Content related to Ben
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
How to Make Tacit Knowledge Accessible for the Enterprise
Much of what an organization’s employees know isn’t written down. Tacit knowledge refers to exactly that: “highly internalized knowledge that is difficult to articulate, record, and disseminate.” It manifests as a deep, institutional understanding of the daily operations, unwritten processes, … Continue reading
Knowledge Cast – Melanie Adams of 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
What’s Next for Search? Moving Beyond Q&A to Context, Discovery, and Action
Beyond the Search Box Enterprise search is in the middle of a reset. For years, most enterprise search programs were built to return documents and links across repositories, and success was measured by whether employees could locate the right file. … Continue reading
Building the Semantic Layer: Scaling Enterprise Intelligence at a Global Investment Firm
The Challenge A global investment firm with a $330 billion dollar portfolio and 50,000+ employees struggled with fragmented data. Investment professionals were losing critical time hunting for assets across disconnected systems. Detailed deal records were scattered as a mix of … Continue reading
GraphRAG in the Enterprise
Retrieval-Augmented Generation (RAG) is a commonly utilized pattern for grounding large language models in enterprise data. Instead of solely relying on a model’s training, RAG collects relevant information from internal sources, documents, knowledge bases, and other systems; it then uses … Continue reading