Separating signal from noise and focusing on what actually works.
I advise on AI integration, enterprise architecture, and transformation, with a focus on systems that can be deployed, maintained, and trusted.
Moving beyond pilots to production. Defining where AI creates value versus risk.
Platform strategy, modernization, and technical due diligence that lasts.
When technology initiatives require organizational change to succeed.
Recent essays on enterprise AI strategy, governance, architecture, and the operational realities of getting systems into production.
Google's Open Knowledge Format points at a real problem. Enterprise knowledge is scattered across documents, wikis, catalogs, and systems of record, so every AI project rebuilds context its own way. The format matters less than the direction: cleaner, more portable knowledge that isn't locked to one vendor.
Read on LinkedInMany companies built their first enterprise AI systems to retrieve internal information and generate plausible answers. That was a reasonable first move. It gets fragile once those same systems start influencing pricing, underwriting, compliance, and other decisions they were never architected to support.
Read on LinkedInThe “RAG is dead” argument is getting ahead of the architecture change. RAG has been one of the main ways companies connect AI models to their own information, but early implementations often treated company knowledge like a pile of searchable files. The next phase requires better decisions about sources, permissions, auditability, and where the AI should go for different kinds of answers.
Read on LinkedInI work with executive teams on AI strategy, enterprise architecture, and transformation. Whether you're scoping a new initiative or stuck on one in flight, a short conversation is often the fastest way to figure out the right next step.