Build Brand Visibility in the AI Era With a Practical Framework for Measurement, Optimization, and Action
Account-based discovery is no longer happening only on Google. Buyers are increasingly turning to AI-powered search and large language models (LLMs) to research vendors, compare options, and shape decisions long before they ever visit a website.
In this pop-up session, we break down what it actually takes to win visibility in the AI era. Kate Quinn, Kevin White, and Ali Lipman walk through how AI engines evaluate brand authority, why traditional SEO metrics fall short, and how teams can benchmark, identify opportunities, and take action with an AEO (AI Engine Optimization) strategy.
The session moves from strategy to execution—covering prompt-based measurement, content and technical readiness, and how to deliver optimized experiences for both human buyers and AI crawlers without degrading either.
What you’ll learn
- Why AI visibility requires a different approach than traditional SEO
- How LLMs assess brand authority using off-site signals and citations
- What metrics matter for AEO—and how they differ from organic traffic
- How to benchmark AI visibility using prompts and prompt clusters
- How to identify low-hanging opportunities across topics and pages
- The technical foundations required for AI-friendly content
- How teams are taking action without sacrificing the human experience
TOC
Table of Contents
1 Why AI Visibility Changes the Rules of Search
2 The Three Pillars of AEO
3 How to Benchmark AI Visibility
4 Finding Low-Hanging Fruit in AI Search
5 Technical Readiness for AEO
6 Taking Action: Optimizing for Humans and AI
1Why AI Visibility Changes the Rules of Search
The buyer experience has fundamentally shifted.
Instead of clicking through pages of search results, users increasingly type a prompt and receive a synthesized answer from an AI model. That shift creates a better user experience—but it also collapses the traditional funnel.
The implication is significant:
- Users may never visit your website
- Brand exposure happens inside AI platforms
- Visibility is driven by citations, mentions, and perceived authority
This doesn’t invalidate SEO best practices—but it does change how success is measured. In the AI era, visibility inside LLM responses becomes just as important as website traffic.
2The Three Pillars of AEO
Three core pillars that consistently influence AI visibility
Brand authority
LLMs take brand authority seriously. They evaluate:
- How often your brand is mentioned
- Where those mentions occur (Reddit, LinkedIn, Wikipedia, forums)
- Whether your brand is cited as a credible source
A key takeaway: authority is built off-site as much as on-site. Helpful, non-promotional participation in communities matters far more than linking to gated landing pages.
Helpful content
AI engines prioritize content that directly answers questions. That means:
- Clear explanations
- Educational framing
- Avoiding overly sales-driven language
Buyers are often completing 70% or more of their research before contacting vendors. Content that helps them understand—not convert—wins early visibility.
Technical accessibility
If AI bots can’t read your content, they can’t use it. JavaScript-heavy pages, poor crawlability, and unstructured content all create barriers to AI consumption.
RevOps should begin with the foundation.
Assess your tech stack
- What tools do you already have that can support ABM?
- Do you have usable first-party data? Intent tools?
- Are there ABM capabilities you already pay for but aren’t using?
A key point: many teams delay ABM because they assume it requires major new tooling. ABM capabilities often exist inside the systems you already have (including HubSpot).
Assess your process and mindset
The biggest change: moving from one entry point (a single lead/MQL) to multiple stakeholders with different needs and influence. ABM readiness includes the ability to:
- stack-rank personas inside the buying committee
- identify champions vs. influencers vs. blockers
- engage based on “digital body language” (intent signals), not just form fills
3How to Benchmark AI Visibility
Traditional SEO benchmarks focus on organic traffic. AEO requires a different unit of measurement.
Prompts as the core metric
In AEO, prompts replace keywords.
- A prompt represents a real user question
- Visibility is measured by whether your brand appears in AI-generated answers
Prompts can be grouped into clusters and evaluated across dimensions such as:
- Funnel stage
- Persona or ICP
- Topic area
- Region or language
- AI model
This framework allows teams to benchmark where they show up—and where they don’t.
Measuring what matters
Key AEO metrics include:
- Brand citations in LLM responses
- Domain citations as a trusted source
- Prompt-level visibility by topic and funnel stage
- LLM referral traffic as a downstream signal
4Finding Low-Hanging Fruit in AI Search
Once benchmarks are established, teams can identify opportunities by comparing:
- Search volume by topic
- Current brand presence within those topics
High-volume topics with low brand presence represent strategic gaps.
Another opportunity lies in existing high-authority pages. By analyzing AI bot traffic, teams can see:
- Which pages AI models already crawl
- Which pages influence AI visibility
Those pages become prime candidates for optimization and expansion.
5Technical Readiness for AEO
Technical optimization plays a critical role in AI visibility.
Once high-impact pages are identified, teams should assess:
- Crawlability and rendering
- JavaScript dependencies
- Content structure and clarity
In some cases, creating AI-friendly versions of content, such as Markdown or structured formats, can dramatically improve how models interpret and reuse information.
6Taking Action: Optimizing for Humans and AI
A common concern is whether optimizing for AI degrades the human experience. The session highlights a more mature approach.
Advanced teams are:
- Detecting AI bot traffic
- Serving AI-optimized versions of content to crawlers
- Preserving rich, visual experiences for human users
This dual-experience model allows teams to meet AI requirements without compromising UX.
The maturity curve looks like:
- Identify AI search trends
- Measure AI visibility and traffic
- Audit technical barriers
- Fix what AI can’t read
- Deliver optimized experiences for AI agents
Accordion
Is AEO just rebranded SEO?
Many SEO best practices still apply, but AEO introduces new metrics and visibility surfaces. Measuring prompt-level visibility and brand citations inside AI platforms is a fundamental shift.
What are the most important early AEO metrics?
Brand mentions, citations, and prompt coverage matter first. LLM referral traffic is useful, but it represents the last mile—not the full picture.
Where should teams start if AEO feels overwhelming?
Start with benchmarking. Define prompts, assess where your brand shows up today, and focus on high-impact gaps rather than trying to optimize everything at once.
How important is off-site content for AI visibility?
Extremely important. LLMs rely heavily on third-party sources like forums, social platforms, and knowledge bases to assess credibility.
How does New Breed support AEO efforts?
New Breed offers AEO assessments that evaluate AI visibility, competitive presence, technical readiness, prompt coverage, and performance—helping teams prioritize action with clarity.