Blog Archives
Shifting to an AI-First Operating Model
Enterprise capital investments in artificial intelligence continue to outpace measurable operational returns because technology alone does not generate enterprise value. Organizations are spending heavily on new technology, but enterprise-wide productivity remains flat. Research from the Federal Reserve Bank of St. … Continue reading
Your AI Can Reach Everything and Understand Nothing
Connectivity is solved. Understanding and precision are not. Why retrieval systems need a semantic foundation. I met up with a colleague at a conference recently. She was newer to the world of semantics and mostly excited by what she was … Continue reading
KM Strategy for AI Readiness
Enterprise Artificial Intelligence (AI) readiness depends on the maturity of its underlying knowledge management (KM) ecosystem, not algorithmic power. Without structured, semantically enriched data, advanced models fail due to misalignment, data fragmentation, and hallucinations. Executive leaders face immense pressure … Continue reading
Beyond Static Permissions: Why Agentic AI Needs Intent-Based Access Control
From Static Boundaries to Dynamic Intent AI agents don’t just need permission to access resources. They need constraints on how they use that access. Intent-Based Access Control (IBAC) is an emerging authorization model that enforces the principle of least privilege … Continue reading
The New Role of Knowledge Management and its Professionals
The world of Knowledge Management is changing rapidly. For those that can seize the moment and embrace the change, this presents an incredible opportunity. For those that instead react with fear or resistance, they will quickly find themselves in obsolescence. … Continue reading
Ontology and Knowledge Graph in the Age of AI and Agents
As organizations accelerate investments in AI, semantic data models, advanced analytics, and agentic transformation, lots of jargon gets thrown around, and this sometimes results in confusion about how data driven systems work. In the realm of semantic layers, one of … Continue reading
Why AI Projects Fail Without a Common Language: The Case for Taxonomy Standards
As organizations rush to adopt AI solutions and technologies, the necessary structures to support such solutions are often overlooked. Gartner predicts that by 2026, 63% of organizations will not have the right data management practices for AI. This gap shows … Continue reading
How to Scale a Semantic Layer with Interoperable Ontologies
A Semantic Layer is the framework for connecting data from multiple sources and formats in both a human- and machine-readable way that enables organizations to understand the meaning of their data, extract contextualized information, and discover new insights. A key … Continue reading
A Practical Guide to a Taxonomy Remodel
For anyone who has undertaken any form of home remodel or loves to watch television shows featuring them, the general phases of a home renovation are familiar: visualizing the target state of the remodeled home, carrying out structural work, demolition, … Continue reading
Taxonomies vs. Ontologies for Enabling AI-Readiness
AI solutions need to be grounded in an organization’s context. It is difficult to reliably distill context from the entirety of an organization’s knowledge assets, including facts, documents, datasets, and other structured records. Without a specific directive on what matters … Continue reading