AI Search: The Six Vital Content Traits For Citation

June 28, 2026June 28, 2026 | admin

There is a question that more business owners are starting to ask, and it matters more than most of them realise.

When a potential customer opens ChatGPT, Claude or Perplexity and asks which local solicitor handles commercial leases, which car dealer in Norwich carries approved used BMWs, or which estate agent consistently sells in their road, whose name comes up?

In most categories, the same handful of businesses get cited again and again. Everyone else is invisible.

This is not random. AI engines do not pick names out of the air. They pull from pages that are structured in a very specific way. If your website is not built that way, it will not be quoted, regardless of how long you have been in business or how good your Google ranking is.

The question is: what does a citable page actually look like?

Having studied in detail how AI search works across multiple platforms, six traits appear on every page that earns consistent citations. Miss one and your chances drop. Miss three and you disappear entirely.

Trait one: The heading is the real question

Not a clever marketing line. Not a vague label. The actual question your customer would type.

If someone asks an AI which estate agents in Worthing sell the most family homes, your page heading should reflect exactly that. “Our services” does not get you cited. “Which estate agents sell the most family homes in Worthing?” might.

AI engines match queries to headings before they read a single word of your copy. If the heading does not fit the question, the page is skipped.

Trait two: The first sentence answers the heading directly

No warm-up. No scene-setting. No “it is a great question” preamble.

The answer goes in the first sentence. Two sentences at most. Then you can expand.

Most business websites do the opposite. They spend three paragraphs building context before they say anything useful. By then, the AI has moved on to a competitor who answered in line one.

Trait three: One specific fact that only you own

This is the one that most businesses overlook, and it is probably the most important.

Generic claims do not get pulled. “We have years of experience” earns nothing. “We have sold 47 properties within half a mile of the seafront in the last 18 months, with an average of 11 days to offer” earns citations.

The fact does not need to be dramatic. It needs to be specific, true, and yours alone. A price. A ratio. A timeline. A count. A measured outcome from your own business.

AI engines are looking for something they can lift and use without having to verify it against ten other sources. A first-party number is exactly that. A generic claim is not.

Trait four: A real person behind the page

A named author. A photograph. A bio that says something specific about their experience. Ideally a small piece of markup behind the scenes that tells AI platforms this page was written by an identifiable human being, not generated by a machine.

Claude and Perplexity both weight this signal. A page with a named author who has a verifiable background gets more trust than an identical page attributed to a faceless brand.

This is straightforward to fix if you have not done it. Add your name to the pages that matter. Write three sentences about your actual background. Make it specific. “20 years in automotive retail, including 12 years managing franchised BMW and Audi sites in the south east” is useful. “Passionate about cars” is not.

Trait five: A structure the AI can scan

Short paragraphs. Clear subheadings. The occasional list where it genuinely helps. White space.

A 900-word page with seven tight sections consistently outperforms a 1,400-word page with three sections and one dense block of copy beneath each, because the engine can locate the relevant passage quickly rather than working through a wall of text.

This is not about dumbing down your writing. It is about making it easy to extract. The underlying thinking can be as sharp as you like. The structure needs to let the engine find the answer without having to dig.

Trait six: No filler

Every filler phrase weakens your entire page, not just the sentence it appears in.

“In today’s competitive landscape.” “Navigating the world of.” “We are committed to delivering excellence.” “At the heart of everything we do.”

AI platforms have learned to classify these phrases as low-signal text. When they appear, the surrounding paragraphs lose credibility. The page reads as generic content, produced to fill space rather than answer a question.

Cut them. Every single one.

What this means in practice

Run through your three most important pages, the ones a customer lands on when they are close to making a decision.

Read the first sentence under each subheading. Does it answer the heading directly? If not, rewrite it so it does. Then add one specific first-party fact to each section. Then check whether a named author with a real bio appears on the page.

That is an afternoon’s work. Not a redesign. Not a new content strategy. Just making what content you already have citable.

The businesses that show up when a potential customer asks an AI for a recommendation are not necessarily the biggest, the oldest, or the best-ranked on Google. They are the ones whose pages are easy to quote.

At the moment, most of your competitors have not made that adjustment. That gap will not stay open for long.

Steve Coulter is a GEO and AI search consultant at State of the Art Digital. He works with automotive retailers, estate agents and professional services firms on AI visibility strategy.

AI Search: Learn About AI Agents and PageSpeed

June 28, 2026June 28, 2026 | admin

PageSpeed and AI Agents: What Your Website Now Has to Do

Your website was built for human visitors. AI agents have different expectations entirely.

They do not just crawl your pages. They read them, follow your links, extract information and decide whether your business is worth mentioning in the answer they deliver to a real person.

This piece covers what your site now needs to do to stay visible in that process.

For most of the last decade, website speed was straightforward. Faster pages meant people stayed longer, bounced less and bought more. Google rewarded the fast ones. Slow sites fell behind.

That logic still holds. But there is now a second audience your website has to satisfy, and it has entirely different requirements.

AI assistants, AI search engines and autonomous AI agents are increasingly browsing the web on your customers’ behalf. They do not just look at your pages. They read them, follow your internal links, extract information, compare it with information from other sites and deliver a final answer back to the user.

Whether your business gets mentioned in that answer depends, in part, on whether your website is built in a way that makes sense to a machine.

This is what people mean when they talk about agentic browsing. And it changes what a good website actually needs to do.

Why PageSpeed Still Matters

Google’s PageSpeed Insights tool measures how quickly and reliably your pages load and behave. It draws on real-world data from Chrome users as well as its own laboratory testing.

The three figures that matter most are:

Largest Contentful Paint. How quickly does the main content appear on screen?

Interaction to Next Paint. How fast does the page respond when someone does something?

Cumulative Layout Shift. Does the page jump around while it loads, or stay stable?

These were designed with human visitors in mind. But AI systems have a very similar set of requirements. Pages that load fast, stay stable and render cleanly are easier for AI crawlers to process correctly. Pages that are slow, bloated or dependent on complex JavaScript are more likely to be misread, partially read or ignored.

What an AI Agent Actually Does

A traditional search engine sends a crawler to read your page and add it to an index. That crawler is not trying to understand your business. It is collecting text and signals.

An AI agent does considerably more.

It might land on your homepage, follow a link to a service page, read a case study, extract a specific fact, cross-reference it with something on a competitor’s site and then produce a summary recommendation for the person who asked. All without a human clicking a single link.

This is closer to how a researcher works than how a crawler works. And it means your website has to be navigable, logical and explicit in a way that most sites currently are not.

What Your Website Needs

None of this requires a complete rebuild. Most of it is good web practice that was being neglected long before AI arrived.

Pages that load quickly. AI crawlers have a limited processing budget. Excessive scripts, oversized images and slow servers consume that budget before the page is properly read. Keep things lean.

Layouts that stay still. If your page shifts around while it loads, an AI system has to recalculate where everything is. That increases the chance of misinterpretation. Stable pages are more reliably understood.

A clear content structure. Every page should have one main heading, supported by logical subheadings, concise paragraphs and, where relevant, bullet lists, tables and FAQs. This is not about formatting for its own sake. It is about making it unambiguous what each section is trying to say.

Proper HTML. Semantic HTML elements such as header, main, article, nav and footer are not decorative. They tell machines what role each part of the page plays. An AI system reading a well-structured HTML document has far less guesswork to do than one reading a div-soup layout built entirely for visual effect.

Content that exists in the HTML. If important information is hidden behind a JavaScript widget, loaded on interaction or stored inside an image, there is a reasonable chance an AI system will never see it. Critical content needs to live in the actual page source.

Good internal linking. AI agents follow links. If your most important pages are buried three clicks deep with no logical path to them, they may simply never be found. Connect your content properly.

Schema markup. Structured data tells AI systems explicitly what type of content they are reading, who it is from and what it refers to. It does not replace good content, but it removes ambiguity.

An llms.txt file. This is a relatively new development. An llms.txt file sits on your website and tells AI systems which pages are most important and most trustworthy. Think of it as a curated map of your site, written specifically for AI models rather than human visitors.

The JavaScript Problem

A significant number of modern websites are built on JavaScript frameworks that assemble the page in the browser rather than delivering it ready-made from the server.

Human visitors rarely notice this. Their browsers handle it.

AI crawlers are less forgiving. If a crawler cannot fully execute the JavaScript, it may see a blank page or a stripped-down version of your content. The risk is that your most important information simply does not exist, as far as the AI is concerned.

The practical answer is to ensure that important content is rendered server-side before it reaches the browser. Your developer will know what this means. If they are building or rebuilding your site, it is worth asking the question directly.

A Simple Test

If you want to get a sense of how well your site works for AI systems, try this.

Imagine an AI assistant has been asked to find a business like yours, understand what you offer, identify a specific piece of information and reach your contact or booking page.

Can it do all of that by reading and following the structure of your site? Or would it get stuck, misled or simply run out of useful content to follow?

Most businesses, if they are honest, will find the answer somewhere in between. The gap between where they are and where they need to be is the work.

The Broader Point

Traditional search optimisation was about helping Google find your pages.

AI optimisation is about helping intelligent systems understand your pages, trust them and use them when answering questions on behalf of real people.

PageSpeed Insights remains a useful benchmark. But performance is now only part of the picture. Speed, structure, accessibility, explicit content and clear internal architecture are becoming the baseline for any business that wants to remain visible as AI search becomes the default.

The businesses that get this right early will not just rank better. They will be cited, recommended and surfaced by AI systems in ways that their slower-moving competitors will not.

Steve Coulter is an independent AI search consultant based in the UK. Through State of the Art Digital, he helps business owners understand how AI systems find, read and cite their websites, and what to do when they do not. His clients include car dealers, car dealer groups, estate agency groups and other SME businesses. His retained advisory service gives clients ongoing strategic guidance as AI search continues to change the rules. If you would like to understand where your business stands please contact me.

AI SEARCH: Free Report. Your Business Is Becoming Invisible

June 22, 2026June 22, 2026 | admin

AI search has changed how customers find businesses. Most business owners haven’t noticed yet.

When a potential customer uses Google AI Overviews, ChatGPT, Perplexity etc. to find a product or service, they get a recommendation – not a list of links. If your business isn’t structured in a way that AI systems can read, understand and trust, you won’t be in that recommendation. Someone else will.

I’ve spent the last 18 months developing OPTIMUM, a framework built specifically to audit and improve AI search visibility. The findings across every client audit point to the same problems.

*Your Business Is Becoming Invisible* is a short report that explains what’s happening, why it matters, and what a structured response looks like.

If you’d like a copy, text or e-mail with title: INTEL (see advert)

No automated sequence. No obligation. Just the report.

About AI Citation Gap Analysis

June 17, 2026June 17, 2026 | admin

One of the biggest misconceptions in the GEO market is that AI citations can be treated like traditional search rankings.

They can’t.

AI systems do not operate like search engines. You cannot buy ‘AI Citations for £199 per month’ – Citations are influenced by a complex mix of trust, authority, entity recognition, content quality, technical accessibility and third-party validation. No credible provider can guarantee when, where or how often a business will be cited.

This understanding sits at the heart of OPTIMUM AI Citation Gap Analysis.

Rather than chasing citation guarantees, OPTIMUM identifies the gaps across a business’s digital footprint that may affect its ability to be retrieved, grounded and cited by AI systems.

The project has been in development significantly longer than many GEO products currently entering the market, with research and methodology established well before AI visibility became a mainstream marketing trend.

The goal has never been to sell hype.

It has always been to provide businesses with a defensible, evidence-based assessment of the factors that influence AI discoverability and authority.

In a market increasingly crowded with promises, measuring reality matters more than ever.

The OPTIMUM Ecosystem offers a cost effective solution to this problem, it analyses your current AI citation position and supplies an action plan to move your business into the citable sources pool.

AI Search: The Entity Identity Problem

June 9, 2026June 9, 2026 | admin

Why AI Can’t Find You: The Entity Identity Problem

Some of the world’s most recognised brands are functionally invisible to AI. Meanwhile, companies nobody has heard of get cited constantly. The difference isn’t marketing spend or domain authority. It’s whether an AI language model can construct a coherent, confident answer to the question: what is this thing, and who is it for?

The citation gap nobody is talking about

When a user asks ChatGPT, Perplexity, or Google’s AI Overview to recommend a tool, suggest a service provider, or explain a category, the model doesn’t retrieve a list of popular brands and rank them by fame. It constructs an answer from the information it has been able to learn, infer, and retain about each entity in that space.

If your entity – your brand, business, or product – is ambiguously defined in the sources that trained and inform those models, you will not appear. It doesn’t matter how long you’ve been trading, how many customers you have, or how much you’ve invested in traditional SEO. AI systems operate on a different logic. They need to be able to explain you clearly before they will cite you confidently.

This is the entity identity problem. Most businesses haven’t even begun to address it.

What entity identity actually means in GEO terms

In Generative Engine Optimisation (GEO), entity identity refers to how clearly and consistently an AI model can characterise a brand across three dimensions:

Definition: Does the AI know exactly who you are and who you serve? Not a vague category description a precise, differentiated statement of what you do and for whom.
Competitive context: Can the AI place you in a landscape? Does it understand what you’re better at than your alternatives, and which use cases or audiences you’re the right choice for?
Problem ownership: Is there a specific, recurring problem that the AI associates with your name? AI models cite solutions to problems. If you aren’t anchored to a problem, you’re unlikely to be cited when that problem is raised.

Why ‘brand awareness’ doesn’t transfer to AI citation

Traditional brand awareness strategies built reputation through repetition and reach. The more people saw your name, the more likely it was to appear in relevant contexts. Search engines amplified this by rewarding domain authority, backlink profiles, and engagement signals which are proxies for real-world trust.

AI language models don’t work this way. They don’t weight your brand higher because your display advertising has saturated a market, or because your name-search volume is strong. They weight you higher when the training and retrieval data they rely on contains clear, consistent, non-contradictory descriptions of what you do – descriptions authored by you, confirmed by third parties, and structured in ways that are easy to ingest.

The implication is uncomfortable for brands that have spent years on awareness. Being known doesn’t mean being understood. In the AI citation economy, it’s understanding that drives inclusion.

The three questions that diagnose your entity identity

Before any technical GEO work begins, there are three questions worth asking about your own brand. Not rhetorically – literally, by querying AI tools directly:

1. How is AI currently describing your brand?

Ask ChatGPT, Claude, and Perplexity: “What is [your brand]?” and “What does [your brand] do?” The answers will often surprise you. If the description is vague, outdated, or simply wrong, that’s the description being served to every potential customer who asks an AI assistant about your category before they’ve heard of you.

2. In what context is AI recommending you?

Ask: “Who is [your brand] best for?” and “When would you recommend [your brand] over [competitor]?” This reveals whether AI models have a coherent sense of your positioning – or whether they’re defaulting to generic category descriptions that give you no competitive advantage.

3. What problem does AI associate you with?

Ask: “Which brands or tools help with [the specific problem you solve]?” If your name doesn’t appear, you don’t own that problem in AI’s understanding. This is arguably the most important gap to close – because AI citation is almost always triggered by a problem query, not a brand query.

How to start building entity clarity

Entity clarity isn’t achieved through a single piece of content or a one-time optimisation. It’s built through consistent, structured signal across your owned and earned presence:

Your About page, homepage headline, and meta descriptions should all carry the same core definition – precise, differentiated, and anchored to the problem you solve.
Third-party citations – directory listings, trade body profiles, press coverage, industry association memberships – should consistently reinforce the same entity description.
Published content should explicitly connect your brand to the specific problems your ideal customers are asking AI tools about. Problem-anchored content is the highest-return GEO investment most businesses aren’t making.
Schema markup, structured data, and Knowledge Panel management are the technical layer and important, but secondary to having a clear, consistent entity story to structure in the first place.

GEO strategy starts with definition, not optimisation

Most GEO conversations start in the wrong place. They focus on technical signals, structured data, and citation tracking before the fundamental question has been answered: does AI actually understand what this brand is?

If the answer is no, or not clearly enough, then all the optimisation work downstream is building on an unstable foundation. Entity identity is where serious GEO strategy begins.

Ask the three questions. Then build from there.

AI Search: What Are Google Preferred Sources?

June 5, 2026June 5, 2026 | admin

Google Preferred Sources: Audience Trust Is Now a Ranking Signal

Google Preferred Sources is not a minor interface update. It is a structural change to how visibility works inside AI search, and it deserves more attention than it has received.

The feature allows users to nominate websites they trust. Once selected, those sites carry a visible badge inside Google search results. It launched in Top Stories, expanded globally in April 2026, and on 27 May 2026 Google extended it into AI Overviews and AI Mode.

Preferred sources now surface with visible markers inside the AI-generated answers themselves, at the exact moment a user is deciding whether to read further or move on.

The numbers are notable. Users have selected more than 345,000 sources, up from around 90,000 at global launch. Google’s own data shows that people click through to preferred sources at twice the rate of other links. Inside an AI Overview, where the generated answer compresses the organic results into a much smaller footprint, that differential is not marginal. It is the difference between being seen and being ignored.

The feature rewards publishers who already command a loyal audience. It does relatively little for newer or smaller sites whose discoverability has been in decline for two years. This is the part of the announcement that sits quietly beneath the positive framing. Preferred Sources is not a rising tide. It reinforces existing authority and presents that reinforcement as user choice.

For SEO and GEO practitioners, the implication is clear. A publisher is no longer competing only with other search results. It is competing with the answer Google has already generated. In that environment, being a recognised and trusted source before the user reaches the search box is a material advantage. Technical SEO remains relevant. It is just no longer sufficient on its own.

Preferred Sources is one more signal pointing in the same direction. Audience trust, topical authority, and citation potential are the metrics that matter in AI search. They are harder to manufacture and harder to reverse-engineer than a keyword strategy, which is precisely why building them now, rather than later, is the work that counts.

AI Search: Welcome Validation From The CEO of Google

June 5, 2026 | admin

Google Zero? I Said So Months Ago. Now Google CEO Has Confirmed It.

Sundar Pichai has now said publicly what anyone paying close attention already knew. Google Zero is not a conspiracy theory. It is a direction of travel that the CEO of Google has validated.

I have been writing about this for months. Not as speculation. As a reading of observable data that pointed one way and kept pointing the same way regardless of how many times the industry and experts tried to reassure otherwise.

AI Overviews now trigger on close to half of all queries, with zero-click rates reaching 80 to 93 percent in some search modes. Publishers are reporting traffic losses of 26 to 55 percent. This is not a future risk. It is a present condition, and it has been for some time.

What Pichai’s interview adds is not new information. It is confirmation from the top. He acknowledged that AI Overviews can be more opinionated than they should be. He admitted the product is still evolving. He leaned on 25 years of user satisfaction data as evidence that Google will course-correct.

With AI search summaries becoming increasingly more accountable, Pichai’s concession, alongside Google’s expansion of Preferred Sources across AI search, points toward a model where verified, trusted, authoritative sources receive preferential citation treatment.

That is the architecture GEO strategy is built around. Not ranking. Citation. Not position one. Source selection.

If your strategy still treats ranking as the end goal, this is your confirmation that the end goal has moved. The work now is becoming the source AI chooses to reference, not just the page that earns a click.

It’s good to have validation, even if somewhat reluctant and late coming.

—

* Blbliography for this article is available should you wish to conduct further research.

* For anyone unfamiliar I’m drafting a description of ‘Google Preferred Sources’ and what that means.

AI SEO: Video Is the Untapped AI Citation Asset Most Local Businesses Are Ignoring

June 4, 2026June 4, 2026 | admin

Punch Above Your Weight With This Two-Presence Video Strategy

Most car dealers and estate agents have been producing video for years. Walk-around stock videos, branch and forecourt tours, meet-the-team clips, market update commentaries. The content exists. The problem is almost none of it is configured to be read by an AI.

That distinction matters enormously right now.

AI search platforms – ChatGPT, Perplexity, Gemini, Google AI Overviews – do not watch video. They read the text surrounding it. They parse the title, the description, the transcript, the structured data markup, and the page context the video sits within. If those elements are absent, incomplete, or inconsistent, the video is invisible to every AI system regardless of its production quality or view count.

This is the gap that presents an immediate competitive opportunity for any local business willing to spend a few hours getting the fundamentals right.

Why YouTube Dominates AI Citation, and How That Helps You

YouTube is currently the single most-cited domain across all major AI platforms. Research from early 2026 shows it appears in roughly 16 per cent of LLM-generated answers, well ahead of any other source. This is not because AI systems are watching the videos. It is because YouTube enforces consistent metadata, generates automatic transcripts, and provides structured, machine-readable content at scale.

The implication for local businesses is significant. A YouTube channel is not just a video hosting platform. Configured correctly, it is a citation asset feeding into every major AI system simultaneously. Your video description, your chapter timestamps, your pinned comment and your auto-generated or manually uploaded transcript are all indexable text that AI crawlers can extract and attribute.

The key is understanding that the same optimisation logic applies to your own website. YouTube gives you citation reach. Your own site gives you citation authority and SEO credit. The winning strategy uses both, with a deliberate canonical structure connecting them.

The Canonical Problem Nobody Is Solving

The most common video mistake local businesses make is treating YouTube and their own website as two separate, unconnected things. A video goes on YouTube. Someone embeds it on a web page. Neither has proper metadata. Neither has a transcript. There is no structured data. The two versions compete with each other in search, and neither builds authority.

The correct approach is to establish a canonical video page on your own website and treat everything else as supporting distribution. Each video gets a dedicated page with a clear, keyword-informed title, a substantive description written in full sentences, a complete transcript published as readable text, VideoObject schema implemented in JSON-LD (Javascript Object Notation for Linked Data), and the YouTube embed as the playback mechanism.

The VideoObject schema uses the canonical page URL as its @id, which signals to search engines and AI crawlers that your site owns this content. The YouTube channel amplifies reach and feeds AI citation platforms. Your site gets the SEO equity.

This dual-presence model is the structural backbone of effective video GEO for local businesses.

What AI Systems Are Actually Reading

Understanding what an AI system extracts from a video page clarifies exactly what you need to produce. When ChatGPT, Perplexity or Google’s AI Mode retrieves a page containing a video, it is reading several distinct text layers.

The first is the page title and H1 heading. These should answer a specific, naturally phrased question. Not “Ford Focus Walkround July” but “What specification is a Ford Focus 1.0 EcoBoost? A full walk-around and honest assessment.”

The second is the video description. On YouTube this needs to be at least 200 words and should front-load the most important information. AI systems give disproportionate weight to the first third of any page’s content. The same description, or a fuller version of it, should appear on your canonical web page.

The third layer is the transcript. This is the most underused asset in local business video SEO. A 90-second walk-around video contains 150 to 200 words of spoken content. Published as visible text on the page, that content becomes indexable, citable, and attributable to your business. For a market commentary video from an estate agent, the spoken words represent genuine information gain – the kind of factual, expert content that AI systems prefer to cite.

The fourth layer is structured data. VideoObject schema implemented in JSON-LD tells AI crawlers and search engines precisely what the video contains, when it was published, how long it is, who produced it, and what page should be treated as the canonical source. Without it, AI systems are guessing at context. With it, they have a machine-readable brief. Fabulous entity and topical, semantic signals for AI citation uplift.

The Local Business Advantage

Large national brands have video teams, SEO departments and agency relationships. A used car dealer in West Sussex or a three-branch estate agent in Essex is not competing with them directly. What local businesses have is hyper-specific local expertise and genuine informational authority in a narrow geography.

An estate agent producing a weekly two-minute video on what is happening in their local property market – pricing, stock levels, buyer activity – and publishing it with a proper transcript, VideoObject schema, and a canonical page is building exactly the kind of factual, locally specific, expert-attributed content that AI systems prioritise when answering questions like “What is the housing market like in Worthing right now?” On the canonical URL page add in extra questions and answer such as; “What are the best local Secondary Schools?” and “Where are the best beaches?”

That is an answerable query. The business that has published consistent, well-structured local content over six months will own the AI citation for it. The business that has uploaded unoptimised clips to YouTube or not at all and done nothing else will not.

The gap between those two outcomes is not one of budget or resource. It is one of consistent process.

AI Search Summaries: How Smaller Brands Are Competing

June 1, 2026June 1, 2026 | admin

How To Get Your Brand Into AI Summaries: A Practical ‘EEAT’ Playbook

There is a version of this article that opens with a statistic about zero-click search rates, references a McKinsey report, and tells you that AI is disrupting the landscape.

You will not be reading that version.

Here is the thing that actually matters: AI search systems, whether Google’s AI Overviews, Perplexity, ChatGPT Search, or any of the others gaining users at pace, are not random. They are not black boxes that reward whoever shouts loudest. They have a logic, and that logic is remarkably close to something Google has been telling marketers for years: demonstrate Experience, Expertise, Authoritativeness, and Trustworthiness. (EEAT). The principles did not change. The stakes did.

When an AI system constructs a summary answer to a user’s question, it is making a series of editorial judgements. Which sources understand this topic? Which ones can be trusted? Which ones have said something specific and citable rather than something vague and generic? Your job, as a brand or business, is to make those judgements easy. Here is how.

Start With Positioning, Not Content

The single most common mistake brands make when trying to appear in AI-generated answers is trying to appear in too many of them. They produce content that covers broad territory. They write ultimate guides to entire industries. They want to rank for everything and end up trusted for nothing.

AI systems are not impressed by breadth. They are looking for signal, and signal requires specificity.

Consider the difference between a software company that describes itself as an all-in-one video platform and one that positions itself as the best tool for podcast editing. The first company is competing with every video tool on the internet. The second has a defined audience and a defined set of questions it can answer better than anyone else. When an AI system is asked what is the best tool for editing podcasts, the second company appears in that answer. The first probably does not.

This is not a content decision. It is a positioning decision. Narrow your claim. Own a space. The content follows from that; it does not create it.

Make Content That Is Actually Useful

Helpful content has become such an overused phrase in SEO that it has nearly lost its meaning. So here is what it actually means in the context of AI citation.

AI systems have seen every version of the generic blog post, the thin listicle, and the padding-heavy answer page. They have also seen genuinely useful writing: the forum post that actually solved someone’s problem, the how-to page that answered the tricky edge case, the comparison article that laid out real trade-offs rather than pretending every option was great in its own way.

The content that gets cited is the content that answers real questions with real specificity. Not how do I use this tool, but why does the audio desync when I import from a particular file format and how do I fix it. Not what is content marketing, but what content formats actually drive enquiries for a small professional services firm with a long sales cycle.

Your content plan should be built from questions your customers actually ask, not from keyword volume alone. Talk to your sales team. Read your support tickets. Go through your reviews. The questions are already there. Answer them with enough depth and clarity that someone with the problem right now would genuinely find it useful.

Build Pages AI Can Point To

There is a structural side to this that is often overlooked. AI systems do not just need good content; they need content in forms they can extract, attribute, and cite.

Three content types consistently perform well as AI citation targets.

Comparison pages. Comparison questions are among the most common queries AI systems receive. If you have a well-structured, honest comparison page covering your product against alternatives, you have created something AI systems can use to answer a question asked thousands of times a day. The key word is honest. Comparison pages that declare the author’s product best in every category are not useful. Pages that acknowledge genuine trade-offs are.

How-to content. Step-by-step explanations with clear sequencing and concrete actions are easier for AI systems to cite and summarise than opinion pieces or narrative articles. This does not mean how-to content cannot have a point of view; it means it should also be practical and functional.

Use-case content. Pages that describe specific applications of your product or service in specific situations give AI systems something to work with when a user’s query is about a context rather than a category. How a small accountancy firm uses project management software to handle client onboarding is more citable than a generic features page.

All of this works considerably better when supported by proper structured data. JSON-LD schema is not optional decoration. It is the vocabulary that tells AI systems what your content is, who it is about, and what it claims. If your site lacks structured data, you are asking AI systems to guess. Some will; many will not, when a better-structured competitor exists.

Use Real Visuals

AI systems with visual capabilities can process and reference visual content. More immediately, the people who train, evaluate, and ultimately trust AI systems use visuals as a quality signal.

Real product screenshots, genuine interface recordings, actual before-and-after examples, and video walkthroughs all contribute to perceived authenticity. They also make your content more useful, which loops back to the citation question. Content that helps people understand something is more likely to be cited than content that merely claims something.

There is also a simpler point here. Brands that use generic stock imagery look like every other brand. AI systems have encountered the same stock photo of a handshake or a lightbulb across thousands of websites. Real product visuals, screenshots from actual use, and genuine demonstrations stand apart. They signal that this content is about a real thing, produced by people who have actually used it.

Expand Your Presence Beyond Your Own Site

Your website is one signal. AI systems are reading many others.

Third-party mentions, reviews, press coverage, and independent creator content all contribute to the trust picture an AI system builds around a brand. When Perplexity or ChatGPT Search decides whether to include your brand in a response, it is not only reading your website. It is reading what others have written about you, in contexts you did not control and cannot directly edit.

This means PR is not separate from your visibility strategy; it is part of it. Getting covered in trade publications, being reviewed on independent platforms, appearing in podcast episodes, and being mentioned in the forums where your customers actually spend time all contribute to your perceived trustworthiness in ways AI systems can detect and weigh.

The practical implication is straightforward. Treat off-site presence as a deliberate programme rather than a nice-to-have. Identify the publications, communities, review platforms, and creators your target audience already trusts. Build genuine relationships with them. Create things worth mentioning. Earn the references rather than manufacturing them.

The Underlying Logic

Everything above serves a single purpose: making it easy for AI systems to understand what you do, trust what you say, and cite you as the source of a useful answer.

The businesses doing well in AI-mediated search right now are not necessarily the biggest ones. They are the ones already doing the work that EEAT has always demanded: positioning clearly, creating content with genuine depth, building structural credibility, and maintaining a consistent presence across the sources their audience trusts.

When AI systems can understand your positioning, trust your content, and point to specific pages you have built, the playing field levels considerably. You are not competing on budget. You are competing on clarity, depth, and genuine usefulness.

Those are things any business can build. Most simply have not started yet.

I offer an AI Risk Intelligence Briefing and Retained Advisory service to ensure your brand or business is making the most of the early-mover opportunity from AI Summary inclusion and citation. Please DM for more information and understand what this early adoption advantage is.

Below I’ve summarised my article for partner, board or C-Suite presentations.

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