Large language models have changed the B2B buyer research process more substantially than any technology shift since the professionalisation of Google Search. Where buyers previously searched Google to find individual sources and formed their own synthesis, they now increasingly ask AI systems to synthesise for them — receiving structured answers that cite specific sources and make recommendations. For B2B marketers, this is not a future concern. 68% of UK B2B buyers used an AI research tool in their last vendor evaluation. The question is whether your brand is being cited when they do.
How buyers actually use LLMs in vendor research
Understanding the specific ways buyers use AI research tools is the first step to optimising your content for citation in those tools.
Buyers use AI to build category knowledge quickly: "Explain the difference between ABM and demand generation," "What is intent data and how does it work in B2B marketing?", "What should I look for in a marketing attribution tool?" These queries return synthesised definitions drawn from multiple sources. Brands whose educational content is cited in these responses earn top-of-funnel brand awareness that did not previously exist as a scalable channel.
Buyers ask AI to suggest vendors: "What are the best ABM platforms for mid-market B2B companies in the UK?", "Which marketing automation tools integrate well with Salesforce?", "Who are the leading B2B SEO agencies in London?" AI responses to these queries draw on review platform data, analyst coverage, directory listings, and content presence. Brands with strong G2 presence, analyst citations, and comprehensive content coverage appear in these responses. Brands without do not.
Once a shortlist is formed, buyers use AI to compare specific vendors: "Compare HubSpot and Salesforce for a B2B company scaling from £5M to £30M ARR," "What are the common complaints about [competitor] according to G2 reviews?", "What does [agency] specialise in and who do they work with?" These queries require AI systems to draw on specific, attributed sources — case studies, review content, and detailed service descriptions that can be verified and cited.
Senior buyers use AI to help build internal investment cases: "What is the typical ROI of intent data for B2B mid-market companies?", "What are the key objections to a marketing attribution investment and how should I address them to the CFO?", "What are the main risks of switching from HubSpot to Salesforce?" These queries draw heavily on original research, benchmark data, and practitioner content with specific claims and evidence.
What content AI systems cite — and why
AI systems are not random in their citation behaviour. They preferentially cite content with specific characteristics that signal credibility and informational authority.
"68% of UK B2B buyers used AI research tools in their last vendor evaluation" is more citable than "most B2B buyers now use AI tools." AI systems preferentially surface specific, attributed statistics — which is why original research with proprietary data generates disproportionate citation value. A single compelling original statistic from your own research can appear across dozens of AI-generated answers.
AI systems are trained on data that reflects existing web authority signals. Domains with high organic authority, many quality backlinks, and existing citation by other authoritative sources are more likely to be represented in training data and therefore in generated responses. This is why SEO and AEO are complementary — organic authority building increases AI citation probability.
Content structured as "What is X? [direct answer]" or "How do you [process]? [step-by-step answer]" is optimised for AI extraction. AI systems look for content that directly answers the query they are responding to. FAQPage schema explicitly signals this structure to both search engines and AI systems — making it the single highest-impact technical implementation for AEO.
G2, Capterra, and Trustpilot content is heavily represented in LLM training data. When a buyer asks AI to compare vendors, review platform ratings and specific review content are among the most reliable signals AI systems have about real-world product performance. A strong, recent review presence on relevant platforms directly influences AI citation in comparison queries.
How to win in an LLM-assisted buying journey
The marketing activities that generate AI citation are not a separate channel — they are extensions of the organic and content marketing activities that already generate search rankings and brand authority.
A domain that thoroughly covers a subject area is more likely to be cited across multiple related queries than a domain with isolated well-written pieces. Map your target topic area comprehensively — every question a buyer might ask about your category — and build content that addresses each question directly. This is the foundation of both traditional SEO authority and LLM citation frequency.
Commission an annual survey of your customer base or ICP. Publish the findings with full methodology. Promote the key statistics across LinkedIn, press releases, and industry publications. Original statistics from credible sources are the most durable citation asset available — they appear in AI answers months and years after publication, and each citation drives awareness among buyers who would otherwise not have discovered you.
Format every content page with clearly marked question-and-answer sections and implement FAQPage structured data markup. This is the technical signal that most reliably improves AI Overview citation rates and Google featured snippet capture. It can be implemented on existing content without rewriting it — simply restructure headings as questions and add the schema markup.
Identify your strongest customer advocates and ask them to leave detailed G2 reviews that describe specific use cases, quantified outcomes, and the buyer's company type. AI systems cite specific review content when answering comparison queries. "A marketing director at a 200-person SaaS company" citing a specific outcome is more citable than a generic positive review. Quality of review content matters as much as volume.
Implications for B2B marketing strategy
- Content quality over content volume: LLMs are trained on and cite authoritative, detailed content — not thin, keyword-optimised content that dominated SEO strategies a decade ago. A smaller number of genuinely useful, deeply researched content pieces generates more AI citation than high volumes of derivative content. This should shift content budget toward fewer, better-resourced pieces.
- AEO infrastructure is now table stakes, not cutting edge: FAQPage schema, topical authority architecture, and original research publishing were experimental in 2023. In 2026, they are baseline requirements for any B2B brand that wants to maintain visibility as AI research tools become the default first step in the buying process.
- Track AI citation as a marketing metric: Add LLM citation monitoring to your measurement framework. Test your target queries in Perplexity, ChatGPT, Claude, and Gemini monthly. Track whether your brand is cited, in which contexts, and whether citation frequency is growing or declining. This is the equivalent of tracking search rankings — a leading indicator of top-of-funnel brand presence.
- Review generation is now an AEO investment: G2 and Capterra content is directly cited in AI comparisons. A systematic review generation programme — automated review requests, guided review templates, response strategy — is no longer just a sales enablement tool. It is AEO infrastructure that influences how your brand appears in the AI-generated comparison answers your buyers are reading.
- The dark funnel is getting darker: As AI research tools replace explicit Google searches, attribution for the earliest stages of the buyer journey becomes even harder. A buyer who forms an initial impression of your brand through an AI-generated category overview will not appear in your analytics. Self-reported attribution, brand lift measurement, and direct buyer interviews become increasingly important to understand the full journey.
Our AEO and GEO programmes build the content structure, schema, and topical authority that earns you citations across Perplexity, ChatGPT, Google AI Overviews, and beyond.