Most enterprise AI initiatives die between pilot and production. Drawing from doctoral research on organizational capabilities for AI scale-up and real experience implementing AI-augmented systems in banking, this session maps what separates companies that ship AI from those stuck in perpetual POC. We examine real architectural patterns: tiered AI strategies that minimize API costs, RAG implementations that work in regulated environments, and the evaluation frameworks (ensemble scoring, chain-of-thought validation) that make AI outputs trustworthy enough for production decisions. This is not an “intro to AI” - it is an architect’s field guide to shipping AI in environments where getting it wrong has consequences.

You will learn:

• How to identify the organizational capabilities (not just technical) that determine whether AI pilots reach production
• How to design tiered AI integration strategies that balance cost, latency, and accuracy for enterprise environments
• How to implement governance and evaluation frameworks for AI-augmented decision-making in regulated industries