What B2B Organisations Need to Know Now about AI and SEO

The way buyers find vendors is changing faster than most marketing teams realise. Here is what is happening, why it matters, and what to do about it.

For an increasing share of our B2B queries, particularly the research-type 'who is' questions driving our purchasing decisions, the first answer we see comes from an AI system. Our search results are no longer a list of ranked links. This is not a future scenario. It is the present state of the market.

According to BrightEdge, the enterprise SEO platform, AI agent requests have reached 88% of human organic search activity and could surpass human-driven search entirely by the end of 2026. A separate analysis estimates AI agents account for approximately 15% of all web traffic today, a figure that has grown substantially in under two years.

For B2B organisations, the implications are significant. Buyers researching your services, whether marketing directors, IT procurement leads, or operations managers evaluating vendors, are increasingly starting their journey not with a Google search but with a question to Perplexity, ChatGPT, Claude, or via Google's AI-powered browser assistant. If your organisation does not appear in those answers, you will be excluded from consideration before the conversation even starts.

Below, we explain what answer engines are, how they work, and what the discipline of AI SEO requires in practice.

Some stats to begin with

• Google reported AI Overviews reaching over 1 billion users per month across more than 100 countries as of early 2025. They framed this as expanding rather than cannibalising traffic - see our article on the crocodile jaws effect.
• Google's own data showed AI Overviews appearing on roughly 15–20% of all searches at peak rollout in the US, with higher rates on informational and research queries. This aligns with the current BrightEdge data.
• In 2024, Gartner predicted that by 2026, traditional search engine volume would drop 25% as users shift to AI assistants - again closely aligned to the BrightEdge results.

Let's begin...What Is an Answer Engine?

An answer engine is a search or AI system that synthesises a direct response to a query rather than returning a ranked list of links. When a user asks a question, the engine generates or retrieves a single, consolidated answer, often without the user having to click through to any website.

Examples operating at scale today include Google's AI Overviews, Perplexity, ChatGPT's search mode, and Microsoft Copilot. Each operates somewhat differently under the hood, yet the user experience remains consistent: ask a question, receive an answer.

The practical consequence for businesses is that organic traffic from traditional blue-link results is declining for informational queries, as users find answers before they ever reach your site. For categories where buyers start with research, i.e. by searching Google, which describes most of B2B, this is a material change in how demand is created and captured.

How are answer engines different from a traditional search engine?

Traditional search engines like Google index web pages and rank them by relevance and authority. We read the results and decide which answers align with our question - usually the top results, and we rarely move to page 2. Answer engines collapse that step: the system reads the various sources and synthesises an answer, often citing two or three references rather than surfacing ten links.

The competitive dynamic shifts from ranking highly (which is still important) to being the source the engine trusts enough to cite or paraphrase. For B2B buyers researching suitable vendors, this means first impressions are increasingly shaped by AI, not by your search engine rankings and web presence. We may form a shortlist based on these results - first impressions, etc.

What Does AI SEO Mean?

AI SEO refers to the set of optimisation practices aimed at making a brand, product, or service visible and credible in AI-generated responses, not just on traditional search engine results pages (SERPs).

This includes being cited in Claude answers, appearing in ChatGPT's browsing results, appearing in Google's AI Overviews, and being accurately understood by large language models (LLMs) when they reason about your market category.

What is an LLM? 

A Large Language Model (LLM) is a type of AI designed to understand, generate, and analyse human language by processing massive datasets, typically using transformer architectures. LLMs function by predicting the next word in a sequence to generate an answer; examples include GPT-4, Claude, and Gemini.

The discipline spans technical site structure, content strategy, entity optimisation, and off-site authority building — with LLM retrieval behaviour as the primary target. It is not a replacement for conventional SEO. It is its logical evolution.

The Rise of Answer Engine Optimisation (AEO) and GEO

Two terms have emerged to describe this new practice area: Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO).

• Answer Engine Optimisation (AEO) is the practice of structuring and positioning content so that AI-powered systems, including Google's AI Overviews, Perplexity, and LLM-based assistants, retrieve and cite it in response to relevant queries. AEO applies established SEO principles (technical health, E-E-A-T signals, structured data) while adding requirements specific to how LLMs process and surface information: clarity of entity definition, directness of on-page answers, consistency of brand signals across the web, and the volume and quality of third-party corroboration.
• Generative Engine Optimisation (GEO) is a closely related term, originating in academic research, that describes strategies to improve a site's visibility within the outputs of generative AI systems. While AEO often focuses on retrieval-augmented generation (RAG) systems like Perplexity that actively crawl live web content, GEO encompasses broader LLM visibility, including how a brand is represented in model weights derived from training data.

In practice, AEO and GEO are used interchangeably by most SEO practitioners. Both converge on the same core recommendation: be clearly authoritative, well-cited, and well-structured.

Why AEO Matters Specifically for B2B

Our B2B buying journeys are heavily research-led. Decision-makers and procurement teams now use AI tools to shortlist vendors, understand market categories, and draft requirements documents (we are seeing this first-hand), often before engaging with any sales process. If your organisation is absent from AI-generated answers in your sector, you are entirely absent from early-stage consideration.

AEO is particularly high-leverage for B2B because the queries that matter, such as "what is the best digital agency for enterprise web projects in Ireland" and "how should I evaluate a CMS migration partner", are exactly the kind of open-ended, research-mode questions that answer engines are built to handle. See an example of our success in Enterprise Web Agencies.

How AI Systems Decide What to Cite

The natural question for any organisation is: what determines whether we appear in an AI-generated answer?

LLMs used in retrieval-augmented generation (RAG) systems (see below) and the architectures underpinning tools like Perplexity and SearchGPT dynamically retrieve web content at query time. Citation decisions are influenced by several overlapping factors: the relevance of the content to the query; domain authority and trust signals; the clarity and extractability of the answer (well-structured pages with direct, unambiguous responses are preferred); entity recognition (the model must be able to identify who you are and what you do); and third-party corroboration, where mentions in reputable publications, directories, and linked sources reinforce model confidence.

There is no single ranking factor; citation is a function of the intersection of all of these.

The Role of E-E-A-T

E-E-A-T - Experience, Expertise, Authoritativeness, and Trustworthiness, introduced by Google as a quality evaluation framework, maps directly onto how LLMs assess source credibility. Models are trained on data that disproportionately represents high-authority sources, so brands that have built genuine E-E-A-T signals over time are more likely to be represented accurately and favourably in model outputs. SEMRUSH provides a breakdown of E-E-A-T.

Practically, this means publishing content with named, credentialed authors; earning editorial coverage and citations from industry publications; accumulating case studies and client evidence; and ensuring your website's factual claims are consistent and verifiable. E-E-A-T is not a checklist; it is a measure of genuine expertise communicated clearly.

Entity Optimisation

In semantic search and LLM terms, an entity is a distinct, uniquely identifiable concept - a person, organisation, product, or place. LLMs reason about the world through entities and the relationships between them.

If your brand is not clearly defined as an entity, with a consistent name, location, service category, and set of associations across your website, social profiles, directories, and third-party mentions, models may misrepresent you, conflate you with competitors, or omit you from relevant answers.

Entity optimisation involves ensuring that all web surfaces describing your organisation are coherent, structured with schema.org markup where appropriate, and corroborated by independent sources.

Structured Data

Schema markup, specifically JSON-LD implemented according to the schema.org vocabulary, provides a machine-readable context that both search engine crawlers and AI retrieval systems can parse with high confidence.

For B2B organisations, the most impactful schema types include Organisation, Service, FAQPage (which directly supports question-and-answer retrieval), and BreadcrumbList. Schema does not guarantee citation, but it reduces ambiguity, and reduced ambiguity is the foundation of AI visibility.

Here's an example - Optimising for Perplexity vs. Google

It is worth distinguishing between the two systems, because optimisation strategies are not identical.

Perplexity operates as a retrieval-augmented generation system: it searches the live web at query time, retrieves a set of sources, and synthesises an answer with inline citations. This makes it behaviourally similar to Google, in that fresh, crawlable, technically sound content is foundational. The key differences are that Perplexity places strong weight on direct answerability, with pages that contain a clear, unambiguous answer to the query near the top of the content cited more frequently; it does not use the same PageRank-style authority model as Google, so domain authority is less determinative than content relevance; and it is used primarily for research-mode queries, meaning long-form, substantive content outperforms thin or promotional copy.

Google's AI Overviews draw on Google's existing index and quality signals, so conventional SEO practice remains important. However, the content that surfaces in an Overview is typically the most directly relevant content from a well-trusted domain, not simply the highest-ranking page.

Reality - The LLMs differ, so you need to select your preferred platforms and develop a plan for each. The more you select, the more testing and work are required. 

What Content Performs Best (Currently!)

Content that directly, clearly, and with appropriate depth answers a specific question consistently outperforms generic or promotional content in answer engine retrieval. Structural characteristics that correlate with citation include a direct answer to the primary query within the first 100 words; headings that mirror natural-language questions; factual claims supported by data or expert attribution; a distinct, authoritative point of view (LLMs tend to cite opinionated, expert sources rather than hedged overviews); and a logical information architecture that enables a retrieval system to extract a coherent answer without requiring the full page context.

FAQ-format content is particularly effective because it maps directly onto how query-response retrieval systems work. A page structured as a series of direct questions and substantive answers is, architecturally, already close to the format that answer engines prefer.

Third-Party Mentions and Off-Site Authority

Third-party corroboration is critical and often underweighted in AI SEO strategies.

LLMs learn what is true about a brand partly from training data and, in RAG systems, partly from what is consistently stated across multiple independent sources. A brand frequently and accurately mentioned in industry publications, directories, review platforms, case studies, and partner sites creates a dense web of corroborating signals that increases model confidence.

For B2B organisations, this means digital PR, awards programmes, trade publication coverage, and analyst recognition are not peripheral marketing activities; they are core AI SEO infrastructure. The principle is analogous to traditional link building, but the beneficiary is model perception, not just PageRank. PageRank is a foundational Google Search algorithm, developed by Larry Page and Sergey Brin, that measures the importance of web pages by analysing the quantity and quality of links pointing to them.

What is RAG?

RAG (Retrieval-Augmented Generation) is the architecture that powers most AI search tools, such as Perplexity, ChatGPT's search mode, and Google's AI Overviews.

This is the core problem it solves: a large language model (LLM) such as GPT-4 or Claude has a knowledge cutoff; it was trained on data up to a certain date and knows nothing beyond that. If you ask it "what are the best digital agencies in Ireland right now," it can't reliably answer based on its training data alone.

RAG fixes this by splitting the job into two:

1. Retrieval - when a query arrives, the system fetches relevant, up-to-date content from the web (or a document database). It returns a set of sources that appear relevant to the question.
2. Generation - the LLM then reads the retrieved sources and synthesises an answer from them, often citing the sources it used.

The result - The model isn't answering from memory; it's reading fresh documents and generating a response based on what it just found.

Why this matters for SEO

In a traditional Google search, the algorithm ranks pages, and the human does the reading. In a RAG-based system, the algorithm retrieves pages, the AI reads them, and then produces a single synthesised answer.

This changes what "winning" looks like. You're no longer competing to be the highest-ranked blue link. You're competing to be the source the retrieval step selects, and then to be the source the generation step trusts enough to cite.

That's why content clarity matters so much in AI SEO; a dense, jargon-heavy page might rank on Google because it has strong backlinks, but a RAG system will prefer the page that most directly and unambiguously answers the query, because that's what's easiest to extract and synthesise.

A simple analogy

Think of a RAG system as a researcher with a very fast internet connection. You ask them a question. They don't answer from memory; they immediately pull up the most relevant sources they can find, read them, and then give you a synthesis. The sources they trust most and quote most clearly are the ones that end up cited in the answer.

The game plan for your AI SEO is to be the source researchers consistently reach for. Check out our article - Own the Answer - Re-Designing Content for AI Search Success

Auditing Your Current AI Visibility

The most direct way to understand your current AI footprint is to conduct systematic query testing. Run the queries your prospective clients would ask across ChatGPT, Perplexity, Google AI Overviews, and Copilot, and evaluate whether your organisation appears and whether the description is accurate.

Specifically, check whether your service category is correctly identified, whether your geography is correct, whether your differentiators are present, and whether you are mentioned alongside the right competitors. 

Discrepancies between how you describe yourself and how AI systems describe you indicate entity confusion. This is usually caused by inconsistent on-site messaging, weak third-party corroboration, or a lack of structured data. A structured AI SEO audit will identify and prioritise the gaps.

Tip: Build a database of queries you'd use to find your business; ask your team and friends to contribute. Prioritise the questions and begin updating your site with new content. Keep adding to your list of questions. The good news: in our experience, results are faster than with traditional SEO. Test often, tick off your success and keep answering the questions.

The Bigger Picture

BrightEdge's projection that AI agent traffic could surpass human organic search by the end of 2026 should be read as directional rather than precise. The exact figure matters less than the underlying dynamic: the share of buyer research mediated by AI systems is growing quickly.

For organisations that have invested in strong content, technical SEO fundamentals, and genuine off-site authority, this transition is manageable. The signals that make a brand credible to Google largely make it credible to LLMs. The additional work is primarily about entity clarity, content directness, and ensuring corroboration across the web.

For organisations that have deferred that investment, the cost of missing AI-generated answers is now visible, in the form of buyers who have already shortlisted competitors before first contact.

The discipline now has a name. Organisations that treat AI SEO as a strategic priority will find themselves in a structurally stronger position as the shift accelerates.

Hope this helps. Good luck.

If you would like to understand your organisation's current AI visibility and what is needed to improve it, get in touch.