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The AI opportunity in ‘go-to-market strategy’ is moving so fast that ‘keeping up’ is currently a daily focus.
Some of the smartest bosses and businesses around however, still pay attention to marketing channels seemingly long forgotten by the gossip around the LinkedIn watercooler.
Watch any Rory Sutherland interview clips and note how quickly he finds a moment to advocate for direct mail.
Similarly, UK industry body for commercial TV, Thinkbox now talks about ‘Total TV’ to include the proliferation of advertising opportunities for marketers across a host of new video channels. And while you’d expect Thinkbox to present evidence whenever possible to prove TV remains consumers’ most trusted marketing channel, independent and credible voices like Professor Mark Ritson follow suit whenever asked.
Still - some revelations still have it in them to surprise me. In marketing channel terms, ‘loyalty programmes’ feel like old news.
Until recently, I assumed loyalty was a marketing channel every brand across every sector had already explored, tested, and deployed in their own way. Like email, paid media, or events, it felt fully established and maybe even taken for granted.
Then I met a really cool business called White Label Loyalty. They’re doing amazing things for whole swathes of the B2B marketing community that are yet to properly investigate or even understand loyalty as a viable marketing channel.
This isn’t a case of B2B marketing teams having lacked ambition or neglecting to value their customers. Rather, in sectors like B2B manufacturing, the traditional model makes loyalty feel an unlikely marketing play.
When your products are sold through retailers and distributors, you rarely own your customer data. And if you don’t see the end buyer, it’s a tough job to truly understand how to excite them.
Without customers’ first-party data, you’ve got zero information on what influences their buying decisions. You can guess that price rules every customer choice, but beyond that rather base assumption and the damaging discounts it encourages, a decent loyalty scheme feels like a non-starter.
This leaves marketing directors under the cosh - being asked to deliver growth while effectively flying blind. They run campaigns without any customer insight, behavioural data, and therefore without a direct relationship with the people who drive their revenue.
Enter White Label Loyalty founder Achille Traore, a soft-spoken, humble yet ridiculously smart visionary. As an ex-professional footballer playing in the Swedish first division and later signed to Barnsley FC - Achille has an amazing story.
After his football career was cut short by injury, he turned his head to business. Achille’s now a celebrated innovator in the tech industry, acknowledged as one of the top 100 Retail Technology Entrepreneurs in UK in Fresh Business Thinking's Shift100 list, associated with KPMG.
As well as the B2B manufacturing sector, White Label Loyalty powers huge loyalty programmes for the likes of Burger King and PepsiCo.
I met Achille late last year and he’s fast become one of my favourite B2B marketing leaders. He’s since worked with OrbitalX on a piece of research, revealing that while many B2B manufacturers believe they actually do have access to customer data, most struggle to activate, understand, or use it to drive repeat business.
So when presented with a loyalty solution that’s dead easy to implement and capable of delivering first-party data and a first real connection with the end customer, his clients jump at the chance. With no disrespect intended, it’s their first opportunity to behave like ‘real’ marketers - generating growth based on real insight rather than price promotions and guesswork.
AI-powered insights, predictive analytics, scaled personalisation and speedy routes to demonstrable ROI take loyalty from a tactic or campaign to a key strategic pillar for growth.
In other words, Achille is bringing loyalty into industries where it’s long been considered out of reach; a marketing channel that seemed to be just for others.
To have found whole sectors - B2B manufacturing and beyond - where old school marketing channels feel like a wide-open frontier and then watching the outlandish success that follows; it’s been a heartwarming experience.
For this 50-year-old, it’s a joyous moment whenever something perceived as old-fashioned makes itself cool again by bringing its phenomenal power and expertise to bear.
The AI opportunity in ‘go-to-market strategy’ is moving so fast that ‘keeping up’ is currently a daily focus.
Some of the smartest bosses and businesses around however, still pay attention to marketing channels seemingly long forgotten by the gossip around the LinkedIn watercooler.
Watch any Rory Sutherland interview clips and note how quickly he finds a moment to advocate for direct mail.
Similarly, UK industry body for commercial TV, Thinkbox now talks about ‘Total TV’ to include the proliferation of advertising opportunities for marketers across a host of new video channels. And while you’d expect Thinkbox to present evidence whenever possible to prove TV remains consumers’ most trusted marketing channel, independent and credible voices like Professor Mark Ritson follow suit whenever asked.
Still - some revelations still have it in them to surprise me. In marketing channel terms, ‘loyalty programmes’ feel like old news.
Until recently, I assumed loyalty was a marketing channel every brand across every sector had already explored, tested, and deployed in their own way. Like email, paid media, or events, it felt fully established and maybe even taken for granted.
Then I met a really cool business called White Label Loyalty. They’re doing amazing things for whole swathes of the B2B marketing community that are yet to properly investigate or even understand loyalty as a viable marketing channel.
This isn’t a case of B2B marketing teams having lacked ambition or neglecting to value their customers. Rather, in sectors like B2B manufacturing, the traditional model makes loyalty feel an unlikely marketing play.
When your products are sold through retailers and distributors, you rarely own your customer data. And if you don’t see the end buyer, it’s a tough job to truly understand how to excite them.
Without customers’ first-party data, you’ve got zero information on what influences their buying decisions. You can guess that price rules every customer choice, but beyond that rather base assumption and the damaging discounts it encourages, a decent loyalty scheme feels like a non-starter.
This leaves marketing directors under the cosh - being asked to deliver growth while effectively flying blind. They run campaigns without any customer insight, behavioural data, and therefore without a direct relationship with the people who drive their revenue.
Enter White Label Loyalty founder Achille Traore, a soft-spoken, humble yet ridiculously smart visionary. As an ex-professional footballer playing in the Swedish first division and later signed to Barnsley FC - Achille has an amazing story.
After his football career was cut short by injury, he turned his head to business. Achille’s now a celebrated innovator in the tech industry, acknowledged as one of the top 100 Retail Technology Entrepreneurs in UK in Fresh Business Thinking's Shift100 list, associated with KPMG.
As well as the B2B manufacturing sector, White Label Loyalty powers huge loyalty programmes for the likes of Burger King and PepsiCo.
I met Achille late last year and he’s fast become one of my favourite B2B marketing leaders. He’s since worked with OrbitalX on a piece of research, revealing that while many B2B manufacturers believe they actually do have access to customer data, most struggle to activate, understand, or use it to drive repeat business.
So when presented with a loyalty solution that’s dead easy to implement and capable of delivering first-party data and a first real connection with the end customer, his clients jump at the chance. With no disrespect intended, it’s their first opportunity to behave like ‘real’ marketers - generating growth based on real insight rather than price promotions and guesswork.
AI-powered insights, predictive analytics, scaled personalisation and speedy routes to demonstrable ROI take loyalty from a tactic or campaign to a key strategic pillar for growth.
In other words, Achille is bringing loyalty into industries where it’s long been considered out of reach; a marketing channel that seemed to be just for others.
To have found whole sectors - B2B manufacturing and beyond - where old school marketing channels feel like a wide-open frontier and then watching the outlandish success that follows; it’s been a heartwarming experience.
For this 50-year-old, it’s a joyous moment whenever something perceived as old-fashioned makes itself cool again by bringing its phenomenal power and expertise to bear.
The AI opportunity in ‘go-to-market strategy’ is moving so fast that ‘keeping up’ is currently a daily focus.
Some of the smartest bosses and businesses around however, still pay attention to marketing channels seemingly long forgotten by the gossip around the LinkedIn watercooler.
Watch any Rory Sutherland interview clips and note how quickly he finds a moment to advocate for direct mail.
Similarly, UK industry body for commercial TV, Thinkbox now talks about ‘Total TV’ to include the proliferation of advertising opportunities for marketers across a host of new video channels. And while you’d expect Thinkbox to present evidence whenever possible to prove TV remains consumers’ most trusted marketing channel, independent and credible voices like Professor Mark Ritson follow suit whenever asked.
Still - some revelations still have it in them to surprise me. In marketing channel terms, ‘loyalty programmes’ feel like old news.
Until recently, I assumed loyalty was a marketing channel every brand across every sector had already explored, tested, and deployed in their own way. Like email, paid media, or events, it felt fully established and maybe even taken for granted.
Then I met a really cool business called White Label Loyalty. They’re doing amazing things for whole swathes of the B2B marketing community that are yet to properly investigate or even understand loyalty as a viable marketing channel.
This isn’t a case of B2B marketing teams having lacked ambition or neglecting to value their customers. Rather, in sectors like B2B manufacturing, the traditional model makes loyalty feel an unlikely marketing play.
When your products are sold through retailers and distributors, you rarely own your customer data. And if you don’t see the end buyer, it’s a tough job to truly understand how to excite them.
Without customers’ first-party data, you’ve got zero information on what influences their buying decisions. You can guess that price rules every customer choice, but beyond that rather base assumption and the damaging discounts it encourages, a decent loyalty scheme feels like a non-starter.
This leaves marketing directors under the cosh - being asked to deliver growth while effectively flying blind. They run campaigns without any customer insight, behavioural data, and therefore without a direct relationship with the people who drive their revenue.
Enter White Label Loyalty founder Achille Traore, a soft-spoken, humble yet ridiculously smart visionary. As an ex-professional footballer playing in the Swedish first division and later signed to Barnsley FC - Achille has an amazing story.
After his football career was cut short by injury, he turned his head to business. Achille’s now a celebrated innovator in the tech industry, acknowledged as one of the top 100 Retail Technology Entrepreneurs in UK in Fresh Business Thinking's Shift100 list, associated with KPMG.
As well as the B2B manufacturing sector, White Label Loyalty powers huge loyalty programmes for the likes of Burger King and PepsiCo.
I met Achille late last year and he’s fast become one of my favourite B2B marketing leaders. He’s since worked with OrbitalX on a piece of research, revealing that while many B2B manufacturers believe they actually do have access to customer data, most struggle to activate, understand, or use it to drive repeat business.
So when presented with a loyalty solution that’s dead easy to implement and capable of delivering first-party data and a first real connection with the end customer, his clients jump at the chance. With no disrespect intended, it’s their first opportunity to behave like ‘real’ marketers - generating growth based on real insight rather than price promotions and guesswork.
AI-powered insights, predictive analytics, scaled personalisation and speedy routes to demonstrable ROI take loyalty from a tactic or campaign to a key strategic pillar for growth.
In other words, Achille is bringing loyalty into industries where it’s long been considered out of reach; a marketing channel that seemed to be just for others.
To have found whole sectors - B2B manufacturing and beyond - where old school marketing channels feel like a wide-open frontier and then watching the outlandish success that follows; it’s been a heartwarming experience.
For this 50-year-old, it’s a joyous moment whenever something perceived as old-fashioned makes itself cool again by bringing its phenomenal power and expertise to bear.
London
Apr 15, 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.*
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Apr 7, 2026
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