Enterprise AI fails on messy data and context, not on the model
Rajxai · reddit · 2026-08-18
A Reddit discussion arguing the model is rarely where enterprise AI projects break.
The real culprits the author sees: messy data, two systems calling the same thing different names, nobody sure which number is right, and half the critical context living undocumented in someone's head — yet teams expect agents to make confident decisions amid all that. He has watched teams spend months evaluating models while the actual problem sat behind the model.
The interesting part: once the underlying data and context are fixed, the AI piece often becomes the easy bit — simpler, faster, more reliable. He asks the community: when an enterprise AI project stalls, what's the real reason you've seen?
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