Digital Marketing

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: WTH Is Going On With Business Leaders?

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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

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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

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


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

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

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

The Chaff Effect, in numbers rather than theory

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

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

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

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

Two dealers, same forecourt, different outcomes

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

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

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

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

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

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

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

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

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

The deadline that isn’t abstract

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

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

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

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

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

 

RETAIL AUTOMOTIVE: An Industry Unprepared

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

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

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

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

Corroboration, not copywriting

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

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

Where dealer website providers are behind

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

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

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

The signals AI agents are actually checking

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

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

This is a bigger job than the industry has clocked

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

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

What the dealer actually has to do

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

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

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

AI SEARCH REPORT: The Fan Out Effect

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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

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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!

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

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

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

My Linked-In post today.

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

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

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

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

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

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

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

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

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

More next week.

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

AI SEARCH: Why Small Brands Still Have a Chance

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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.

AI Search: The Six Vital Content Traits For Citation

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

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

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

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

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

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

Trait one: The heading is the real question

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

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

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

Trait two: The first sentence answers the heading directly

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

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

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

Trait three: One specific fact that only you own

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

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

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

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

Trait four: A real person behind the page

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

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

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

Trait five: A structure the AI can scan

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

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

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

Trait six: No filler

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

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

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

Cut them. Every single one.

What this means in practice

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

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

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

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

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

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

About AI Citation Gap Analysis

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

They can’t.

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

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

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

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

The goal has never been to sell hype.

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

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

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

AI Search: Welcome Validation From The CEO of Google

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

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

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

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

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

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

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

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

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

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

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

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

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Punch Above Your Weight With This Two-Presence Video Strategy

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

That distinction matters enormously right now.

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

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

Why YouTube Dominates AI Citation, and How That Helps You

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

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

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

The Canonical Problem Nobody Is Solving

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

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

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

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

What AI Systems Are Actually Reading

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

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

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

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

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

The Local Business Advantage

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

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

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

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

AI Search Summaries: How Smaller Brands Are Competing

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

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

You will not be reading that version.

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

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

Start With Positioning, Not Content

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

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

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

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

Make Content That Is Actually Useful

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

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

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

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

Build Pages AI Can Point To

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

Three content types consistently perform well as AI citation targets.

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

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

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

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

Use Real Visuals

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

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

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

Expand Your Presence Beyond Your Own Site

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

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

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

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

The Underlying Logic

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

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

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

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

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

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

Simple Paragraph Summary Bullet Points:

Start With Positioning, Not Content

  • Trying to rank for everything means being trusted for nothing
  • AI systems reward focused, specific positioning over broad claims
  • Decide what question you want to answer better than anyone else
  • Positioning is a business decision; content follows from it

Make Content That Is Actually Useful

  • Generic content has been seen before and AI systems know the difference
  • Answer real, specific questions drawn from real customer language
  • Use support tickets, sales conversations, and reviews to find genuine query patterns
  • Depth and clarity matter more than volume

Build Pages AI Can Point To

  • Honest comparison pages with real trade-offs are high-value citation targets
  • How-to content with clear steps maps naturally to how AI systems construct answers
  • Use-case content tied to specific situations outperforms generic features pages
  • JSON-LD structured data tells AI systems what your content actually is; without it, you are asking them to guess

Use Real Visuals

  • Genuine screenshots, recordings, and product demos outperform stock imagery
  • Real visuals are a trust signal for both AI systems and the people who evaluate them
  • Generic imagery makes your brand indistinguishable from the competition

Expand Beyond Your Own Site

  • AI systems read third-party mentions, reviews, press coverage, and creator content
  • PR is part of your AI visibility strategy, not separate from it
  • Target the publications, communities, and creators your audience already trusts
  • Earn mentions through genuine relationships and content worth referencing

The Underlying Logic

  • Clarity, depth, and usefulness level the playing field against bigger competitors
  • EEAT principles have not changed; the consequences of ignoring them have
  • AI citation is not a budget competition; it is a quality and structure competition
  • Businesses that build this foundation now will have a meaningful head start