Content related to A Structured Content Model and Multi-Channel Publishing for Rapid Content Distribution
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
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
Taxonomy Alignment for SDG Tagging
In the session “Utilizing Taxonomies to Meet UN SDG Obligations“ co-presented at KMWorld 2025 on November 17th, Enterprise Knowledge’s Ben Kass and ASHA’s Mike Cannon discuss how they structured taxonomies within an ongoing auto-tagging implementation to serve content management and … Continue reading
Content Engineering to Improve AI Readiness for a Financial Institution’s Customer Service Center
A well-known financial institution faced challenges in providing frictionless, personalized service to customers due to widespread issues with knowledge base content. Due to outdated legacy systems, both agents and customers experienced difficulties finding and using content; similarly, internal content management teams struggled to maintain and update both agent-facing and customer-facing content … Continue reading
Transforming Content Operations in the Age of AI
AI continues to disrupt and transform the way we work in the modern enterprise, and owners and managers of digital content are no exception. During their presentation, “Transforming Content Operations in the Age of AI,” on November 19th at KM … Continue reading
How to Leverage LLMs for Auto-tagging & Content Enrichment
When working with organizations on key data and knowledge management initiatives, we’ve often noticed that a roadblock is the lack of quality (relevant, meaningful, or up-to-date) existing content an organization has. Stakeholders may be excited to get started with advanced … Continue reading
Defining Governance and Operating Models for AI Readiness of Knowledge Assets
Artificial intelligence (AI) solutions continue to capture both the attention and the budgets of many organizations. As we have previously explained, a critical factor to the success of your organization’s AI initiatives is the readiness of your content, data, and … 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
Top Ways to Get Your Content and Data Ready for AI
As artificial intelligence has quickly moved from science fiction, to pervasive internet reality, and now to standard corporate solutions, we consistently get the question, “How do I ensure my organization’s content and data are ready for AI?” Pointing your organization’s … Continue reading
Auto-Classification for the Enterprise: When to Use AI vs. Semantic Models
Auto-classification is a valuable process for adding context to unstructured content. Nominally speaking, some practitioners distinguish between auto-classification (placing content into pre-defined categories from a taxonomy) and auto-tagging (assigning unstructured keywords or metadata, sometimes generated without a taxonomy). In this article, I use ‘auto-classification’ in the broader sense, encompassing both approaches. Continue reading