Most enterprise AI projects don’t fail because the models are wrong. According to Gartner, 60% of AI projects lacking AI-ready data will be abandoned through 2026, and a Harvard Business Review and Cloudera study found that only 7% of enterprises say their data is completely ready for AI adoption. An AI data pipeline is what sits between raw enterprise data and a working
LlamaIndex, LangChain, and Haystack can each spin up a working RAG prototype in an afternoon. The differences between them stay hidden until the system meets real documents, real query volume, and a compliance review. By then the framework is wired deep into the codebase, and swapping it costs weeks. Getting that choice wrong is common. An MIT study found 95% of enterprise
Most enterprises do not lose to competitors because they lack data. They lose because that data sits in systems that refuse to talk to each other. A finance tool here, a warehouse platform there, a CRM that exports to nobody. The cost shows up quietly. Reports take days instead of minutes, teams argue over whose numbers are right, and AI
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