EIS Webinar Recording When AI Answers 2026 09 24

Summary Your AI is only as good as the content feeding it. If that content is unstructured, duplicated, or written without retrieval in mind, your AI will hallucinate, go silent, or return answers nobody can trust. And the problem compounds: every failed retrieval carries a cost in compute, in staff time, and in the confidence people have in the system. This session gives you a clear picture of what is going wrong and what to do about it. Seth Earley and Rob Hanna of Precision Content bring two complementary perspectives: one on how to score and measure content for AI readiness, and one on how to write and structure content so it performs for both humans and machines. You will come away understanding why AI retrieval is a content problem before it is a technology problem, how to evaluate your own content against the signals AI systems use to decide what is retrievable and citable, and what a practical path to improvement looks like. The session introduces Earley's AI Retrieval Readiness (AIRR) framework, a 10-dimensional scoring model grounded in published standards and vendor guidance, alongside Precision Content's microcontent methodology for structured authoring that scales. If your AI initiatives are underdelivering and you are not sure why, this recording is where to start. Start with a free AIRR content evaluation To learn more about Precision Content's DocIntel Analyzer, contact Rob Hanna at Precision Content: https://www.precisioncontent.com Key Themes and Takeaways AI retrieval failure is a content problem, not a model problem. Ungoverned content creates a measurable tax on token costs, team productivity, and AI reliability. Well-written content and retrievable content are not the same thing. The AIRR framework scores content across 10 dimensions to evaluate AI readiness objectively. Improving content for external AI visibility and improving internal RAG retrieval are the same work. Structured authoring and DITA provide a scalable, governed control plane for AI content pipelines. Lean RAG reduces content duplication so AI can identify a reliable single source of truth. Ground truth must be defined and validated by subject matter experts before AI answer quality can be measured. Your knowledge architecture, ontology, and metadata structures are competitive assets and should not be outsourced to opaque vendor platforms. Session Highlights "When AI retrieval fails, most people look at the model. The model is rarely the problem. You had well-structured, curated content. You didn't have duplicates. Someone managed it, organized it, reviewed it, vetted it. That is why it retrieved well." -- Seth Earley "This content tax has been around for a very long time. We have always put the burden on the consumers of information -- your staff, your customers, your partners -- to work through it. AI does not absorb that burden. It fails the same way humans do, just faster." -- Rob Hanna "If the content is not clean and not well aligned with the user's query, the system will resubmit queries, re-rank, reconfigure. That means 6 to 10 times the token cost. People are going to start realizing that tax is real." -- Seth Earley "Not all content is suitable for structured authoring, and not all content is suitable for your AI. Your technical content should be the knowledge foundation. It has the weight and credibility that marketing and transactional content do not." -- Rob Hanna "Your knowledge architecture, your ontology, your knowledge graph, your metadata structures -- that is what you compete on. When you treat those as a vendor black box, you are outsourcing your competitive advantage and locking yourself into a platform you cannot control." -- Seth Earley