Content related to How to Make Tacit Knowledge Accessible for the Enterprise
Semantic Layer Enablement and Change Management, Part 1: Understanding Change & Measuring Impact for the Semantic Layer
The semantic layer is a framework of standards and tools designed to work and remain behind the scenes. It is the conceptual layer that sits between your organization’s knowledge assets, like content and data, and the people who need to … Continue reading
How to Improve Enterprise AI Adoption: AI Observability & Evaluation
Part 1: Why Agentic AI Demands a New Kind of Visibility The AI Trust Problem No One Is Talking About Your AI product is live. Latency looks fine. Uptime is green. And yet, somewhere in production, your AI is quietly … Continue reading
Taxonomies in the Age of AI: Evolving Your Strategy
The Topic Taxonomy: An Outdated Artifact? As knowledge workers continue to navigate constantly evolving priorities in developing effective AI solutions that complement organizational priorities, semantics have maintained their value—but not without shifts that deserve our attention. Broadly speaking, one reliable … Continue reading
From Slides to Structured Data: Preparing Slide Decks for AI Systems
Slide decks are everywhere in most organizations. They are used to share strategies, summarize decisions, communicate complex ideas, and facilitate decisions. They are packed with information, but they’re also inconsistently formatted and can be difficult for machines to properly “read.” … 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
Breaking Down Enterprise AI, Part II: Action Track
Introduction Organizations have access to a broad and rapidly expanding set of AI capabilities, from systems that monitor and analyze data to tools that generate content, automate decisions, and orchestrate complex workflows. Despite this abundance, many organizations struggle to apply … Continue reading
Unified Entitlements: The Essential AI Enabler
Data and information security is often seen as a necessary evil that slows down the implementation of valuable new systems. We have all waited for security approval before launching a new system. However, AI is changing the narrative, and KM … Continue reading
Knowledge Cast – Melanie Adams, Global Talent Development & Training Operations Lead in Digital Manufacturing Operations at MSD
Enterprise Knowledge CEO Zach Wahl speaks with Melanie Adams, Global Talent Development & Training Operations Lead, Digital Manufacturing Operations, at MSD. With a deep background in science, leading analytical testing teams, and ensuring quality in regulated pharma environments, Melanie blends … Continue reading
What is the Difference Between a Semantic Layer and a Context Layer? When to Use a Knowledge Graph vs. a Context Graph
Before AI became part of everyday conversations, most enterprise knowledge and data projects had a somewhat straightforward goal: to create a “single source of truth.” In theory, this meant that everyone in the company could look at the same search … Continue reading
Leveraging a Semantic Layer for Research Curation and Conversational Experiences
The Challenge A global philanthropic organization focused on health programs struggled to fully leverage knowledge from semi-structured and unstructured documents. Specifically, within a health-related funding program, researchers lacked access to key qualitative data from end-user surveys and transcripts. Consequently, they … Continue reading