“The biggest problem with AI in the enterprise isn't the model. It's the data. Models are useless without access to your company's knowledge.”
“Enterprise search was a solved problem that nobody actually solved. AI gives us a second chance.”
“Every enterprise will need an AI layer that sits on top of their existing tools. That's what we're building.”
“The ROI of AI in the enterprise is not about replacing workers. It's about eliminating the hours people spend searching for information.”
“RAG is not a feature. It's the architecture of enterprise AI. Without retrieval, you're just hallucinating.”
“We went from zero to $100M ARR because every company has the same problem: their knowledge is trapped in siloed tools.”
Nav Jain represents the enterprise AI infrastructure layer — the less glamorous but potentially more valuable application of large language models to the mundane reality of how companies actually work. As CEO of Glean, he has built one of the fastest-growing enterprise AI companies by solving a problem that existed long before ChatGPT: people in large organizations cannot find the information they need.
Jain’s perspective is refreshingly grounded compared to the existential rhetoric that dominates AI discourse. His focus is on practical value: reducing the time knowledge workers spend searching for information, connecting AI to real enterprise data sources, and delivering measurable ROI. This pragmatism has driven Glean to $100 million in annual recurring revenue while many flashier AI startups struggled to find product-market fit.
His emphasis on RAG (retrieval-augmented generation) as the foundational architecture for enterprise AI reflects a sophisticated understanding of why general-purpose chatbots fail in business contexts. Without access to company-specific knowledge, AI models hallucinate corporate information just as they hallucinate legal citations. Grounding responses in actual enterprise data eliminates this problem.
Jain’s background includes co-founding Rubrik (a cloud data management company) and working at Google, giving him deep experience in enterprise infrastructure and data systems. This background shapes his view that AI in the enterprise is primarily a data access problem, not a model capability problem.
His framing of AI’s enterprise value as eliminating information search time rather than replacing workers positions Glean favorably in the politically sensitive landscape of AI and employment. By focusing on augmentation rather than automation, he addresses buyer concerns about AI-driven layoffs while still delivering the productivity gains that justify enterprise AI investment.