Most enterprise AI never makes it past the pilot phase. The systems that do survive production at Fortune 50 scale — across hundreds of thousands of daily transactions, thousands of human operators, and dozens of countries — share architectural patterns that aren’t obvious at the demo stage. This talk distills those patterns from two production AI programs built and deployed over the past decade: a digital supply-chain transformation platform supporting 220,000 orders per day across 392,000 SKUs, documented as MIT Sloan / Ivey case study W24797 (a Harvard Business Publishing bestseller, taught at MIT, Rutgers, NUS, and IE Business School); and STRIDE, a closed-loop AI service architecture supporting customer support organization, recognized with the 2025 TSIA STAR Award for Innovation in Analytics and AI for Service Excellence. Both programs faced the same failure modes that LLM teams hit today: hallucinated outputs, brittle integration boundaries, runaway cost curves, and slow learning loops. Both succeeded by converging on the same underlying architecture — continuous telemetry feeding decision engines feeding learning loops, with humans gating the boundaries rather than sitting in the critical path. The talk walks through the architectural decisions that determined whether each system shipped or stalled, including build-vs-buy under vendor immaturity, phased rollout under quality risk, and tiered automation under fraud exposure.
Ankit Talwar is Director of Product Management for AI at Dell Technologies and a Distinguished Fellow of the Soft Computing Research Society (SCRS). He led the product strategy for the digital supply chain transformation documented by researchers at the Massachusetts Institute of Technology (MIT Digital Supply Chain case study, “Dell: Roadmap of a Digital Supply Chain Transformation,” Case No. W24797), and directed the STRIDE service AI ecosystem recognized with the 2025 TSIA STAR Award for Innovation in Analytics and AI for Service Excellence. His work synthesizes patterns from these programs across eight years (2017–2025). Mr. Talwar serves as a judge for MIT Hack-Nation’s Global AI Hackathon (three consecutive editions and the Venture Lab) and as a proposal reviewer for Manning Publications on agentic AI titles.
