Most organizations understand that AI is important. Far fewer know where to start, which problems are actually worth solving with AI, or whether their data is in any condition to support it. That gap — between knowing AI matters and knowing what to do about it — is where my work begins.
My approach starts with organizational reality: the structure of the company, the state of its data, the pressures it's operating under, and the constraints that any realistic AI initiative has to work within. The question isn't what AI can do in theory — it's what AI can do here, for this organization, with the data and resources it actually has.
That means my work looks different from conventional AI consulting. I lead with strategic analysis and feasibility rather than technology selection. I surface data readiness gaps before they become costly mid-project surprises. And I produce working prototypes — not slide decks — so stakeholders can evaluate what an AI initiative actually feels like before committing to a full build.
I focus especially on cross-border operations between the United States and Japan. Bilingual capability in English and Japanese is rare in the AI strategy space, and the gap between how these two business cultures approach data, communication, and process is significant. I build AI tools that work for genuinely bilingual and bicultural organizations — not translated versions of US-centric tools — is a specific and underserved problem.