[RECORDED] Engineering AI: The Foundations That Enable Value at Scale

Most enterprise AI projects do not fail because of the model. They fail because the foundations are not in place. This session examines why so many organizations are stuck running pilots that never reach production, and what it actually takes to build AI that scales across the enterprise.

1. The Pilot-to-Production Gap: Organizations are investing in AI experiments that succeed in controlled conditions and stall everywhere else. The gap between a working pilot and a production-ready system is almost always a foundations problem, not a technology problem.
2. Knowledge and Information Architecture as Prerequisites: AI depends on structured, governed, and findable information. Without taxonomy, metadata, and consistent terminology in place, AI retrieves inconsistently and at scale those inconsistencies multiply.
3. The Four Domains of AI Readiness: Scaling AI requires readiness across four areas: knowledge, technical infrastructure, operational integration, and governance. Most organizations focus on technical readiness first and discover the other three too late.
4. Information Leverage Points: Every organization has points where improving information flow has a disproportionate downstream impact. Identifying and addressing those points is how AI initiatives produce measurable, lasting value.
5. Governance as an Enabler, Not a Constraint: Lightweight governance, starting with use case inventories, baseline metrics, and a decision-making body, is what allows AI initiatives to move faster and more safely, not slower.