May 20, 2026
Casey Bjorkdahl is one of the pioneering thought leaders in the SEO community. In 2010, Casey co-founded Vazoola after working for a Digital Marketing Agency for five years in New York City. Vazoola is now one of the fastest growing and most widely recognized SEO marketing firms in the country.
AI search has upended the playbook.
Rankings used to tell the story. Now, the real question is simpler and harder at the same time: Why does one brand show up in an answer while another gets ignored?
There is no official rulebook, but there are clear patterns hiding in plain sight. We’re here to cut through the noise, show what is actually working, and explain how agencies can turn it all into action.
Key Takeaways
-
AI systems do not publish ranking factors, but patterns still exist.
-
Off-page signals like brand mentions and authority shape AI outputs.
-
Strong entity recognition improves visibility in AI-generated answers.
-
Digital PR and citations influence how models reference brands.
-
Agencies should shift focus from rankings to inclusion in answers.
Table of Contents
What Are AI Ranking Factors—and Why They’re Hard to Define
Agencies have spent years working with clear SEO playbooks. Google shared signals. Tools tracked rankings. Teams built repeatable systems.
AI search changes that model in a meaningful way.
Large language models don’t release ranking documentation. No official list explains why one brand appears in an answer while another does not. Instead, marketers rely on research, observed behavior, and patterns across training data.
Recent analysis from McKinsey shows that AI search is reshaping how users discover information, with generated answers acting as a new “front door” to the web.
That shift changes what visibility means. Instead of relying on position in a list of results, brands now compete to be included in the sources AI systems reference and synthesize. Agencies need a new mindset to compete in that environment.
Waiting for clarity slows progress. Teams that act on what is already known gain an advantage.
Most teams still track rankings, which misses the real signal. Start documenting when and where your brand appears in AI-generated answers across tools like ChatGPT, Claude, and Google AI Overviews. Patterns will emerge faster than traditional SEO data.
What Google Ranking Factors Still Teach Us
Years of SEO data still matter. Many signals that shaped Google rankings also influence AI behavior.
Several long-standing off-page signals still shape visibility today:
-
Backlinks: Links from other sites signal relevance and trust.
-
Authority: Established domains carry more weight across ecosystems.
-
Credibility: Consistent, accurate information builds long-term trust.
-
Brand mentions: References across independent platforms reinforce recognition.
These signals help search engines understand trust. AI systems rely on similar signals when pulling from training data or retrieval sources.
For a deeper breakdown of how these signals work, check out our overview of SEO ranking factors. The same foundations apply, even as the output format changes.
The connection becomes clear with one shift in perspective. AI doesn’t rank pages. AI selects and synthesizes information. Sources that appear often and carry authority get used more frequently.
Off-Page Signals That Influence AI Responses
Clear documentation may not yet exist, but that doesn’t leave you in the dark. Strong patterns still emerge from AI responses.
Brand Mention Volume and Consistency
Brands that appear often across independent sites build recognition. Consistency matters as much as volume.
Mentions across trusted publications, forums, and industry resources create a footprint. AI models learn from those repeated references. Retrieval systems also prioritize sources that appear reliable and widely cited.
A focused strategy around brand mentions helps reinforce this footprint over time.
One mention on a high-authority site helps. Five mentions across related, credible sites within the same topic area strengthen the signal. AI systems respond to patterns, not one-off wins.
Named Entity Recognition (NER)
AI systems identify brands as entities. Each entity carries context.
A brand linked consistently to specific topics gains stronger association. Over time, that association improves recall within AI-generated responses.
For example, a marketing agency mentioned alongside “link building” across multiple sources becomes tied to that concept. The model learns the relationship.
Without this clarity, mentions lose value.
That loss is why inconsistent messaging across PR, content, and listings weakens visibility. AI systems struggle to form a clear association when a brand shows up with different positioning across sources.
Citations in Authoritative Sources
Not all mentions carry equal weight.
Sources that publish frequently and maintain strong editorial standards influence AI outputs more heavily. Research from MIT FutureTech highlights how AI systems rely on high-quality, frequently referenced sources to generate reliable answers.
This behavior mirrors traditional link authority. Strong sources amplify visibility.
Not all authority is equal. Focus outreach on sites that are frequently cited, updated, and crawled. These sources are more likely to feed both training data and live retrieval systems.
E-E-A-T Behaviors Still Matter
AI models do not apply E-E-A-T in the same structured way as Google. The behaviors behind it still drive outcomes.
Brands that earn expert mentions, build credibility, and maintain consistency appear more often in training data and retrieval systems.
The mechanism differs, but the result aligns.
Over time, those repeated signals shape how a model “understands” a brand’s authority, even if the model never explicitly labels it as E-E-A-T.
Digital PR as a Signal Multiplier
Digital PR strengthens multiple signals at once.
Coverage drives brand mentions, earns citations, and builds authority. Each placement reinforces entity recognition. Over time, these signals compound.
That effect explains why digital PR has become a core off-page strategy in AI search.
PR placements do more than drive traffic. They increase the chance your brand becomes part of future model training datasets. Think beyond immediate ROI.
What’s Different About AI vs. Traditional Search
Search engines rank results, but AI systems generate answers. That difference changes the goal.
From Ranking to Being Cited
Traditional SEO focuses on page position, while AI search focuses on inclusion.
A brand doesn’t need to rank first to appear in an answer. It needs to be trusted enough to be referenced.
Recency Works Differently
AI systems rely on two inputs:
-
Training data, which may have a cutoff date
-
Live retrieval, which pulls recent information
A brand with strong historical authority can still appear even without fresh content. A newer brand may gain visibility through recent mentions if those mentions appear in trusted sources.
This creates a split advantage. Established brands benefit from historical authority, while newer brands can break in through timely, high-quality mentions.
Balance matters here.
Weak Off-Page Presence Limits Visibility
Strong content alone doesn’t guarantee inclusion in AI answers.
A brand without external validation often gets ignored. AI systems look for signals that confirm credibility. Without them, even high-quality content remains unseen.
This is where many brands fall short. They invest heavily in content but neglect the external signals that validate it. Without that validation, AI systems have little reason to trust or surface the source.
What Agencies Can Control Right Now
Yes, uncertainty surrounds AI search. Yet action matters more than waiting for a perfect rulebook.
Agencies can focus on building durable off-page signals that support both traditional search and AI visibility.
-
Strengthen brand mentions across independent sources: Focus on quality placements that reinforce authority.
-
Build consistent entity associations: Align messaging so each mention supports the same expertise.
-
Prioritize digital PR campaigns: Earn coverage in publications that AI systems trust.
-
Maintain backlink quality: Strong links still signal credibility.
-
Monitor visibility patterns: Track where brands appear in AI-generated answers.
These actions create a stable foundation, regardless of how AI models evolve.
How to Talk to Clients About AI Visibility
Clients notice the shift quickly. Inevitably, questions soon follow. Even with that uncertainty, clear communication still builds trust.
Explain to clients that AI visibility depends on broader signals than rankings. Emphasize that authority, mentions, and credibility shape inclusion.
Make sure you set expectations early. Results take time because signals must build across multiple sources.
Ultimately, frame the opportunity as a long-term strategy, not a quick fix. Brands that invest now position themselves for sustained visibility.
Clients often default to rankings because they’re familiar. Shift the conversation to “share of answers.” How often does their brand appear in AI-generated responses for key queries? That metric better reflects how AI search works and resets expectations around success.
The Future of Off-Page Signals in AI Search
As AI search continues to evolve, patterns will become clearer over time.
Early indicators point in one direction: Brands that build strong off-page signals gain an advantage across both traditional search and AI-driven results.
Visibility no longer depends on where a page ranks. It depends on whether a brand becomes part of the answer.
In the end, the question is no longer how to rank. It’s whether a brand earns a place in the conversation.
Short bursts of coverage fade quickly. Consistent mention velocity over time creates stronger signals. AI systems reward sustained presence more than temporary visibility.