Knowing Before Doing ft. Sudhir Hasbe
About this episode
Sudhir Hasbe discusses the challenges of deploying AI in enterprises and the importance of context and knowledge management.
Most enterprises are under board pressure to deploy AI agents. Sudhir Hasbe argues the harder shift is upstream: you cannot scale intelligence on missing context—and graph databases are how organizational data becomes knowledge agents can actually reason over. In this episode, Sudhir joins Josh Tyson and Robb Wilson to map the pathway to organizational AGI (bounded expertise, not omniscient AGI), leaning into feature reduction for token sanity, and explaining why eighty-plus percent of enterprise AI projects fail before the model messes anything up. Graphs emphasize relationships over isolated rows; virtual and native storage let you meet latency where it lives; ontologies plus data plus memory form the backboard for self-learning systems. Josh and Robb press on cost—when compute exceeds employee spend if agents spin without context—and on agent sprawl : without a shared semantic map, every bot maintains its own partial truth. Sudhir connects customer examples— Walmart 's two-million-employee knowledge graph, Quarles & Brady turning unstructured legal corpora into navigable paths—and validates the season's through-line: knowledge before agents, humans included. The demo: a…
People in this episode
Hosts: Josh Tyson, Robb Wilson
Guest: Sudhir Hasbe
Topics covered
- AI deployment
- organizational knowledge
- graph databases
- enterprise AI
- self-learning systems
- knowledge management
Keywords
- AI agents
- graph databases
- enterprise projects
- knowledge graphs
- semantic map
- personalized learning
Mentioned in this episode
Organizations: Walmart, Quarles & Brady
Products: The Learning Machine, OneReach.ai, Neo4j
Books & works: Lean Knowledge Management
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