The AI conversation has moved on. While the industry obsessed over training—bigger models, more parameters, unprecedented compute clusters—the economic center of gravity quietly shifted. Inference now dominates enterprise AI, and it plays by entirely different rules: continuous rather than episodic, latency-critical rather than batch-tolerant, and brutally cost-sensitive at scale.

Real enterprise AI isn't about building the biggest model. It's about deploying the right model exactly where your business happens—increasingly, that means efficient Small Language Models running at the edge, sitting next to your data rather than forcing your data to travel.

This "data gravity" principle demands a fundamental infrastructure rethink. The assumptions that guided cloud-first decisions don't automatically translate to AI-first architectures. When cost-per-inference becomes your competitive advantage, every millisecond of latency hits the margin line directly.

For UK enterprises, this inflection arrives alongside a unique opportunity: sovereign inference is becoming a strategic differentiator, not just a compliance checkbox.

This keynote explores what AI-first infrastructure actually requires—FinOps disciplines purpose-built for inference economics, edge-ready architectures, and operational models that make sovereign AI deployable at scale. The organizations getting this right today are building tomorrow's competitive moats.