Organic Marketing

OPTIMUM: How SMEs Can Ensure AI Mentions Them

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For most small and medium-sized businesses, the biggest risk from AI search is not lower rankings; it is invisibility. Your website can rank on page one and still never appear in the AI‑generated answers that now sit above the links. OPTIMUM is a practical, SME‑friendly framework that shows exactly why that happens and what to fix so your business is cited, named and recommended inside AI summaries on Google AI Overviews, ChatGPT, Perplexity, Gemini and similar engines.

Why traditional SEO is no longer enough

Classic SEO optimises for a list of blue links. AI search optimises for inclusion in the answer itself. Generative engines do not just rank pages; they retrieve, synthesise and attribute information from sources they judge to be clear, trustworthy and easy to cite. That means signals like structured data, entity clarity, citation readiness and GDPR‑friendly crawler access matter as much as, or more than, keyword density and backlinks when it comes to appearing in AI summaries.

An SME that only tracks rankings can therefore look successful while being completely absent from the AI answers that increasingly drive enquiries, shortlists and purchasing decisions. OPTIMUM exists to close that gap.

What OPTIMUM is

OPTIMUM is a structured AI visibility audit and scoring framework created by AI search expert Steve Coulter. It models how AI systems interpret, retrieve and cite your business across a defined set of dimensions, then turns that into a scored report with a prioritised action plan. The output is designed to be read by owners, marketers and technical teams alike: a branded document showing where you stand, where competitors are beating you, and what to do next.

Crucially, OPTIMUM does not produce a single magic number. It produces a structured picture of your AI visibility profile across seven core dimensions, each scored and weighted by its impact on the likelihood of being cited. GDPR compliance is treated as a foundational requirement for both the business owner and the OPTIMUM audit process itself.

The seven OPTIMUM dimensions (and what they mean for an SME)

The framework sets out seven scored dimensions that together determine your AI visibility. GDPR compliance sits alongside these as a non‑negotiable prerequisite for both the business and the audit. For an SME, each dimension translates into concrete questions and actions.

1. Technical AI Accessibility

This dimension asks: can AI crawlers reach, read and process your site without obstruction? It covers:

  • Robots.txt and crawl budget, ensuring AI bots are not unintentionally blocked or throttled
  • Page speed and Core Web Vitals, as slow, unstable pages are less likely to be used as sources
  • llms.txt and related AI‑facing files, emerging standards that help large language models understand your site’s structure and content in a machine‑friendly format. llms.txt as an emerging standard is controversial as there are mixed opinions on effectiveness. Ask how long does it take to add in and make your own decision? It’s minutes.

SME takeaway: If your site is technically hard to crawl or too slow, AI engines may skip it entirely, regardless of how good your content is.

2. Structured Data and Schema

This assesses whether your content is marked up so AI systems clearly understand who you are, what you do and where you are relevant. It includes:

  • Correct schema types for your business, for example LocalBusiness, Product, Service, FAQ, Review
  • Accurate implementation of name, address, phone, opening hours, service areas and key offerings
  • Coverage across important page types: home, location pages, service pages, product pages, about, contact

SME takeaway: Well‑implemented schema makes it far easier for an AI to extract clean, attributable facts about your business and cite them in a summary.

3. E‑E‑A‑T Signal Strength

AI systems favour sources that demonstrate experience, expertise, authoritativeness and trustworthiness. OPTIMUM evaluates:

  • Clear author bylines, bios and credentials on key content
  • A strong About page that explains who you are, your history and why you are qualified
  • External references, citations, press mentions and industry affiliations that corroborate your claims

SME takeaway: Even a small business can score highly here by being explicit about real‑world experience, qualifications, case studies and third‑party validation.

4. Content Authority and Depth

This dimension measures whether your content shows genuine subject‑matter expertise rather than thin, generic copy. AI rewards:

  • Original insight, primary data and clear point of view
  • In‑depth guides, comparisons and explainers that go beyond what every competitor already says
  • Regularly updated, relevant content aligned to your core services and customer questions

SME takeaway: You do not need to publish daily, but you do need a focused set of high‑quality pages that prove you know your niche better than anyone else.

5. Machine Readability and Structure

AI needs content that is easy to parse and extract from. This dimension looks at:

  • Clear heading hierarchy (H1, H2, H3) that mirrors the logical structure of the topic
  • Concise, factual statements that can be quoted without heavy rewriting
  • Use of FAQ‑style sections, bullet points and tables where appropriate to make key facts obvious

SME takeaway: Write for humans first, but format so an AI can quickly identify the answer within your page.

6. Citation Readiness

Citation readiness is distinct from general SEO quality. It asks whether your content has the characteristics that make AI systems comfortable attributing claims to your website, such as:

  • Precise, verifiable statements with dates, figures, sources where relevant
  • Clear ownership of data and opinions, for example Our 2025 survey of 300 local buyers found…
  • Consistent messaging across pages so the AI is not confused by contradictions

SME takeaway: If your claims are vague, inconsistent or unsupported, AI engines will prefer to cite other sources.

7. Cross‑Platform AI Presence

AI engines draw on more than your website when forming answers. This dimension evaluates your presence and mentions across:

  • Industry publications, local news and relevant blogs
  • Review platforms, directories and trade bodies
  • Community platforms, such as Reddit and niche forums, where your brand or services are discussed

SME takeaway: Being talked about elsewhere gives AI systems additional confidence that your business is real, relevant and worth citing.

GDPR compliance: the foundation for both business and audit

GDPR compliance is not an eighth dimension; it is the foundation that makes the other seven meaningful. It applies in two ways:

  • For the business owner: Your site must handle personal data lawfully, with clear privacy notices, appropriate consent mechanisms and robust security. AI crawlers and platforms are increasingly sensitive to sites that appear non‑compliant or opaque about data practices.
  • For the OPTIMUM audit process: The audit itself must be conducted in a GDPR‑compliant manner, ensuring any data collected, stored or analysed during the assessment respects privacy obligations and is used only for the agreed purpose.

SME takeaway: If your site requires interactive consent before content is visible, or your data practices are unclear, many AI crawlers will simply leave, and you will not appear in summaries. Strong GDPR compliance protects both your customers and your AI visibility.

How an OPTIMUM audit helps an SME appear in AI summaries

For a typical SME, an OPTIMUM audit works in three stages:

  1. Measurement and scoring Your site, and often key competitors, is assessed against the seven dimensions. Each dimension receives a score and commentary explaining what is working and what is not. GDPR compliance is verified as a prerequisite.
  2. Gap analysis and benchmarking The report highlights which dimensions are dragging your AI visibility down most and shows where competitors are outperforming you in AI search. This makes it clear whether your main problem is technical, content‑related, trust‑related or a mix.
  3. Prioritised action plan You receive a clear set of recommendations ordered by impact, such as:

Because the recommendations are prioritised, an SME can focus limited time and budget on the changes most likely to move the needle on AI visibility.

What this looks like in practice

Local trades business (for example plumber, electrician)

Before OPTIMUM: Ranks well for plumber in town but never appears when someone asks an AI Who is a reliable plumber in town for emergency call‑outs?

After OPTIMUM fixes:

  • LocalBusiness schema correctly implemented with service areas, emergency call‑out details and reviews
  • Service pages rewritten with clear, factual answers to common AI‑style questions, such as response times, coverage, pricing ranges
  • Stronger Google Business Profile, local directory citations and a few local news mentions

Result: AI engines now have clean, attributable data and third‑party signals that make it safe to name and recommend the business in local AI answers.

Specialist B2B service (for example HR consultancy, IT support)

Before OPTIMUM: Good blog traffic but generic content that mirrors competitors; no clear author credentials; weak schema.

After OPTIMUM fixes:

  • Author bios with real experience and credentials added to key articles
  • Core service pages restructured with clear headings, FAQs and data‑backed claims
  • A small set of original reports or surveys published and promoted to industry sites

Result: AI systems begin to cite the firm’s data and viewpoints when answering questions like What are the main HR compliance risks for UK SMEs in 2026?

How often should an SME run an OPTIMUM audit?

AI search behaviour is changing quickly. For most SMEs:

  • A full OPTIMUM audit every six months is a sensible baseline
  • A lighter review in between is useful if you make significant site changes, launch new services, or notice shifts in how AI answers appear for your key queries

Retained advisory arrangements can include ongoing monitoring, but even a periodic audit gives you a structured way to measure progress and keep your AI visibility aligned with your business goals.

Bottom line for SMEs

If your customers use AI to research providers, compare options or get recommendations, your AI visibility is now a core business metric. OPTIMUM gives SMEs a clear, evidence‑based way to:

  • Understand why they are or are not appearing in AI summaries
  • See exactly which technical, content and trust signals matter most for their sector
  • Implement a focused, prioritised plan to become the brand AI engines cite, not the one they ignore

In a world where the answer is increasingly a single AI‑generated paragraph, OPTIMUM is how an SME makes sure that paragraph includes them.

Why Ranking on Page One Doesn’t Get You Quoted by ChatGPT

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The finding

Multiple independent studies in 2025 and 2026 point to the same conclusion: ranking in Google’s top ten does very little to guarantee a citation in ChatGPT.

Ahrefs analysed 15,000 prompts and found that on average, only 12% of links cited by ChatGPT, Gemini and Copilot appear in Google’s top ten results for the same query. A separate Ahrefs study of ChatGPT’s top 1,000 cited pages found that fewer than one in six of ChatGPT’s chosen sources show up in Google’s SERPs for the matching query at all. Moz’s 2026 analysis found that 88% of Google AI Mode citations do not come from the organic top ten, and put ChatGPT’s overlap with Google’s top results at just 6.5%. A separate correlation study measured the relationship between Google position and ChatGPT citation order at close to zero.

So the pattern holds across several research houses using different methods: a page can dominate page one and still be invisible to ChatGPT, and a page with no meaningful Google ranking can still be the source ChatGPT quotes.

What this means and why it happens

Traditional SEO ranking and AI citation are answering different questions. Google ranking measures relevance and authority for a search query. ChatGPT citation measures whether a passage can be lifted cleanly, attributed with confidence, and used to answer a specific question inside a generated response.

The mechanics behind the gap:

  • Different retrieval systems. ChatGPT does not simply read Google’s results. It draws on its own index, third-party search APIs and training data, then re-ranks candidates by its own criteria, not Google’s ranking signals.
  • Structure over authority. ChatGPT favours reference-style content that is comprehensive, well-organised and easy to extract cleanly: Wikipedia, structured how-to pages, homepages, and app store or review pages score disproportionately well. Ahrefs found Wikipedia alone accounts for around 30% of ChatGPT’s top-cited content.
  • Data density matters. Content with a high number of concrete statistics and figures gets cited far more often than descriptive copy making the same points in general terms.
  • Freshness is weighted. Most ChatGPT citations come from recent content, so an older page that once ranked well can lose citation share even while it keeps its Google position.
  • Backlinks predict Google rank, not AI citation. Brand mentions across other authoritative sources are a stronger predictor of AI citation than the backlink profile that drives conventional ranking.

The practical consequence for any business: a website can be doing everything right by traditional SEO standards and still be absent from the answers customers actually see, because ChatGPT is not scoring the same things Google scores.

How OPTIMUM identifies the gap

OPTIMUM audits a website against seven dimensions of AI visibility and produces a qualitative analysis rather than a generic score out of one hundred. Each finding is written up in plain terms: what the site currently does, where the structure or content fails to meet the standard AI engines actually use for citation, and what specifically needs to change.

Every dimension carries three figures:

  • Now Score – where the website currently stands
  • Future Score – where it could realistically stand once the identified fixes are made
  • Delta – the gap between the two, showing the size of the opportunity in each dimension

This turns the abstract problem of “we don’t show up in ChatGPT” into a structured, dimension-by-dimension breakdown: content structure, citation-readiness, technical accessibility to AI crawlers, data and statistical density, freshness and update cadence, authority signals beyond backlinks, and the areas most likely to be re-ranked by AI systems ahead of Google’s own results.

The Now/Future/Delta format means a client sees not just a diagnosis but a measurable target, and the dimensions with the largest Delta show precisely where effort will produce the greatest citation gain.

Find out where your website stands

If you don’t know whether ChatGPT, Gemini or Claude are citing your website, you’re planning blind. An OPTIMUM audit will show you exactly where the gaps are, dimension by dimension, with a clear route from Now Score to Future Score. Get in touch with State of the Art Digital to book yours.

Why John Lewis Just Built a Studio for AI Search

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When a 160-year-old department store starts building a television studio inside its flagship shop, it’s worth asking why. When the answer turns out to be “so chatbots talk about us more”, it’s worth paying close attention.

That’s exactly what John Lewis has done. On 3 September, the retailer opened The Box Studio at its Oxford Street store, a purpose-built space for video and audio content, and launched a new vodcast called Gift List, hosted by Angela Scanlon with Louis Theroux as the first guest. Six episodes will run in the lead-up to Christmas, each pairing Scanlon with a well-known guest to talk about the gifts they’ve given, received, and occasionally got badly wrong.

On the surface, this looks like a media story. Underneath, it’s a search story, and a very telling one for anyone working in marketing today.

The real reason for the studio

John Lewis has been open about its motive. As reported by The Guardian, the chatshow and studio exist specifically to make the brand and its products more visible to chatbots and more prominent in AI search results. This isn’t a guess or a bit of spin from an agency. It’s the retailer’s own stated purpose.

The numbers explain the urgency. A year ago, AI search tools such as ChatGPT sent John Lewis around 0.3% of its customers. That figure now stands at 2.5%, an eightfold increase in twelve months, and outgoing managing director Peter Ruis has said the growth is happening across every age group, not just younger shoppers experimenting with new tools.

Ruis also pointed to something more interesting than the show itself: the value of the studio’s speed. Being able to film inside a working shop means John Lewis can respond to a cultural moment within days rather than weeks. He gave the example of the Bayeux Tapestry exhibition at the British Museum reviving interest in cross stitch, and the timing of a new Harry Potter television series ahead of Christmas. He now rates that capability alongside the Christmas advert and the “never knowingly undersold” pledge as central to the business.

Why chatbots need feeding, not persuading

This is the part that matters most for anyone running a business. Large language models don’t work like traditional search engines. You cannot pay for a mention inside a ChatGPT or Gemini answer, and you cannot optimise a single web page and expect a citation to follow automatically. These models build their answers by drawing on what other credible sources are already saying about a brand or product, favouring content that is fresh, independently produced, and genuinely discussed rather than simply published.

John Lewis has grasped this. Gift List follows the same model as Waitrose’s Dish podcast, which the group has already used successfully to generate exactly this kind of third-party discussion. The studio isn’t there to make adverts. It’s there to manufacture the ongoing, citable conversation that AI systems rely on when they decide what to recommend.

This is the mechanism I describe to clients as the Chaff Effect: as more businesses compete for the same limited citation space, the brands producing genuine, timely, discussable content are the ones that keep getting picked, while the rest become background noise the models scroll past. John Lewis is positioning itself deliberately on the right side of that split, and doing it before most of its competitors have even noticed the shift is happening.

What this validates for everyone else

The interesting bit isn’t that a retailer has launched a podcast. Retailers launch podcasts all the time. The interesting bit is why, at board level, with a named presenter and a dedicated studio, and with the explicit reasoning stated in public.

John Lewis is a business built on caution and long-term thinking, not on chasing trends. When a brand with that reputation commits real budget to AI visibility, purely because the traffic and the sales it delivers can no longer be ignored, it stops being a niche marketing theory and becomes a board-level growth channel.

For smaller businesses, the lesson isn’t “build a studio”. It’s that the underlying principle applies at any scale. AI search rewards businesses that produce content worth citing: honest reviews, real demonstrations, genuine expert commentary, and material that answers the questions customers are actually asking an AI assistant. You don’t need Louis Theroux in the room to benefit from that same logic. You need to be present in the conversation before your competitors are.

John Lewis has just shown, very publicly, that the businesses treating AI search as an afterthought are the ones falling behind. The ones treating it as seriously as their Christmas advert are the ones about to pull ahead.

 

How Any Business Can Punch Above Its Weight in AI Search

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The car market example of accelerated Chinese EV adoption makes the point well because the numbers are so stark. Brands with no UK footprint two years ago now sit ahead of manufacturers with a century of history, partly because they built their AI presence properly while the established names were still marketing for a search and click world that’s mostly gone.

The good news for smaller or newer businesses is that the same mechanics work in every sector, not just cars. A brand with no legacy and a modest budget can be cited by an AI assistant ahead of a competitor with fifty years of reputation, if it does the groundwork the AI is actually checking for. Here’s what that groundwork looks like in practice.

Understand what the AI is actually checking

An AI assistant doesn’t rank pages the way a search engine used to. It builds an answer from sources it trusts, then cites the ones it used. Trust here isn’t about age or size, it’s about three things: whether your business is described consistently everywhere it appears online, whether other credible sites mention or reference you, and whether your own content is structured clearly enough for a model to lift a fact from it with confidence.

None of that requires a big marketing budget. It requires consistency and discipline.

Fix your entity data first

Before anything else, make sure your business name, description, address, services and key facts are identical everywhere: your website, Google Business Profile, LinkedIn, directories, press mentions, review sites. Inconsistency here is the single biggest reason AI systems either ignore a business or get basic facts about it wrong. This is unglamorous work and it’s exactly the kind of thing larger, older organisations tend to have let drift over the years, which is an opening for smaller ones.

Build for citation, not just for reading

Most business content is still written to be read by a human who lands on the page. Content built for AI citation needs to answer a specific question clearly, in a self-contained paragraph, with the key fact stated plainly near the top. Think of every page as something that might be lifted whole into someone else’s answer. If a competitor’s page buries the useful fact in paragraph six behind three paragraphs of introduction, and yours states it in the first two lines, yours is more likely to get used.

Earn third party mentions deliberately

AI systems weigh what other sites say about you more heavily than what you say about yourself. That means the old habit of chasing backlinks purely for search ranking is dated. Getting mentioned in trade press, directories, comparison sites, review platforms and partner websites builds the kind of external authority that gets a business named in an AI generated answer. A newer or smaller business that’s proactive about this can out-cite a much bigger name that’s relying on reputation alone.

Answer the questions your buyers are actually asking an AI

Think about what someone genuinely asks an AI assistant before they get in touch: best value for X, is Y worth it, how does Z compare. These are usually research stage questions, not “buy now” questions, and they’re exactly where AI Overviews and chat assistants intervene most. Build content around those specific questions rather than generic service pages, and make sure the answer is genuinely useful rather than thinly disguised sales copy.

Keep checking, because this moves fast

AI citation isn’t a one off project. Which sources get cited for a given query shifts as models update and as competitors catch up. Businesses that treat this as ongoing, checking regularly which queries they’re being cited for and where they’re being missed, stay ahead of those that treat it as a single fix and move on.

The real opportunity

The car market shift happened because newer brands treated AI visibility as seriously as product development, while established names assumed reputation would carry them. That assumption is what’s costing them.

For any business without a household name or decades of history, that’s the opening. The playing field for being trusted by an AI assistant is far more level than the playing field for being trusted by a human who’s heard of you for thirty years. Getting the entity data right, the content structured for citation, and the third party authority in place costs far less than building a brand the traditional way, and it can work far faster.

The businesses that get this right in the next year or two will be cited well ahead of competitors many times their size. The ones that don’t will find themselves in the same position as the legacy car makers: good product, real history, and increasingly invisible at the exact moment a buyer is deciding who to trust.

How AI Agents Are Rewriting Automotive Loyalty

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A century of brand equity is being outrun by a car company that most of Britain hadn’t heard of two years ago. That isn’t a provocation, it’s simply what the registration figures show. Chinese-owned brands now account for around 15 to 16% of new UK car registrations, up from a rounding error not long ago. BYD sold more cars in the UK in 2025 than Tesla did. Jaecoo went from 0.7% market share to over 3% in five months. The Chery Group, which had no UK presence before late 2024, reached a share milestone in eighteen months that took Kia twenty-five years to reach.

Consumer interest tells the same story from a different angle. Search volume for the seven leading Chinese marques nearly quadrupled year on year, growing roughly ten times faster than search interest in established brands.

Something has genuinely changed in how trust gets built, and I don’t think it’s the explanation most people reach for first.

It isn’t really “social media.” It’s proof, delivered faster than reputation used to travel.

The obvious answer is influencer culture: TikTok ‘unboxings’, YouTube reviewers, lifestyle content on Instagram. That’s real and it plays a part. But it sits on top of something more structural.

For most of the last century, a brand name stood in for information an individual buyer had no other way of getting. You couldn’t independently check a manufacturer’s build quality, resale value or five year reliability record, so you borrowed trust from the badge, from what your father drove, from the dealer that had been on the same forecourt for thirty years. Heritage did the job of due diligence, because proper due diligence wasn’t otherwise within reach.

That constraint has largely gone. Spec comparisons, owner forums, warranty terms and real running costs are now easy to line up side by side. Once the facts are visible and checkable, the badge does less of the work. A new brand doesn’t need eighty years of dealer trust if it can put verifiable proof, video, data, owner testimony, in front of a buyer within a few minutes of scrolling.

This shows up most clearly at the affordable, high spec end of the market, sold on PCP and pay monthly finance to buyers who were never particularly loyal to a badge to begin with. Autotrader’s own figures show new entrant demand is being driven by spec and value rather than rock bottom pricing. These buyers aren’t trading down, they’re trading sideways, getting more equipment for the same monthly payment from a brand with no history behind it. For someone weighing up a finance deal rather than an outright purchase, affordability plus visible proof of quality tends to beat a heritage they never felt any loyalty to in the first place.

The layer most of the industry hasn’t caught up with: the AI is deciding who gets a mention

Here is the part I think most dealers and legacy marketing teams haven’t fully registered yet, and it’s the part I work inside every day.

Around a quarter of new car buyers already use AI tools such as Google AI Overviews, ChatGPT or Claude somewhere in their research, and the large majority of those buyers expect AI to shape car buying even further in future. In the UK, well over half of shoppers who saw an AI Overview during their research said it gave them genuinely useful information. These aren’t marginal figures and research into AI generated car buying answers points to something buyers rarely notice consciously: the AI names specific brands in almost every response, whether the shopper asked for one or not.

That’s the bit worth dwelling on. When someone asks an AI assistant “best value family SUV under £30k” or “is a plug in hybrid worth it right now,” the system doesn’t hand back a neutral list. It writes a synthesised answer and cites sources, and it has already decided, on the buyer’s behalf, which brands are worth including. If a legacy manufacturer’s content, structured data and third party mentions aren’t built with that retrieval process in mind, the brand can simply be left out of the answer. Not ranked lower, not on page two, just missing from the one summary the buyer actually reads.

This is exactly where the newer brands have done their homework. Companies built in the last few years, in a market where digital first discovery was already normal, have generally been far more disciplined about the things AI systems check before citing a source: consistent entity data across the web, third party mentions that build authority, structured comparison content, and a visible footprint across the review and forum sites that large language models draw on. Legacy manufacturers, by contrast, are often still marketing for a search and click world that AI Overviews have already replaced in large part. In categories where AI Overviews have rolled out, more than 80% of searches now end without a single click through to a website. If your content strategy assumes someone will land on your page, and increasingly no one does, your actual signal to the AI is much weaker than your dealer network or heritage would suggest.

Put simply, legacy brands built trust through decades of visible, physical presence. New entrants are building trust through a different kind of presence, one engineered specifically for the systems that now sit between a buyer and their decision.

The uncomfortable question for established brands

If an AI Overview is the moment that narrows a shopper’s shortlist, and your brand isn’t cited in it, your heritage never gets the chance to matter. You aren’t losing on trust. You’re losing on visibility to the thing that now decides what gets trusted in the first place.

For a younger buyer financing a high spec car on a monthly plan, that AI generated shortlist may be the entire research journey before they ever set foot in a showroom. If your brand isn’t on it, a century of reputation counts for nothing, because it was never given the chance to be weighed.

The brands taking market share right now aren’t necessarily building better cars. Plenty of them are good, but so are many of the legacy alternatives they’re displacing. What they’ve done differently is treat AI visibility as seriously as product development, building the entity signals, third party authority and structured proof that get a brand named by the system a growing number of buyers now trust to do their first round of vetting for them.

Badge trust took a century to build. It’s being overtaken by brands that worked out, in about eighteen months, how the new gatekeeper actually makes its decisions.

Simple Google Search Console Set Up Guide

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How To Set Up Google Search Console (GSC): A Step-by-Step Guide

If you own a website, Google Search Console (GSC) is one of the most useful free tools available to you, and one that many site owners never set up properly. It’s Google’s direct line of communication about how your site performs in search: what’s indexed, what’s being clicked, and what’s broken.

This guide walks through setting it up properly, from choosing the right property type to knowing when you’ll actually see data.

What Is Google Search Console?

Google Search Console is a free tool from Google that shows you how your website performs in Google Search. It tells you which pages are indexed, which search queries bring people to your site, what your click-through rates look like, and whether Google has flagged any technical problems, from broken links to mobile usability issues to security warnings.

If you’re serious about SEO, or about how AI search engines and answer engines find and cite your content, GSC is where you start. You can’t optimise what you can’t measure.

Step 1: Sign In With a Google Account

Head to search.google.com/search-console and sign in with a Google account. Use a business account if you have one, or one that several team members can access. You want this to be a shared, permanent login, not tied to one person who might leave the company down the line.

Step 2: Choose a Property Type

This is the part that trips people up, so it’s worth slowing down here. When you click “Add Property,” GSC gives you two options.

Option 1: Domain Property (the more thorough option)

A Domain property covers everything under your domain: all subdomains (www, blog, shop, and so on) and both protocols (http and https) in one view. It’s the most complete way to monitor your site.

The catch is verification. A Domain property requires DNS record verification, meaning you need to log into your domain registrar (GoDaddy, 123 Reg, Cloudflare, etc.) and add a TXT record. It’s more powerful, but it’s an extra technical step that not everyone will be comfortable doing on their own.

Option 2: URL-Prefix Property (the simple option)

This is the one most people should start with. A URL-prefix property covers just the exact URL you enter, for example https://www.yourwebsite.com/. It doesn’t automatically include other versions, such as the non-www version or your subdomains.

Verification is much easier here. You can verify using:

  • An HTML file uploaded to your website
  • An HTML tag added to your homepage code
  • Your Google Analytics or Google Tag Manager account, if already installed
  • Your domain name provider, as an alternative

For most small business owners, bloggers, and anyone managing a single, straightforward website, a URL-prefix property with the “Add URL only” approach is the fastest way in. Add your homepage URL, verify with whichever method suits your setup (Google Analytics is usually the quickest if it’s already installed), and you’re live.

Start with a URL-prefix property to get moving today. You can add a Domain property alongside it later, once you’re comfortable, to get the fuller picture across all subdomains and protocols.

Step 3: Verify Ownership

Once you’ve entered your property, GSC will ask you to prove you own the site. Pick whichever verification method suits your setup:

  • If Google Analytics is already installed on your site, this is usually the quickest option.
  • If not, downloading the small HTML verification file and uploading it to your site’s root directory is the next easiest route.
  • Your web developer or website platform (WordPress, Shopify, Squarespace, Wix) will often have a simple field for pasting the verification code in directly.

Click “Verify” once it’s in place.

Step 4: Submit Your Sitemap

This step is easy to skip, and you shouldn’t. A sitemap is a map of your website that tells Google which pages exist and where to find them, which speeds up discovery and indexing considerably.

  1. In the left-hand menu, click Sitemaps.
  2. Enter the path to your sitemap file, usually something like sitemap.xml (most website platforms generate this automatically; check yours at yourwebsite.com/sitemap.xml to confirm).
  3. Click Submit.

If you’re not sure whether your site has a sitemap, most modern CMS platforms (WordPress with Yoast or Rank Math, Shopify, Squarespace) generate one automatically. If you’re on a custom-built site, ask your developer to confirm the sitemap URL.

Step 5: Be Patient. Data Takes Time

This is the step people find hardest: nothing happens instantly.

Once you’ve verified your property and submitted your sitemap, here’s roughly what to expect:

  • Indexing: Google typically starts crawling and indexing your submitted pages within a few days, though a full crawl can take up to a couple of weeks, especially on larger or newer sites.
  • Performance data: the Performance report (clicks, impressions, average position) usually starts populating within 2 to 3 days, though it can take up to 48 hours minimum before you see anything at all.
  • A full, reliable picture: realistically, allow 2 to 4 weeks before you have enough data to draw meaningful conclusions. Search Console also only shows a rolling 16 months of history going forward, so the earlier you set this up, the more historical data you’ll have to work with a year from now.

If the dashboard looks empty on day one, that’s expected. Google is gradually building its picture of your site.

Quick Recap

  1. Sign in at search.google.com/search-console
  2. Choose your property type: URL-prefix with “Add URL only” is the simplest starting point; Domain property is more complete but technically involved
  3. Verify ownership using whichever method matches your setup
  4. Submit your sitemap under the Sitemaps section
  5. Wait. Expect first data within a few days, and a genuinely useful picture within 2 to 4 weeks

Once GSC is live and collecting data, you’ve got a first-party window into how Google sees your site, which is the foundation everything else in SEO is built on.

 

OPTIMUM & Supporting AI Search Research Study

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OPTIMUM and AI Search, Today and Tomorrow: A Framework and a Body of Research, Built to Work Together

State of the Art Digital is pleased to note the growing International recognition for two connected pieces of work: the OPTIMUM audit framework and the flagship research study AI Search, Today and Tomorrow.

OPTIMUM gives businesses a rigorous, seven-dimension diagnostic of their AI search visibility, scoring where they stand now, where they could stand, and the gap between the two – the improvement Delta. AI Search, Today and Tomorrow is the evidence underneath it: a consolidated ongoing study tracking how generative engines are reshaping discovery, recommendation and trust online.

One maps the terrain. The other tells you exactly where you sit on it, and what to do about it. Use them together and AI SEO/GEO stops being guesswork and becomes something you can actually measure, argue for, and act on.

If you’re a business owner wondering whether AI engines can even find you, this is where to start.

State of the Art Digital | AI Search Consultancy

AI SEARCH: Google PPC ROI Going Backwards? Read On.

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Google Is Eating Its Own PPC Business. Here’s Where the Budget Should Go Instead.

For twenty years the deal was simple. Google gave you the organic result for free and sold you the paid one next to it. Two products, one page, no conflict of interest.

That deal is breaking down. AI Overviews now sit above both. They answer the query before the user reaches either the organic listing or the ad. Google has built a product that competes with its own advertisers, and the data on how badly is now solid enough to act on.

The numbers advertisers need to see

Paid click-through rate on queries where an AI Overview appears has collapsed. Independent tracking across thousands of queries found paid CTR falling from 19.70% to 6.34% once an AI Overview took the top of the page, a drop of more than two thirds. Queries without an AI Overview saw nowhere near that decline.

At the same time, cost per click has gone the other way. Average Google Ads search CPC reached $2.96 in Q1 2026, up 12% year on year, the steepest rise since 2021. Three forces are driving it: AI Overviews compressing organic click volume by 8 to 12%, which pushes displaced traffic into the paid auction; Performance Max expanding into more inventory and intensifying competition for it; and Smart Bidding escalation, where every advertiser’s algorithm chases the same efficient frontier at once.

Put the two trends together and the picture is unambiguous. Advertisers are paying more for a placement that converts less, because the AI layer above it is absorbing the click before the ad is ever seen.

This is cannibalisation, not disruption

Disruption implies an outside force changing the market. This is Google restructuring its own page to prioritise a product it fully controls, at the expense of a revenue line it also fully controls. The AI Overview is not a neutral feature sitting between organic and paid. It is a third product competing with both, built by the same company that sells the other two.

Performance Max makes the squeeze worse because it removes advertiser control at the exact moment control matters most. There are no keyword lists, no placement level budgets, no manual bid adjustments. You feed it assets and a goal, and it chases inventory automatically across Search, Display, YouTube, Gmail, Discover, Maps and now Waze. Every new surface Google adds becomes something PMax bids into without asking you first. That is inventory expansion working exactly as designed. It is also auction pressure you did not choose and cannot switch off.

Why the fix isn’t “spend more on PPC”

The instinctive response to falling CTR is to bid harder to hold position. That response is now actively unprofitable. You are bidding into a shrinking pool of clicks that survive the AI Overview, at a price inflated by every other advertiser doing the same thing. It is spend chasing a contracting opportunity, not a stable one.

The more useful question is where the AI Overview’s traffic actually goes. It does not vanish. It goes to whichever source the AI Overview or the AI App result cites. Brands cited inside an AI Overview earn measurably more of the residual organic clicks than brands mentioned nearby but not cited. The click that used to be won with a bid is now won, or lost, before the auction even happens, based on whether your content was structured well enough to be pulled into the answer.

This is the mechanism the OPTIMUM framework has been built around since 2024. Tier 1 is the technical and structural foundation that makes a page machine-readable and citation-eligible in the first place, structured data, entity clarity, crawlable architecture. Tier 2 is the citation-cluster layer built on top of it, content and product pages architected around the fan-out queries an AI system actually generates when it decomposes a user’s question. A business with weak Tier 1 is invisible to the citation engine regardless of PPC spend. A business with strong Tier 1 and no Tier 2 is citable but thin, present without authority.

Where the budget should actually go

The case for reallocating a portion of PPC spend toward AI-optimised website and product page architecture is not a hedge. It is arithmetic.

PPC now buys a shrinking, increasingly expensive slice of clicks that survive an AI Overview. Citation-cluster investment buys presence inside the AI Overview and the AI App result itself, the layer that decides who gets seen before any auction takes place. One is renting a diminishing asset. The other is building a durable one, since citation eligibility, once structurally embedded, keeps compounding across every future query in that cluster without an incremental cost per click.

The businesses already ahead on this split tend to be the larger operators with the resource to run both tracks simultaneously, approaching what the framework calls Epoch 2, where every competitor has completed Tier 1 and competitive advantage shifts entirely to Tier 2 execution. Smaller operators are still trying to out-bid a machine that has already decided not to show their ad to most of the people searching. That is not a strategy. It is a losing bet against an opponent who has already changed the rules.

The practical allocation

This is not an argument for abandoning PPC. High-intent, bottom-funnel commercial queries where no AI Overview appears remain a legitimate paid channel, and will for some time. The argument is for stopping the reflex of defending top-of-funnel and informational query positions with bid increases, when those are precisely the queries AI Overviews have already absorbed and are not coming back.

A sensible reallocation moves budget away from defending CTR on AI-Overview-saturated queries and into citation-cluster sitemap architecture, structured data completion, and entity clarity work, the technical groundwork that determines whether a business is even eligible to be cited when the AI Overview forms its answer. Spend on being the answer, not on renting the space next to it.

Google built the auction. It also built the thing that is quietly emptying it. The only rational response is to stop paying to be seen in a place fewer people are looking, and start building to be cited in the place they are.

Steve Coulter is the founder of State of the Art Digital, an independent AI search visibility consultancy built around the OPTIMUM framework for AI citation readiness. He has been working in search since 1999, with a sales and marketing background spanning senior roles at Renault UK and automotive industry management before moving into independent consultancy in late 2024. His work focuses on automotive retail and estate agency, with a growing client base across financial services, publishing and other UK SME sectors. ‘AI Search Today and Tomorrow’ is a three part thesis which anchors published research and AI Search SaaS products.

stevecoulter.co.uk | steve@stevecoulter.co.uk | +44 (0)7407 038877

AUTOMOTIVE: AI Search – Shock Therapy

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The third in a series of three articles explaining how the retail automotive industry appears unaware and wholly unprepared for the paradigm shift in search from SERPS results to AI Summaries, and how dealers are not preparing for something already underway they are mostly unaware of. Read about a future where your business is left with only the poorest enquiries.


Outside the citation pool: what happens to the dealers AI stops mentioning

A buyer in Wolverhampton asks ChatGPT which dealer to use for a used Golf. Somewhere on a nearby high street sits a dealership that has traded for thirty years, sponsors the local football club, and has a reputation built up over three decades of word of mouth. ChatGPT doesn’t mention them. Not ranked lower. Not on page two. Simply absent from the answer, as though they don’t exist.

This is already happening. It will happen more often, to more dealers, in less time than most people in this industry think.

The Chaff Effect, in numbers rather than theory

The wider thesis behind this shift is the Chaff Effect: as AI systems answer more buyer questions directly, organic traffic to any individual dealer website falls, and what does arrive skews towards the buyers AI couldn’t confidently place elsewhere.

Play that forward and the outcome is uglier than a simple decline in visitors. The buyers still landing on a dealer’s own site are disproportionately price shoppers, tyre-kickers and people outside the dealer’s actual catchment, the enquiries AI wasn’t confident enough to resolve on its own. The buyers AI is confident about, the ones ready to commit, go straight to whichever dealer got named. No shopping around. No comparison. One dealer gets the sale before the buyer has spoken to anyone.

The dealer who wasn’t cited never gets the chance to make the case, because there was no shortlist. There was one name.

This compounds. Fewer conversions mean less marketing spend, which weakens exactly the signals that would have earned citation next quarter. A dealer outside the pool doesn’t stay in a stable, disadvantaged position. They drift further out, and the gap gets harder to close the longer it’s left.

Two dealers, same forecourt, different outcomes

Picture two dealerships eighteen months from now. Same stock levels, same staff, same marque, same town. One is cited consistently across ChatGPT, Google’s AI Overviews and Perplexity whenever a buyer in their area asks who to trust. The other is cited by none of them.

The first dealer sees enquiry volume hold or grow, with leads that already trust the dealership before a single call is made. Cost per sale falls, because trust was established before the buyer ever engaged. The second dealer sees a slow decline in enquiries that doesn’t show up as a single alarming drop, just a gradual thinning that’s hard to pin on any one cause.

That’s the part that should genuinely worry dealers. A falling Google ranking shows up in Search Console with a clear before and after. Falling out of AI citation shows up as nothing. There’s no dashboard alert that reads “AI stopped recommending you.” The dealer simply sees fewer enquiries, assumes it’s the market, and carries on doing what they were doing, unaware that the actual cause is structural and getting worse.

Why your website provider can’t fix this at the pace required

This is not a once-a-year update. AI platforms change crawler behaviour, citation criteria and schema expectations on a rolling basis, closer to continuous than annual. A platform provider running template updates twice a year, built for hundreds of dealers on the same underlying system, cannot track and respond to that pace, and most aren’t trying to.

The honest question is whether dealers can keep up with this on their own. Mostly, no. Not without treating it as an ongoing operational discipline rather than a website feature ticked off once and forgotten.

What your provider should be offering, and probably isn’t

Hold your website provider to this list. If none of these are on offer as a standing service rather than a one-off project, you’ve been sold a website built for a web that no longer exists.

  • Ongoing schema audits, not a single implementation project
  • Citation monitoring across ChatGPT, Google AI Overviews, Perplexity and Gemini as a continuous service
  • llms.txt and crawler configuration maintained as platform rules change
  • Entity consistency monitoring across third-party directories, not just the dealer’s own site aka ‘uncorroborated claims’
  • Quarterly reporting on AI citation share against named local competitors, not just organic traffic figures
  • Talking to you about how AI Agents answer questions for prospects without visiting your website – The Zero Click Effect.

The deadline that isn’t abstract

Search doomsday, the point at which AI answers overtake traditional click-through search as the default buyer journey, is projected for Q3 2027. That’s roughly a year away.

The dealers doing this work now are building a citation history that compounds in their favour. The dealers who wait until 2027 to start will be trying to establish trust signals in a market that has already decided who the trusted names are. By then, the citation pool isn’t open for new entrants in the way it is today. It’s a list that gets harder to join the longer it’s already been settled.

The question worth asking isn’t whether AI search will affect car sales. It already does. The question is whether your dealership is inside that pool or outside it, and whether anyone at your business would currently be able to tell you which.

‘Your Business Is Becoming Invisible’, ‘Search Doomsday’ and ‘The Chaff Effect’ are three reports detailing AI Search today and its effect on enquiries in the near future.

State of the Art Digital works with automotive retailers on the OPTIMUM Seven Dimension AI Summary and Citation Audit. Contact steve@stevecoulter.co.uk or +44 (0)7407 038877.

 

RETAIL AUTOMOTIVE: An Industry Unprepared

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The second in a three part series explaining how the retail Automotive industry appears unaware of the paradigm shift in AI search summaries, citation and search behaviour – and are not implementing change to accomodate a shift they are mostly unaware of.

Why your dealer website provider isn’t ready for AI search, and why that’s your problem too

Every dealer website says roughly the same thing. Decades of trading. Manufacturer approved. Trusted by thousands of customers. None of that means anything to an AI agent unless it can be corroborated.

This is the part of AI search that automotive retail hasn’t caught up with yet. ChatGPT, Google’s AI Overviews and Perplexity don’t take a dealer’s word for its own expertise, trust or authority. They cross-reference it. A claim only counts if it’s backed by structured data, consistent entity information and verifiable signals scattered across the dealer’s own site and the wider web. Say it without the backing, and an AI model simply won’t repeat it, or worse, just won’t mention the dealer at all.

Corroboration, not copywriting

Most dealers still think of trust signals as a writing problem: get the tone right, mention the years in business, add a testimonials page. AI agents work differently. They check whether a claim is structurally supported, not whether it reads well.

A dealer stating compliance, regulatory or association membership needs that claim reflected consistently across the FCA and Companies House records, the Google Business Profile, manufacturer directories and the dealer’s own site, all pointing to the same verifiable entity. A dealer claiming forty years of trading needs that history to show up somewhere an AI model can check it, not just as a line on the About page. This is closer to infrastructure than marketing, and it sits squarely in technical SEO territory, which is exactly where most dealer sites are weakest. Critically, making claims that cannot be corroborated by an AI Agent will dramatically reduce the likelihood of an AI mention or citation.

Where dealer website providers are behind

Dealer platform providers built their systems for a different web. Fast stock feeds, finance calculators, lead capture forms. That’s what dealers have been sold, and it’s what most providers still optimise for.

Structured data on these platforms is typically limited to basic vehicle schema, enough to get a car listing showing correctly in a regular search result. Beyond that, the gaps are consistent across the sector: no proper Organization schema tied to a verifiable entity, no Person schema for staff or specialists, Review and AggregateRating markup either missing or poorly implemented, LocalBusiness data that’s inconsistent across branch pages and Google Business Page, and no FAQPage schema answering the actual questions buyers now put to ChatGPT rather than search – or are intercepted and answered in an AI Overview generated by Google and sitting above the results page. Never mind ensuring content is not generic and is so-called non-commodity. AI Agents tend to cite based on the content of the top 30% of a web page, particularly the first 10%, also if the page fans out with follow up questions. Then duplicated in the machine-readable code. These concepts are virtually non-existent in automotive.

A platform built to serve hundreds of dealers from one template cannot produce dealer-specific authority, because authority is not a template feature. It comes from a dealer’s own history, staff and reputation, and a generic site simply has nowhere to put that.

The signals AI agents are actually checking

The OPTIMUM Seven Dimension AI Summary and Citation Audit exists precisely because these signals need to be checked individually, not assumed. The dimensions covering technical readiness and schema implementation look at entity consistency across every external reference to the dealer, citation density on trusted third-party sources, staff and authorship credibility, review authenticity and volume, robots.txt and llms.txt configuration that isn’t accidentally blocking AI crawlers, and structured data depth on every page, not just the homepage.

Most dealer sites fail several of these dimensions without anyone noticing, because the site still looks fine to a human visitor. The problem is invisible until it’s tested against how an AI agent actually reads the page.

This is a bigger job than the industry has clocked

The honest assessment is that automotive retail is not ready for this. Fixing vehicle schema is an afternoon’s work for a competent developer. Building genuine entity consistency, credible authorship signals and page-level structured data across an entire dealer site, and keeping it that way as stock, staff and locations change, is an ongoing technical and editorial commitment. Most dealer groups have neither budgeted for it nor assigned anyone to own it, and most platform providers are treating it as a features list item rather than the structural rebuild it actually is.

That gap matters because the timeline is short. The wider thesis behind this shift, ‘Search Doomsday’, points to Q3 2027 as the point where AI answers overtake traditional click-through search as the default buyer journey. That is not a distant horizon. It’s roughly a year away, and the work required here is not the kind that gets done in a sprint. It will affect any industry where informational search queries are the first part of a prospect’s journey.

What the dealer actually has to do

None of this can be outsourced entirely to a platform provider, however good the provider is. The dealer is the entity being corroborated, so the dealer has to own the consistency of that entity everywhere it appears. That means auditing what currently exists, fixing trust and schema gaps page by page, and treating technical SEO as a live discipline rather than a one-off build.

An OPTIMUM Citation Gap Analysis is the starting point for any dealer wanting to know where they currently stand, rather than assuming their website provider has this covered. Most haven’t.

State of the Art Digital works with automotive retailers on the OPTIMUM Seven Dimension AI Summary and Citation Audit. Contact steve@stevecoulter.co.uk or +44 (0)7407 038877.

AI Search Doomsday: Don’t Look Up!

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Ever seen the film Don’t Look Up? A black comedy drama which portrays a group of astronomers (including; Leo DiCaprio & Jennifer Lawrence)  who accidentally notice a distant asteroid is on a collision course with Earth that might be diverted – but fail to get their warning across to a sceptical world and leaders. Inevitably civilisation ends. There’s a lot of running around near the end.

AI Search Doomsday Q3 2027 won’t end the world, but my detailed and peer reviewed research suggests it could end or do serious damage SME and MME businesses who are not already working on increasing the likelihood of their web pages’ AI Summary inclusion and citation or appearing on AI App outputs. Few are being assisted by the website provider industry – the reluctant gatekeepers of change

The people who I have spent time with over the last three months are truly the outliers and thinkers in industry. They have noticed an unprecedented and difficult to interpret drop in Click Through Rates from vital search phrases and/or recognise the threat AI Search poses to their industry. We have a remedies underway. Where is your business on this paradigm shift?

My Linked-In post today.

In late 2024 I started digging into a question most business owners haven’t asked yet:

What happens to search when AI stops sending clicks to websites and just answers the question itself.

The authoritative and published data I’ve been tracking since is stark. Zero-click searches have gone from 56% to 69% of all Google queries in a year. When an AI Overview appears, that jumps to 83%. Organic click-through rates on those queries are down 61%. HubSpot, one of the best SEO operations in the world, lost 70 to 80% of its organic traffic in under 12 months, while its rankings barely moved.

That’s the part people are starting to notice. What I’ve spent the last year and a half researching is what it actually means for a business, and I’ve written it up across three reports which are all now published.

– Your Business Is Becoming Invisible sets out the diagnosis. Ranking and being cited by AI are now two separate problems. Across 20 UK automotive and estate agency audits I ran, the average AI readiness score was 5.5 out of 10, and not one of those businesses knew it.

– Search Doomsday Is Q3 2027 puts a date on it. AI is currently intercepting around 17% of the clicks that would once have gone to a website. Every data source I’ve reviewed converges on the same figure: 40% by Q3 2027. That is the point where the question changes from “do we rank?” to “do we get cited?”

– The Chaff Effect is the sharpest and damning finding of the three. Clicks are not disappearing evenly. AI resolves informational, early-stage searches 74% of the time, but only 31% of transactional ones. Businesses are losing the awareness-stage traffic that used to feed everything else, while the pool that is left quietly runs dry behind them.

Next week I’m releasing a benchmark that lets any SME or MME leader, and the marketers working for them, see exactly where they stand against this shift, and what to do about it before the inflection point arrives.

If your enquiries have been sliding while your rankings look fine, this is why.

More next week.

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Feel free to contact me if you are the director or employee in the business pushing to ensure yours is included in AI Summaries, Citations and on AI Apps.

AI Search: The Six Vital Content Traits For Citation

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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: Free Report. Your Business Is Becoming Invisible

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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.

Your Business Is Becoming Invisible AI SEO Report

AI Search: The Entity Identity Problem

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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?

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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.