Research and advisory organizations publish thousands of high-quality reports each year. But even the strongest insights can underperform in AI retrieval. Leaders saw three core problems:
Analyzed hundreds of category, problem-solving, and comparison queries; the same types executives and analysts ask generative AI tools.
Reviewed thousands of assets for:
These are the signals AI systems rely on to understand relevance.
Tested a broad set of prompts across internal search, commercial engines, and multiple LLMs. This revealed which content surfaced, which didn’t, and why.
Pinpointed the changes that most improved visibility:
Leaders received dashboards showing visibility gaps, top-performing assets, priority reports to restructure, and improvements over time.
Showing up in AI answers requires more than keywords or metadata. It demands LLM-aware content, tested prompts, structured summaries, clean entities, and analysts who understand how models retrieve, rank, and cite information.
Teams that combine technical prompt engineering, LLM behavior analysis, and structured content design gain a visibility advantage traditional SEO can’t match.
increase in AI/search visibility after improving summaries and citations
reduction in analyst time-to-find key reports
improvement in organic search engagement
in accurate citations from LLMs
If you want your best content to appear in AI answers (not just search results) you need structure, clarity, and LLM-aware signals. Our 90-Day AI Visibility Sprint helps teams apply these principles quickly and measurably.
