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If you're playing the long game, because B2B buyers make decisions based on familiarity and trust, this is good news. But you'd be forgiven for missing that, given the volume of hot takes that followed. New hacks. New fears. And, as ever, a rush to share the latest ‘quick wins’ while declaring the old playbook obsolete!
LinkedIn published a full technical breakdown of the update on their Engineering blog. It’s worth reading before anyone else's commentary shapes your thinking.
Here’s the update in their own words:
“While the Feed has long been AI-powered, recent LLM advances gave us the opportunity to rethink what's possible. That's why we're rolling out a new advanced ranking system, powered by LLMs and GPUs, that better understands what a post is actually about and how it relates to a member's evolving interests and career goals."
The algorithm doesn't define success. Your content does.
So what does LinkedIn actually value? The algorithm filters for relevance, evaluating what your post is about, who it's likely to matter to, and whether you're the kind of voice that shows up consistently on that topic. What the new LLM-powered system changes is the precision of that matching, not the underlying logic.
LinkedIn's algorithm update is designed to be more adaptive to evolving user interests, rather than being guided purely by historical engagement data. In practical terms, the old system rewarded familiarity. If someone had engaged with your posts before, they were more likely to see them again.
In my training, I always emphasised engagement as the key to building future visibility. That principle still holds, but this new system now broadens the opportunity, matching content to current interests means your posts can reach relevant audiences beyond those who already know you.
There's also a clear signal on what's being deprioritised. Engagement bait, posts that prompt users to "Comment 'Yes' if you agree”, or posts that feature a video with no relation to the accompanying text, is being actively filtered out. Recycled thought leadership posts that add little in terms of substance or insight will also be downranked. A welcome shift (and long overdue).
If your activity has been built around gaming engagement rather than earning it, this update is a genuine problem. For everyone else, it's an improvement.
The update is good news for anyone producing quality content: when industry news breaks and relevant posts gain traction, the updated system surfaces them within minutes, not hours or days.
But here's what most people are missing
The feed algorithm is one piece of a much larger picture of change. According to Semrush, LinkedIn is now the second most cited domain across AI search tools, sitting ahead of Wikipedia, major news publishers, and other social platforms (Reddit is currently occupying first place).
Semrush analysed 325,000 prompts across three AI tools, ChatGPT Search, Google AI Mode, and Perplexity, identifying 89,000 LinkedIn URLs cited in responses. On average, 11% of AI responses reference LinkedIn content.
Why does this matter? AI responses tend to mirror the meaning of original LinkedIn content closely, your framing, your positioning, your expertise can surface directly inside an AI-generated answer that a potential buyer is reading right now. That's a different kind of visibility than feed reach, and it's one most B2B marketers haven't yet accounted for.
So what earns that kind of citation? Educational, original content published consistently, long-form articles and substantive posts that share practical knowledge rather than promotional messaging.
The research also found that roughly 75% of cited authors post frequently, and about half have over 2,000 followers, so follower count matters less than you might expect. Authoritative content from smaller accounts still breaks through.
One nuance worth flagging for those managing both a brand presence and a personal profile: not all AI tools draw from the same sources. Perplexity tends to cite Company Pages most often, while ChatGPT Search and Google AI Mode more frequently surface content from individual creators. That's a compelling data point to have in your back pocket when making the internal case for investing in both, it's not either/or, it's both/and.
What this means for you
The latest algorithm update and the AI visibility data are pointing in the same direction. Stop optimising for the feed. Start optimising for building and maintaining trust.
That means posting consistently on the topics where you have genuine expertise. It means writing with clarity of thought, not volume of words. It means publishing original insight rather than recycled takes. And it means treating LinkedIn less like a broadcast channel and more like a body of work. one that both human readers and AI platforms are actively drawing from.
A word on consistency. Playbooks might tell you to post three times a week, always on Tuesday mornings, and between 8 and 10am. That guidance has its place, but it misses the point. Consistency that matters is consistency of topic and perspective, not frequency for its own sake. Showing up once or twice a week with genuine insight on the things you actually know will always outperform daily posting that says nothing in particular. The algorithm, and your audience, will notice the difference.
Start by auditing your last 90 days of LinkedIn activity against a simple test: does each post reflect genuine expertise, answer a question your audience is actually asking, and come from a consistent point of view? If the answer is mostly yes, this update works in your favour. If not, that's where your energy is better spent.
And don't overlook your profile. The system uses everything LinkedIn knows about you, your headline, summary, skills, and experience, to understand who you are and what you should be associated with. If your profile still reflects where you were three years ago rather than where you are now, the algorithm is working with outdated information. A profile that clearly signals your expertise and focus area makes everything else work harder.
If you're playing the long game, because B2B buyers make decisions based on familiarity and trust, this is good news. But you'd be forgiven for missing that, given the volume of hot takes that followed. New hacks. New fears. And, as ever, a rush to share the latest ‘quick wins’ while declaring the old playbook obsolete!
LinkedIn published a full technical breakdown of the update on their Engineering blog. It’s worth reading before anyone else's commentary shapes your thinking.
Here’s the update in their own words:
“While the Feed has long been AI-powered, recent LLM advances gave us the opportunity to rethink what's possible. That's why we're rolling out a new advanced ranking system, powered by LLMs and GPUs, that better understands what a post is actually about and how it relates to a member's evolving interests and career goals."
The algorithm doesn't define success. Your content does.
So what does LinkedIn actually value? The algorithm filters for relevance, evaluating what your post is about, who it's likely to matter to, and whether you're the kind of voice that shows up consistently on that topic. What the new LLM-powered system changes is the precision of that matching, not the underlying logic.
LinkedIn's algorithm update is designed to be more adaptive to evolving user interests, rather than being guided purely by historical engagement data. In practical terms, the old system rewarded familiarity. If someone had engaged with your posts before, they were more likely to see them again.
In my training, I always emphasised engagement as the key to building future visibility. That principle still holds, but this new system now broadens the opportunity, matching content to current interests means your posts can reach relevant audiences beyond those who already know you.
There's also a clear signal on what's being deprioritised. Engagement bait, posts that prompt users to "Comment 'Yes' if you agree”, or posts that feature a video with no relation to the accompanying text, is being actively filtered out. Recycled thought leadership posts that add little in terms of substance or insight will also be downranked. A welcome shift (and long overdue).
If your activity has been built around gaming engagement rather than earning it, this update is a genuine problem. For everyone else, it's an improvement.
The update is good news for anyone producing quality content: when industry news breaks and relevant posts gain traction, the updated system surfaces them within minutes, not hours or days.
But here's what most people are missing
The feed algorithm is one piece of a much larger picture of change. According to Semrush, LinkedIn is now the second most cited domain across AI search tools, sitting ahead of Wikipedia, major news publishers, and other social platforms (Reddit is currently occupying first place).
Semrush analysed 325,000 prompts across three AI tools, ChatGPT Search, Google AI Mode, and Perplexity, identifying 89,000 LinkedIn URLs cited in responses. On average, 11% of AI responses reference LinkedIn content.
Why does this matter? AI responses tend to mirror the meaning of original LinkedIn content closely, your framing, your positioning, your expertise can surface directly inside an AI-generated answer that a potential buyer is reading right now. That's a different kind of visibility than feed reach, and it's one most B2B marketers haven't yet accounted for.
So what earns that kind of citation? Educational, original content published consistently, long-form articles and substantive posts that share practical knowledge rather than promotional messaging.
The research also found that roughly 75% of cited authors post frequently, and about half have over 2,000 followers, so follower count matters less than you might expect. Authoritative content from smaller accounts still breaks through.
One nuance worth flagging for those managing both a brand presence and a personal profile: not all AI tools draw from the same sources. Perplexity tends to cite Company Pages most often, while ChatGPT Search and Google AI Mode more frequently surface content from individual creators. That's a compelling data point to have in your back pocket when making the internal case for investing in both, it's not either/or, it's both/and.
What this means for you
The latest algorithm update and the AI visibility data are pointing in the same direction. Stop optimising for the feed. Start optimising for building and maintaining trust.
That means posting consistently on the topics where you have genuine expertise. It means writing with clarity of thought, not volume of words. It means publishing original insight rather than recycled takes. And it means treating LinkedIn less like a broadcast channel and more like a body of work. one that both human readers and AI platforms are actively drawing from.
A word on consistency. Playbooks might tell you to post three times a week, always on Tuesday mornings, and between 8 and 10am. That guidance has its place, but it misses the point. Consistency that matters is consistency of topic and perspective, not frequency for its own sake. Showing up once or twice a week with genuine insight on the things you actually know will always outperform daily posting that says nothing in particular. The algorithm, and your audience, will notice the difference.
Start by auditing your last 90 days of LinkedIn activity against a simple test: does each post reflect genuine expertise, answer a question your audience is actually asking, and come from a consistent point of view? If the answer is mostly yes, this update works in your favour. If not, that's where your energy is better spent.
And don't overlook your profile. The system uses everything LinkedIn knows about you, your headline, summary, skills, and experience, to understand who you are and what you should be associated with. If your profile still reflects where you were three years ago rather than where you are now, the algorithm is working with outdated information. A profile that clearly signals your expertise and focus area makes everything else work harder.
If you're playing the long game, because B2B buyers make decisions based on familiarity and trust, this is good news. But you'd be forgiven for missing that, given the volume of hot takes that followed. New hacks. New fears. And, as ever, a rush to share the latest ‘quick wins’ while declaring the old playbook obsolete!
LinkedIn published a full technical breakdown of the update on their Engineering blog. It’s worth reading before anyone else's commentary shapes your thinking.
Here’s the update in their own words:
“While the Feed has long been AI-powered, recent LLM advances gave us the opportunity to rethink what's possible. That's why we're rolling out a new advanced ranking system, powered by LLMs and GPUs, that better understands what a post is actually about and how it relates to a member's evolving interests and career goals."
The algorithm doesn't define success. Your content does.
So what does LinkedIn actually value? The algorithm filters for relevance, evaluating what your post is about, who it's likely to matter to, and whether you're the kind of voice that shows up consistently on that topic. What the new LLM-powered system changes is the precision of that matching, not the underlying logic.
LinkedIn's algorithm update is designed to be more adaptive to evolving user interests, rather than being guided purely by historical engagement data. In practical terms, the old system rewarded familiarity. If someone had engaged with your posts before, they were more likely to see them again.
In my training, I always emphasised engagement as the key to building future visibility. That principle still holds, but this new system now broadens the opportunity, matching content to current interests means your posts can reach relevant audiences beyond those who already know you.
There's also a clear signal on what's being deprioritised. Engagement bait, posts that prompt users to "Comment 'Yes' if you agree”, or posts that feature a video with no relation to the accompanying text, is being actively filtered out. Recycled thought leadership posts that add little in terms of substance or insight will also be downranked. A welcome shift (and long overdue).
If your activity has been built around gaming engagement rather than earning it, this update is a genuine problem. For everyone else, it's an improvement.
The update is good news for anyone producing quality content: when industry news breaks and relevant posts gain traction, the updated system surfaces them within minutes, not hours or days.
But here's what most people are missing
The feed algorithm is one piece of a much larger picture of change. According to Semrush, LinkedIn is now the second most cited domain across AI search tools, sitting ahead of Wikipedia, major news publishers, and other social platforms (Reddit is currently occupying first place).
Semrush analysed 325,000 prompts across three AI tools, ChatGPT Search, Google AI Mode, and Perplexity, identifying 89,000 LinkedIn URLs cited in responses. On average, 11% of AI responses reference LinkedIn content.
Why does this matter? AI responses tend to mirror the meaning of original LinkedIn content closely, your framing, your positioning, your expertise can surface directly inside an AI-generated answer that a potential buyer is reading right now. That's a different kind of visibility than feed reach, and it's one most B2B marketers haven't yet accounted for.
So what earns that kind of citation? Educational, original content published consistently, long-form articles and substantive posts that share practical knowledge rather than promotional messaging.
The research also found that roughly 75% of cited authors post frequently, and about half have over 2,000 followers, so follower count matters less than you might expect. Authoritative content from smaller accounts still breaks through.
One nuance worth flagging for those managing both a brand presence and a personal profile: not all AI tools draw from the same sources. Perplexity tends to cite Company Pages most often, while ChatGPT Search and Google AI Mode more frequently surface content from individual creators. That's a compelling data point to have in your back pocket when making the internal case for investing in both, it's not either/or, it's both/and.
What this means for you
The latest algorithm update and the AI visibility data are pointing in the same direction. Stop optimising for the feed. Start optimising for building and maintaining trust.
That means posting consistently on the topics where you have genuine expertise. It means writing with clarity of thought, not volume of words. It means publishing original insight rather than recycled takes. And it means treating LinkedIn less like a broadcast channel and more like a body of work. one that both human readers and AI platforms are actively drawing from.
A word on consistency. Playbooks might tell you to post three times a week, always on Tuesday mornings, and between 8 and 10am. That guidance has its place, but it misses the point. Consistency that matters is consistency of topic and perspective, not frequency for its own sake. Showing up once or twice a week with genuine insight on the things you actually know will always outperform daily posting that says nothing in particular. The algorithm, and your audience, will notice the difference.
Start by auditing your last 90 days of LinkedIn activity against a simple test: does each post reflect genuine expertise, answer a question your audience is actually asking, and come from a consistent point of view? If the answer is mostly yes, this update works in your favour. If not, that's where your energy is better spent.
And don't overlook your profile. The system uses everything LinkedIn knows about you, your headline, summary, skills, and experience, to understand who you are and what you should be associated with. If your profile still reflects where you were three years ago rather than where you are now, the algorithm is working with outdated information. A profile that clearly signals your expertise and focus area makes everything else work harder.
London
Apr 7, 2026
Rich Fitzmaurice
Letters
Dear Rich,
I am under pressure from my CEO and CCO because they are increasingly obsessed with AI chatbots. Apparently the chatbots don't know much about our firm but can answer questions about our competitors. I am interim head of marketing and I'm feeling like I don't have long to address this before it harms my prospects for the gig full time.
I understand organic content is still valuable but how exactly do I get our firm and our products into ChatGPT or Claude?
Rebecca, Manchester
Dear Rebecca,
Your CEO and CCO have stumbled onto something increasingly real. To be fair to them, I think they are right, you need to treat this as a priority.
Prospective buyers are using AI tools to shortlist vendors before they ever land on your website. A CFO types "which platforms offer AI-powered forecasting" into Copilot. A procurement director asks ChatGPT "who are the main providers of X in the UK." None of them went to Google first (who would have said that just a year ago?!). And when the AI answered, it named specific brands. If yours wasn't one of them, you lost ground in a conversation you didn't know was happening.
The discipline you need is called AEO. Answer Engine Optimisation. It's what SEO was in 2008, which means the window to get ahead of your competitors is open right now, but it won't stay open forever. You can’t open LinkedIn or Instagram or any social media without someone talking about it or pitching a solution.
Here's what you actually do.
First, understand how AI decides what to say. Tools like ChatGPT were trained on web data up to a certain point. What they know about your company comes from that training: your content, your press mentions, your directory listings, third-party coverage. Retrieval-based tools like Perplexity pull live web data. Google's AI Overviews blend both. No single fix works across all of them. But the underlying principle is consistent. AI rewards clarity, consistency, and credibility.
Start with an audit. Open ChatGPT, Perplexity, and Claude. Ask the questions your buyers actually ask. "What are the best platforms for [your category]?" "Which providers work with [your target industry]?" "Tell me about [your brand name]." Note where you appear. Note where your competitors appear instead. Run fifteen to twenty prompts. The gaps become your priority list. This also gives you something concrete to take back to your CEO this week, which, given your situation, is not a small thing. It shows you are on it.
(Important Note: We see the need, so B2B Marketing United have decided to build our own ‘AI Search Scout Report’ tool to conduct this audit for free and get you started. We’ll release it soon so sign up to the newsletter on our website for updates.)
Then look at your own website. AI systems parse content differently from humans. They break pages into individual passages and evaluate each one independently. Clear headings, direct answers at the top of each section, and specific factual statements all increase the chance of being cited. A page that says "our platform processes two million transactions per day with 99.9% uptime" is far more citable than one that says "we offer industry-leading reliability." Specific beats vague. Every time. Go through your most important pages and make them legible to a machine. This means leading each section with a direct answer, adding FAQ sections that mirror the actual questions buyers ask, and replacing any claim that a journalist couldn't quote with one that they could.
Build your authority footprint outside your own site. Here's the thing most marketers miss. What others say about you matters at least as much as what you say about yourself. Often more. AI models weight sources by perceived credibility. Coverage in respected industry publications, bylines on high-authority sites, mentions from recognised experts: these all increase the probability that AI treats your brand as worth citing. One well-placed article in a credible trade publication does more for your AI visibility than ten posts on your own blog. I said it in our AEO how-to and I'll say it again here: PR is making a comeback, and this is the big reason why.
Fix your entity consistency. This is the unglamorous work that nobody wants to do and that most companies haven't done. Audit every place your brand appears online. Your website, your LinkedIn page, your Google Business Profile, your directory listings, your press mentions. Make sure your brand name, description, category, and key facts are identical everywhere. If your founding year, product description, or company category varies between sources, AI loses confidence in citing any of them. Content and Comms teams must be loving that all their hard work insisting on ‘core scripts’ and ‘factbooks’ are now more than justified and back in vogue.
Use the language your buyers use. AI categorises content using semantic relationships. If your website speaks in internal jargon and your buyers are searching in plain English, the connection AI needs to make between your brand and their queries simply won't be there. Write for their vocabulary, not yours.
The pressure you're under is real. But the good news is that fixing this is visible work. You can show your CEO a before and after. The audit alone demonstrates that you understand the problem and are taking action. The content and authority work demonstrates that you're addressing it. Most of your competitors haven't even started. That's your advantage, and your argument for the full-time role.
Move fast. Document what you do. Show the change. Get that job permanently!
Onwards!
Rich
For a fast read on the full AEO playbook, our how-to is here: How to use AEO to get your B2B brand into AI answers. And if you want to know exactly where you stand right now, the B2BMU AI Scout Report will audit your AI visibility for free just get in touch with the team via the website at www.b2bmarketing.com
Dear Rich,
I am under pressure from my CEO and CCO because they are increasingly obsessed with AI chatbots. Apparently the chatbots don't know much about our firm but can answer questions about our competitors. I am interim head of marketing and I'm feeling like I don't have long to address this before it harms my prospects for the gig full time.
I understand organic content is still valuable but how exactly do I get our firm and our products into ChatGPT or Claude?
Rebecca, Manchester
Dear Rebecca,
Your CEO and CCO have stumbled onto something increasingly real. To be fair to them, I think they are right, you need to treat this as a priority.
Prospective buyers are using AI tools to shortlist vendors before they ever land on your website. A CFO types "which platforms offer AI-powered forecasting" into Copilot. A procurement director asks ChatGPT "who are the main providers of X in the UK." None of them went to Google first (who would have said that just a year ago?!). And when the AI answered, it named specific brands. If yours wasn't one of them, you lost ground in a conversation you didn't know was happening.
The discipline you need is called AEO. Answer Engine Optimisation. It's what SEO was in 2008, which means the window to get ahead of your competitors is open right now, but it won't stay open forever. You can’t open LinkedIn or Instagram or any social media without someone talking about it or pitching a solution.
Here's what you actually do.
First, understand how AI decides what to say. Tools like ChatGPT were trained on web data up to a certain point. What they know about your company comes from that training: your content, your press mentions, your directory listings, third-party coverage. Retrieval-based tools like Perplexity pull live web data. Google's AI Overviews blend both. No single fix works across all of them. But the underlying principle is consistent. AI rewards clarity, consistency, and credibility.
Start with an audit. Open ChatGPT, Perplexity, and Claude. Ask the questions your buyers actually ask. "What are the best platforms for [your category]?" "Which providers work with [your target industry]?" "Tell me about [your brand name]." Note where you appear. Note where your competitors appear instead. Run fifteen to twenty prompts. The gaps become your priority list. This also gives you something concrete to take back to your CEO this week, which, given your situation, is not a small thing. It shows you are on it.
(Important Note: We see the need, so B2B Marketing United have decided to build our own ‘AI Search Scout Report’ tool to conduct this audit for free and get you started. We’ll release it soon so sign up to the newsletter on our website for updates.)
Then look at your own website. AI systems parse content differently from humans. They break pages into individual passages and evaluate each one independently. Clear headings, direct answers at the top of each section, and specific factual statements all increase the chance of being cited. A page that says "our platform processes two million transactions per day with 99.9% uptime" is far more citable than one that says "we offer industry-leading reliability." Specific beats vague. Every time. Go through your most important pages and make them legible to a machine. This means leading each section with a direct answer, adding FAQ sections that mirror the actual questions buyers ask, and replacing any claim that a journalist couldn't quote with one that they could.
Build your authority footprint outside your own site. Here's the thing most marketers miss. What others say about you matters at least as much as what you say about yourself. Often more. AI models weight sources by perceived credibility. Coverage in respected industry publications, bylines on high-authority sites, mentions from recognised experts: these all increase the probability that AI treats your brand as worth citing. One well-placed article in a credible trade publication does more for your AI visibility than ten posts on your own blog. I said it in our AEO how-to and I'll say it again here: PR is making a comeback, and this is the big reason why.
Fix your entity consistency. This is the unglamorous work that nobody wants to do and that most companies haven't done. Audit every place your brand appears online. Your website, your LinkedIn page, your Google Business Profile, your directory listings, your press mentions. Make sure your brand name, description, category, and key facts are identical everywhere. If your founding year, product description, or company category varies between sources, AI loses confidence in citing any of them. Content and Comms teams must be loving that all their hard work insisting on ‘core scripts’ and ‘factbooks’ are now more than justified and back in vogue.
Use the language your buyers use. AI categorises content using semantic relationships. If your website speaks in internal jargon and your buyers are searching in plain English, the connection AI needs to make between your brand and their queries simply won't be there. Write for their vocabulary, not yours.
The pressure you're under is real. But the good news is that fixing this is visible work. You can show your CEO a before and after. The audit alone demonstrates that you understand the problem and are taking action. The content and authority work demonstrates that you're addressing it. Most of your competitors haven't even started. That's your advantage, and your argument for the full-time role.
Move fast. Document what you do. Show the change. Get that job permanently!
Onwards!
Rich
For a fast read on the full AEO playbook, our how-to is here: How to use AEO to get your B2B brand into AI answers. And if you want to know exactly where you stand right now, the B2BMU AI Scout Report will audit your AI visibility for free just get in touch with the team via the website at www.b2bmarketing.com
Dear Rich,
I am under pressure from my CEO and CCO because they are increasingly obsessed with AI chatbots. Apparently the chatbots don't know much about our firm but can answer questions about our competitors. I am interim head of marketing and I'm feeling like I don't have long to address this before it harms my prospects for the gig full time.
I understand organic content is still valuable but how exactly do I get our firm and our products into ChatGPT or Claude?
Rebecca, Manchester
Dear Rebecca,
Your CEO and CCO have stumbled onto something increasingly real. To be fair to them, I think they are right, you need to treat this as a priority.
Prospective buyers are using AI tools to shortlist vendors before they ever land on your website. A CFO types "which platforms offer AI-powered forecasting" into Copilot. A procurement director asks ChatGPT "who are the main providers of X in the UK." None of them went to Google first (who would have said that just a year ago?!). And when the AI answered, it named specific brands. If yours wasn't one of them, you lost ground in a conversation you didn't know was happening.
The discipline you need is called AEO. Answer Engine Optimisation. It's what SEO was in 2008, which means the window to get ahead of your competitors is open right now, but it won't stay open forever. You can’t open LinkedIn or Instagram or any social media without someone talking about it or pitching a solution.
Here's what you actually do.
First, understand how AI decides what to say. Tools like ChatGPT were trained on web data up to a certain point. What they know about your company comes from that training: your content, your press mentions, your directory listings, third-party coverage. Retrieval-based tools like Perplexity pull live web data. Google's AI Overviews blend both. No single fix works across all of them. But the underlying principle is consistent. AI rewards clarity, consistency, and credibility.
Start with an audit. Open ChatGPT, Perplexity, and Claude. Ask the questions your buyers actually ask. "What are the best platforms for [your category]?" "Which providers work with [your target industry]?" "Tell me about [your brand name]." Note where you appear. Note where your competitors appear instead. Run fifteen to twenty prompts. The gaps become your priority list. This also gives you something concrete to take back to your CEO this week, which, given your situation, is not a small thing. It shows you are on it.
(Important Note: We see the need, so B2B Marketing United have decided to build our own ‘AI Search Scout Report’ tool to conduct this audit for free and get you started. We’ll release it soon so sign up to the newsletter on our website for updates.)
Then look at your own website. AI systems parse content differently from humans. They break pages into individual passages and evaluate each one independently. Clear headings, direct answers at the top of each section, and specific factual statements all increase the chance of being cited. A page that says "our platform processes two million transactions per day with 99.9% uptime" is far more citable than one that says "we offer industry-leading reliability." Specific beats vague. Every time. Go through your most important pages and make them legible to a machine. This means leading each section with a direct answer, adding FAQ sections that mirror the actual questions buyers ask, and replacing any claim that a journalist couldn't quote with one that they could.
Build your authority footprint outside your own site. Here's the thing most marketers miss. What others say about you matters at least as much as what you say about yourself. Often more. AI models weight sources by perceived credibility. Coverage in respected industry publications, bylines on high-authority sites, mentions from recognised experts: these all increase the probability that AI treats your brand as worth citing. One well-placed article in a credible trade publication does more for your AI visibility than ten posts on your own blog. I said it in our AEO how-to and I'll say it again here: PR is making a comeback, and this is the big reason why.
Fix your entity consistency. This is the unglamorous work that nobody wants to do and that most companies haven't done. Audit every place your brand appears online. Your website, your LinkedIn page, your Google Business Profile, your directory listings, your press mentions. Make sure your brand name, description, category, and key facts are identical everywhere. If your founding year, product description, or company category varies between sources, AI loses confidence in citing any of them. Content and Comms teams must be loving that all their hard work insisting on ‘core scripts’ and ‘factbooks’ are now more than justified and back in vogue.
Use the language your buyers use. AI categorises content using semantic relationships. If your website speaks in internal jargon and your buyers are searching in plain English, the connection AI needs to make between your brand and their queries simply won't be there. Write for their vocabulary, not yours.
The pressure you're under is real. But the good news is that fixing this is visible work. You can show your CEO a before and after. The audit alone demonstrates that you understand the problem and are taking action. The content and authority work demonstrates that you're addressing it. Most of your competitors haven't even started. That's your advantage, and your argument for the full-time role.
Move fast. Document what you do. Show the change. Get that job permanently!
Onwards!
Rich
For a fast read on the full AEO playbook, our how-to is here: How to use AEO to get your B2B brand into AI answers. And if you want to know exactly where you stand right now, the B2BMU AI Scout Report will audit your AI visibility for free just get in touch with the team via the website at www.b2bmarketing.com
Content
Mar 23, 2026
Content
How to's
Here is the number that should be on every B2B CMO's desk this quarter. In G2's August 2025 buyer survey, 87% of B2B software buyers said AI chatbots like ChatGPT, Perplexity, Gemini and Claude are changing how they research purchases. In the same study, only 12% of B2B SaaS brands appeared when buyers ran category-level searches inside those tools. The other 88% were simply absent from the moment their buyers were forming opinions.
That gap, between where buyers are researching and where brands are visible, is the single biggest unpriced risk in B2B marketing right now. It is also not a distant problem. A multi-source analysis published in March 2026, covering 680 million AI citations and almost two million browsing sessions, found that 73% of B2B buyers are already using AI tools inside their purchase research process. Forrester's 2025 buyer study put a number on the downstream consequence. 61% of the B2B buying journey now completes before a buyer ever contacts a vendor, and that figure climbs every time an AI tool synthesises a shortlist on their behalf.
Meanwhile, McKinsey estimates that around 50% of Google searches already include AI summaries, rising above 75% by 2028, and projects that $750 billion in US revenue will funnel through AI-powered search by 2028. This is a rewiring of how B2B buyers discover, compare and choose, not a marginal channel shift.
The discipline that governs whether you are visible inside those AI-generated answers has a name. It is Answer Engine Optimisation (AEO), and it is a different discipline to SEO.
Why SEO tactics don't translate (and why that matters to your budget)
For most of the last two decades, B2B marketers have optimised for a specific user behaviour. Type a query into Google, scan a list of ten blue links, click one. The entire SEO playbook (keyword targeting, backlinks, domain authority, ranking positions) is a set of levers pulled against that behaviour.
Answer engines don't work that way. When a buyer asks ChatGPT "what are the best account-based marketing platforms for mid-market B2B?", the model doesn't show them ten blue links. It synthesises an answer, names two or three vendors, and sometimes cites a handful of sources. The buyer leaves with a shortlist instead of a search results page.
That one behavioural change breaks several of the core assumptions B2B marketers have been budgeting against.
Rankings don't map to citations. A page that ranks #1 on Google may never be cited by ChatGPT, and a page that doesn't rank in Google's top 20 can be cited repeatedly by Perplexity. The 2025 AI Visibility Report from The Digital Bloom found that only 11% of domains cited by ChatGPT were also cited by Perplexity for the same queries. These are different retrieval systems with different signals.
Backlinks are no longer the dominant signal. In the same study, brand search volume was the strongest predictor of AI citations, with a correlation of 0.334. That is materially stronger than any backlink-based metric. Models are increasingly using brand familiarity as a proxy for trust.
Content freshness suddenly matters in a way SEO never rewarded. Roughly 65% of log hits to cited content were for pages published in the last year, and 79% were from the last two years. AI systems lean toward recent, actively maintained sources.
Traditional measurement is silent on the question that matters. GA4 will not tell you how often ChatGPT recommends you. Your rank tracker will not tell you whether Perplexity is citing your competitor. The measurement stack most B2B marketing teams rely on was built for a world where discovery happened on a SERP.
These differences add up. The inputs, the signals, the measurement and the skills required are distinct enough that treating AEO as "the SEO team's next project" is already producing a second wave of wasted budget across the industry.
The four pillars of AEO for B2B marketers
The methodology breaks down into four pillars. They work as concurrent workstreams rather than sequential steps, and each one pulls a different lever.
1. Entity positioning: make the model understand who you are
Before an answer engine can recommend you, it has to understand what you are. Large language models reason in terms of entities: companies, products, categories, people. If the model's internal representation of your brand is vague, incomplete or confused with a competitor, no amount of content will fix it.
Entity positioning is the work of making sure every AI system has an unambiguous understanding of who you are, what category you compete in, what you do better than alternatives, and who your ideal customer is. The practical work includes Wikidata entries, Knowledge Panel optimisation, structured data (Organization, Product, Software Application schemas), and consistent entity descriptions across high-authority third-party sources.
Diagnostic question for your team: open ChatGPT and ask "What does [your brand] do?" Then ask "Who are [your brand]'s main competitors?" If the answers are vague, wrong, or list your brand alongside companies you don't actually compete with, you have an entity problem. Entity sits upstream of every other AEO lever, so this is usually where the work starts.
2. Answer architecture: structure content so models can extract and cite it
Most B2B content is written for humans scrolling on a laptop. Answer engines don't scroll. They extract. Content that wins citations is content that answers specific questions directly, in a structure the model can parse.
The brands that get cited repeatedly across platforms usually aren't the ones producing the most content. They are the ones whose content is architected for extraction: clear definitions, direct answers at the top of sections, FAQ blocks with schema, concise paragraphs, well-structured headers, and explicit comparisons. Seer Interactive's data on AI Overviews found that brands cited in Google's AI answers earn 35% more organic clicks and 91% more paid clicks. The same Seer analysis showed that when AI Overviews are present and your brand is not cited, organic CTR drops 61% and paid CTR drops 68%. Answer architecture is what determines which side of that line you sit on.
3. Source signal: earn mentions in the places LLMs actually weight
Not all sources are weighted equally. Each AI platform draws from a different constellation of trusted inputs, and the differences are significant. The same 2025 cross-platform analysis found that Reddit accounted for 46.7% of top Perplexity citations but under 10% on ChatGPT after a September 2025 rebalancing. ChatGPT leans heavily on Wikipedia and long-standing editorial sources. Google AI Overviews favour a diversified cross-platform presence.
Source signal is the discipline of earning mentions, reviews and references in the specific places each platform treats as authoritative for your category. For a B2B SaaS brand, that usually means G2 and Gartner Peer Insights (G2's own internal study confirms they are heavily cited across LLMs), plus category-specific industry publications, relevant subreddits, and any analyst coverage that ends up in the training corpus. This is PR, earned media and community work done with a very specific retrieval target in mind.
4. Measurement: track what your rank tracker can't see
If you can't measure AI visibility, you can't manage it, and you certainly can't defend the budget in front of a board. The four metrics we track for clients map directly to the commercial question every B2B CMO cares about: are we showing up when our buyers are choosing?
AI Mention Rate. For a defined set of 20 to 50 buyer-intent queries, what percentage of the time does your brand appear in the answer across ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews?
Citation Authority. When those platforms cite sources, how often is your domain among them, and how does that compare to your top three competitors?
Entity Clarity Score. When you ask the model directly "What does [brand] do?", is the answer accurate, complete and differentiated, or generic and interchangeable?
Answer Ownership. For the queries that matter most to your pipeline, are you the primary recommendation or a supporting mention?
This is the layer most B2B marketing teams are missing entirely. Only 22% of marketers currently track AI visibility and traffic, and only 25.7% plan to develop content specifically for AI citations. That gap is, for the moment, the single biggest competitive opportunity in B2B marketing.
You can read the full methodology behind each of these pillars on the [growthvibe AI search optimisation methodology page](https://www.growthvibe.com/ai-search-optimization-services/).
A worked example: what this looks like in martech
Consider a mid-market martech category, say, account-based marketing platforms. Ask ChatGPT, Perplexity and Google's AI Overview the same question: "What are the best account-based marketing platforms for B2B companies with 200 to 2,000 employees?"
Run it once and you'll notice a pattern. Two or three vendors show up consistently across all three engines. A handful appear on one platform but not the others. And a long tail of category players, some of them with significantly better products, bigger marketing budgets and stronger Google rankings, don't appear at all.
When we dig into the brands that win across all three platforms, the same four things are usually true. Their entity is clear (the model knows what they do without hesitation). Their owned content is structured for extraction, with direct answers, comparison pages and FAQ schema. They have strong, recent source signal across G2, Gartner coverage, analyst mentions, and in Perplexity's case, organic Reddit discussion. And they are measuring AI visibility as a discipline rather than guessing at it.
The brands that lose? Usually strong on traditional SEO, weak on entity clarity, inconsistent on answer architecture, and invisible in the places the models trust. Their dashboards look healthy. Their pipeline is quietly leaking.
The mistakes I see most often
After running diagnostics across dozens of B2B brands, the same patterns recur.
Treating AEO as a content problem. Most teams respond to the AI search shift by publishing more blog posts about AI. That is content marketing with the word "AI" in the title. AEO requires work across entity, architecture, source signal and measurement. Content is one of four levers.
Over-indexing on a single platform. Teams obsess over ChatGPT and ignore Perplexity, or vice versa. The retrieval systems are different enough that you need a cross-platform view. The 11% citation overlap figure is the most important number most B2B marketers haven't heard.
Letting the incumbent SEO agency redefine AEO as "SEO plus schema". This is the most expensive mistake in the market right now. Schema helps, but it is a single lever inside the second pillar. If your AEO strategy can be executed by adding FAQ markup, it is not an AEO strategy.
Waiting for measurement to get easier. It will not. The teams winning in 2026 are the ones who built a measurement layer manually, accepted the rough edges, and used the data to make decisions. Waiting for a perfect dashboard is a way of conceding ground to the brands who didn't wait.
A 30-minute AEO self-audit for B2B marketers
If you want to stress-test your own position before the next board meeting, here is a condensed version of the audit we run for clients. It takes about half an hour.
1. The entity test. Open ChatGPT, Perplexity and Google's AI Overview. Ask each one "What does [your brand] do?" and "Who are [your brand]'s main competitors?" Note the answers verbatim. If any of them are wrong, vague or list competitors you don't actually compete with, flag an entity issue.
2. The category test. Run your three most important buyer-intent queries across all three platforms. Example: "What are the best [your category] platforms for [your ICP]?" Note whether your brand appears, and if so, where in the answer.
3. The competitor test. Run the same three queries again, but replace your brand name with each of your top three competitors. Compare who gets cited, how often, and in what position.
4. The source test. For every citation that appears across those queries, note the source. Which domains are being pulled? Is your domain among them? If not, which domains are, and do you have a presence on those surfaces?
5. The freshness test. Check the publication dates on your most important category pages. If your pillar content hasn't been updated in the last 12 to 18 months, you are likely losing ground to competitors whose content is fresher.
If you do nothing else this quarter, run this audit. It will tell you, in thirty minutes, whether you have an AEO problem, and roughly how big it is.
The window is open, and it won't stay open
AEO is in the phase every new marketing discipline goes through before it becomes table stakes. The market data is clear enough that ignoring it is a choice. The measurement tooling is rough but workable. The playbooks are being written in real time by the brands who decided not to wait.
B2B marketing teams that treat AEO as a discipline now, with its own strategy, its own measurement, and its own owned workstream inside the marketing function, will compound an advantage every month their competitors spend arguing about whether it matters. The ones that treat it as a side project, or hand it to an agency still thinking in terms of rankings and links, will spend 2027 trying to catch up on ground they didn't realise they were losing.
The brands being cited by AI today are the brands being chosen tomorrow. Everything else is a dashboard.
---
*Sources: [G2: Does G2 Get Ranked in AI LLM Search?](https://learn.g2.com/tech-signals-does-g2-get-ranked-in-ai-llm-search); [PR Newswire: 73% of B2B Buyers Use AI Tools in Purchase Research](https://www.prnewswire.com/news-releases/73-of-b2b-buyers-use-ai-tools-in-purchase-research-multi-source-analysis-finds-302733319.html); [The Digital Bloom: 2025 AI Visibility Report](https://thedigitalbloom.com/learn/2025-ai-citation-llm-visibility-report/); [McKinsey: The new front door to the internet](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search); [Seer Interactive: AIO impact on Google CTR](https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update); [SparkToro: 2024 Zero-Click Search Study](https://sparktoro.com/blog/2024-zero-click-search-study-for-every-1000-us-google-searches-only-374-clicks-go-to-the-open-web-in-the-eu-its-360/); [Adobe: The explosive rise of generative AI referral traffic](https://business.adobe.com/blog/the-explosive-rise-of-generative-ai-referral-traffic); Forrester 2025 B2B Buyer Journey Study.*
Here is the number that should be on every B2B CMO's desk this quarter. In G2's August 2025 buyer survey, 87% of B2B software buyers said AI chatbots like ChatGPT, Perplexity, Gemini and Claude are changing how they research purchases. In the same study, only 12% of B2B SaaS brands appeared when buyers ran category-level searches inside those tools. The other 88% were simply absent from the moment their buyers were forming opinions.
That gap, between where buyers are researching and where brands are visible, is the single biggest unpriced risk in B2B marketing right now. It is also not a distant problem. A multi-source analysis published in March 2026, covering 680 million AI citations and almost two million browsing sessions, found that 73% of B2B buyers are already using AI tools inside their purchase research process. Forrester's 2025 buyer study put a number on the downstream consequence. 61% of the B2B buying journey now completes before a buyer ever contacts a vendor, and that figure climbs every time an AI tool synthesises a shortlist on their behalf.
Meanwhile, McKinsey estimates that around 50% of Google searches already include AI summaries, rising above 75% by 2028, and projects that $750 billion in US revenue will funnel through AI-powered search by 2028. This is a rewiring of how B2B buyers discover, compare and choose, not a marginal channel shift.
The discipline that governs whether you are visible inside those AI-generated answers has a name. It is Answer Engine Optimisation (AEO), and it is a different discipline to SEO.
Why SEO tactics don't translate (and why that matters to your budget)
For most of the last two decades, B2B marketers have optimised for a specific user behaviour. Type a query into Google, scan a list of ten blue links, click one. The entire SEO playbook (keyword targeting, backlinks, domain authority, ranking positions) is a set of levers pulled against that behaviour.
Answer engines don't work that way. When a buyer asks ChatGPT "what are the best account-based marketing platforms for mid-market B2B?", the model doesn't show them ten blue links. It synthesises an answer, names two or three vendors, and sometimes cites a handful of sources. The buyer leaves with a shortlist instead of a search results page.
That one behavioural change breaks several of the core assumptions B2B marketers have been budgeting against.
Rankings don't map to citations. A page that ranks #1 on Google may never be cited by ChatGPT, and a page that doesn't rank in Google's top 20 can be cited repeatedly by Perplexity. The 2025 AI Visibility Report from The Digital Bloom found that only 11% of domains cited by ChatGPT were also cited by Perplexity for the same queries. These are different retrieval systems with different signals.
Backlinks are no longer the dominant signal. In the same study, brand search volume was the strongest predictor of AI citations, with a correlation of 0.334. That is materially stronger than any backlink-based metric. Models are increasingly using brand familiarity as a proxy for trust.
Content freshness suddenly matters in a way SEO never rewarded. Roughly 65% of log hits to cited content were for pages published in the last year, and 79% were from the last two years. AI systems lean toward recent, actively maintained sources.
Traditional measurement is silent on the question that matters. GA4 will not tell you how often ChatGPT recommends you. Your rank tracker will not tell you whether Perplexity is citing your competitor. The measurement stack most B2B marketing teams rely on was built for a world where discovery happened on a SERP.
These differences add up. The inputs, the signals, the measurement and the skills required are distinct enough that treating AEO as "the SEO team's next project" is already producing a second wave of wasted budget across the industry.
The four pillars of AEO for B2B marketers
The methodology breaks down into four pillars. They work as concurrent workstreams rather than sequential steps, and each one pulls a different lever.
1. Entity positioning: make the model understand who you are
Before an answer engine can recommend you, it has to understand what you are. Large language models reason in terms of entities: companies, products, categories, people. If the model's internal representation of your brand is vague, incomplete or confused with a competitor, no amount of content will fix it.
Entity positioning is the work of making sure every AI system has an unambiguous understanding of who you are, what category you compete in, what you do better than alternatives, and who your ideal customer is. The practical work includes Wikidata entries, Knowledge Panel optimisation, structured data (Organization, Product, Software Application schemas), and consistent entity descriptions across high-authority third-party sources.
Diagnostic question for your team: open ChatGPT and ask "What does [your brand] do?" Then ask "Who are [your brand]'s main competitors?" If the answers are vague, wrong, or list your brand alongside companies you don't actually compete with, you have an entity problem. Entity sits upstream of every other AEO lever, so this is usually where the work starts.
2. Answer architecture: structure content so models can extract and cite it
Most B2B content is written for humans scrolling on a laptop. Answer engines don't scroll. They extract. Content that wins citations is content that answers specific questions directly, in a structure the model can parse.
The brands that get cited repeatedly across platforms usually aren't the ones producing the most content. They are the ones whose content is architected for extraction: clear definitions, direct answers at the top of sections, FAQ blocks with schema, concise paragraphs, well-structured headers, and explicit comparisons. Seer Interactive's data on AI Overviews found that brands cited in Google's AI answers earn 35% more organic clicks and 91% more paid clicks. The same Seer analysis showed that when AI Overviews are present and your brand is not cited, organic CTR drops 61% and paid CTR drops 68%. Answer architecture is what determines which side of that line you sit on.
3. Source signal: earn mentions in the places LLMs actually weight
Not all sources are weighted equally. Each AI platform draws from a different constellation of trusted inputs, and the differences are significant. The same 2025 cross-platform analysis found that Reddit accounted for 46.7% of top Perplexity citations but under 10% on ChatGPT after a September 2025 rebalancing. ChatGPT leans heavily on Wikipedia and long-standing editorial sources. Google AI Overviews favour a diversified cross-platform presence.
Source signal is the discipline of earning mentions, reviews and references in the specific places each platform treats as authoritative for your category. For a B2B SaaS brand, that usually means G2 and Gartner Peer Insights (G2's own internal study confirms they are heavily cited across LLMs), plus category-specific industry publications, relevant subreddits, and any analyst coverage that ends up in the training corpus. This is PR, earned media and community work done with a very specific retrieval target in mind.
4. Measurement: track what your rank tracker can't see
If you can't measure AI visibility, you can't manage it, and you certainly can't defend the budget in front of a board. The four metrics we track for clients map directly to the commercial question every B2B CMO cares about: are we showing up when our buyers are choosing?
AI Mention Rate. For a defined set of 20 to 50 buyer-intent queries, what percentage of the time does your brand appear in the answer across ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews?
Citation Authority. When those platforms cite sources, how often is your domain among them, and how does that compare to your top three competitors?
Entity Clarity Score. When you ask the model directly "What does [brand] do?", is the answer accurate, complete and differentiated, or generic and interchangeable?
Answer Ownership. For the queries that matter most to your pipeline, are you the primary recommendation or a supporting mention?
This is the layer most B2B marketing teams are missing entirely. Only 22% of marketers currently track AI visibility and traffic, and only 25.7% plan to develop content specifically for AI citations. That gap is, for the moment, the single biggest competitive opportunity in B2B marketing.
You can read the full methodology behind each of these pillars on the [growthvibe AI search optimisation methodology page](https://www.growthvibe.com/ai-search-optimization-services/).
A worked example: what this looks like in martech
Consider a mid-market martech category, say, account-based marketing platforms. Ask ChatGPT, Perplexity and Google's AI Overview the same question: "What are the best account-based marketing platforms for B2B companies with 200 to 2,000 employees?"
Run it once and you'll notice a pattern. Two or three vendors show up consistently across all three engines. A handful appear on one platform but not the others. And a long tail of category players, some of them with significantly better products, bigger marketing budgets and stronger Google rankings, don't appear at all.
When we dig into the brands that win across all three platforms, the same four things are usually true. Their entity is clear (the model knows what they do without hesitation). Their owned content is structured for extraction, with direct answers, comparison pages and FAQ schema. They have strong, recent source signal across G2, Gartner coverage, analyst mentions, and in Perplexity's case, organic Reddit discussion. And they are measuring AI visibility as a discipline rather than guessing at it.
The brands that lose? Usually strong on traditional SEO, weak on entity clarity, inconsistent on answer architecture, and invisible in the places the models trust. Their dashboards look healthy. Their pipeline is quietly leaking.
The mistakes I see most often
After running diagnostics across dozens of B2B brands, the same patterns recur.
Treating AEO as a content problem. Most teams respond to the AI search shift by publishing more blog posts about AI. That is content marketing with the word "AI" in the title. AEO requires work across entity, architecture, source signal and measurement. Content is one of four levers.
Over-indexing on a single platform. Teams obsess over ChatGPT and ignore Perplexity, or vice versa. The retrieval systems are different enough that you need a cross-platform view. The 11% citation overlap figure is the most important number most B2B marketers haven't heard.
Letting the incumbent SEO agency redefine AEO as "SEO plus schema". This is the most expensive mistake in the market right now. Schema helps, but it is a single lever inside the second pillar. If your AEO strategy can be executed by adding FAQ markup, it is not an AEO strategy.
Waiting for measurement to get easier. It will not. The teams winning in 2026 are the ones who built a measurement layer manually, accepted the rough edges, and used the data to make decisions. Waiting for a perfect dashboard is a way of conceding ground to the brands who didn't wait.
A 30-minute AEO self-audit for B2B marketers
If you want to stress-test your own position before the next board meeting, here is a condensed version of the audit we run for clients. It takes about half an hour.
1. The entity test. Open ChatGPT, Perplexity and Google's AI Overview. Ask each one "What does [your brand] do?" and "Who are [your brand]'s main competitors?" Note the answers verbatim. If any of them are wrong, vague or list competitors you don't actually compete with, flag an entity issue.
2. The category test. Run your three most important buyer-intent queries across all three platforms. Example: "What are the best [your category] platforms for [your ICP]?" Note whether your brand appears, and if so, where in the answer.
3. The competitor test. Run the same three queries again, but replace your brand name with each of your top three competitors. Compare who gets cited, how often, and in what position.
4. The source test. For every citation that appears across those queries, note the source. Which domains are being pulled? Is your domain among them? If not, which domains are, and do you have a presence on those surfaces?
5. The freshness test. Check the publication dates on your most important category pages. If your pillar content hasn't been updated in the last 12 to 18 months, you are likely losing ground to competitors whose content is fresher.
If you do nothing else this quarter, run this audit. It will tell you, in thirty minutes, whether you have an AEO problem, and roughly how big it is.
The window is open, and it won't stay open
AEO is in the phase every new marketing discipline goes through before it becomes table stakes. The market data is clear enough that ignoring it is a choice. The measurement tooling is rough but workable. The playbooks are being written in real time by the brands who decided not to wait.
B2B marketing teams that treat AEO as a discipline now, with its own strategy, its own measurement, and its own owned workstream inside the marketing function, will compound an advantage every month their competitors spend arguing about whether it matters. The ones that treat it as a side project, or hand it to an agency still thinking in terms of rankings and links, will spend 2027 trying to catch up on ground they didn't realise they were losing.
The brands being cited by AI today are the brands being chosen tomorrow. Everything else is a dashboard.
---
*Sources: [G2: Does G2 Get Ranked in AI LLM Search?](https://learn.g2.com/tech-signals-does-g2-get-ranked-in-ai-llm-search); [PR Newswire: 73% of B2B Buyers Use AI Tools in Purchase Research](https://www.prnewswire.com/news-releases/73-of-b2b-buyers-use-ai-tools-in-purchase-research-multi-source-analysis-finds-302733319.html); [The Digital Bloom: 2025 AI Visibility Report](https://thedigitalbloom.com/learn/2025-ai-citation-llm-visibility-report/); [McKinsey: The new front door to the internet](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search); [Seer Interactive: AIO impact on Google CTR](https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update); [SparkToro: 2024 Zero-Click Search Study](https://sparktoro.com/blog/2024-zero-click-search-study-for-every-1000-us-google-searches-only-374-clicks-go-to-the-open-web-in-the-eu-its-360/); [Adobe: The explosive rise of generative AI referral traffic](https://business.adobe.com/blog/the-explosive-rise-of-generative-ai-referral-traffic); Forrester 2025 B2B Buyer Journey Study.*
Here is the number that should be on every B2B CMO's desk this quarter. In G2's August 2025 buyer survey, 87% of B2B software buyers said AI chatbots like ChatGPT, Perplexity, Gemini and Claude are changing how they research purchases. In the same study, only 12% of B2B SaaS brands appeared when buyers ran category-level searches inside those tools. The other 88% were simply absent from the moment their buyers were forming opinions.
That gap, between where buyers are researching and where brands are visible, is the single biggest unpriced risk in B2B marketing right now. It is also not a distant problem. A multi-source analysis published in March 2026, covering 680 million AI citations and almost two million browsing sessions, found that 73% of B2B buyers are already using AI tools inside their purchase research process. Forrester's 2025 buyer study put a number on the downstream consequence. 61% of the B2B buying journey now completes before a buyer ever contacts a vendor, and that figure climbs every time an AI tool synthesises a shortlist on their behalf.
Meanwhile, McKinsey estimates that around 50% of Google searches already include AI summaries, rising above 75% by 2028, and projects that $750 billion in US revenue will funnel through AI-powered search by 2028. This is a rewiring of how B2B buyers discover, compare and choose, not a marginal channel shift.
The discipline that governs whether you are visible inside those AI-generated answers has a name. It is Answer Engine Optimisation (AEO), and it is a different discipline to SEO.
Why SEO tactics don't translate (and why that matters to your budget)
For most of the last two decades, B2B marketers have optimised for a specific user behaviour. Type a query into Google, scan a list of ten blue links, click one. The entire SEO playbook (keyword targeting, backlinks, domain authority, ranking positions) is a set of levers pulled against that behaviour.
Answer engines don't work that way. When a buyer asks ChatGPT "what are the best account-based marketing platforms for mid-market B2B?", the model doesn't show them ten blue links. It synthesises an answer, names two or three vendors, and sometimes cites a handful of sources. The buyer leaves with a shortlist instead of a search results page.
That one behavioural change breaks several of the core assumptions B2B marketers have been budgeting against.
Rankings don't map to citations. A page that ranks #1 on Google may never be cited by ChatGPT, and a page that doesn't rank in Google's top 20 can be cited repeatedly by Perplexity. The 2025 AI Visibility Report from The Digital Bloom found that only 11% of domains cited by ChatGPT were also cited by Perplexity for the same queries. These are different retrieval systems with different signals.
Backlinks are no longer the dominant signal. In the same study, brand search volume was the strongest predictor of AI citations, with a correlation of 0.334. That is materially stronger than any backlink-based metric. Models are increasingly using brand familiarity as a proxy for trust.
Content freshness suddenly matters in a way SEO never rewarded. Roughly 65% of log hits to cited content were for pages published in the last year, and 79% were from the last two years. AI systems lean toward recent, actively maintained sources.
Traditional measurement is silent on the question that matters. GA4 will not tell you how often ChatGPT recommends you. Your rank tracker will not tell you whether Perplexity is citing your competitor. The measurement stack most B2B marketing teams rely on was built for a world where discovery happened on a SERP.
These differences add up. The inputs, the signals, the measurement and the skills required are distinct enough that treating AEO as "the SEO team's next project" is already producing a second wave of wasted budget across the industry.
The four pillars of AEO for B2B marketers
The methodology breaks down into four pillars. They work as concurrent workstreams rather than sequential steps, and each one pulls a different lever.
1. Entity positioning: make the model understand who you are
Before an answer engine can recommend you, it has to understand what you are. Large language models reason in terms of entities: companies, products, categories, people. If the model's internal representation of your brand is vague, incomplete or confused with a competitor, no amount of content will fix it.
Entity positioning is the work of making sure every AI system has an unambiguous understanding of who you are, what category you compete in, what you do better than alternatives, and who your ideal customer is. The practical work includes Wikidata entries, Knowledge Panel optimisation, structured data (Organization, Product, Software Application schemas), and consistent entity descriptions across high-authority third-party sources.
Diagnostic question for your team: open ChatGPT and ask "What does [your brand] do?" Then ask "Who are [your brand]'s main competitors?" If the answers are vague, wrong, or list your brand alongside companies you don't actually compete with, you have an entity problem. Entity sits upstream of every other AEO lever, so this is usually where the work starts.
2. Answer architecture: structure content so models can extract and cite it
Most B2B content is written for humans scrolling on a laptop. Answer engines don't scroll. They extract. Content that wins citations is content that answers specific questions directly, in a structure the model can parse.
The brands that get cited repeatedly across platforms usually aren't the ones producing the most content. They are the ones whose content is architected for extraction: clear definitions, direct answers at the top of sections, FAQ blocks with schema, concise paragraphs, well-structured headers, and explicit comparisons. Seer Interactive's data on AI Overviews found that brands cited in Google's AI answers earn 35% more organic clicks and 91% more paid clicks. The same Seer analysis showed that when AI Overviews are present and your brand is not cited, organic CTR drops 61% and paid CTR drops 68%. Answer architecture is what determines which side of that line you sit on.
3. Source signal: earn mentions in the places LLMs actually weight
Not all sources are weighted equally. Each AI platform draws from a different constellation of trusted inputs, and the differences are significant. The same 2025 cross-platform analysis found that Reddit accounted for 46.7% of top Perplexity citations but under 10% on ChatGPT after a September 2025 rebalancing. ChatGPT leans heavily on Wikipedia and long-standing editorial sources. Google AI Overviews favour a diversified cross-platform presence.
Source signal is the discipline of earning mentions, reviews and references in the specific places each platform treats as authoritative for your category. For a B2B SaaS brand, that usually means G2 and Gartner Peer Insights (G2's own internal study confirms they are heavily cited across LLMs), plus category-specific industry publications, relevant subreddits, and any analyst coverage that ends up in the training corpus. This is PR, earned media and community work done with a very specific retrieval target in mind.
4. Measurement: track what your rank tracker can't see
If you can't measure AI visibility, you can't manage it, and you certainly can't defend the budget in front of a board. The four metrics we track for clients map directly to the commercial question every B2B CMO cares about: are we showing up when our buyers are choosing?
AI Mention Rate. For a defined set of 20 to 50 buyer-intent queries, what percentage of the time does your brand appear in the answer across ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews?
Citation Authority. When those platforms cite sources, how often is your domain among them, and how does that compare to your top three competitors?
Entity Clarity Score. When you ask the model directly "What does [brand] do?", is the answer accurate, complete and differentiated, or generic and interchangeable?
Answer Ownership. For the queries that matter most to your pipeline, are you the primary recommendation or a supporting mention?
This is the layer most B2B marketing teams are missing entirely. Only 22% of marketers currently track AI visibility and traffic, and only 25.7% plan to develop content specifically for AI citations. That gap is, for the moment, the single biggest competitive opportunity in B2B marketing.
You can read the full methodology behind each of these pillars on the [growthvibe AI search optimisation methodology page](https://www.growthvibe.com/ai-search-optimization-services/).
A worked example: what this looks like in martech
Consider a mid-market martech category, say, account-based marketing platforms. Ask ChatGPT, Perplexity and Google's AI Overview the same question: "What are the best account-based marketing platforms for B2B companies with 200 to 2,000 employees?"
Run it once and you'll notice a pattern. Two or three vendors show up consistently across all three engines. A handful appear on one platform but not the others. And a long tail of category players, some of them with significantly better products, bigger marketing budgets and stronger Google rankings, don't appear at all.
When we dig into the brands that win across all three platforms, the same four things are usually true. Their entity is clear (the model knows what they do without hesitation). Their owned content is structured for extraction, with direct answers, comparison pages and FAQ schema. They have strong, recent source signal across G2, Gartner coverage, analyst mentions, and in Perplexity's case, organic Reddit discussion. And they are measuring AI visibility as a discipline rather than guessing at it.
The brands that lose? Usually strong on traditional SEO, weak on entity clarity, inconsistent on answer architecture, and invisible in the places the models trust. Their dashboards look healthy. Their pipeline is quietly leaking.
The mistakes I see most often
After running diagnostics across dozens of B2B brands, the same patterns recur.
Treating AEO as a content problem. Most teams respond to the AI search shift by publishing more blog posts about AI. That is content marketing with the word "AI" in the title. AEO requires work across entity, architecture, source signal and measurement. Content is one of four levers.
Over-indexing on a single platform. Teams obsess over ChatGPT and ignore Perplexity, or vice versa. The retrieval systems are different enough that you need a cross-platform view. The 11% citation overlap figure is the most important number most B2B marketers haven't heard.
Letting the incumbent SEO agency redefine AEO as "SEO plus schema". This is the most expensive mistake in the market right now. Schema helps, but it is a single lever inside the second pillar. If your AEO strategy can be executed by adding FAQ markup, it is not an AEO strategy.
Waiting for measurement to get easier. It will not. The teams winning in 2026 are the ones who built a measurement layer manually, accepted the rough edges, and used the data to make decisions. Waiting for a perfect dashboard is a way of conceding ground to the brands who didn't wait.
A 30-minute AEO self-audit for B2B marketers
If you want to stress-test your own position before the next board meeting, here is a condensed version of the audit we run for clients. It takes about half an hour.
1. The entity test. Open ChatGPT, Perplexity and Google's AI Overview. Ask each one "What does [your brand] do?" and "Who are [your brand]'s main competitors?" Note the answers verbatim. If any of them are wrong, vague or list competitors you don't actually compete with, flag an entity issue.
2. The category test. Run your three most important buyer-intent queries across all three platforms. Example: "What are the best [your category] platforms for [your ICP]?" Note whether your brand appears, and if so, where in the answer.
3. The competitor test. Run the same three queries again, but replace your brand name with each of your top three competitors. Compare who gets cited, how often, and in what position.
4. The source test. For every citation that appears across those queries, note the source. Which domains are being pulled? Is your domain among them? If not, which domains are, and do you have a presence on those surfaces?
5. The freshness test. Check the publication dates on your most important category pages. If your pillar content hasn't been updated in the last 12 to 18 months, you are likely losing ground to competitors whose content is fresher.
If you do nothing else this quarter, run this audit. It will tell you, in thirty minutes, whether you have an AEO problem, and roughly how big it is.
The window is open, and it won't stay open
AEO is in the phase every new marketing discipline goes through before it becomes table stakes. The market data is clear enough that ignoring it is a choice. The measurement tooling is rough but workable. The playbooks are being written in real time by the brands who decided not to wait.
B2B marketing teams that treat AEO as a discipline now, with its own strategy, its own measurement, and its own owned workstream inside the marketing function, will compound an advantage every month their competitors spend arguing about whether it matters. The ones that treat it as a side project, or hand it to an agency still thinking in terms of rankings and links, will spend 2027 trying to catch up on ground they didn't realise they were losing.
The brands being cited by AI today are the brands being chosen tomorrow. Everything else is a dashboard.
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*Sources: [G2: Does G2 Get Ranked in AI LLM Search?](https://learn.g2.com/tech-signals-does-g2-get-ranked-in-ai-llm-search); [PR Newswire: 73% of B2B Buyers Use AI Tools in Purchase Research](https://www.prnewswire.com/news-releases/73-of-b2b-buyers-use-ai-tools-in-purchase-research-multi-source-analysis-finds-302733319.html); [The Digital Bloom: 2025 AI Visibility Report](https://thedigitalbloom.com/learn/2025-ai-citation-llm-visibility-report/); [McKinsey: The new front door to the internet](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/new-front-door-to-the-internet-winning-in-the-age-of-ai-search); [Seer Interactive: AIO impact on Google CTR](https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update); [SparkToro: 2024 Zero-Click Search Study](https://sparktoro.com/blog/2024-zero-click-search-study-for-every-1000-us-google-searches-only-374-clicks-go-to-the-open-web-in-the-eu-its-360/); [Adobe: The explosive rise of generative AI referral traffic](https://business.adobe.com/blog/the-explosive-rise-of-generative-ai-referral-traffic); Forrester 2025 B2B Buyer Journey Study.*
Content
Apr 7, 2026
Content
