Translating algorithmic complexity into fiduciary trust
From Enterprise IT to AI Governance
For over three decades, I have operated at the intersection of complex technology and executive strategy. My foundational career in enterprise IT involved leading massive digital transformations, system validations, and global ERP rollouts across the pharmaceutical, manufacturing, and textile industries.
As the technology landscape shifted, so did my focus toward equipping the people who manage it. Over the last 15 years, I have trained thousands of professionals across 300+ batches and mentored over 15,000 leaders and CXOs in data-driven decision-making and strategic leadership.
Today, I am bringing that experience to the most critical challenge facing modern leadership: AI Governance. Boards are approving AI initiatives without frameworks, accepting vendor assurances without the right questions, and carrying fiduciary risk they haven’t fully measured.
Operating out of Sunnyvale, CA, my practice is built around one question that keeps coming up in boardrooms everywhere: Who is accountable when AI gets it wrong? My work is to change that—practically, quickly, and without the jargon
The Research Backing the Methodology
I am currently completing my Doctorate in Business Administration (DBA), with research specifically focused on Explainable AI (XAI) for Board Directors. This research sits precisely at the intersection of AI transparency, fiduciary duty, and strategic risk. It ensures that every workshop, audit, and advisory engagement I conduct is grounded in the latest governance frameworks, complemented by my standing as a CPMAI, PMP, and PgMP certified practitioner