Why AI needs contextual intelligence — not just bigger models

A CTO argues that AI systems require robust contextual data infrastructure—organized taxonomies, access controls, and system mappings—to function effectively, not just larger models. Drawing on real examples, including discovering that 50% of one engineering team's sprint time involved manual workarounds rather than actual bugs, the author contends that "contextual intelligence" or "context engineering" captures how work actually happens, separate from official organizational charts. Most enterprise AI deployments fail because they lack this foundational data layer, which remains unglamorous but essential work that has evolved little in two decades.