Beyond the Model: Why Enterprise AI Lives in Its Infrastructure

Enterprise AI implementations often fail not because of poor models, but because teams underinvest in the platform infrastructure surrounding the agent—evaluation pipelines, shadow testing, governance, and monitoring. A real case study reveals how a payroll AI team recovered from 70% accuracy to 98% not by switching models, but by building proper infrastructure: the accuracy gap stemmed from production data distributions that test sets never covered. This pattern reflects a decades-old technology adoption problem: visible capabilities get prioritized while the unglamorous infrastructure that ensures durability gets starved.