Method
Augmentation, Not Replacement
AI earns its place by making skilled people more effective, not by substituting for them. The goal is leverage, not headcount reduction. I've watched organizations chase AI adoption the wrong way: deploy fast, ask questions later, and wonder why the tools sit unused six months in. The problem usually isn't the technology. It's that nobody stopped to ask whether AI was the right answer in the first place. My approach starts before anything is built. I sit with teams, listen to where the friction actually lives, and ask a question that sounds simple but rarely gets asked: should AI be involved here at all? Sometimes the answer is no, and that is a win too.
Governance, Not a Free-for-All
Ungoverned AI adoption creates risk, inconsistency, and technical debt. Structure and guardrails aren't bureaucracy. They're what make adoption sustainable. Every agent I deploy has a defined charter: who it serves, what decisions it supports, and what it must not decide on its own. Usage is tiered by risk. Outputs are reviewed before they reach production, stakeholders, or public channels. The governance model is what separates a tool that earns trust from one that creates liability.
Living Systems, Not Set-and-Forget
AI systems that aren't maintained become graveyards. I build for iteration: clear owners, feedback loops, and a plan for what happens when things change. Launch is the beginning, not the finish line. I establish usage signals, review cadences, and update protocols so the system evolves alongside the organization rather than calcifying at v1. A well-governed AI system should get better over time. Most don't, because nobody planned for maintenance.
Intent Before Architecture
I listen before I configure. Every AI engagement starts the same way: understanding how people actually work. Before I recommend a model, build an agent, or automate a workflow, I want to know where the friction is, what success looks like, and whether AI belongs there at all. Sometimes the best solution is an AI system. Sometimes it is a simpler process, a clearer handoff, a better prompt, or a conversation that removes the need for technology altogether. That judgment is the work. Building the solution is often the easy part. Knowing what should be built and what should not is where experience creates value.