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

Описание: 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10:42 Why outdated documentation breaks AI systems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?