Back to Blogs
AI answer engines can only recommend a B2B brand when they can find, understand, and trust its information. Guy Yalif and Omer Gotlieb organize AEO around four pillars: content, technical structure, authority, and measurement. Their framework gives resource-constrained marketing teams a practical way to answer buyer questions, strengthen machine readability, earn citations, and track visibility.
Buyers increasingly ask AI systems detailed questions instead of entering a few keywords and reviewing a page of links. They may describe their role, company size, existing technology, budget, security requirements, and business problem before asking which solution fits.
That shift changes what it means for a B2B brand to be discoverable. Ranking for a broad keyword remains valuable, but an answer engine also needs enough information to determine who the product serves, what problem it solves, how it differs, and whether credible sources support the company’s claims.
In a Renegade Marketers Unite conversation about AEO in B2B, Webflow Chief Evangelist Guy Yalif and Salespeak co-founder Omer Gotlieb organized the work around four pillars: content, technical structure, authority, and measurement.
The framework makes AEO more manageable. It also shows why publishing more AI-generated articles is not an adequate strategy.
AEO begins with the questions buyers bring to an answer engine. Those questions are usually more specific than traditional search terms because buyers can include their circumstances in the prompt.
A technology buyer might ask whether a platform works for a regulated enterprise, integrates with an existing system, supports a particular use case, fits within a defined budget, or offers an advantage over a named competitor. A website that only presents broad brand language may not provide enough evidence for the answer engine to include it.
Omer described the starting point in direct terms: “Content is everything.” He then distinguished question-led content from the keyword-led production that has filled many B2B websites.
Sales calls, support tickets, customer interviews, win-loss analysis, site searches, and sales emails can reveal the questions buyers actually ask. The first step does not require hundreds of new pages. A team can identify 10 or 20 recurring questions, determine which ones matter most to the buying process, and evaluate whether the website answers them clearly.
The useful answer may need to include:
Omer’s website research found gaps in subjects such as pricing, competition, company vision, ideal customer profiles, personas, and use cases. Those omissions create room for another source to define the company.
“The main understanding right now for companies is that if you don't have data, somebody else is going to steal your narrative. Somebody else is going to provide their point of view on that.”
The implication extends beyond AEO. Buyers already want this information. Answer engines make the cost of withholding it more visible because they may fill the gap with third-party commentary or a competitor’s framing.
A resource-constrained team can begin with recorded sales calls. Pull the most common questions from recent conversations and compare them with the current website. Select two or three unanswered questions tied to important buying decisions, then create useful responses grounded in internal expertise and customer evidence.
Guy suggested starting at that scale so the team can develop the new habit without creating an entirely separate content operation.
Strong content can still be difficult for an answer engine to interpret when the site provides little structural guidance. Technical AEO helps machines identify what each page contains and how the information relates.
Guy explained the site’s expanding role:
“Websites now have two audiences: you need a visually stunning, emotionally evocative, engaging experience for humans and an interface for machines where it needs to be well-structured, concise, cheaper for them to crawl.”
Serving machines does not mean replacing the human experience with a plain-text site. It means making the existing information easier to identify, retrieve, and interpret.
Structured data and schema can label a page as a product page, article, author biography, FAQ, organization profile, event, or use-case page. Clear headings, descriptive page titles, accessible text, internal links, transcripts, and fast performance can further reduce ambiguity.
A video or webinar page, for example, may offer a strong human experience but give machines little usable information. Adding a transcript, summary, speaker details, and clearly structured questions gives the answer engine more evidence to work with.
Technical improvements can start with several manageable checks:
The objective is not to create hidden content for machines. The human and machine-readable versions need intellectual consistency. Both audiences need access to the same accurate company story.
A company cannot establish authority entirely through claims published on its own domain. Answer engines also consider what external sources say, which sources cite the company, and whether the wider information environment supports the brand’s position.
Guy described authority as an evolution from a narrow focus on backlinks toward widespread, credible mentions. Traditional links still matter, but plain-text references, reviews, media coverage, community discussions, research citations, and recognized expert commentary can contribute to the picture.
Platforms such as Wikipedia, Reddit, G2, TrustRadius, analyst sites, industry publications, customer communities, and professional forums may influence what an answer engine finds. The appropriate sources depend on the category and the questions buyers ask.
Authority cannot be manufactured safely through corporate promotion disguised as community participation. Guy noted that marketing language performs poorly on Reddit, where audiences tend to value directness, specificity, and a willingness to acknowledge limitations.
His team began by observing the communities, learning their norms, and identifying where Webflow could participate credibly. The goal was not to flood another channel with branded copy. It was to contribute useful expertise in a form the community would accept.
Original research can also create authority because it gives journalists, analysts, practitioners, and answer engines something distinctive to cite. Customer evidence, benchmarks, proprietary data, expert analysis, and clearly articulated points of view help the company become a source rather than another summary of existing material.
Guy connected that distinction with how teams use AI:
“You can use an LLM to outsource your thinking, or you can use an LLM to think more deeply and do so more rapidly.”
The first path produces more interchangeable content. The second can help a team analyze customer evidence, test ideas, refine an argument, and distribute genuinely useful expertise more effectively.
Choose five high-priority buyer questions and review which sources answer them today. Note the publications, communities, reviewers, analysts, and companies being cited. The gaps can reveal where stronger content, digital PR, expert participation, or third-party validation could improve authority.
AEO measurement is less stable than traditional keyword tracking. An AI system may include a company in one response and omit it when the same question is asked again. The wording, user context, model, and timing can all affect the result.
That makes a single test misleading. Guy recommended moving from keyword rankings to share of voice across a defined basket of buyer questions. Sentiment matters as well because inclusion is not automatically positive.
A useful AEO measurement program can track:
The exact tools will continue changing. The durable element is the question set. Those questions connect measurement to buying behavior rather than reducing AEO to another platform score.
Omer suggested that teams with limited budgets start with free assessment tools and basic measurement before committing substantial resources. The baseline helps identify whether the immediate weakness lies in missing content, technical structure, authority, or all three.
Measurement then becomes a learning loop. The team identifies important questions, checks current visibility, improves the supporting information, and tests again over time.
AEO builds on many practices that effective SEO already rewarded: original information, clear structure, useful answers, technical accessibility, and external authority. Search engines also remain important to how answer engines retrieve current information.
Guy’s advice was unequivocal: “Don't abandon SEO. It still matters.”
The practical change is to expand the optimization target. Keyword rankings remain one signal, while question coverage, machine readability, third-party mentions, share of voice, and sentiment become additional measures.
This prevents AEO from becoming a competing content factory. The same team can strengthen existing high-value pages, update outdated material, add missing buyer answers, improve structure, and build authority around subjects the company already wants to own.
The four pillars can become a focused initial program:
Omer brought the framework back to a familiar marketing principle:
“Design for the buyer, design for the buyer, design for the buyer.”
AEO is new, but the foundation is not. Buyers want useful information, credible evidence, and a clear understanding of how a solution fits their situation. Answer engines reward brands that make those elements available.
The four pillars are content, technical structure, authority, and measurement. Together, they address what the company says, how machines interpret it, whether external sources support it, and how frequently the brand appears in relevant AI answers.
No. Search visibility, crawlability, original content, and technical SEO still matter. AEO expands the work to include question-led content, machine-readable structure, external mentions, citations, sentiment, and share of voice across AI answers.
Useful AEO content directly answers specific buyer questions with clear language, relevant context, credible evidence, and enough detail for a person or answer engine to understand when the solution fits.
The team can identify 10 recurring buyer questions, measure current visibility, improve two or three important answers, add basic schema, and review the external sources being cited. This creates an initial learning cycle without requiring a separate AEO department.
A practical starting point is share of voice across a consistent group of buyer questions. Teams can also track citations, sentiment, competitor inclusion, AI-referred traffic, and which site content answer engines access.
Listen to the full conversation with Guy Yalif and Omer Gotlieb.
CMO Huddles helps B2B marketing leaders win by bringing together peers, fresh perspectives, and opportunities to build stronger personal brands. Want to join the huddle? Learn more about CMO Huddles and join CMO Huddles Starter.
[a]Please attribute it to someone
[b]Please attribute it to someone