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Content related to NERDy Content for the Enterprise

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

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

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

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

Semantic Layer for Content Discovery, Personalization, and AI Readiness

A professional association needed to improve their members’ content experiences. With tens of thousands of content assets published across 50 different websites and 5 disparate content management systems (CMSes), they struggled to coordinate a content strategy and improve content discovery. They could not keep up with the demands of managing content … Continue reading