Let’s discuss the PoC-to-Production Engineering Gap. AI leaders are under pressure to move beyond prompt-driven tools, generative chat interfaces and proofs of concept, but most agentic initiatives stall before reaching production. Today, we aim to talk about what is actually working in production and where autonomous workflows struggle to deliver tangible value and fall short of fully replacing human execution. We know success requires reliable orchestration, AI-ready data, robust evaluation frameworks, cost controls, governance, and engineering teams that can balance applying expert and institutional judgment with the automation capabilities of AI. So let’s discuss the orchestration layers and guardrails required to manage access controls and permissions for autonomous software pipelines without choking innovation. And let’s see how y'all are building the practical operating models needed to scale AI from experimentation to measurable business impact.