Website Development

State of the Art’s OPTIMUM v2.4 v GEO Agencies

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Before you sign a GEO retainer, know what you are paying to fix

Most UK GEO agencies sell you a retainer. I think you should buy the diagnosis first.

GEO, or generative engine optimisation, is the work of getting your business named and cited when people ask ChatGPT, Gemini, Claude or Perplexity for a recommendation. Before you pay anyone to do that work, you need to know why AI engines are not citing you now. That is what OPTIMUM v2.4 is for. It scores your site, shows the evidence behind each score, and gives you a report you can hand to any developer or agency.

This post compares that approach with how UK GEO agencies typically sell. It includes a ready reckoner you can keep, and it says plainly where an agency is the better choice.

How UK GEO agencies sell

UK agencies usually sell a one-off audit, then a monthly retainer. Published UK price guides put the typical retainer at £2,000 to £5,000 a month, with audit or onboarding fees of £1,500 to £5,000 on top. One London guide gives £2,000 to £4,000 for entry-level work and £5,000 to £12,000 or more for advanced programmes.

Few agencies publish prices. In one October 2026 comparison of 20 UK agencies, most said request a quote and a handful showed a monthly fee. The published fees ranged from about £450 to £5,950 a month plus VAT, depending on what they covered. Narrow packages, such as a few tracked prompts or off-site outreach only, sat at the bottom. Broad programmes with content, technical work and PR sat at the top. Minimum terms ran from one month’s notice to twelve months.

These fees buy different work, so they are not like for like. Many agencies also run their own AI tracking tool, so each measures visibility in its own way.

Where agency offers leave gaps

The buyer guides that agencies publish themselves point to the same weak spots, and OPTIMUM is built around them.

  • No standard scope. Two fees are not comparable without the scope behind them. OPTIMUM has a published rate card and a defined set of deliverables.
  • Each agency measures its own way. A higher visibility score can simply reflect a different sample of questions. OPTIMUM uses one 0 to 10 rubric and a dated Evidence Log, so scores can be compared over time.
  • SEO with a new label. Several guides warn that some agencies repackage SEO as GEO. A fixed audit lets you test any proposal against the same standard.
  • The audit is commonly a baseline before a retainer. The OPTIMUM report stands alone and can be taken to any supplier.
  • Sector depth is uneven. Most agencies work across sectors. OPTIMUM carries frameworks for automotive retail, estate agency, fintech, health tech, accountancy and professional services, and for other industries that rely heavily on website clicks.

The ready reckoner

The table below sets a typical UK GEO agency beside OPTIMUM v2.4, row by row. Keep it, print it, or take it into your next supplier meeting.

  Typical UK GEO agency OPTIMUM v2.4
Entry offer Audit or onboarding at £1,500 to £5,000, then a retainer Audit £700 (£350 introductory), two-hour discovery included
Ongoing work Retainer, typically £2,000 to £5,000 a month Advice at £350 a day or £50 an hour. Monthly Citation Watch POA
Price visibility Most say request a quote Published rate card, no VAT
Scoring The agency’s own tracker and metrics Fixed 0 to 10 rubric. Now Score, Future Score and Delta
Evidence Reports from the agency’s tracker Dated Evidence Log behind every score
Who fixes it The agency Your developers or agency, using the report as the brief
Third parties Often bundled into the retainer Flagged in the report. Caps the Future Score
Compliance Varies GDPR and crawler permission checks in every audit
Founder visibility Varies Checked in every audit
Sector depth Mostly cross-sector Automotive retail, estate agency, fintech, health tech, accountancy, professional services. Other industries heavily reliant on website clicks
Commitment One month’s notice to twelve months Fixed-fee audit, then advice by the day if wanted

Agency figures come from published UK GEO price guides, October 2026. OPTIMUM prices come from the State of the Art Digital rate card dated 19 August 2026, with no VAT. POA means price on application.

What OPTIMUM v2.4 gives you

  • A low cost of entry. The audit is £700, or £350 introductory for early clients, and includes a two-hour discovery meeting. That is well below the £1,500 to £5,000 agencies charge for an audit or onboarding.
  • A fixed score. Every dimension is scored 0 to 10 on one rubric, with a Now Score, a Future Score and the Delta between them.
  • Evidence behind every score. A dated Evidence Log shows how each number was reached, so a re-test is like for like.
  • Honesty about what you do not control. Where a gain depends on a third party, the Future Score is capped. The report does not promise results nobody can guarantee.
  • Compliance and person-level checks. GDPR status, crawler permissions and founder visibility are checked in every audit.
  • A brief you can reuse. Buyer guides advise sending every shortlisted agency the same brief. The OPTIMUM report is that brief, with scores to hold each proposal against.
  • Advice without a retainer. Day-rate advice is £350 a day or £50 an hour, on a two-hour minimum. Compare that with £2,000 to £5,000 a month.

If you want more, the next steps are priced on application: the Citation-Cluster Sitemap Blueprint, Build Oversight and Certification, and Monthly Citation Watch.

Where agencies are the better choice

To be fair, agencies win on delivery.

  • Delivery capacity. Many agencies bundle digital PR, content and technical work. OPTIMUM designs the work. It does not write pages or place coverage at volume.
  • Scale and reach. Larger agencies field big teams with multi-language support, which suits multi-market brands.
  • Joined-up channels. Some agencies coordinate GEO with paid search and paid social under one plan.
  • Continuous tracking. Many run in-house trackers that monitor answers all month. OPTIMUM re-tests on a schedule and relies on consultant time.
  • Small, narrow entry packages. Some start at a few hundred pounds a month plus VAT for tightly defined work.

Which option fits which situation

Your situation Best fit
You do not know why AI engines ignore you and want a plan before spending OPTIMUM audit
You have developers or an agency but no brief OPTIMUM audit as the brief
You need content and PR delivered at volume An agency retainer, scoped using an OPTIMUM audit and judged against its scores
Enterprise, multi-language, or GEO joined to paid media A larger agency, with an OPTIMUM audit as the baseline
Tight budget and a narrow need An OPTIMUM-LITE snapshot from £75 introductory, or a small agency entry package

The two work together. OPTIMUM diagnoses and an agency delivers. It should tell you what that team is being paid to fix.

So before you sign a GEO retainer, ask yourself: do you know what you are paying to fix?

 

Questions people ask about GEO retainers

How much does a GEO retainer cost in the UK?

One UK price guide puts entry-level programmes at £800 to £2,000 a month, most small and medium businesses at £2,000 to £5,000, competitive B2B work at £5,000 to £10,000, and enterprise programmes above that. Audit or onboarding fees of £1,500 to £5,000 are common on top. Fees buy different scopes, so compare what is included before you compare prices.

What does a GEO audit include?

A typical GEO audit reviews how AI engines describe your business, how your content is structured, your technical setup, and the authority signals that influence citations. It should end in a prioritised list of fixes. An OPTIMUM v2.4 audit scores seven dimensions, backs each score with a dated Evidence Log, and includes a two-hour discovery meeting and a written report.

What is the difference between GEO and SEO?

SEO aims to rank your pages in search results. GEO aims to get your business named and cited inside AI answers on tools such as ChatGPT, Gemini, Claude and Perplexity. The two overlap, and strong SEO foundations make GEO easier, but the measures of success differ.

How can I tell whether an agency is selling SEO with a new label?

Look for four warning signs: no baseline of how AI engines describe you today, no tracking of AI questions, reporting that covers only Google rankings, and a very low price for what is called full GEO. One UK price guide suggests that well under £1,500 a month usually means rebranded SEO. Ask which questions and which AI engines will be tracked, and how often.

Can anyone guarantee that I will appear in ChatGPT?

No one controls what AI engines say, so a guarantee of specific answers should end the conversation. Some agencies offer conditional guarantees on a defined measure. Check the metric, the eligibility, the period and the remedy. OPTIMUM takes the cautious route: where a gain depends on a third party, the Future Score is capped.

How long does GEO take to show results?

UK sources vary. Some report early signs within a few weeks, with more consistent movement over two to three months. Work that depends on third-party coverage usually takes longer. Treat any promise of dramatic change in weeks with caution.

How do I measure whether GEO is working?

Track a fixed set of buyer questions every month. Record whether you are mentioned, whether you are recommended, and whether your own website is cited, and compare that with your main competitors. Add AI referral traffic in your analytics and, most importantly, enquiries. OPTIMUM re-tests against the same Evidence Log, so the before and after are like for like.

Do I need an agency, or can my own team do the work?

It depends on who can carry out the fixes. If you have developers and writers waiting for a brief, a diagnostic report may be enough. If nobody has the capacity, you need delivery, and an agency is the better fit. Either way, work out the gap before you buy.

What should I ask an agency before I sign?

  • Can you show me how AI engines describe my business and my competitors today?
  • Which questions and which AI engines will you track, and how often?
  • What does the fee include: strategy, content, technical work, PR, tools?
  • How does this fit with our existing SEO?
  • What results have you achieved for businesses like ours, and how were they measured?
  • What is the minimum term, and what happens if we stop?

Is there a cheaper way to find out where I stand?

Yes. OPTIMUM-LITE has two snapshot diagnostics. The AI Readiness Diagnostic reviews how ready your website is for AI engines. The AI Citation Diagnostic runs 12 prompts across three AI apps: ChatGPT, Claude and Gemini. Each is £150, or £75 introductory for early clients.

Which industries is OPTIMUM built for?

Automotive retail, estate agency, fintech, health tech, accountancy and professional services, plus other industries that rely heavily on website clicks for business.

Stop Searching Yourself in ChatGPT

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AI consultants or ‘the loudest voice’, ‘aka the new industry expert’ in a specific business forum seem to be telling business owners the same thing right now.

“Open ChatGPT, type your business name, see what comes back.”

Half the time nothing does, and the point of the exercise is apparently to make you feel bad about that.

It’s a lame party trick dressed up as insight.

It tells you nothing about why you’re missing, and even less about what to do next. It just produces a bit of dread, which is handy if you’re about to sell someone a fix or look clever.

Here’s what’s actually going on underneath. Old-school SEO worked on a fairly crude logic: say the right words often enough, in the right places, and Google would eventually decide you deserved to rank. Keywords, backlinks, meta descriptions, all of it built around repetition being treated as proof. Type it enough times and the machine believes you.

AI search agents don’t work like that. They’re not hunting for the phrase that matches the query, they’re trying to work out whether the things being said about you actually check out. Does your own website agree with what your Google listing says, agree with what a review site says, agree with what a directory says? Are other people online independently backing up the claim, rather than you just repeating it about yourself in twenty different places?

That’s a completely different game. It has more in common with how a journalist stands up a story than with how a search engine used to rank a page. One source saying something is a tip. Three unconnected sources saying the same thing is closer to a fact.

Which is why, if you did get cited somewhere by an AI tool this week, I wouldn’t get too excited about it. It might have nothing to do with anything you’ve done. A particular query happened to fan out in a particular direction, and a page of yours happened to line up with something else on the web that day. That’s luck, not a strategy, and luck doesn’t show up again next month on its own.

That’s really the bit nobody wants to say, because it’s inconvenient for anyone selling a quick fix. Appearing once proves almost nothing. What actually matters is whether the same business turns up again for a different question, on a different platform, a month later. If it doesn’t, that first appearance was noise. A fluke pairing that happened to hold together for five minutes.

So what does actually move the needle, if it’s not the ChatGPT search and it’s not chasing a single mention?

Mostly it comes down to whether the story about your business is consistent everywhere it appears, and not just because you’ve written it the same way on every page you control. It’s whether other people, sites and listings you don’t control are saying the same thing about you unprompted. That agreement between independent sources is roughly what these systems are built to go looking for.

It also helps enormously if what you’re claiming is specific enough to check. Calling yourself a trusted local expert doesn’t give anyone anything to verify, and everybody says it anyway, so it carries no weight. Saying you’ve handled probate sales in a particular postcode for fifteen years is a claim someone, or something, could actually test against other evidence.

Who’s saying it about you matters more than it used to. A named, traceable review sits differently to an anonymous one, and the systems doing this checking are getting better at telling the two apart.

None of that is complicated, and none of it needs a screenshot of ChatGPT to prove or disprove. It’s closer to building a reputation that would survive a sceptical journalist asking around than it is to gaming an algorithm. The old approach rewarded saying things a lot. This one rewards things that are still true when somebody checks.

So by all means search yourself if it makes you feel something – remember to be Incognito mode. Just don’t mistake the result for an answer.

Why So Much Of The AI Citation Debate Misses The Point

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There is a recurring theme in the AI SEO industry and LinkedIn discourse around AI citations: people arguing about what makes a website, brand or piece of content “citable” by AI.

Much of the debate is understandable. AI Search is still immature, the platforms are changing rapidly and nobody has a complete picture of exactly how systems such as Google AI Overviews, ChatGPT, Perplexity and Gemini select their sources. But there is an important distinction that often gets lost.

Being eligible to be cited is not the same as giving an AI system a reason to cite you.

This is where the OPTIMUM framework separates AI Search readiness into two broad tiers.

Tier 1 is the foundation. It is about making a business understandable and accessible to machines. Crawlability, site architecture, structured data, entity clarity, consistent business information, technical signals and trust all matter. Without a coherent digital identity and a website that AI systems can access and interpret, there is little point worrying about sophisticated citation strategies.

But Tier 1 is increasingly becoming the entry ticket.

Tier 2 is where the competition starts.

Tier 2 is about creating the information and authority that gives an AI system a reason to choose one business over another. It moves beyond individual keywords and isolated articles towards connected areas of useful information that answer the questions surrounding a customer’s original query.

An AI system does not necessarily have to answer a user’s question by finding one page containing the perfect keyword. It can break that question into supporting questions, explore different sources and assemble an answer from the information it considers useful, relevant and trustworthy.

That changes the game.

The objective is no longer simply to produce a page that ranks for “best estate agent in Brighton” or “best used Porsche dealer.” It is to build a body of credible information that helps an AI system understand who you are, what you know, what you do, where you operate, who you serve and why your information is worth using.

This is why I think some of the current LinkedIn arguments about AI citations are premature.

People are trying to identify the citation factor when the reality is much more complicated. Technical accessibility matters. Entity clarity matters. Authority matters. Relevance matters. Information quality matters. Intent matters. Reputation matters. The relationship between all of these signals matters too.

We are still in the early stages of this transition.

The businesses that understand this distinction will have an advantage. They will not simply ask, “How do I get cited?”

They will ask a much better question:

“What would make an AI system choose me as a useful and trustworthy source?”

That is the real AI Search challenge.

Want to understand what this means for your business? DM me for more information about AI Search, GEO and the OPTIMUM framework. I’m building OPTIMUM in public and thanks to social media increasingly well connected with academics internationally for peer reviews and development.

#AISearch #GEO #DigitalMarketing #SEO #DigitalTransformation #OrganicGrowth

About OPTIMUM

OPTIMUM is State of the Art Digital’s AI SEO and GEO audit framework, scoring a business across seven dimensions of AI search readiness with a Now Score, a Future Score and the Delta between them. It is the methodology behind the research and terminology referenced throughout this newsletter.

About AI Search, Today and Tomorrow

AI Search, Today and Tomorrow is State of the Art Digital’s combined research study, bringing together three flagship reports on how AI search is reshaping business visibility, including the Zero-Click Economy findings referenced above. It is updated and republished on a quarterly basis as the AI search landscape develops. A copy is available on request.

Why I Built The AI Citation Content Engine

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Search behaviour has changed faster than most content strategies have caught up with. People increasingly ask an AI system a question rather than typing a keyword into Google, and the AI system does not browse the web the way a human does. It retrieves passages, checks them against each other, and decides which claims are safe enough to state as fact. Content written for a search engine ranking does not automatically survive that process. Content written for AI citation does.

This is the reason the OPTIMUM AI Content Engine exists.

What AI bots actually look for

An AI answer engine breaks a page into chunks, turns each chunk into a vector, and retrieves the ones closest to the query. It does not read a page top to bottom the way a person does. That single fact changes what good content means.

A chunk needs to answer a question on its own, with no dependency on the sentence before it. A pronoun that only makes sense once you have read the previous paragraph is a chunk that fails on its own. A claim without a figure attached to it is weaker than a claim with one. And a fact stated once, in one place, is a claim. The same fact stated twice, in different words, in two separate passages that both survive retrieval, is a corroborated claim. Corroboration is what gives an AI system the confidence to cite something as settled rather than as one source’s opinion.

Most content on the web was written before any of this mattered. It opens with context, builds towards an answer three paragraphs in, and states its most important facts exactly once. That structure suited a human reader working through the page from top to bottom. It does not suit being cut into pieces and checked for agreement against other pieces.

Consistency is the part everyone underestimates

The instinct when writing about the same subject twice is to vary it: change the numbers slightly, phrase the range differently, keep it interesting. For a human reader that is good style. For an AI retrieval system it looks like contradiction, and contradiction is the fastest way to be dropped from a summary altogether. If one page states a fee range as one to three per cent and a related page on the same site states one to two and a half, a system checking both has no reliable way to decide which figure is correct. The safer outcome, from its point of view, is to cite neither.

This is why the Content Engine treats consistency as a validation rule rather than a style preference. Every fact that appears more than once in a piece, or across a set of pages on the same site, has to agree. Not roughly agree. Agree exactly, restated in different phrasing each time, so retrieval keeps finding it corroborated instead of catching it contradicted.

Why fan-out changes the unit of work

A single answer to a single query used to be enough. It no longer is, because an AI system rarely stops at the seed query. It fans a question out into the sub-questions and adjacent facets a real customer would ask next, retrieves against each of those separately, and builds its answer from whichever passages agree across the widest spread of related queries. A page that answers only the seed query wins one retrieval slot out of several and offers nothing to corroborate the answer beyond itself.

The Content Engine’s job is to write to the whole subject, not the single question. Each fan-out branch gets its own answer-first block, and the facts that matter most are carried across blocks on purpose, so the piece reads as several independent, mutually agreeing sources rather than one long answer padded out to look thorough.

Where OPTIMUM fits

The Content Engine is not a standalone tool. It is the production layer of OPTIMUM, the audit framework that already scores a business across the dimensions that determine whether AI systems can find, trust, and cite it. An OPTIMUM audit identifies where a business is invisible to AI retrieval and why. The Content Engine closes that gap: it produces the extractable, corroborated, consistently stated content the audit says is missing, built to the same rules the audit checks for. A business does not need two separate processes, one to diagnose the problem and another unrelated one to fix it. OPTIMUM does both against the same standard.

That is the reason this exists. Not to write content that reads well, although it should, but to write content that survives being taken apart, checked against itself, and cited with confidence. Search worked for twenty years on the assumption that a human would read the whole page in order. That assumption is no longer safe to build on, and content strategy needs to stop pretending otherwise.

Steve Coulter, a U.K. based AI Search Consultant and creator of OPTIMUM which identifies the hidden SEO, GEO and entity issues preventing companies from appearing prominently in AI-driven discovery. Local search and competitive industry expert. Four decades Automotive industry sales and 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.

 

How To Get Cited By Chat GPT

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To get cited by ChatGPT, your website needs three things: content structured around the specific questions people ask, clear factual claims that AI models can lift and attribute without misreading them, and enough independent signals elsewhere on the web to convince the model your business is a trustworthy source on the topic. Get those three right and citation follows. Get them wrong and you can have the best product in your market and still never appear in an answer.

That’s the short version. Most businesses stop there and wonder why nothing changes. The reason is that “structure your content properly” isn’t a task, it’s a framework, and without one you’re guessing at what a language model actually rewards.

Why This Is Different From SEO

Traditional SEO optimises for a ranking algorithm working through keywords, backlinks and page authority. ChatGPT and other AI engines don’t rank pages, they synthesise answers. That means the unit of value shifts from “does this page rank” to “does this page contain a clean, extractable fact that answers a specific question.” A page can rank on Google and be invisible to ChatGPT. A page can also barely rank on Google and still get pulled into an AI answer, because the model found one paragraph that answered the query precisely.

This is the gap most agencies haven’t caught up with. They’re applying 2015 SEO thinking to a 2026 problem.

The OPTIMUM Framework

OPTIMUM is the audit methodology I built at State of the Art Digital to diagnose exactly where a business stands on AI visibility, and where the opportunity sits. It scores a site across seven dimensions, each rated on where it is now and where it could realistically get to, with the gap between the two showing you which dimension is worth fixing first.

The dimensions cover things most businesses have never audited before: whether your factual claims are stated in a form a model can extract cleanly, whether the right entities (your business, your people, your credentials) are unambiguously identified across the web, whether your content answers real user intent or just targets a keyword, and whether independent third parties are corroborating what you say about yourself. A business can have a beautifully designed website and score badly on every one of these, because good design and AI-readiness are unrelated problems.

The value of scoring it this way is prioritisation. Nobody has the budget to fix everything at once. OPTIMUM tells you which dimension has the biggest gap between where you are and where you could be, so the work you commission actually moves the number that matters.

Fan-Out Query Mapping

Here’s the part that catches almost everyone out. When someone asks ChatGPT a question, the model rarely answers from a single search. It breaks the question into several related sub-queries, runs each one, and blends what it finds into one response. This is called query fan-out, and it’s the reason a business can rank well for its main keyword and still be absent from the AI answer entirely.

Take “best accountant near me.” A model won’t just search that phrase. It will fan out into sub-queries covering qualifications, pricing structure, specialisms, client reviews, and response times, then decide which businesses show up consistently across those separate searches. If your website only ever addresses the headline query and says nothing concrete about pricing or specialism, you vanish the moment the model fans out into those directions, even though your homepage was built for exactly the customer asking.

Fan-Out Query Mapping is the process of working out what the real sub-queries are for a given topic and making sure your content answers each one directly, in language a model can extract without ambiguity. It’s not about guessing keywords. It’s about thinking the way the model thinks: breaking one question into the five or six it will actually go and check.

Where to Start

If you want to know exactly where your business stands, an OPTIMUM audit gives you a Now Score, a Future Score and a clear list of what closes the gap between them, dimension by dimension. That’s the honest starting point. Everything else is guesswork dressed up as strategy.

Steve Coulter, a U.K. based AI Search Consultant and creator of OPTIMUM which identifies the hidden SEO, GEO and entity issues preventing companies from appearing prominently in AI-driven discovery. Local search and competitive industry expert.

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

AI SEARCH: The AI Citation Quick Fix Strategy

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The Quick Fix: How to Close 55 to 60 Per Cent of Your AI Citation Gap Without Spending on New Content

An insight leap that is saving my clients money AND gaining AI Summary mentions and citations.

Most businesses hear “AI citation readiness” and assume it means a full content programme: new pages, new expert bios, new research, a marketing budget they don’t have. It doesn’t have to start there.

Every OPTIMUM audit we run scores a business across seven dimensions, from Technical Foundation through to Structured Data and Local Relevance. Each dimension carries a Now Score and a Future Score, the improvement available once the recommended work is done. Add the seven dimensions together and you get a total Delta, the full distance between where a business sits today and where it could sit with every recommendation actioned.

What we’ve found, repeatedly, is that a large share of that Delta doesn’t need new content at all. It needs the content that already exists to be made visible to the systems doing the citing.

The gap isn’t authority. It’s translation.

A business with genuine credibility, real reviews, real awards, a properly researched catalogue, often scores badly on AI citation not because the substance is missing but because none of it is in a format AI systems can read. Reviews sit in a widget with no schema behind them. A landmark piece of research sits on one standalone page with no links leading to it. The blog isn’t even in the sitemap.

This is a translation problem, not a credibility problem. And translation problems are cheap to fix.

The Quick Fix approach

Instead of working through all seven dimensions in full, the Quick Fix strategy identifies the subset of recommendations that meet three conditions:

  • They require developer or marketing time already on staff, not new spend
  • They surface authority the business already holds rather than creating anything new
  • They sit in the dimensions where structural fixes carry the most weight

In practice, that means four areas of work:

Development fixes to the sitemap and robots.txt. Correcting protocol mismatches, adding excluded content types such as the blog, removing dead entries. None of this touches content. All of it affects whether a crawler can find content that already exists.

Structured data implementation. This is where the Quick Fix does most of its work. Organisation schema, Review and AggregateRating schema pulling from an existing review feed, Product schema across a catalogue, BreadcrumbList schema across an existing category structure. Every one of these exposes information the business already holds. None of it is new writing.

Cross-linking existing assets. Research, expert credentials, standout pages that carry real authority but sit isolated with no links pointing to them. Connecting them into the site’s existing structure costs an afternoon, not a project.

Verification tasks. Confirming a Google Business Profile exists, confirming sitemap variants are correctly referenced. Near-zero cost, genuine accuracy gains.

Why this reliably closes 55 to 60 per cent of the Delta

Structured Data consistently carries one of the largest Deltas of the seven OPTIMUM dimensions, and it’s also the one requiring the least new production. That combination is what makes the Quick Fix approach work. When a business has no confirmed schema in place, implementing it properly doesn’t nudge the score, it moves it substantially, because you’re taking signals from completely invisible to fully machine-readable in one pass.

Technical Foundation and Entity Clarity behave the same way. Both carry meaningful Delta, and both are largely solved by developer time rather than content production.

Set against that, the dimensions the Quick Fix approach deliberately leaves out, Content Quality and Topical Authority, are the two that genuinely require new writing: author credentials, pedagogy-led explainer content, expanded pillar pages. There’s no shortcut there, and we don’t pretend there is one.

Add it up across a typical seven-dimension audit and the pattern holds consistently: the Quick Fix list captures somewhere between 55 and 60 per cent of the total available improvement, for a fraction of the cost of the full programme.

Why this matters right now

We’re in what we call Epoch 1, the open, early-mover phase of AI citation, before the competitive field normalises. Tier 1, the technical and machine-readable foundation, is currently the gate. Get through it and a business becomes eligible to be cited at all. Miss it and even genuinely excellent content, research, and reputation stay invisible to the systems doing the summarising.

The businesses that move now, cheaply, on the structural layer, put themselves in the citation pool while their competitors are still deciding whether AI search is worth taking seriously. The Quick Fix strategy isn’t a compromise. It’s the correct first move for almost anyone starting this process, budget aside.

An actual case anonymised OPTIMUM Quick Win AI Citation Task List. 

Want to see what a Quick Fix list would look like for your business? Get in touch at steve@stevecoulter.co.uk.

AI SEARCH: AI Summaries & Future of SERP Links

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Why AI Overviews, zero-click search and the changing shape of organic results are rewriting how visibility, traffic and demand are won online, and what it means for citation readiness under the OPTIMUM framework

Executive Summary

Search has entered a new phase. Across recent studies, around two-thirds of Google searches now end without a click, and the presence of AI summaries is a major reason why. When an AI Overview appears, users are more likely to get what they need on the results page and less likely to visit a website.

That matters because organic search has always been measured by what it sends to the open web. Once the answer is delivered before the click, the old model of “rank first and expect traffic to follow” becomes much less dependable.

The consequence is not simply fewer visits. It is a change in how intent is intercepted, how brands are discovered, and how value is distributed across the results page. This briefing sets out the evidence, introduces the OPTIMUM framework’s model for understanding it, and outlines the practical response for businesses that still want to be found, cited and chosen.

The New Shape of Search

Search is not disappearing, but the way it distributes attention is changing fast. Independent analyses in 2026 show that roughly 65 to 68 per cent of Google searches now end without any click to a website, up from about 60 per cent just two years earlier.

AI summaries, Google’s AI Overviews and similar features in other engines, are a key driver of this shift. When an AI Overview is present:

•      Click-through rates to websites fall sharply, often by 50 to 60 per cent.

•      Even the top organic position loses a meaningful share of its traffic, not just the long tail.

•      Informational and commercial research queries, the “what is…”, “how does…”, “best X vs Y” searches that used to feed blog posts, comparison pages and product discovery, are especially exposed.

The pattern is not confined to Google. In experimental AI-only search modes, the share of queries that produce no outbound clicks at all is even higher, as the interface is designed to resolve intent entirely in-platform.

The result is a search ecosystem where visibility and traffic are increasingly decoupled: a brand can be present in the results, yet receive little or no session.

‘A recent CMA ruling permits publishers to opt out of AI Overviews altogether. Few will. That opt-out simply hands citation share to competitors who stay in, which is a sharper reason to act than the regulatory story on its own.’

What the Research Is Showing

The data behind this trend are now consistent across multiple sources:

•      Zero-click searches (queries that end without any click) are estimated at 68 per cent of US Google searches in early 2026, up from around 60 per cent in 2024.

•      AI Overviews now appear on 20 to 42 per cent of queries, depending on market and dataset.

•      When an AI Overview is shown, organic CTRs fall by roughly 60 per cent, and even position 1 can lose around 29 per cent of its previous clicks.

•      For informational queries, the zero-click rate has reached around 79 per cent, with AI Overviews triggered on 58 per cent of these searches.

•      For commercial investigation queries (“best…”, “X vs Y”), the zero-click rate has risen from 42 to 51 per cent since 2024, and AI Overviews appear on 67 per cent of these queries.

These figures support a clear thesis: AI is systematically intercepting searches that would previously have produced clicks, and doing so at scale.

A Clearer Way to Understand the Trend

Most commentary stops at the phrase “zero-click search”. State of the Art Digital’s research takes the analysis further by separating the immediate loss of clicks from the deeper structural change in how search works. This is the model behind two of our published intelligence reports, Search Doomsday Is Q3 2027 and The Chaff Effect, both available free at stevecoulter.co.uk, and it sits at the centre of every OPTIMUM audit we run for clients.

This view of the market has three parts:

1. AI Interception / Zero-Click

This is the first layer: AI summaries absorbing simple informational queries before they ever reach a website. The user’s intent is intercepted and resolved on-platform, so the brand may be visible but receives no session. This is already measurable in the rising zero-click rates and falling CTRs described above.

2. Search Doomsday

This is the medium-term systemic risk, set out in full in Search Doomsday Is Q3 2027. As AI summaries scale, the pattern of interception becomes so dominant that traditional organic traffic is materially weakened as a channel. The clickstream that has powered SEO, attribution, lookalike audiences and performance marketing begins to decay.

For businesses that rely on consistent organic acquisition, that is not a distant theory; it is for now, not tomorrow – because tomorrow will be too late.

3. The Chaff Effect

This is the most original part of the analysis, formalised in the second edition of The Chaff Effect, and it is often misunderstood.

This is not the dilution of AI-generated and regenerated and quoted content. That is a separate problem, content pollution, and deserves its own name.

The Chaff Effect is the residual pool of balance and transactional clicks that remains after the simple, easy-to-resolve informational clicks have been removed. In practice:

•      AI summaries take the majority of straightforward informational queries, the “what is…”, “how does…”, basic definition and explanation searches.

•      What is left is a smaller, more contested pool of more detailed, complicated and less common clicks: deeper research, nuanced comparisons, local and transactional queries, and long-tail decision journeys.

•      These remaining clicks are shared around a much larger market of businesses, most of which do not appear in AI summaries at all.

In other words, the Chaff Effect describes the world after AI has taken the “easy” clicks: the leftover demand that still has commercial value, but is now spread thinner across more competitors and harder to capture with traditional ranking alone. A nightmare scenario for any business where a higher volume of clicks drives the KPIs.

Separately, there is indeed a problem of diluted AI-generated content and citation noise, a kind of content pollution that makes it harder for high-quality sources to stand out. But that is distinct from the Chaff Effect, which is about what remains of the click pool once AI has intercepted the bulk of informational intent.

Why This Matters for Your Business

The practical implication is clear. Traditional SEO remains important, but it is no longer enough to focus on rank alone.

Visibility inside AI summaries, citation in trusted third-party sources, and branded demand all matter more than they used to.

•      If a business depends heavily on informational traffic, it is most exposed to AI interception.

•      If it depends on balance or transactional traffic, it is competing in the Chaff Effect: a smaller, more contested pool of clicks that still have commercial value.

This is why performance measurement needs to move beyond sessions and rankings. Businesses need to understand:

•      Where they appear (AI Overview, organic, local, third-party citations).

•      Whether they are cited in AI summaries.

•      How much of the remaining demand they can still capture.

Tools such as Semrush and its peers are useful for measuring the size of the citation gap. They are not built to close it. That is the distinction OPTIMUM was built around: a seven-dimension audit framework, covering Technical Foundation, Content Quality and E-E-A-T, Entity and Brand Clarity, Topical Authority, Trust and Credibility Signals, Structured Data and Machine Readability, and Local and Contextual Relevance, that scores where a business stands now and what closing the gap would achieve.

The Strategic Response

Winning in this environment means shifting from “rank and hope” to “be cited, be trusted, be chosen”. A practical agenda includes:

•      Build content that is authoritative enough to be cited, not merely indexed. Clear structure, strong evidence, and genuine expertise matter more than ever.

•      Strengthen entity signals, structured data and brand clarity so AI systems understand who you are, what you offer, and why you are credible.

•      Prioritise the queries that still generate meaningful commercial demand, especially those that are more detailed, specific and decision-led, the clicks that live in the Chaff Effect rather than the intercepted informational layer.

•      Measure visibility across AI summaries, not just classical blue-link rankings. Track appearance in AI Overviews, citation frequency, and branded search as leading indicators.

In practice, this is what an OPTIMUM audit and a citation-cluster sitemap architecture are designed to deliver: a structural map of where a business can still be cited once the easy clicks have gone, built and monitored dimension by dimension rather than left to chance.

Closing Note

Search is not disappearing, but the way it distributes attention is changing fast. Businesses that understand AI summaries early will be better placed to keep visibility, protect demand and win the clicks that still matter.

That is why this is more than a technical SEO story. It is a strategic shift in how brands are discovered, compared and chosen. And it is why working with someone who has a clear, research-backed model, AI Interception, Search Doomsday and the Chaff Effect, is an advantage, not a luxury.

Originally published at stevecoulter.co.uk/news For the full published research behind this briefing, including The Chaff Effect (Second Edition), Search Doomsday Is Q3 2027, and Your Business Is Becoming Invisible, visit stevecoulter.co.uk. For a citation-readiness audit of your own site under the OPTIMUM framework, contact Steve Coulter at steve@stevecoulter.co.uk or +44 (0)7407 038877.