Most AI retrieval failures get blamed on the model. The real problem is the content underneath it. This session introduces two ways to score your content, one for how AI engines retrieve it and one for how people use it, and shows how to find the gaps, fix them, and measure the improvement inside and outside the enterprise.
- AI Retrieval Failure is a Content Problem, Not a Model Problem: When content is unstructured, duplicated, or ungoverned, AI systems re-query, re-rank, and reconfigure to find an answer. That can multiply token costs six to ten times, and the system may still return a weak answer or go silent.
- Well-Written is Not the Same as Retrievable: The AIRR framework scores content across 10 dimensions, including semantic clarity, structure, verifiability, and temporal signals. Content that reads well to a person can still score below 5 and be invisible to AI engines.
- Buyers Are Moving From Search Results to Single Answers: If your content cannot be retrieved, it does not show up in AI-generated answers. LLM-referred visitors are fewer than search visitors but convert at four to nine times the rate, which makes visibility worth measuring now.
- Structure and Citations Give AI Something to Trust: Good content for AI is good content for humans. Small, single-idea chunks with a clear purpose and metadata work for both, and answers should cite a single source of truth that subject matter experts have validated.
- Measure Before and After to Prove the Value: Baseline your content, remediate, and measure again. One field service organization that cleaned up its content cut the time engineers spent searching by 50%, saving tens of millions of dollars.
Speakers
- Seth Earley, CEO and Founder, Earley Information Science
- Rob Hanna, CEO and Chief Architect, Precision Content
