Content related to Enterprise AI Architecture Series: How to Inject Business Context into Structured Data using a Semantic Layer (Part 3)
Semantic Layer Symposium 2026: How Metadata Management Is Changing in the Era of AI
In October of this year, Enterprise Knowledge held our annual Semantic Layer Symposium (SLS) in Vienna, Austria, bringing together industry thought leaders, data experts, and practitioners to explore the transformative potential, and reflect on successful implementations of, Semantic Layers. This … Continue reading
The Six Archetypes You’ll Encounter When Addressing Your Organization’s AI Challenges
Innovations in AI-powered solutions and approaches haven’t made knowledge management challenges disappear. Instead, they have evolved. As such, the mitigation strategies that organizations may take to address these challenges must also evolve. That said, it can be daunting to know … Continue reading
Semantic Layer Symposium 2026: Welcome & Opening Remarks
In October of this year, Enterprise Knowledge held our annual Semantic Layer Symposium (SLS) in Vienna, Austria, bringing together industry thought leaders, data experts, and practitioners to explore the transformative potential, and reflect on successful implementations of, Semantic Layers. This … Continue reading
Enterprise Semantic Layer for Firm-Wide Risk Management at a Financial Services Firm
One of the top global leaders in automotive manufacturing faced significant challenges in managing and accessing critical knowledge across its diverse teams. The company engaged Enterprise Knowledge (EK) to conduct a Knowledge Management (KM) Strategy and solution implementation project plan after the failure of multiple KM initiatives. The engagement’s long-term goal is to establish a shared Knowledge Management System (KMS) to streamline access to crucial information, better leverage experts’ institutional knowledge and experience, and decrease new employees’ time to proficiency. Continue reading
Steps to Improve AI Outcomes with Context Engineering
When it comes to successfully implementing AI solutions, a common refrain in most implementations is that your results are only as good as your inputs. To avoid this pitfall, we have seen both data and content teams ask “are my … Continue reading
Cutting Through the Noise: RDF & LPG
When do you choose a Resource Description Framework (RDF) graph over a Labeled Property Graph (LPG), and vice versa? And when does it make sense to combine both to model your knowledge domain? RDF and LPG serve distinct purposes. RDF … Continue reading
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
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