Enterprise AI Needs Business Rules—and Human Judgment | Robert Long

What does it take to turn enterprise data into better business decisions—and how much control should a company retain over the AI it depends on? In this episode of the Enterprise Data Podcast, Billy Thomas sits down with Robert Long, founder of Ava, to discuss data science, business optimization, and the connections between trusted data and useful AI. Robert explains how internal and third-party data can support decisions about pricing, product launches, manufacturing, logistics, and resource allocation. The conversation explores the work behind those capabilities: managing data at scale, connecting metadata and business terminology, and designing systems for the demands of AI-generated queries. Billy and Robert also discuss where automation can shorten implementation work and why discovering business rules, validating requirements, and accepting tests still require human expertise. They close by exploring the local impact of data centers, Robert’s perspective on quantum computing and security, and his case for sovereign AI. For leaders weighing enterprise AI investments, the discussion raises practical questions about infrastructure ownership, vendor dependence, operating costs, and which workloads need a frontier model. 0:00 Meet Robert Long, Founder of Ava 1:12 Explaining Data Science in Business Terms 4:40 Connecting Internal and Third-Party Data 10:24 Engineering for Large-Scale Data 15:48 Designing Systems for AI’s Query Demands 19:35 Semantic Layers, Metadata, and Business Meaning 23:41 Business Rules, Human Expertise, and AI Implementation 28:21 Data Centers and Their Community Impact 37:00 Robert’s Perspective on Quantum and Security 41:04 Sovereign AI and Enterprise Control