I’ve worked with complex platforms, workflows, and configurations where the challenge is often larger than any single interface or feature.
Over the past several years, my work has increasingly centered on AI: researching where it can create value, identifying potential use cases, evaluating AI-enabled experiences, recommending solutions, and helping translate evidence into decisions about what should actually be built. I’m particularly interested in the questions that come before implementation—where AI belongs, where it doesn’t, what work should remain human, and how intelligent systems can reduce unnecessary complexity without introducing new problems.
I believe this moment creates a meaningful opportunity for systems thinkers. AI can streamline processes, reduce manual work, connect fragmented information, and change how organizations operate—but only when the underlying problem is understood first. My approach brings together research, behavioral insight, systems thinking, and practical experimentation to help organizations make those decisions more deliberately.
When I’m not exploring some corner of AI, I’m usually looking for a very different kind of experience: off-roading around Red River Gorge, swimming with dolphins, traveling, or settling into a seat for a Broadway musical.
Where the credibility comes from
Research
Doctoral-level training and a decade of mixed-method work inside complex enterprise systems — customer, partner, and employee research where the question is rarely the one first asked.
AI
AI-enabled product research, agent and workflow design, evaluation and test-set development, research automation, guardrails, and human-in-the-loop systems that keep judgement where it matters.
Strategy
Turning evidence into product direction, roadmap sequencing, system improvements, adoption strategy, and the organizational recommendations that make any of it survivable.
Current areas of focus
- AI opportunity identificationWhere intelligence changes the shape of a workflow, not just its speed.
- Human-in-the-loop workflowsWhere judgement belongs, and what it costs to exercise it.
- AI evaluationShared definitions of quality agreed before the launch debate begins.
- Research operationsMaking evidence cheap enough that teams actually use it.
- Enterprise systemsDepth for experts without abandoning the people arriving new.