Enterprise AI's real cost crisis: leaky pipelines, not cheap tokens

The enterprise AI conversation has matured rapidly in two years. What began as a maximalist experimentation phase—deploying as many pilots and agents as possible to find which workflows would transform—has collided with fiscal reality. Uber's spending tightening, aggressive token-price cuts by Anthropic, OpenAI, and Google, and Cursor's decision to factor cost into model selection all signal that the industry has moved from "How do we adopt AI?" to "How do we derive ROI from AI while controlling burn?" Yet the article argues that cheaper tokens misdiagnose the real problem. The core issue is architectural: enterprises lack the data governance and master data management infrastructure to feed clean, curated context to AI agents. Instead, they send raw, noisy data through prompts—a practice that multiplies token consumption by 5–10×. This inefficiency is baked into the pipeline itself: context windows bloat, agents wander through ungoverned data, large models handle simple lookups, stateless interactions require reprocessing, and semantic duplicates regenerate answers unnecessarily. The implication is that the next phase of competitive advantage in enterprise AI will not belong to those who negotiate cheaper tokens or deploy the largest models, but to those who invest in the unsexy but essential work of master data management, data governance, and semantic architecture—turning raw AI consumption into measurable, governed business value.