What does it actually take to prove that AI delivers real value in the industries that keep the world running?

In this episode of Eye on AI, Craig Smith sits down with Debdas Sen, CEO of TCG Digital and Joint Managing Director of Lummus Digital, to explore what serious enterprise AI looks like when it is applied to some of the most complex, high-stakes problems on the planet. Problems like compressing years of catalyst research into weeks, predicting refinery failures before they happen, and accelerating drug development timelines that could determine how long a life-saving medicine takes to reach patients.

Debdas has spent nearly 30 years in data and AI, living through every hype cycle from the data warehousing era of 1997 to today's agentic revolution. He makes a compelling case that the AI community has one defining job right now: prove the ROI, or risk another AI winter.

We also get into what makes TCG Digital's platform mcube™ different. It is not a horizontal tool. It is a domain-first, agentic AI ecosystem built for the kinds of massive, multi-variable problems that horizontal platforms cannot touch. Debdas breaks down how mcube™ bridges legacy enterprise infrastructure with cutting-edge agentic systems, why hybrid modeling beats pure AI in energy and life sciences, and how the platform keeps private enterprise data protected while still drawing on the best of what public LLMs have to offer.

Finally, Debdas shares where he sees the industry heading next, a future where agents from different providers can reason together in a neutral space, where inference and reasoning keep improving, and where the companies that go deepest into domain will pull furthest ahead.

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(00:00) Introduction and Meet Debdas Sen

(01:30) 30 Years in Data and AI: From Data Warehousing to Agentic Systems

(03:02) What TCG Digital Actually Does (04:32) Inside mcube™: How the Platform Works

(10:06) Domain vs Horizontal: Why Specificity Wins in Enterprise AI

(18:29) Catalyst R&D: Collapsing 12 Months of Research Into One

(30:38) Predicting Plant Failures Before They Happen