From Risk to Readiness: Anatomy of an AI-Ready Knowledge Asset
The hidden risk in your AI investment isn’t the technology; it’s the content. The core issue is that humans and AI systems process information differently: humans infer context and build on an ever-expanding web of knowledge; AI systems require explicit … 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
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
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
Expert Analysis: What is Enterprise AI-Ready Content?
Scaling Your AI Pilot with the Right Contextual Foundations There’s a rush to build AI solutions: recommendation engines, chatbots, analytics dashboards, and virtual agents. But chasing shiny tools, without understanding the full picture can be risky. The organizations that truly … Continue reading
Optimizing Historical Knowledge Retrieval: Extracting Knowledge by Making Connections
The Challenge From POC to Production A Federally Funded Research and Development Center (FFRDC) faced significant challenges with low-quality or incomplete metadata for managing and cataloging scientific reports, hindering researchers’ ability to parse repositories and efficiently discover relevant content. As … Continue reading
Enterprises, KM, & AI: From Fragmented Knowledge to Intelligent Systems
In the session “Enterprises, KM, & AI: From Fragmented Knowledge to Intelligent Systems,” Jess DeMay (Enterprise Knowledge) and Rachel Teague (Emory Consulting LLC.) co-presented at KMWorld 2025, exploring how organizations can evolve from disconnected information environments into intelligent and adaptive … Continue reading
How to Ensure Your Content is AI Ready
In 1996, Bill Gates declared “Content is King” because of its importance (and revenue generating potential) on the World Wide Web. Nearly 30 years later, content remains king, particularly when leveraged as a vital input for Enterprise AI. Having AI-ready … Continue reading
How to Ensure Your Data is AI Ready
Artificial intelligence has the potential to be a game-changer for organizations looking to empower their employees with data at every level. However, as business leaders look to initiate projects that incorporate data as part of their AI solutions, they frequently … Continue reading