Google is now showing up to four ads in a single AI Mode response, according to recent reports from the search industry. On the surface that may sound like a minor tweak, since four ads have appeared on standard Google results for years. But the context matters. Google has told investors it plans to show more ads, more often, in AI Mode, and this looks like an early sign of that strategy in action. For any business advertising on Google, it is worth understanding what is changing and why.

What Is Actually Different?

Until now, AI Mode users would typically see one or two ads, and occasionally three. A screenshot shared by Sachin Patel on Search Engine Watch shows four ads sitting at the bottom of an AI Mode answer, which suggests the ceiling has been raised. The picture is not entirely clear, though. Four ads has been the official maximum on traditional search results since 2016, so this may simply be AI Mode catching up. Either way, advertisers should expect more ad inventory in this space, not less.

Why Google Is Doing This

The commercial logic is simple. Industry commentators suggest Google has not monetised AI Mode clicks as effectively as traditional search clicks, and showing more ads is one way to close that gap. More is coming as well. Google is also introducing multiturn ads, which appear on follow-up questions within the same conversation, starting in the US. That means a single AI Mode session could offer several chances for your brand to appear.

What Advertisers Should Do Now

More ads means more opportunity, but also more competition for attention in a smaller, more conversational space. If your campaigns already use AI-powered formats such as Performance Max or AI Max, you may be eligible to appear without changing anything. Check that your product data and landing pages answer the detailed questions people are asking, and keep a close eye on cost per click as demand grows.

AI Agent Testing can connect AI to a site, giving it the ability to test AI machine readiness, including whether systems can extract and understand content and whether there are any technical obstacles.

Another function of AI testing is that it also has the ability to perform tasks on a site, such as making a purchase, filling out a form, etc. It can then report on any issues it faces in this regard; this is important as users may start using AI to complete tasks for them more regularly in the future, not just using it as a research tool.

How does it work?

Agentic testing works by connecting an AI Agent to your website using a Model Context Protocol (MCP). The best way to describe an MCP is that it’s like a USB for AI. It gives AI the ability to connect to different software, files or browsers. Chrome DevTools is a set of web development tools provided by Google Chrome. By connecting AI to Chrome DevTools MCP, the AI can then use these tools to analyse your website, report on its findings, and complete tasks such as purchasing, ordering, newsletter sign-up, etc.

What can you learn from AI Agent Testing

• Crawlability and discovery – Can the website’s content and pages be discovered by AI
• Machine readability – Can systems extract and understand the information, H1s, Titles, descriptions, and Schema
• AI Search content readiness – Editorial changes to improve chances of content being used in AI
• Technical and AI-agent obstacles – Technical problems stopping AI from completing tasks on the site
• Improved Conversion Journeys – Can AI interact with the site such as purchases, form fill-outs, etc.? Is everything working properly

How to set up AI Agent Browser Testing

• Install Node.js – Node.js lets you run javascript locally on your computer, which you will need to run the JavaScript program that powers the Dev Tool MCP.
• Install AI Agent – For some of the more popular AI agents such as ChatGPT and Claude, you can’t always connect an MCP to the web version of the app. In such cases, you will need to either download the desktop version or connect to them via your computer’s command centre, PowerShell. (If you are on Microsoft)
• Set up Chrome Dev Tools MCP – Connect Chrome Dev Tools to your chosen AI Agent. If using the desktop app there should be some built-in app connections or you may need to connect it manually using PowerShell commands.
• Prompt the AI – Once your AI Agent and MCP is fully connected, you need to make sure you prompt the AI correctly so it pulls through the sort of information you want, but don’t

Results

AI Agent Testing provides businesses with a way to understand how prepared their websites are for an increasingly AI-driven internet. By connecting an AI agent to Chrome DevTools MCP, businesses can assess how easily AI systems can extract and understand their content, identify technical issues and test whether AI agents can complete tasks such as navigating pages, filling out forms and making purchases.

If your ChatGPT referral traffic seemed to fall sharply in the second week of October, you are not alone, and you probably did not lose those visitors. For about a week, ChatGPT stopped labelling the links it sends people to. Analytics tools could not tell where those visits came from, so they filed them as “direct” traffic instead.

OpenAI has now started fixing the problem. But the episode is a useful warning for anyone who reports on AI search performance: the data is more fragile than it looks, and a dip in a chart is not always a dip in demand.

What happened

In June, OpenAI began adding a small tracking tag (utm_source=chatgpt.com) to links inside ChatGPT answers. That tag is what lets Google Analytics 4, Matomo and similar tools recognise a visit as coming from ChatGPT. It has quickly become the main way marketers measure AI referral traffic.

Shortly after GPT-6 launched on 8 October, that tag disappeared from many links. Cedric Huesler at Adobe found that links were also being opened in a way that strips out the referrer, the other signal analytics tools use to identify where a visitor came from. With no tag and no referrer, the visit lands in the direct bucket.

The scale was significant. Across around 500 pages where ChatGPT normally drove at least 5% of visits, Huesler saw ChatGPT visits fall by about 31,000 a day between 9 and 13 October, while direct visits on the same pages rose by about 26,000 a day. Other trackers told the same story. LLM Pulse recorded around 46% fewer ChatGPT sessions, and Profound found that the share of ChatGPT answers containing a tagged link fell from about 98% to 66% in less than a week.

It was a bug, but the gap in your data is permanent

On 14 October, OpenAI told Huesler privately that this was a known bug, and a fix began rolling out the next day. Tagged ChatGPT clicks returned to near-normal levels within hours. OpenAI has not made a public statement, and some analysts were still seeing untagged links while the fix rolled out.

Two points matter for reporting:

• Visits recorded as direct between roughly 8 and 15 October will not be reclassified. No backfill has been announced, so October reports will understate ChatGPT and overstate direct traffic.
• The referrer is still missing on most ChatGPT clicks. The tracking tag is now effectively the only signal, so if it breaks again, the same thing will happen.

Why this matters beyond one bad week

AI search is becoming a meaningful source of high-intent visitors. Someone who clicks through from a ChatGPT recommendation has often already shortlisted you. That makes it tempting to treat the “AI assistant” line in GA4 as a reliable performance metric.

This episode shows its limits. AI platforms control how their links behave, they change them without notice, and they are under no obligation to help you measure them. A product update on their side can make your AI channel appear to collapse overnight, even when nothing has changed in how many people find you.

The bigger risk is a bad decision. A business that saw ChatGPT traffic halve in October might conclude that its AI visibility work had stopped paying off and cut budget, when the visitors were still arriving all along.

What to do now

• Annotate 8 to 15 October in GA4 and Matomo so nobody misreads the dip in future month-on-month or year-on-year comparisons.
• Report ChatGPT and direct traffic together for that week. If the combined total held steady, you lost measurement, not visitors.
• Check the pages ChatGPT normally sends people to. A rise in direct entrances to deep pages, such as a specific treatment or course page, is a strong sign of misattributed AI traffic, because people rarely type those URLs directly.
• Judge AI visibility on more than one number. Pair referral data with visibility tracking (how often you are mentioned and cited in AI answers), branded search trends and enquiry or conversion data.
• Do not add UTM tags to your own internal links. They overwrite the original source and make attribution worse.
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AI has permeated every stage of the marketing process, undeniably. Whether that’s teams ideating, polishing email correspondence, or utilising it to quickly mock up visuals. Raising questions about the actual impact it could have on B2B marketing strategies. And most importantly, the business minds making the decisions based on a process that is now deeply entrenched with AI-influenced processes.

Does AI change the acquisition process for B2B buyers?

AI can absolutely change the acquisition process for stakeholders, individuals within a business, along with the vendors and sellers. In fact, AI now comes in as a fourth party to the three key figures within a business’s buying arc.

 AI now automates processes like researching, collating a prospect list and even has the ability to compare proposals. Its purpose is to then become a tool for businesses to vet and ultimately decide what business deals are practical and well-fitting.

Forrester’s 2026 Buyer Insight report discovered that roughly 94% of buyers rely on AI at some stage of the buying process to assist with decision-making. That’s an astounding figure that absolutely reinforces the notion that AI is remapping B2B marketing processes.

Search Journeys shift, impacting discovery and interaction

The zero-click trend, with AI engines, research tools, and generative engine optimisation, all highlight the shifts around search journeys. AI deeply influences the basic processes of inquiring, exploring and analysing.

Where previously buyers would type in keywords, click through to a number of sources and inform decisions based on directly collected information, now AI changes how much of that exploration can happen, with individuals simply clicking enter.

Brand, trust and reputation

So if buyers aren’t directly engaging with content on prospects’ websites, it begs the question of how they build trust, interpret reputations and truly understand branding.

This is where traditional trust signals still stand strong. These can include:

• Reviews
• Testimonials
• Recommendations
• Credentials and accolades (qualifications and awards)
• Verifiable individual profiles
• Clarity of offering: from a buyer or prospect perspective, this is about the transparency around offerings, costs, as well as identifying what and why those decisions are made
• Consistent authoritative content: which, in terms of AI, may mean that the same business names come up across varied searches

What shouldn’t change for B2B marketing

AI is most definitely changing the ways buyers are researching and making decisions, but it doesn’t necessarily change the fundamental reasons behind making a decision. Businesses still need to understand their audiences, communicate their values, and build reputations that instil confidence in buyers choosing them.

This means B2B marketers still need to account for both sides of the equation, recognising the role AI plays in influencing the buyer’s journey, while continuing to prioritise the things that make a business worth choosing. This is often credibility, expertise, clarity and an understanding of their target markets.

AI may increasingly influence which businesses enter a buyer’s consideration, but it is still the strength of the business, its reputation and the value it can demonstrate that ultimately helps turn consideration into a decision.

AI search systems do not look at your images the way a human browsing your website does. They scan them, extract what they can identify, and then use that information to decide whether your page is a credible source for the answer they are assembling. A photo that looks great to a person can be functionally invisible to a machine if it cannot detect what the image shows or confirm that the page text backs it up.

This is not a niche technical concern. With AI Overviews now reaching 2.5 billion monthly users, the question of what AI systems can see on your website has real commercial consequences. And images are increasingly the entry point, not an afterthought.

The change worth understanding

Google holds a patent filed in 2023 and published in April 2026 describing a mechanism where the cited source for an AI-generated answer is chosen by image match first, with surrounding text then pulled in to build the answer. In other words: the picture on your page may be what gets your page selected, and the text is used to fill out the response.

This is a patent application, not confirmed live behaviour. Google files thousands of patents and many never ship exactly as described. But as a directional signal it matters, because it is consistent with what practitioners are observing across AI search environments: visual content is playing a larger role in how AI systems identify and retrieve relevant sources, not just how humans navigate pages.

The scale of visual search reinforces why this direction makes sense. Google Lens processes around 20 billion visual searches a month. Pinterest handles 1.5 billion visual searches a month, converting at 62% better than text searches because a camera finds what words struggle to describe. When that volume of search intent is arriving through images, the systems that respond to it need to be able to read images reliably – and they are increasingly being designed to do exactly that.

What AI sees when it looks at your images

There are two distinct questions to ask about any image on your website. The first is whether the AI can correctly identify what is in the photograph. The second is whether you put the right things in the photograph to begin with.

These sound similar but they are different problems. The first is about legibility – whether the machine can read the image accurately. The second is about intent – whether the image actually communicates the story you want associated with your brand or service. A striking lifestyle image that reads beautifully to a human might tell a machine a very different story if the composition is ambiguous, if the background objects do not match your brand, or if the image contains text that cannot be read at the resolution it is being served.

A practical example: a dental clinic that uses a stock image of a general healthcare setting tells an AI system something much less specific than a clinic that uses its own photography showing its actual treatment rooms, equipment and team. The AI is reading both, but the signal from the first is weaker and shared with every other site using the same image. Stock images are a known problem for AI citation specifically because they appear on thousands of sites – the uniqueness of your image is part of what makes it citable.

The specific things that affect machine readability

There are several factors that determine how well an AI system can read and use your images. Most of these are straightforward to address:

• Image uniqueness. Your own photography, showing your actual products, premises, people or work, performs better than stock images in AI citation environments. If AI systems use image matching as a selection signal, uniqueness is part of what makes your image the one that gets selected rather than a generic version of the same thing.
• Text legibility within images. If your images contain text – packaging, signage, labels, credentials – machine vision systems read it. If that text is blurry, small, or obscured by glare, the system misses it. The same applies to before-and-after images common in aesthetics and dental contexts: the treatment result needs to be visually clear enough for the machine to register what it shows.
• What else is in the frame. AI systems do not just see the main subject. They see everything in the frame. What surrounds your product or service in a photograph forms part of the context signal the machine reads. A treatment room image with equipment visible in the background that does not match the service being described is sending a mixed message. This is worth considering when reviewing or commissioning photography.
• The text immediately around the image. The patent mechanism describes the surrounding text as what gets pulled into the AI answer after the image match. If your most relevant, specific information is buried three paragraphs below an image rather than in a caption or direct label alongside it, the machine may use weaker text to build its answer. Putting the key fact directly next to the image is a simple change with a direct effect.
• Alt text. Alt text for AI search is not just an accessibility or keyword exercise. It is a claim the machine can extract and use. Writing it as a clear description of what the image shows and what it means commercially is more useful than a keyword string, and more useful than a generic description that could apply to any image.

Which types of site this affects most

The multimodal AI search direction is most immediately relevant to businesses where the visual representation of a product or service is central to the buying decision. Aesthetics, dental, healthcare, hospitality, workspace, food and education all fall into this category. For these businesses, the photograph is not decoration supporting a text-based page – it is a primary source of information that shapes what a prospective customer believes about quality, credibility and relevance.

E-commerce is the clearest case: if an AI system can use image matching to identify a product and pull in the surrounding text to build an answer, the businesses with clear, unique, well-labelled product photography on pages with specific supporting copy have a structural advantage over those relying on shared manufacturer images and thin descriptions.

But the principle is broadly applicable. Any business that cares about being recognised and recommended by AI systems should consider whether their images are helping or hindering that goal. The question is not whether your images look good to your customers – it is whether they give AI systems the right signals to identify, retrieve and cite your pages.

What to do with this information

The most impactful single change for most businesses is replacing generic stock images with original photography. Not because stock images look bad, but because uniqueness matters in AI citation and stock images are inherently shared across thousands of sites. Your own photography of your actual work, space or team is the version only you have.

Beyond that, the practical checklist is short. Caption your images with specific, factually accurate descriptions rather than mood-led copy. Place the key information the image illustrates in the text directly alongside it, not separated by paragraphs of other content. Write alt text as a clear description of what the image shows, not as a keyword list. Check that any text visible within your images – credentials, labels, treatment names – is clear enough to be read at the sizes you are serving.

None of this requires expensive tooling or a complete photography overhaul in one go. The highest-value starting point is auditing your most important pages – typically treatment or service pages, product pages, and homepage – to check whether the images on those pages are unique, legible and contextually accurate. That review will surface the specific gaps worth addressing before worrying about anything more advanced.

Google has announced Gemini 4 Argon, its most capable AI model to date, and what sets it apart from previous releases is not just raw performance but where that performance is being directed. Rather than a general-purpose upgrade, Argon has been built specifically to handle the kinds of complex, multi-step tasks that businesses struggle with, such as large-scale software development, financial research, legal work, and cybersecurity defence. It is currently rolling out to a select group of trusted testers, with broader availability for developers, enterprise customers and consumers to follow.

Built for Real Work, Not Just Benchmarks

The headline capability is an industry-leading output limit of one million tokens, a significant leap from the previous 64,000. In practical terms, this means the model can sustain deep, uninterrupted reasoning across far longer and more complex tasks without losing context. Google has already put Argon to work internally, where it has been used to migrate large codebases, optimise memory usage across data centres, and assist quantum computing researchers, in one case beating a published performance baseline by 40% in minutes. On AutomationBench, Zapier’s benchmark measuring execution across core business functions, Argon ranks first with a score of 51.3%. It also leads on benchmarks covering financial research, legal drafting and long-form video understanding.

A New Frontier for Cybersecurity

One of Argon’s most significant capabilities is its ability to autonomously identify, validate and patch software vulnerabilities, something previous models have not been able to do reliably. Google has already deployed it through its Fairwind Program with cybersecurity firm Wiz, where it uncovered a critical vulnerability in healthcare software used by hospitals worldwide, a risk that earlier frontier models had missed entirely. For organisations managing complex digital infrastructure, this represents a meaningful shift in what AI-assisted security can deliver in practice.

What This Means for Businesses Watching the AI Space

Argon is not yet publicly available, and Google is being deliberately cautious about the rollout, investing in safeguards against misuse before opening broader access. Pricing starts at $2 per million input tokens and $10 per million output tokens at the introductory rate. For businesses tracking how AI is reshaping the tools they rely on, from campaign automation to data analysis to content workflows, Argon signals the direction of travel clearly. The gap between what AI can handle and what previously required specialist human expertise is closing faster than most organisations have planned for.

For the first time, we have controlled evidence of what Google’s AI Mode does to website traffic. When searchers were moved onto AI Mode, the share of searches that ended in a click to an external website fell by 18.8 percentage points. Users also trusted Google less and were more likely to try another search engine.

What happened

Researchers from the University of Pennsylvania and Northeastern University published the first randomised controlled experiment into how Google’s AI features change search behaviour. The paper, AI in Search Reduces Publisher Referrals Without Improving User Experience, was released in August 2026 and preregistered before the data was collected.

Most studies of AI search compare traffic before and after a change, which makes it hard to separate AI from seasonality, algorithm updates and everything else going on. This one did something different. A browser extension randomly placed US Google users into one of three groups for a week:

1. No AI: AI Overviews hidden and AI Mode redirected to normal results.
2. Current Google: search left exactly as it is today.
3. AI Mode: every search redirected to AI Mode.

Because the groups were assigned at random, differences in clicks can be attributed to the AI experience itself rather than to who the users were or what they searched for. That is why this study carries more weight than the many traffic analyses published over the past year.

What the study found

AI Mode reduced clicks to almost every type of destination. Removing AI from the results did the opposite, and users did not miss it.

The no-AI group clicked through 8.8 percentage points more often than people using Google as normal, with no measurable change in satisfaction. Put simply, the AI layer is taking clicks without making search feel better.

Users in the AI Mode group rated Google lower on trust, satisfaction, usefulness and relevance, and said they were more likely to switch to Bing. They also searched less often, by almost one session a day.

The test ran for one week, and the sample skewed younger and more educated than the UK search population. AI Mode was forced on people when, at the start of the study, it accounted for only 0.6% of searches. Google also changed its page code mid-study, so only around half of AI Overviews were successfully hidden in the no-AI group. That means the 8.8 point uplift is probably an underestimate.

Why it matters

This moves the AI search debate from opinion to evidence. Google has repeatedly said AI features send “higher quality” clicks and that people are searching more. This study shows that, when AI Mode becomes the default experience, fewer searches lead anywhere outside Google.

For most businesses, today’s impact is limited because AI Mode is still opt-in for most users. The risk lies in what happens if Google makes AI Mode more prominent, or the default. A site that relies on informational content to bring people in at the top of the funnel would see a meaningful drop in visits, even if its rankings stayed exactly the same.

There is a reporting consequence too. If traffic falls while rankings and impressions hold steady, it is easy to assume something has gone wrong with the SEO. In reality, the search results page is simply answering more of the question itself. Teams that track only sessions will misread what is happening.

Paid search is not immune either. The researchers recorded a large drop in ad clicks in the AI Mode group, but the study was run before Google began placing ads inside AI Mode, so that figure says more about where ads sit today than where they will sit next year.

What we recommend

There is no need to panic. These are the steps to take now so that a wider AI Mode rollout does not catch you off guard.

1. Separate visibility from visits in your reporting. Track impressions, rankings and AI citations alongside sessions. If impressions hold and clicks fall, that is a change in the results page, not a loss of position.
2. Find out which pages are exposed. Pages that answer simple, informational questions are the most likely to lose clicks to an AI summary. Pages built around a decision, a booking or a purchase are much harder for AI to replace.
3. Give people a reason to click. Original data, pricing, case studies, tools, expert opinion and clear next steps all offer something a summary cannot. Generic explainers are the content most at risk.
4. Earn the citation. Being named or linked in an AI answer is now a form of visibility in its own right. That means clear, well-structured pages, a credible author and mentions on trusted third-party sites.
5. Diversify where demand comes from. Email, social, referral partners and direct traffic all reduce reliance on a single results page that Google can redesign at any time.
6. Watch the conversion rate, not just the volume. If fewer but better-informed people arrive, enquiries per visit should rise. Measure leads and revenue, not sessions alone

The emergence of AI technology in recent years has seen the rise of numerous different AI chatbots with varying degrees of popularity. These chatbots differ significantly in terms of research, writing, coding and productivity. In this article, we’ll run through these chatbots, discussing their pros and cons so you can decide which chatbot is the best for your needs.

ChatGPT

ChatGPT, made by OpenAI, is currently the most popular chatbot being used and is a strong general-purpose assistant, though perhaps not as specialised as some of the other AI chatbots. ChatGPT can use the web to research in real time and also has strong coding and data analysis tools.

However, just like all AI chatbots, given their infancy, ChatGPT has its drawbacks. It can still produce inaccurate information, and some of its more advanced capabilities require paid plans to access.

Claude

Claude is rapidly rising in popularity as one of the most popular chatbots. Claude is generally considered to be the best chatbot for coding capabilities and is used by many software developers to aid workflows. Similarly to ChatGPT, Claude also has strong capabilities when it comes to writing and analysing/ proofreading documents.

Claude is, however, less impactful than some competitors such as ChatGPT and Perplexity when it comes to web research, as this isn’t central to the experience like it is with Perplexity.

Google Gemini

Gemini has some distinct advantages compared to other AI chatbots, due to it being part of Google’s ecosystem, which dominates online search. If your work already revolves around Gmail, Drive, Docs and other Google services. This also gives Google the advantage of being able to perform real-time deep research.

If you don’t typically use Google products and use Microsoft products more readily, such as Office and Outlook, Gemini’s big advantage disappears.

Perplexity

Perplexity stands out from other AI chatbots because of its focus on search and research. Rather than simply generating an answer, it searches the web for relevant information and provides citations that allow you to check the original sources. This makes it particularly useful for research, fact-finding and questions where up-to-date information is important.

However, Perplexity is more search-focused than some of its competitors. While it can still help with writing and other everyday AI tasks, users looking primarily for creative writing, coding, or a broader general-purpose assistant may find alternatives such as ChatGPT or Claude more suitable.

Microsoft Copilot

Much like Gemini for Google, Microsoft Copilot is particularly useful for businesses and professionals who already work with Microsoft products. Its integration with Microsoft 365 means it can assist with tasks across applications such as Word, Excel, PowerPoint, Outlook and Teams, making it a practical AI assistant for everyday workplace productivity.

The main drawback is that much of Copilot’s appeal comes from these Microsoft integrations. If you aren’t heavily invested in the Microsoft ecosystem, other AI chatbots may offer greater flexibility for general-purpose use.

Ultimately, the best AI chatbot depends on what you want to use it for. ChatGPT is a strong all-round option with a wide range of capabilities, while Claude stands out for writing and working with large amounts of information. Gemini is particularly useful for those already using Google’s ecosystem, whereas Microsoft Copilot makes sense for businesses and professionals who regularly use Microsoft 365. For research and finding information online, Perplexity offers a search-focused approach with clear access to its sources.

As AI chatbots continue to develop, the differences between them are becoming less about which one is simply the best and more about which one fits your individual needs and existing workflow. Since most of these platforms offer free versions, it is worth trying a few before deciding which chatbot works best for you.

A survey of 343 marketing decision-makers published this month by Fractl contains one finding that cuts through all the noise about what to call AI search optimisation: 81% of marketers still describe their AI search visibility strategy as SEO. Not GEO. Not AEO. Not LLM optimisation. Just SEO.

This matters, but probably not in the way the commentary usually suggests. The reaction from most of the industry has been to frame this as a problem – a gap between where search is heading and where practitioner vocabulary has got to. But the data tells a more nuanced story, and the practical implications for anyone managing digital marketing in 2026 are worth reading carefully.

What the data actually says

The headline number – 81% still calling it SEO – sits alongside several others that are worth considering together. Marketers are allocating an average of 24% of their total search or content budget to AI search visibility. Up to 82% have committed at least some budget to it. 43% are allocating more than 20%. This is not a situation where the vocabulary gap reflects an investment gap. The money is moving. The terminology hasn’t caught up.

The terminology gap is also unevenly distributed. Senior leaders use newer terms like GEO and AEO at roughly three times the rate of individual contributors. C-suite marketers are more likely to have encountered these acronyms in board decks or conference presentations and to have adopted them into their own language. The people actually doing the work – the SEO specialists, the content managers, the performance marketers – are largely still working within the SEO frame. That’s a meaningful distinction for anyone trying to have a coherent conversation about strategy across a mixed team.

Half of marketers surveyed said they had researched a term they didn’t know after seeing it used by a peer or competitor. Only 27% of marketing teams have formally adopted a term beyond SEO. 42% have actively decided against it. 31% haven’t decided. This is not the profile of an industry that has broadly accepted a new vocabulary. It’s the profile of an industry where the vocabulary is being imposed top-down and hasn’t bedded in.

What this means for how clients and internal teams behave

The most commercially useful finding in the research is about vendor credibility. When decision-makers evaluate a supplier or agency for AI search work, the credibility signals they trust are strikingly familiar. Case studies with measurable, verifiable results were the top signal, selected by 34% of respondents. Clear methodology came second at 22%. Team expertise and track record came third at 15%. Terminology fluency ranked at just 9%.

The red flag data is equally direct. The biggest single turnoff when evaluating a vendor was heavy use of buzzwords without a clear explanation of what each term means – cited by 36% of respondents. Another 21% pointed to a lack of case studies or trackable results. 20% flagged vague or unsubstantiated performance claims. And 16% specifically raised concerns about repackaged SEO services with new AI branding.

The practical implication is clear. Clients and internal decision-makers want to understand what has changed, what you are doing about it, and what happened as a result. The shift from rankings to recognition in how AI systems surface brands is a real and significant change. But calling it GEO in a pitch deck and not explaining what that means in practice is actively counterproductive. The acronym doesn’t do the work. The explanation does.

The platform prioritisation problem

Platform focus among marketing teams is currently fragmented in a way that reflects the broader unsettled state of the category. ChatGPT leads, with 34% of marketers naming it as their primary AI search visibility priority. Gemini is second at 16%. Claude is at 6%, Copilot and Bing AI at 5%. Only 1% named Perplexity, despite the significant attention it receives within SEO and AI professional circles. 14% of teams haven’t picked a target platform at all.

This matters in context of something we looked at earlier in the year: 91% of AI citations appear in only one engine. If you’re cited on ChatGPT, the odds are you’re not being cited on Perplexity or in AI Overviews. A single-platform AI visibility strategy is almost certainly leaving material gaps. But the research suggests most teams haven’t yet developed a multi-platform approach, and a significant minority haven’t even committed to a primary platform to focus on first.

The research authors’ advice is sensible here: avoid both extremes. Don’t dismiss AI search because your traffic hasn’t collapsed. But don’t overcorrect by chasing every new answer engine separately. Start by identifying where AI search is already influencing discovery in your category, then build outward from there.

The feedback loop that changes what visibility means for agencies

The most practically significant finding for agencies is buried toward the end of the research. Two thirds of marketing decision-makers said they had personally used an AI tool – ChatGPT, Perplexity, or Gemini – to research or evaluate a marketing vendor or agency. Adoption is highest among the groups most likely to influence search investment: SEO specialists (89%), performance marketers (84%) and C-suite leaders (81%).

This creates a feedback loop that redefines what agency visibility means. A prospective client may search Google, ask peers in a Slack community, and then prompt ChatGPT before they ever fill out a contact form. Those surfaces are not separate. They reinforce each other. If an agency is recommending AI search visibility to clients but is not itself visible in AI search answers about marketing agencies, that is a credibility problem that will compound over time.

The practical audit for any agency is to run the prompts your prospective clients are likely to run. Ask which agencies specialise in AI search visibility, which SEO firms have credible case studies, how a specific agency compares with competitors, and which vendors are trusted for this kind of work. Then look at the sources shaping those answers, and identify where you are and aren’t present.

The useful takeaway from the naming debate

The argument about what to call AI search optimisation has consumed a remarkable amount of industry energy for something the data suggests buyers largely don’t care about. The research is direct on this: vocabulary fluency ranked at 9% as a credibility signal, while case studies, clear methodology, and track record together accounted for 71%.

What the naming debate does reveal, though, is something more useful. The fact that 81% of practitioners still default to SEO is not just a vocabulary lag. It reflects a genuine continuity in what the underlying work involves. The content has to be specific and authoritative. The technical accessibility has to be right for crawlers and AI systems alike. Third-party credibility signals still matter. The entities – brand, people, place – need to be correctly understood and associated. These are things SEO has always been about. What has changed is where the work surfaces and how it gets measured.

The teams that are doing well in AI search are not necessarily the ones that adopted GEO terminology earliest. They are the ones that understood what changed in how AI systems decide what to cite, built their content and credibility accordingly, and can demonstrate the results. That is a methodology problem, not a naming problem. And it is the one worth spending time on.

AI has its applications, but recently, more and more AI-generated menus, posters, and marketing materials have been cropping up. This has led to many online and offline comments about a lack of creativity, inaccurate representation, and a generally lazy approach to asset creation.

The core issues with AI-generated content

Beyond customers disengaging from AI-generated assets, there are several issues that are apparent with this approach. The key one is creative opportunities being taken from skilled designers, photographers and copywriters. Outsourcing content creation to AI snubs very capable content creators out of work.

So why is this an issue? Well, aside from the lack of work, it also raises issues around quality, styling, and effort. All of which creatives provide when designing marketing materials.

Other issues that present with AI-generated designs are the inconsistent quality. We observe a lack of textural accuracy with things like food items, with burger buns looking ultra shiny, burger patties looking bumpy, poorly coloured, and just generally not like food. Proportions are off, the colour palettes are often entirely different from the products, and “creative” liberties are taken with the overall styling.

What it highlights is the need for accurate media materials, especially when marketing materials like food menus are being distributed. There’s an art to food photography, which is excellent for inducing cravings and highlighting the nature of food. Think splashing drinks, dripping sauces, bites from food, which actually look like they’ve come from a person. These particular stylings help to sell a product, and AI rarely gets it right.

A key issue around AI-generated content, which we are yet to see the fallout for, is the fact that these engines will utilise training assets to develop designs. This could result in copy being plagiarised, designs verging on copyright infringement, and IP being stolen and repurposed for a cheap and quick solution.

Each compounded issue can very quickly erode the trust of customers, especially without explicit labelling that AI has been used. A core value of a great business is transparency, and without that, customers can be left wondering and assuming what else is disingenuous about the business.

Why do AI-generated assets bother customers?

As we touched on, distrust is a core problem raised with AI use. In addition to distrust, AI-slop can make customers feel:

• Like the materials don’t align with their experience, with the expectation versus reality causing a strong response and dislike.
• Feeling misled in their decision, whether that’s from inaccurate copy or added ingredients in generated food images.
• The erosion of the human experience; it’s like a chain reaction of distrust. If the menu is AI-generated, then what will be next? People want human experiences to be at the forefront of their experience, not being led by AI, which feels cold and lazy.

An insight into the audience’s response

We see a number of users across channels showing the same disdain for AI-generated content.

Keeping the Human Element in Marketing

AI can offer businesses useful ways to streamline content creation, but the quality and authenticity of the final result still matter. When marketing assets feel disconnected from the real product or experience, they can affect how customers perceive a brand.

Human creativity, expertise and attention to detail remain valuable in creating content that accurately represents what a business offers. By using technology alongside these skills, rather than allowing it to replace them entirely, businesses can benefit from greater efficiency without losing the human element that customers connect with.