Why AI breaks without context — and how to fix it

Presented by Zeta GlobalThe gap between what AI promises and what it delivers is not subtle. The same model can produce precise, useful output in one system and generic, irrelevant results in another. The issue is not the model. It's the context.Most enterprise systems were not built for how AI operates. Data is scattered across tools. Identity is inconsistent. Signals arrive late or not at all. Systems record events but fail to connect them into a continuous view.AI depends on that continuity. Without it, the model fills in the gaps so the result looks polished but lacks relevance. This is where most teams get stuck.A better model does not fix fragmented, stale, or commoditized data. Gartner estimates organizations lose an average of $12.9 million annually due to poor data quality. AI doe