My approach

Understand. Build. Test.
Help people use it.

I connect research with hands-on building, contextual testing, and the guidance people need to adopt a solution.

01
Understand the work
Define the problem from actual practice.
I use interviews, in-person observation, surveys, process maps, and landscape research to understand what people need and where the current process breaks down.
02
Build the solution
Make the opportunity concrete.
I map the workflow, identify where AI or automation can help, and build a version that can be evaluated. That may be an agent, an application, a process redesign, or a training resource. I work directly with vendors and technical teams when the solution requires collaboration.
03
Test in context
Look beyond the first successful result.
Testing examines routing, data relationships, output accuracy, exceptions, and the experience of the person doing the task. I compare prototypes with existing processes, review requirements with specialists, and use feedback to revise the work.
04
Enable adoption
Give people what they need to use the work.
I create setup guides, prompts, demonstrations, and training, and present findings to decision-makers. I follow up with users, maintain guidance as systems change, and document the engineering or distribution needs for broader adoption.

Working principles

Trust is part of the design.

Keep judgment visible. Define what AI drafts and what a person decides.
Be precise about results. Distinguish a prototype, a delivered resource, and a measured outcome.
Design for the real task. The workflow matters as much as the tool.
Make the next step clear. A finding is useful when someone can act on it.

Let’s connect

I’m interested in AI innovation and solutions roles where research becomes useful systems.