RAG failure points are an architecture problem, not a model problem

RAG sits under 51% of enterprise AI, yet 80% of projects are projected to fail. A NAACL 2025 study found chunking configuration influences retrieval quality as much as the embedding model. The controlling variable is your data pipeline, not your model.

RAG beats fine-tuning for most enterprise use cases

RAG runs in 51% of production enterprise AI deployments against 9% for fine-tuning, and the same retrieval setup swings from 3% to 40% hallucination on corpus quality alone. The corpus is the variable, not the model.

Harness engineering is the missing discipline in enterprise AI adoption

Access to AI is near universal at 88% of organizations, but only 6% reach high performance. The difference is harness engineering, not model choice. BCG puts 70% of AI value in process and pipeline design, 10% in the algorithm itself.

Good knowledge = good engineering

Most AI pilots fail not because the models are wrong but because engineering data is unclassified, ungoverned, and full of gaps. A problem that naive AI accelerates, not solves.