A recent study from MIT’s NANDA initiative highlights a persistent challenge in enterprise AI adoption. Despite heavy investment and experimentation, roughly 95% of corporate AI efforts fail to deliver clear, measurable value. Models and agents dominate the current AI conversation, but the true success or failure of real-world AI applications hinges on their underlying data architecture.
Developers can spin up agents, run experiments, and build intelligent workflows in hours using frameworks like LangChain, CrewAI, or LangGraph. Although…

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