Enterprise AI RAG Playbook: Reduce AI Failure Rates

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Progress Software Corporation brings this intelligence to enterprise AI, data, security, and architecture leaders through Intent Amplify, where decision-makers access research, analysis, and expert perspectives on AI deployment, retrieval quality, grounding, governance, and production readiness.

The number is not new. It has been cited in boardrooms, budget reviews, and AI strategy meetings for two years. But in 2026, as enterprise AI moves from experimentation to production, it has become a direct accountability question for every AI, data, security, and architecture leader responsible for turning pilots into measurable business value.

80% of enterprise AI projects fail to deliver business value. ¹

The most consistent culprit is not the LLM. It is not the vendor. It is the retrieval layer, and specifically the failure to build a properly grounded, production-grade RAG pipeline that can carry enterprise AI from a promising demo into a reliably p…

For AI/ML leaders, the issue is model-output reliability. For data leaders, it is fragmented knowledge, metadata quality, and access to a trusted enterprise context. For architecture leaders, it is whether the RAG pipeline can scale across systems, u…

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