#558 AI Is Easy to Build, Hard to Deploy: Data, Evaluation, and ROI with Bryan Wood

AI models are becoming commoditized, but deploying AI systems that deliver real ROI remains hard. In this episode, Mehmet sits down with Bryan Wood, Principal Architect at Snorkel AI, to unpack why data-centric AI, evaluation, and domain expertise are now the true differentiators.Bryan shares lessons from working with frontier AI labs and highly regulated enterprises, explains why most AI projects stall before production, and breaks down what it actually takes to deploy AI safely and at scale.⸻👤 About the GuestBryan Wood is a Principal Architect at Snorkel AI, where he works closely with frontier AI labs and enterprises to design high-quality, AI-ready datasets and evaluation frameworks.He brings over 20 years of experience in financial services, with a unique background spanning banking, engineering, and fine art. Bryan specializes in data-centric AI, programmatic labeling, AI evaluation, and deploying AI systems in high-compliance environments.https://www.linkedin.com/in/bryanmwood/⸻🧠 Key Takeaways • Why AI success is less about models and more about data and evaluation • How enterprises misunderstand ROI and why most projects stall before production • The difference between benchmark performance and real-world trust • Why evaluation must be bespoke, not off-the-shelf • How frontier labs approach data as true R&D • Why partnering beats building AI entirely in-house today • What’s realistic (and unrealistic) about autonomous agents in the near term⸻🎯 What You’ll Learn • How to move from AI experimentation to production deployment • How to design data that reflects real enterprise workflows • How to identify where AI systems actually fail, and why • Why regulated industries are proving grounds, not laggards • How startups can overcome data and talent constraints • Where AI is heading beyond today’s LLM plateau⸻⏱️ Episode Highlights & Timestamps00:00 – Introduction & Bryan’s background02:30 – Why data is now the real AI bottleneck05:00 – Models are commoditized. So what actually matters?07:45 – Why AI evaluation is harder than building AI11:30 – Enterprise misconceptions about AI readiness15:10 – Hallucinations, RAG failures, and finding the real problem18:40 – Why most AI projects fail to show ROI22:30 – Partnering vs building AI in-house26:00 – AI in regulated industries: myth vs reality30:10 – Startups, cold start problems, and data moats33:40 – Scaling data operations with small teams36:00 – What’s next: agents, data complexity, and AI timelines39:00 – Final thoughts and where AI is really heading⸻📌 Resources Mentioned • Snorkel AI – Data-centric AI and programmatic labeling: https://snorkel.ai/ • Enterprise AI evaluation frameworks • Frontier AI lab research practices • MIT studies on AI ROI and enterprise adoption