Arvind Jain is the CEO of Glean, an enterprise AI platform that acts as a conversational assistant for employees. Glean integrates with a company's internal data and the world's knowledge to answer questions, provide information, and enable the creation of custom AI agents. Jain previously co-founded Rubrik ($16B public company), a cybersecurity and cloud data management company, and was a distinguished engineer at Google. He is now focused on helping organizations navigate the complexities of AI adoption. Our conversation ranges from his thoughts on what future of AI at work looks like, to how companies will look in future and why he picked the problem of solving Enterprise Search in 2019 and a lot more.
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Quotes from the episode
Glean is like ChatGPT, but inside your company, answering questions using internal context and the world's knowledge.
Before AI models became powerful, Glean functioned as a "Google for your workplace," surfacing information rather than providing direct answers.
The initial pain point for Glean was employee productivity issues stemming from fragmented knowledge within rapidly growing companies.
Enterprise AI requires ensuring models receive accurate, up-to-date information as the source of truth to prevent "garbage in, garbage out."
AI is evolving towards proactive assistance, embedding itself into daily workflows rather than requiring users to actively seek it out.
What you’ll learn
Understand how Glean evolved from an enterprise search engine to a conversational AI assistant for businesses.
Discover the core problem of enterprise productivity that inspired the creation of Glean.
Learn about the techniques Glean employs to reduce AI hallucinations and ensure data accuracy.
Explore how AI agents are revolutionizing various departments, from sales and customer service to legal and engineering.
Gain insight into the future of work, where AI acts as a proactive personal companion for employees.
Grasp the challenges and strategies involved in building a robust AI agent platform reliant on extensive integrations.
Understand the spectrum of AI applications, from deterministic workflows to truly ad hoc agents.
Hear about the importance of embedding AI across platforms and devices for seamless user experience.
Discover how companies can foster an AI-first culture through education, goal setting, and experimentation.
Learn about the key metrics Glean focuses on to measure success and guide future development.
Takeaways
Glean addresses enterprise productivity by providing a unified conversational AI interface that leverages both internal company data and external knowledge.
The initial thesis for Glean was rooted in solving the critical problem of fragmented information hindering employee productivity in fast-growing organizations.
Reducing AI hallucinations is achieved through general model improvements and a rigorous process of retrieving and validating high-quality, up-to-date enterprise information.
AI agents are becoming powerful workflow tools, automating tasks across sales, customer service, engineering, and legal departments, democratizing AI adoption.
The future of work involves AI becoming a proactive, deeply integrated personal companion that anticipates needs and offers assistance before being asked.
Building a successful AI agent platform hinges on extensive integrations with enterprise systems, coupled with robust security and governance measures.
AI agent platforms should support a spectrum from fully deterministic workflows to dynamic, ad hoc agent behaviors for maximum utility.
Cross-platform and cross-application integration is crucial for AI to become a truly pervasive and helpful assistant, accessible on various devices.
Cultivating an AI-first culture requires a multi-pronged approach including education, setting achievable goals, celebrating AI wins, and dedicated onboarding programs.
Customer satisfaction, user engagement, and the expansion of AI agent creation are key metrics Glean tracks to ensure it's heading in the right direction.
In this episode, we cover
(00:01) Introduction to Arvind Jain and Glean
(01:13) Glean's core functionality as an enterprise AI assistant
(03:43) The evolution from enterprise search to conversational AI
(04:19) The problem of enterprise productivity and knowledge fragmentation
(06:46) Glean's early adoption of AI and its mission
(09:06) The narrative versus experience of AI adoption
(10:26) The strategy of tackling hard problems when starting a company
(12:37) Addressing AI hallucinations and ensuring accuracy in enterprise data
(17:31) Glean's model hub and supported AI models
(20:16) Use cases for AI agents across different enterprise departments
(24:42) The concept of workflow agents and their advantages
(26:00) The challenge of enterprise integrations for AI platforms
(29:04) The spectrum of AI applications: workflows vs. agents
(31:27) The evolution of AI form factors: chat, RAG, and agents
(31:57) The future of AI: proactive and embedded assistance
(35:38) AI companions and their role in daily work
(37:14) Cross-platform and cross-application AI integration
(38:22) Enterprise hardware for AI and privacy concerns
(39:52) The business of AI: hiring and scaling in a fast-growing startup
(43:39) Fostering an AI-first culture within a company
(47:04) Future ideas and opportunities in the AI space
(49:49) Key metrics for tracking success in a fast-growing AI company
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