I write about how Finance and Business Operations change as AI moves from experiments and copilots into governed workflows, decision systems, context layers, and operating models — so the business can move faster without giving up trust.
AI will compress a large part of Finance preparation work — gathering data, reconciling numbers, explaining variance, drafting narratives, assembling evidence, preparing reviews. But productivity is not the strategic prize. The bigger shift is from Finance as a periodic reporting function to Finance as governed decision and value-capture infrastructure — which takes trusted data, deterministic calculations, reusable context, workflow ownership, evidence trails, controls, and clear human accountability. The operating model matters as much as the technology.
I write about AI transformation from the perspective of someone trying to make it work inside large enterprise functions — the parts usually left under-discussed: trust, context, governance, decision rights, evidence, workflow ownership, and the last mile between insight and action.
AI can produce an answer that looks right. Getting one the business can trust is the harder problem — what it takes to automate executive reporting without letting AI invent, alter, or mishandle governed numbers.
Read → 2026The durable advantage in Finance AI is not the first useful agent. It is the accumulated context future agents can inherit — the case for a shared context layer where assumptions, corrections, source choices, and judgment compound.
Read →A working concept for how Finance could operate when AI is built into the function's core workflows, not bolted onto the side — decision systems, value-capture workflows, governed agents, certified data, deterministic calculations, evidence, and human accountability. Not a chatbot demo — a model for how Finance could become faster without becoming less trustworthy.
Open the portal →I help companies redesign how work gets done with AI.
I am a strategy and operations executive with 15+ years of experience leading transformation across global B2B SaaS and technology companies. The core thread of my career has been turning enterprise strategy into cross-functional execution: aligning leadership teams, building operating models, and driving change across GTM, Product, Services, Customer Success, Business Operations, and Finance.
My current work focuses on AI transformation inside enterprise functions. I am especially interested in what happens after the pilot: how AI moves into recurring workflows, how teams govern agentic systems, how trusted data and context are reused, and how operating models change when preparation work becomes dramatically cheaper.
At Workday, I have led AI transformation work across Business Operations and Finance — production agents for reporting, forecasting, and business analysis; shared data and knowledge layers; natural-language analytics; redesigned workflows; and governed AI platforms for trusted deployment.
My view is simple: the biggest opportunity in enterprise AI is not task automation by itself. It is changing how teams access intelligence, how decisions are prepared, how work is governed, and how operating models scale.
That is the work I am interested in: building the systems, workflows, and operating models that let AI become useful in the parts of the enterprise where trust actually matters.