AI adoption is mandatory, but new budget is scarce, and production AI costs are averaging 380% over pilot projections. The GPUs and models get the attention, while the systems underneath get far less. LLM calls, RAG and agentic workflows all run against applications and data infrastructure built for human-speed workloads: Kafka, Flink, Spark, Cassandra, Trino and the Java estate that runs them. Agentic work can multiply internal traffic 10–100x, and Gartner expects 40% of enterprise applications to embed task-specific agents by the end of 2026. We call this the “Ferrari problem”: you’ve bought the engine, but the chassis is the constraint.
This session looks at six pressures AI is putting on engineering organizations: cost, performance, security, modernization, code velocity, and DevOps productivity. They converge on one layer, the application runtime, which decides whether the pressure gets absorbed or becomes the bottleneck. An optimized application infrastructure lets you absorb AI demand in place, with no code changes and no re-architecture. It speeds patching, cuts AI infrastructure and cloud spend, and frees scarce engineering teams to focus on AI. You’ll leave with a way to find funding for your AI investments in the infrastructure you already run.