AI is not simply another tool to add to existing work. Its promise depends on understanding the problem, redesigning the process, and building the skills and culture needed to use it well.
Why this work fits together
My path runs from management and technology consulting through business intelligence, data science and machine learning, and now AI transformation and adoption. That range helps me connect executive priorities to workflows, data, technical delivery, and the people who will actually use a solution.
I have co-authored Rutgers research on state AI readiness, published a systematic review of explainable AI in healthcare, and conceived and led a live Camden health-equity data platform. Each has reinforced the same lesson: useful technology needs evidence, a real use case, and an organization capable of acting on it.
How I work
I begin with the outcome: what needs to be solved, achieved, or improved? From there I examine the process, identify a specific use case, consider the capability people need, choose appropriate technology, and attend to the culture that supports experimentation and sustained change.
Sometimes the right contribution is a framework or a conversation. Sometimes it is a platform, a data product, or an applied research effort. I am interested in both the thinking and the making.
Finding your way around
The transformation framework explains how I think. The technical portfolio shows what I build. Research and writing develop the ideas, while the reading room gathers some of what is shaping my perspective.
If one of those threads connects with a problem you are working on, let's talk.