With 25+ years in tech across Capgemini, HPE, and TCS, and now as Chair and Co-founder of a cloud/data/AI consulting firm, IntelStack, I’ve spent my career helping large enterprises deploy technology safely, profitably, and at scale. My work advising banks, insurers, and mid-market firms puts me directly inside the boardrooms now wrestling with AI trust, evaluation, and governance. I’m seeing firsthand the gap between how fast AI is rolling out and how unprepared organisations are to judge whether these systems are safe, reliable, or economically sound. That gap is turning into one of the biggest career opportunities of this decade.

I’m teaching this because the market needs thousands of people who can evaluate AI, not just build it — and mid-career professionals are perfectly positioned to step into these roles. Students learn from real cases, real systems, and real organisational dynamics we’re working on every week. You get practical tools, language executives respect, and a clearer path to promotions and pivots in the emerging AI-trust landscape..

I have spent nearly 20 years building and governing data and AI systems across McKinsey, Deloitte, EY and now as founder of multiple AI and data-engineering companies. I've worked directly with banks, insurers, and public-sector organisations on large-scale data modernisation, governance, and AI-driven decision systems. Today I am the Founder of Towards AGI, a research and AI lab that builds real evaluation tooling, agent workflows, and trust frameworks used inside enterprises that are struggling to deploy AI safely.

I teach this because I see the same pattern everywhere: AI is moving faster than organisations can evaluate or control it. I will bring the technical depth behind metrics, drift, failure modes, and evaluation pipelines — all grounded in the real-world systems I'm shipping.

Students get practical, field-tested guidance on how AI behaves in production and how to turn evaluation skill into career leverage.