Why Do 90% of Enterprise AI Implementations Fail? [Ft. Eva Nahari, Former CPO, Vectara] скачать в хорошем качестве

Why Do 90% of Enterprise AI Implementations Fail? [Ft. Eva Nahari, Former CPO, Vectara]

RAG isn't just another AI buzzword, it's the architectural foundation that determines whether enterprise AI delivers value or burns budget. Eva Nahari, former Chief Product Officer at Vectara and four-year venture investor, explains why separating data from models matters more than the models themselves, and why 90% of AI implementations fail at the execution layer, not the technology layer. The standard approach, dumping an 80-page PDF into a custom GPT, fails because accuracy requires proper data architecture, not better prompts. RAG addresses this by feeding models precise context rather than expecting them to ingest everything at once. But implementation creates new problems: multiple teams building isolated RAG systems across the same enterprise, creating governance nightmares when those hobby projects need to scale. The companies succeeding aren't the ones with the best AI talent, they're the ones who treated data management seriously before the AI hype arrived. 00:00 Episode Trailer 00:52 Why 90% of AI implementations fail 01:03 What is RAG and why it matters for enterprise 02:20 Why custom GPTs still hallucinate with your data 03:30 Accuracy determines AI adoption, not features 04:43 How enterprises use RAG for compliance traceability 06:37 RAG sprawl: when every team builds their own system 08:52 Enterprise empathy lessons from the big data era 10:42 Why outdated documentation breaks AI systems 12:34 From VC hype to enterprise execution reality 15:33 How to provide context and guardrails for agents 17:36 Guardian agents and real-time governance systems 19:52 Multi-dimensional accuracy monitoring in RAG pipelines 22:34 Building trust through deployment and rollout phases 25:37 Why Vectara is building an agent operating system 29:05 Fast forward to 2030: from RAG to cross-system agents 30:43 Will LLMs be replaced or evolve?