Recorded Webinar
Search is changing from a page of links to a single generated answer. When someone asks ChatGPT, Gemini, Claude, or Perplexity a question your organization could answer, does your content show up? And when your own internal AI assistant reaches into your knowledge base, does it find the right content, understand it, and cite it correctly? For most organizations the answer is no, and they cannot see it happening.
Here is the uncomfortable truth executives need to hear: this is not a technology failure. It is a content failure, and it plays out on two fronts. On the outside, as buyers shift from search to AI assistants, content that AI cannot retrieve quietly stops showing up, and a competitor's shows up instead. On the inside, AI assistants hallucinate or return useless answers because the RAG system cannot find or interpret the organization's own knowledge. In the great majority of cases, that is a content problem, not a model problem. Earley Information Science calls the hidden cost of all this the content chaos tax.
In this joint session, Seth Earley, CEO of Earley Information Science and author of The AI-Powered Enterprise, and Rob Hanna, founder of Precision Content, show why content is the foundation of every AI system, and how to prove your content is ready using two complementary approaches. Earley Information Science scores content for AI retrieval readiness with the AIRR-10 framework, evaluating from the retrieval system's point of view across a document, a corpus, or an entire website, and across both external discovery and internal RAG. Precision Content engineers the content itself through structured authoring, DITA, and a governed content supply chain, then proves the result with human performance testing that measures accuracy, speed, and confidence before and after. One approach is the X-ray. The other is the stress test. Together they close the loop from diagnosis to remediation to measurable proof.
You will leave with a clear-eyed view of why AI initiatives really underdeliver, a shared language for making the case to the executives who control the budget, and a practical first move you can run this week on one high-value slice of your content.
What you will learn:
- Why AI retrieval, whether it is ChatGPT on the outside or your own assistant on the inside, is a content and information architecture problem, not a model problem, and what that means for where you invest
- The difference between external GEO visibility and internal enterprise RAG readiness, and why conflating the two leads to the wrong fix
- How AIRR-10 scores content for AI retrieval readiness, and how it differs from measuring "good content"
- How Precision Content proves content quality with usability testing and performance benchmarks that executives cannot wave away
- Why improving content for humans improves it for AI, and how structured authoring, DITA, and the CCMS become the control plane for a Lean RAG content supply chain
- The most damaging content mistake teams are making right now in the rush to feed AI, and how to avoid it
- A practical, low-risk way to baseline, remediate, and re-measure one slice of content, and how to get your content and AI teams in the same room
Who should attend: Executives and transformation leaders responsible for AI outcomes, and the content operations, technical documentation, knowledge management, and information architecture leaders who make those outcomes real.
