https://drive.google.com/file/d/1gpPJPutlfJqb2xUH-P2-WqtB1BUImwhc/view?usp=share_link
The AI gold rush is on. Every boardroom is placing bets on agentic AI—autonomous systems that perceive, reason, and act without human hand-holding. The market is bullish. The slide decks are stunning. And almost no one is doing the actual work. AI is a knowledge tool running on data infrastructure, and that math doesn’t add up. While executives trade on AI futures, their organizations are still wrestling with messy data lakes, fragmented metadata, and semantic layers held together with hope. The industry keeps stirring data soup, expecting intelligence to magically emerge. It won’t.

The path to agentic AI runs straight through knowledge infrastructure: taxonomies, ontologies, controlled vocabularies, linked data, and the cultural commitment to sharing that makes it all work. It’s unsexy. It’s labor-intensive. And it’s the only real bet worth making.

Understand why AI is fundamentally a knowledge tool—not a data tool—and why that distinction matters for every AI initiative. Gain a framework for what knowledge infrastructure actually requires and recognize the gap between where most organizations are and where they need to be. Discover knowledge management, in all its glory, and why library science and knowledge management hold the methodologies the tech industry keeps trying to reinvent. Stop trading on margin with collateral you don’t have. Start building.