The Model Is Not the Moat: Alvin Wang Graylin on the Stanford Enterprise AI Playbook

Data is the real moat. Frontier models are becoming a commodity.

In this episode of the ipushpull Capital Markets FinTech Forum podcast, Matthew Cheung sits down with Alvin Wang Graylin, Digital Fellow at the Stanford Digital Economy Lab and co-author of The Enterprise AI Playbook: Lessons from 51 Successful Deployments

With over 35 years of experience across AI, semiconductors, immersive computing, and cybersecurity at HTC, Intel, IBM, and Trend Micro, four founded startups, and investments in 100+ early-stage firms, Alvin shares the findings from a five-month Stanford study of 51 enterprise AI deployments across 41 organisations and 9 industries on how AI is actually being adopted inside large organisations.

The conversation covered:

• -Why MIT's "95% AI failure" headline collapsed under deeper methodology
• -Why data, not model choice, is the durable enterprise moat
• -The productivity gap between full automation and human-in-the-loop
• -Why two-thirds of successful AI rollouts started as failures
• -Why HR, legal, and compliance are the biggest internal blockers to enterprise AI
• -Why 65% of white-collar work in the US, UK, and East Asia is exposed to AI