As traditional search gives way to conversational and generative AI systems, professionals face a new challenge: being surfaced as an expert even when a user does not know their name. To address this shift, Dr. Tamara “Tami” Patzer has introduced AI Discoverability, a framework that explains how artificial intelligence identifies experts through category signals, contextual cues, and machine recognition rather than keywords.

AI Discoverability reflects a major change in how people find information. Instead of searching for “back pain doctor Tampa,” users now ask AI systems to “find the best non-surgical pain solution near me.” Instead of looking up “author name + book title,” they ask: “Who is writing about identity and AI?”

“AI Discoverability is not search visibility,” Patzer said. “It’s the ability to appear when someone asks a question, even if they’ve never heard of you. The system connects identity, expertise, and context to elevate you as the answer.”

The framework is part of Patzer’s broader discipline, AI Identity Engineering, which explains how modern AI platforms determine authority, relevance, and trust. Unlike traditional SEO, which depends on keywords and website structure, AI Discoverability focuses on:

• category alignment

• authority saturation

• contextual expertise signals

• identity clarity

• cross-platform corroboration

• public-interest relevance

This shift has grown more pronounced since the November 2025 update to ChatGPT 5.1, which strengthened category-based reasoning, trust layers, and entity recognition; and since Google expanded its AI Overviews to prioritize expert explanations over ranked links. Meta, Microsoft Copilot, and Perplexity have also adopted more aggressive expert-identification systems that select individuals based on the totality of their digital identity.

“AI is not choosing the most optimized website,” Patzer said. “It’s choosing the most recognizable and verifiable expert. If the system believes you fit the category, you surface. If not, you remain invisible, regardless of your credentials.”

AI Discoverability highlights several issues facing professionals in 2025:

• Experts with weak identity signals often fail to appear even when they are the top authority in their field.

• Professionals with similar names to celebrities or influencers may be suppressed by Identity Collision, another risk identified in Patzer’s AI Reality Check.

• Experts whose work is not machine-readable or corroborated may be deprioritized.

• Category experts in emerging fields may go unrecognized if AI lacks sufficient structured information about them.

Research institutions throughout 2025 have emphasized related concerns.
Updates from the Poynter Institute, Nieman Lab at Harvard University, Columbia Journalism Review, International Fact-Checking Network, American Press Institute, Trust Project, News Literacy Project, Knight Foundation, and the Reuters Institute for the Study of Journalism each addressed the need for clearer expert identification and stronger digital trust signals.

These journalism organizations have warned that as AI becomes a primary information distributor, experts risk becoming invisible unless their identity, expertise, and public relevance are documented and verifiable at scale.

“People believe they are discoverable because they exist,” Patzer said. “But AI determines discoverability based on signals most professionals have never optimized. Visibility is no longer a function of popularity—it is a function of identity structure.”

AI Discoverability outlines what those signals are, how they function across platforms, and how experts can ensure that AI systems surface them consistently across categories, topics, and public-interest queries.

About Dr. Tamara Patzer
Dr. Tamara “Tami” Patzer is a Pulitzer Prize–nominated journalist and founder of AI Identity Engineering. She is the creator of the AI Reality Check, AI Discoverability, Identity Collision, the AI Suggestibility Score, the AI Trust Score, and the FirstAnswer Authority System. Her work integrates journalistic verification standards with AI-driven authority and identity systems.

LinkedIn: https://www.linkedin.com/in/tamarapatzer/