Inside Enterprise AI’s Adoption Problems with Shuchi Agrawal
In the first episode of Simform’s Enterprise Cloud and AI Forum (ECAF), Shuchi Agrawal, Head of AI Execution at SMBC Group, joins host Rameshwar Balanagu, Co-Founder, Dallas CTO Club, for a conversation that doesn’t start with AI hype, but with the execution gap.
Every enterprise technology leader has a version of the same story right now: the AI pilot worked, the business case was there, and production never happened. Or it happened and it cost far more than anyone planned, on a problem that probably wasn’t the right one to begin with.
With 25 years building AI and data systems across aviation, global banking, and financial services, Shuchi has a specific answer to why enterprise AI programs stall between pilot and production. It’s probably not what your team is looking for.
Key Takeaways for the Viewers
The four-part failure map
There is a specific, predictable sequence of ways enterprise AI programs break before they ship. Shuchi names all four and the order turns out to matter more than most teams expect.
AI governance as a competitive advantage
Shuchi runs a multi-disciplinary AI governance council at SMBC Group where legal, security, compliance, and risk are all at the table before a use case gets approved. She has a specific view on how that structure becomes an advantage rather than a brake.
Token economics isn't a line item
You'll hear why the real cost of running AI in a regulated enterprise isn't what shows up in your initial model budget and why the organizations that discover this accidentally are the ones left holding a very expensive demo.
The pilot-to-production gap isn't a technical problem
Most enterprises assume that if a pilot works, production is an engineering challenge. Shuchi's framework built across aviation, banking, and global financial services, challenges that assumption directly. What's missing is almost never the thing teams go looking for first.
The human-first problem that hasn't changed in 25 years
Change management for AI in 2026 faces exactly the same structural tension it faced during the industrial revolution, the cloud migration, and every major technology shift before it. You'll hear why that's not a bug to fix and what it tells you about how to lead AI adoption inside your organization.
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