Internet Search

Why John Lewis Just Built a Studio for AI Search

| admin

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

| admin

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 To Get Cited By Chat GPT

| admin

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

| admin

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.

| admin

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

| admin

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: WTH Is Going On With Business Leaders?

| admin

With the unstoppable rise of AI Summaries, here are stats that should be concerning many a boardroom.

71% of UK websites score below 50/100 for AI visibility.

Not one has hit “AI-Ready” status. Zero, out of 31,000+ sites audited.

Meanwhile:
→ AI Overviews now intercept 1 in 3 Google searches
→ Zero-click has hit 60% of all searches
→ For informational queries, it’s 70%+

Does your customer’s journey begin with research?

Fewer than 5% of UK SMEs have done anything about this.

Not “not enough.” Anything.

That means 95% of business leaders are watching prospects get answered and half-persuaded by AI before a human ever reaches their website.

No dashboard flags this. No familiar metric dips. It’s just quiet, compounding absence.

Structured sites get cited 3x more than unstructured ones.

That’s not a marginal edge. That’s the difference between being in the conversation and not existing in it.

The businesses winning this aren’t the biggest. They’re the ones built in a language AI can read and trust.

Everyone else finds out the hard way. One invisible enquiry at a time.

Is your site one of the 71%? I run AI visibility audits against the OPTIMUM framework.

Message me with your business URL and contact details and I’ll tell you exactly where you stand.

 

AI SEARCH: Google AI Overviews Is Killing Your Clicks

| admin

Google is quietly keeping 58 of every 100 clicks that used to be yours.

That’s not a projection. It’s Ahrefs’ December 2025 data: when an AI Overview appears, the page ranking first loses 58% of its click-through rate. You can hold position one and still lose the majority of the traffic that used to come with it.

Google’s AI Overviews aren’t a niche feature anymore. A 2026 study of over 55,000 queries found they now trigger on 13.7% of all searches, and 64.7% of question-form searches.

Ask a question on Google today, and there’s a two-in-three chance the answer is written before you ever reach a website.

Here’s the part that should worry you more. Independent audit data shows 77% of U.K. SME websites are sending confusing signals to AI search platforms right now: inconsistent business descriptions, missing schema, unclear entity structure. These aren’t cosmetic issues. They are the exact signals AI systems use to decide who gets cited and who gets skipped entirely. They’re exactly what OPTIMUM is designed to surface.

Most businesses are still fighting for rankings on a channel that’s shrinking, while the channel replacing it is being handed out to whoever happens to have their structural signals sorted. Right now, almost nobody does. That’s not a long-term advantage window. It closes.

I’ve mapped this in three reports:

1.Your Business Is Becoming Invisible
2. The Chaff Effect
3. Search Doomsday Is Q3 2027

Together they show the same thing from three angles: the shift is already measurable, most businesses have no defence against it.

Stop reading about it. Get your website OPTIMUM AI Risk audited before your competitor does.

To open the conversation message me for the FREE reports and let’s find out where you actually stand.

Once we know the remedy I have affordable consultancy and retained advisory hours to help you.

Don’t wait until Q3 2027 to find out your website is invisible and your enquiries have vanished.

AUTOMOTIVE: AI Search – Shock Therapy

| admin

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

| admin

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.

AUTOMOTIVE: The AI Marketing Imperative

| admin

Car Dealers: build AI authority on your own forecourt, not someone else’s

Car dealers are being urged to rethink their digital strategy as AI-driven search changes how buyers find vehicles and choose who to trust with their money.

ChatGPT, Google’s AI Overviews and Perplexity increasingly give buyers a direct answer rather than a page of listings. Visibility is no longer about rankings or how many stock photos sit on AutoTrader. It comes down to whether AI systems recognise a dealer as a trustworthy source at all.

That is the shift behind the thesis that your ‘Business Is Becoming Invisible’. Not invisible in the old sense of slipping down the rankings, but absent from the answer altogether. When an AI Overview or Answer gives a buyer one or two named and fully researched recommendations instead of ten blue links, a dealer either is that recommendation or doesn’t exist for that buyer. There is no page two. Increasingly a link isn’t clicked at all.

AI SEO and GEO (Generative Engine Optimisation) are converging into a single discipline that most dealer groups still treat as an afterthought. That is a mistake with a shelf life. ‘Search Doomsday’, the point at which AI answers displace traditional click-through search as the default buyer journey, is projected for Q3 2027. Dealers who wait for that shift to arrive will be optimising for a channel that has already moved on.

Optimisation matters, and it matters where you do it

Too many dealers pour content, reviews and stock data into third-party portals without realising who benefits. List a car on AutoTrader, Motors.co.uk or a manufacturer’s certified used platform, and the AI trust signals generated by that content accrue to the portal, not the dealership. The dealer builds someone else’s AI visibility with their own budget.

This is the gap the OPTIMUM Seven Dimension AI Summary and Citation Audit was built to expose. OPTIMUM breaks a dealership’s digital presence into seven measurable dimensions: technical readiness, content structure, citation patterns, schema implementation, off-site authority, competitor benchmarking and platform-specific performance across ChatGPT, Google AI Overviews, Perplexity and Gemini. For a dealer group, that means knowing whether AI platforms cite the dealership’s own website when a buyer asks who to trust, or whether every citation flows to a portal the dealer doesn’t control.

Long-term visibility lives on your own domain

Optimising AI SEO and GEO directly on a dealership’s own website builds long-term visibility and brand equity the dealer actually owns, rather than rents.

There is a third thesis worth knowing here too: ‘The Chaff Effect’. As AI answers more queries directly, organic traffic to any given website falls, and what remains skews towards harder-to-convert buyers, because AI has already filtered out the easy wins upstream in the information phase, in old money that’s higher up the sales funnel. Dealers feeding third-party platforms without a parallel strategy on their own site are handing over the signals that decide who is authoritative, while their own slice of a shrinking pool gets thinner still.

A buyer asking ChatGPT which dealer to trust for a used BMW is answered by whichever domain the AI has learned to cite, and increasingly that is not the dealer’s own.

An OPTIMUM Citation Gap Analysis makes this visible rather than theoretical. It shows exactly where a dealer’s own website is skipped over in favour of third-party sources, then sets out what needs to change, from schema markup and structured pages through to the first-party content AI models treat as citable.

The dealers who move first will own the conversation – AKA The Early Mover Advantage

Dealers who invest early in their own AI visibility will be far better placed as buyers increasingly ask AI tools who to trust to sell them a car, service their vehicle, or handle their part-exchange.

Independent dealers must navigate this shift in search and behaviour, the question is straightforward: is your website building AI authority you own, or funding someone else’s?

—

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 REPORT: The Fan Out Effect

| admin

The Fan Out Effect and AI Citations

Executive summary

AI search is changing how brands are discovered and referenced. The main lesson from recent research into query fan out is that citation in AI answers depends less on broad authority and more on whether a page is retrieved early, matches the query closely, and is structured in a way the model can use.

For State Of The Art Digital, the practical takeaway is clear. Content strategy now needs to be built for citation as well as ranking. That means sharper page intent, better heading alignment, and a stronger focus on direct answers rather than broad topic coverage.

Report overview

Recent industry research into query fan out examines how AI systems move from a user prompt to the sources they cite. The analysis draws on a large set of queries and retrieved pages, giving a useful picture of how citation decisions are made in practice.

The findings show that AI search does not work like a simple keyword ranking system. Instead, the model expands a prompt into related sub-queries, retrieves a broad set of pages, and then narrows down to the sources that best fit the answer.

What the data suggests

The strongest signal appears to be retrieval position. Pages that surface near the top of the retrieval set are much more likely to be cited than pages that appear lower down. In other words, if a page is not visible early in the retrieval process, it is unlikely to feature in the final response.

Heading relevance also matters a great deal. Pages whose headings closely match the user’s query are cited more often than pages with weaker or more generic section titles. This suggests that clear, question led structure is a practical advantage, not just a stylistic preference.

The research also indicates that traditional authority signals do not carry the same weight here as they do in standard SEO. Domain authority and backlinks may still help overall visibility, but they do not seem to be the deciding factor when AI systems choose which page to cite.

Why this matters

This changes how content teams should think about optimisation. Long, all-purpose pages are not automatically better, especially if they dilute the relevance of the page to one specific question. A tighter page that answers one intent directly may have a better chance of being surfaced and cited.

It also means that content quality alone is not enough. A well written page still needs to be easy for the model to interpret, with headings, structure, and topical focus that closely mirror the user’s likely prompt.

Practical implications

For brands that want to improve AI visibility, the first priority should be page alignment. Each important page should be built around one clear intent, with headings that reflect the way people actually ask the question.

The second priority is structure. Short, well ordered sections make it easier for AI systems to identify useful passages and extract them into an answer. This is especially important for service pages, FAQ sections, and comparison content.

The third priority is clarity over breadth. Rather than trying to cover everything on one page, it is often better to create focused pages that answer a single task or question properly.

Recommendations for clients

  • Build pages around one primary search intent.
  • Use headings that closely match real user questions.
  • Keep sections concise and logically ordered.
  • Refresh important pages regularly so they stay current.
  • Support core pages with internal links and related content.
  • Treat AI citations as a visibility goal alongside organic rankings.

Conclusion

The key message from the research is that AI search rewards precision. Brands are more likely to be cited when their content is easy to retrieve, easy to interpret, and clearly matched to the prompt being answered.

For State Of The Art Digital, this is an opportunity to position clients for the next phase of search visibility. The winning approach is no longer just to rank, but to become the clearest source a model can confidently quote.

Please contact me if you would like your business website structure and navigation adapted for your industry to suit early AI bot retrieval.

AI SEARCH: Fake Plastic Models

| admin

Fake Reviews Aren’t for People Anymore. They’re for the Models.

Reddit is being flooded with AI-generated posts and reviews. According to a recent MediaPost piece, brands are planting them there because ChatGPT and Google’s AI treat Reddit as a trusted source of real human opinion. The target isn’t a reader. It’s an algorithm looking for something to cite.

If that sounds familiar, it should. It’s the SEO playbook from the 2000s, one layer up.

Back then, ranking on Google was something you gamed: keyword stuffing, invisible text, link farms, thousands of dodgy backlinks. It worked, for a while. Then Google shipped Panda and Penguin, and most of those tactics stopped working overnight. Sites built on tricks collapsed. Sites built on genuinely useful content held steady.

We’re watching the same cycle repeat, with forums instead of backlinks. The models now read Reddit as ground truth, so that’s where the fake signal gets planted. And the platforms are already fighting back. Reddit is blocking 23 million spam views a day, removing close to 2 million fake votes daily, and catching around 25,000 spammy posts every day. The enforcement window that took Google years is now measured in seconds.

The reason this is happening is simple: search has moved into the answer box. Zero-click queries rose from 56% to 69% in under a year, and news sites lost 600 million monthly visits in the process. Being findable now means being cited, and brands are scrambling to work out how.

The lesson from the SEO era still applies: astroturfing is the wrong answer to the right question. It works until the platform catches it, and platforms are catching it in seconds now. You’re building your visibility on someone else’s moderation policy.

The better question is the one Panda and Penguin eventually forced everyone to ask: why would an AI cite you at all? Usually because the information about your product is clear, structured and genuinely useful, not because you gamed a forum.

Keyword stuffing didn’t survive. Fake reviews won’t either. The brands that win the citation race will be the ones the models actually understand, not the ones spamming Reddit.

AI Search Doomsday: Don’t Look Up!

| admin

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.

—

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: Why Small Brands Still Have a Chance

| admin

For brands also read businesses. Your business.

Apple, Reddit and HubSpot are not scrapping for a place in AI answers, and there is one reason for this: training data.

AI engines will cite and quote a brand under two conditions:

  1. The model already knows the brand (training data)
  2. The model can find the answer it needs on the brand’s website (retrieval)

Large brands have already secured their position in the AI citation game. They dominate answer engine optimisation for free, more or less, because these models were trained on data in which those brands are already deeply embedded.

Sixty per cent of ChatGPT answers never touch the live web at all, which tells you how much weight training data carries.

For a smaller brand, competing on training data is a losing battle. The real opportunity lies in the second path: retrieval.

When an AI engine cannot find the answer in its training data, or needs something current, it runs a web search. This is where a smaller brand gets its chance to be cited and quoted. The route to that chance has not changed: SEO. But it is a specific flavour of SEO that matters here.

Retrieval depends on technical foundations:

Technical SEO. If a crawler cannot access, render or parse your site cleanly, none of your content is retrievable, no matter how good it is. Fast load times, clean site architecture, proper canonicalisation and a crawlable structure are the entry ticket, not an afterthought.

Structured pages. Content built around one clear question per page, with a direct answer near the top, gets pulled into AI responses far more easily than content buried in long, unfocused articles. Structure your pages the way you would structure an answer if someone asked you directly.

Comprehensive schema markup. Schema tells AI engines exactly what your content means, not just what it says. FAQ schema, Article schema, Organisation schema and Product schema, used properly and thoroughly across the site, give models the clearest possible signal that your page holds the answer they are looking for.

llms.txt. This is the newest lever available. An llms.txt file gives AI models a direct, structured route to your most important content, much as a sitemap does for traditional search engines. It will not guarantee a citation, but it removes friction and makes your best answers easier to find and trust.

There is a genuine glimmer of hope here. Retrieval can beat fame. A smaller brand with a more retrievable answer, backed by solid technical SEO, well-structured pages and thorough schema, will win the citation over a bigger brand that only earns a passing mention.

The golden rule for winning at retrieval is simple:

Own your niche completely. Every topic. Every subtopic. Every question a customer might ask. The instant an AI model spots a gap in your coverage, it moves on and cites whoever has filled that gap instead.