ChatGPT Ads has hit $1 billion in annualised revenue in under 200 days. To put that in context, it took Google AdWords several years to reach comparable scale. OpenAI has built a meaningful advertising business at remarkable speed, and this week it expanded self-service access to India, Europe, the Middle East, and North Africa, meaning businesses in those regions can now launch campaigns directly through Ads Manager without routing the buy through an agency or managed-sales team. For marketers watching where audiences are spending their time and attention, this is a development worth taking seriously.

Why ChatGPT Ads Is a Different Kind of Advertising

The proposition OpenAI is building is fundamentally different from traditional search advertising. Rather than intercepting someone who has typed a keyword, ChatGPT Ads places your brand inside an active conversation. OpenAI describes its users as arriving already partway through a decision, comparing options, planning a purchase, researching a job change. An ad served in that moment is not interrupting a search result; it is entering an existing thought process. Ads are clearly labelled as sponsored and appear separately from ChatGPT’s answers, and OpenAI states that advertising does not influence what the AI tells users. Targeting is based on the content of the current conversation and, where users have consented, their broader interests and preferences.

The Numbers Behind the Platform

Tens of thousands of advertisers are already active on the platform, which is now live in more than 40 countries. The majority of campaigns run on cost-per-click or outcome-optimised bidding. OpenAI has pointed to early results from advertisers including one ecommerce brand that reportedly achieved a 3x return on ad spend over 28 days, and a technology partner that found over 80% of ad-driven ChatGPT traffic came from new customers. Small and medium-sized businesses represent a material share of total ad revenue, and international advertisers account for a growing proportion of spend.

The Regulatory Dimension for European Advertisers

Alongside the commercial expansion, the European Commission has designated ChatGPT as a Very Large Online Search Engine under the Digital Services Act, giving it the same enhanced regulatory status as Google Search and Bing. OpenAI now has until January 2027 to meet the additional obligations that come with that designation, covering areas including algorithmic risk assessment, protection of minors, and safeguarding users’ rights under EU law. For brands advertising into European markets through ChatGPT, this is worth monitoring. Increased regulatory scrutiny tends to bring greater transparency requirements, which in turn affects how platforms can target and how advertisers can measure. Getting familiar with the platform now, before those rules bed in, puts you in a stronger position than waiting to see how it develops.

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.

Why doesn’t a new website, an SEO campaign or even substantial improvements to an existing site not translate into immediate ranking gains? This can be a core frustration of clientele in the SEO industry that are using their marketing budget and expecting immediate results. In this article, we will set expectations for how long it really takes to rank on Google and why Google doesn’t rank changes immediately.

How long does it take to rank on Google 

Google’s John Mueller recently stated that it can take many months for a website to rank on Google after making changes,

“Fixing issues around bigger algorithmic changes can take quite some time – sometimes many months – until you see the effects.”

Mueller’s comments were in response to a redditor who is currently encountering errors with his site including having his Google My Business profile suspended and his rankings dropping due to accidentally disavowing some important backlinks.

However, Mueller’s comments don’t only matter when it comes to ranking drops, any SEO changes to a website can take a long time to take effect including starting a new site, running campaigns or making large scale changes. 

Why doesn’t Google change rankings immediately

Google can’t change rankings immediately because there is a process that needs to take place to make sure rankings are accurate and reliable for Google’s users. 

Google first has to crawl and index your pages. This is basically the process where Google uses bots to read and understand your content so it can show it to the right audience. However there are billions of pages on the internet and Google is constantly crawling them every day. Your site may not be crawled for some weeks and even then Google has to understand and categorise your content and decide how it stacks up against other competing content.   

You can help fast track how quickly your site is crawled and indexed by Google by submitting your sitemap to Google via Search Console so it knows which pages to crawl, but still don’t expect your rankings to immediately improve. 

When should you be concerned about lack of rankings

Obviously just because we have said you need to be patient to achieve your rankings, doesn’t mean that you should just “wait a few months” and not identify any issues that could potentially cause you not to rank. Some key issues that could stop you from ranking include.

• Important pages aren’t crawled and indexed
• Crawling is being blocked
• Migration causes ranking losses
• Pages disappear from Google’s index

How Long Should You Give SEO?

There isn’t a universal SEO timeline. A low-competition local website and a brand-new business targeting highly competitive national keywords are completely different propositions, however making sure you have submitted your sitemap to Google and major pages are available to be indexed on your site is your quickest route to ranking improvements. 

SEO is a fast paced ever changing industry and those that work inside of it constantly need to adapt to survive to the whims of search engines such as Google and Bing. This statement is now more true than ever with the rise of AI, the way SEOs interact with their work has changed forever. Integrated AI Machine learning, MCPs and Chatbots now mean tasks that once took weeks or months can now be completed in a matter of hours or days. Many in the SEO industry are frightened by the way that AI is being used to automate many tasks, leaving the question; is there a future for the SEO industry?

How GEO impacts the future of SEO

The immediate answer to how AI is going to shape the future of the SEO industry is already upon us with Generative Engine Optimisation. GEO is essentially a new branch of SEO that is focused on optimising content for generative answers engines, such as AI Overviews and ChatGPT, rather than traditional search engines. 

SEOs will need to pivot to learning how to optimise for GEO, as more and more users (especially of a younger generation) are turning to chatbots to answer any queries they might have online. While traditional search engines remain the default for most people, studies show that roughly 37% to 50% of consumers regularly start their research or purchase decisions with AI tools rather than standard Google searches, as per Danny Goodwin for Search Engine Land.

Optimising for GEO is not too dissimilar from optimising your content from SEO however they both have their own set of priorities. Some main aspects of optimising for GEO include: 

• Frontloading answers – Write directs answers to users questions in your content so they can be pulled out and extracted by chatbots
• Writing conversationally – Matching how people use language in their chat prompts
• Format for machines – Use bullet points, tables etc. that chatbots can easily scan for information to extract 
• Add statistics and citations – Make sure to link to authoritative pages and use accurate data points so AI sees your content as credible

Why the Industry is safe from AI

How about further beyond into the future, say 10, 20 years, what will the landscape for SEO look like then? Here at Intelligency, we believe that the industry will adapt but we don’t believe it will be wiped out. SEO may become obsolete and evolve permanently into GEO, but the question remains will AI permanently take over the sector and we believe the answer to that is no.

Companies that hand over all their SEO operations to AI run the risk of producing generic, low quality content that is duplicated causing their rankings to drop, and while the use of chatbots is rising, traditional search still has a massive grip on the market meaning any significant ranking drops will see a steep decline in  business. 

Even if businesses become more reliant on AI in the future (which is already happening) ultimately if they are competing for customers online, there will always need to be a n SEO to feed the information to the AI that it needs to make SEO decisions and to control the output, avoiding hallucinations. The SEO industry may adapt, change and become more reliant on AI over the years, but it will survive. 

Google AI Mode, the conversational search experience powered by Gemini, has now passed one billion monthly users. That is not a figure to file away for later. It represents a fundamental shift in how people research and discover products online, and for ecommerce brands it raises an urgent and largely misunderstood question: how do you actually advertise in it? The answer is not what most paid media teams expect, and the work required often sits in a part of the business that advertising teams rarely control.

Your Product Feed Is Now Your Ad Creative

In Google AI Mode, nobody writes the ads. Google’s Gemini AI builds them in real time from your Merchant Center product feed, assembling a response tailored to the specific question a shopper has asked. The factor that determines whether your product appears is not your bidding strategy or targeting setup. It is the quality and specificity of your product data. This matters more than ever because searches in AI Mode run roughly three times longer than traditional searches. Shoppers are not typing “running shoes.” They are asking for a neutral running shoe with extra cushioning, under a certain budget, that does not squeak on wet pavement. Only a detailed, well-structured feed can answer that.

The New Ad Formats and How Eligibility Works

Google introduced four new Gemini-powered ad formats at its Marketing Live event, three of which are directly relevant to ecommerce: Conversational Discovery Ads, which appear as a sponsored response within the AI conversation; Highlighted Answers, which place your product alongside organic AI recommendations; and AI-powered Shopping Ads, which run on Search for considered purchases with a Gemini-written explainer tailored to each query. You cannot target these placements directly. Eligibility comes from running Performance Max, AI Max for Search and Shopping, and Shopping campaigns, all with Smart Bidding enabled. Once eligible, Gemini decides when your product is a strong enough match to surface.

What to Prioritise in Your Feed Right Now

Google has added optional conversational attributes to Merchant Center built specifically for AI surfaces. The most valuable is the question and answer attribute, which lets you submit FAQ-style pairs as structured data, exactly the format a conversational AI wants to pull from. Most brands already have this content in their product page FAQs or customer service logs. Turning it into structured feed data is one of the highest-value actions available right now. Beyond that, focus on richer titles, detailed descriptions naming materials and use cases, and the specific attributes AI-powered Shopping reads when writing its per-query explainers. Start with your highest-spend SKUs. Those are the products most likely to be asked about, and where a stronger feed pays back fastest.

Google updated its tracking tools at the end of August in a way that makes accurate measurement meaningfully more accessible. If your website uses Google Ads or Google Analytics, this affects how you set up and manage conversion tracking – the data that tells you whether your marketing is actually working.

What changed in plain English

Google runs two separate tracking tools: Google tag, which is a snippet of code that sits on your website, and Google Tag Manager, a more powerful system that lets you manage all your tracking in one place without touching your website code repeatedly. Until now, they have operated as separate tools. Google is bringing them together.

The most significant change is that you can now set up conversion tracking by clicking through your website the same way a customer would, and Google will configure the technical setup for you. You navigate to the thing you want to track – a purchase, a form submission, a button click – and Google works out the rest. No code required. No developer needed.

Google has also simplified the Tag Manager interface, making it easier to see at a glance which of your Google tools are receiving data and where potential problems might be. And data now moves from your website to Google faster, which improves both measurement accuracy and, in some cases, page load speed.

Why conversion tracking matters so much

Conversion tracking tells Google’s advertising system which clicks led to results – a booking, an enquiry, a purchase. Google’s automated bidding uses this data constantly to decide where to show your ads and how much to spend. If your tracking is incomplete or misconfigured, the system is effectively making decisions based on partial information. It will spend your budget less efficiently than it otherwise could.

This is why the no-code event tracking matters commercially, not just technically. Getting tracking right has historically required either a developer or a working knowledge of tag management tools. That means many smaller advertisers have been running campaigns on imperfect data without realising it. Visual, code-free tracking directly reduces that problem.

What to do if you already use Google Tag Manager

Google will prompt you to optimise your existing setup, but it will not make any changes automatically. You will see a banner in your account offering to optimise your container. Before you accept, review what changes are being proposed. If your setup is straightforward, the optimisation is unlikely to cause problems. If you have a more complex configuration – custom triggers, unusual variable setups, or anything built by a previous developer or agency – take the time to understand what is changing before publishing.

The preview function shows you exactly what will change. Use it. Measurement is one of those things where getting it wrong quietly is worse than not changing anything, because bad data looks like good data until you start making decisions based on it.

The broader point worth taking from this

Google is consistently moving toward making accurate measurement accessible to businesses that do not have dedicated analytics or technical teams. That is a useful direction. Accurate conversion data is one of the few inputs that reliably improves campaign performance across everything else you do, and measuring what matters commercially rather than what is technically easy to count has always been the right starting point. These tools make it easier to get that right without needing a specialist every time something needs updating.

If your conversion tracking has been set up but you are not sure exactly what it is measuring or whether it is capturing everything it should, now is a reasonable time to review it. The tools for doing that have just become easier to use.

The number one priority for technical SEOs in 2026 should be making sure that AI bots can find and retrieve your content. AI is growingly rapidly and not only are AI chatbots such as Claude and ChatGPT continuing to grow in popularity but Google is also slowly becoming dominated by AI; first with AI Overviews and now with the introduction of AI Mode, making sure AI chatbots can retrieve your content is therefore, more paramount than ever.

Crawlability and Indexation 

The main focus for SEOs when technically optimising for AI chatbots should be to make sure the crawlability and indexability of your site is fully optimised. This essentially means that AI can go through the content on your website and retrieve any relevant information that gives it an insight into what your website is about and therefore which queries it should use to place you in front of the correct audience. 

The most imperative step you can take to make sure your content can be easily crawled and indexed is to make sure your robots.txt file allows for AI crawlers. A robots.txt file is A robots.txt file is a plain text file placed in a website’s root directory to tell web crawlers and search engine bots which pages or folders they can and cannot access. A robots.txt file can inadvertently block crawlers as old robots.txt files can have rules written in them that block content retrieval bots. Make sure you have parsed through your robots.txt and it is fully optimised to allow AI crawlers to access your content. 

An XML sitemap also helps AI crawlers crawl your site efficiently. This is basically what it says in the name; a map of your site that tells crawlers how your site is structured and what content needs visiting and is important. You can make sure your sitemap is routinely up to date by submitting it to Google Search Console whenever you make changes. 

JavaScript rendering is also a problem when it comes to crawlability for AI visibility. None of the main AI crawlers render JavaScript which means if your site is built using heavy JavaScript injections, AI chatbots will not be able to understand your content properly. To fix this make sure you are using HTML for critical content.  

Schema Markup 

Schema markup is structured data injected into the HTML of a webpage to give crawlers and search engines bots more information about what your web page represents. Schema is important for AI because it helps give specific information about each page on your website to AI chatbots.

It’s important when you’re applying a schema to your site that it is applied correctly. Common mistakes people make include using the wrong type of schema. Such as using product schema for a normal service page or using irrelevant schema just because it’s available. A simple way to explain correct vs incorrect application is: good schema accurately describes what the page and its entities actually represent; bad schema tries to tell search engines something different, exaggerated, irrelevant, or outdated. 

Ultimately, optimising for AI doesn’t mean abandoning traditional technical SEO principles. In many cases, it means getting the fundamentals right. If AI crawlers can’t access your pages, retrieve your content or clearly understand the entities and information on them, even the best content strategy will struggle to achieve consistent AI visibility.

Start by making your website as easy as possible for both search engines and AI systems to access and interpret. Keep your robots.txt and XML sitemap clean, make important content available within the HTML rather than relying heavily on JavaScript, and use accurate schema markup to provide additional context.

Google released the August 2026 spam update on 18 August, logged on its Search Status Dashboard at 9:28am Pacific time. It applies globally and to all languages, and Google says the rollout may take a few days to complete. This is the third spam update Google has released in 2026, following updates in March and June.

If your rankings are stable, you can broadly relax. But if you have seen movement in the last few days, or if your site is in an area where spam tends to accumulate, it is worth knowing what these updates actually target and how to respond sensibly.

What a spam update is and what it is not

The distinction between a spam update and a core update matters because they work differently and require different responses.

• A core update changes how Google weighs quality signals across the whole web. It is a recalibration of the algorithm’s judgement about what constitutes a good, helpful, trustworthy result. Core updates affect sites that are technically fine but that Google has decided to rank differently against its quality criteria. Recovery from a core update usually requires improving the overall quality and relevance of your content.
• A spam update is different. It improves the detection systems that identify violations of Google’s existing spam policies. Think of it as the bank upgrading its fraud detection rather than rewriting what counts as fraud. No new spam categories have been announced with this update. The policies themselves, which have not changed since December 2025, remain the reference point. Google is simply getting better at catching sites that violate rules that were already in place.

The practical implication is that if your site was not violating spam policies before this update, you are unlikely to have been hit by it. And if you have been hit, the path forward is not to improve content quality in the core update sense but to identify and remove whatever the policy violation is.

What the 2026 spam updates have been targeting

Google has been quiet about the specific targets of all three 2026 spam updates, which is standard practice. It confirmed no new policy types alongside the August release and described it on LinkedIn as a normal spam update. But the context around the June update, which came shortly after Google formally extended its spam policies to cover AI Overviews and AI Mode, is worth noting.

On 15 May 2026, Google clarified that its existing spam policies now explicitly include attempts to manipulate generative AI responses in Google Search. That means tactics designed to force brand mentions into AI Overviews or AI Mode responses carry the same demotion risk as other forms of search spam. Google did not confirm that the June update enforced this provision, and has not confirmed it for August either. But the timing of three spam updates in a year that saw AI spam policies extended is not a coincidence worth ignoring.

This connects to something we have been tracking across this series: the difference between manufactured AI visibility tactics and genuine brand recognition. If Google’s spam systems are being improved partly to catch artificial AI citation manipulation, that reinforces the case for building real presence rather than gaming the signal.

Google spam policies: what they actually cover

If you want to check your site against what Google’s spam updates enforce, the reference document is Google’s spam policies page. The main categories it covers are:

• Cloaking. Showing different content to Googlebot than to users. A persistent and straightforward violation that spam updates have targeted for years.
• Doorway pages. Pages created to rank for specific queries that funnel users to a different destination. A common pattern in local SEO abuse where hundreds of near-identical location pages are generated with minimal unique content.
• Hacked content. Content placed on a site without the owner’s knowledge. Spam updates improve detection of sites that have been compromised and are being used to host or redirect spam. Relevant given the Premier Laser site incident this week where a related site was hacked.
• Hidden text and links. Content that is visible to crawlers but hidden from users. Still actively targeted despite being one of the older spam categories.
• Scaled content abuse. Creating large volumes of low-quality content primarily to manipulate rankings. This category was formalised in March 2024 and is directly relevant to AI-generated content published at scale without meaningful human review or original value.
• Link spam. Buying, selling or exchanging links to manipulate PageRank. A long-standing category with an important asymmetry: once Google neutralises spammy links, the ranking benefit they conferred does not return. Recovery restores the site to where it would have been without them, not to its prior position.
• Site reputation abuse. Publishing third-party content on an established site to exploit its authority signals. This runs on its own enforcement track since September 2024 and is separate from standard spam updates.
• AI manipulation of search features. Tactics designed to artificially influence appearances in AI Overviews or AI Mode. Formally added to spam policy in May 2026. Enforcement scope in recent updates has not been officially confirmed but the policy exists.

How to read your data during a rollout

One important piece of context for the August update: Search Console performance reports experienced a logging failure starting around 12 August that caused a decrease in reported impressions and clicks. Google confirmed this on 17 August. The logging issue was a data problem, not a ranking event. Sites reading their August data for evidence of an unannounced update were partly reading a broken meter, not their actual search performance.

This matters for how you interpret any movement you see around this period. If you have seen impression or click drops since 12 August, check whether they fit the pattern of the logging issue rather than assuming a ranking change. Google has said the logging problem is being resolved.

For the spam update itself, the sensible approach is:

• Wait until Google confirms the rollout is complete before drawing conclusions. Previous spam updates have taken between 19 hours and 48 hours. Google’s estimate for this one is a few days with no completion date named.
• Look for sustained changes rather than single-day movement. Volatility during a rollout is normal and some of it reflects ranking churn that settles after completion.
• If you see a genuine, sustained drop, compare it against the spam policy categories above. The most common causes are link-related or content-volume-related rather than technical.
• If you are clean, the rollout will complete and nothing will have changed for you. Spam updates do not affect sites that are not violating the policies they enforce.

The broader pattern behind three updates in one year

Three spam updates in eight months is a higher cadence than Google has run in previous years. The March update was the fastest confirmed rollout in dashboard history at under 20 hours. The June update followed within days of Google formally extending spam policy to AI features. The August update has arrived with no new policy announcements but in a year where the signals that drive genuine search and AI visibility are increasingly distinct from the signals that spam tactics try to manufacture.

The consistent message across all three updates is that Google is investing in better spam detection rather than changing the rules. For sites operating within the guidelines, that is straightforwardly good news. Better detection means less noise from spam competitors in the results, cleaner ranking signals and more reliable performance data. The businesses most affected by spam updates are those trying to take shortcuts that the policies have prohibited for years. The rest can monitor this one as it completes and then get back to building the kind of presence that compounds over time.

Google has rolled out a series of AI-powered updates across both Google Ads and Google Analytics, making it easier for advertisers to understand how their campaigns are performing and where the opportunities for improvement lie. While the changes are incremental rather than transformational, they represent a clear direction of travel: Google wants its platforms to surface the right information at the right time, without you having to go looking for it. For businesses managing ad spend across multiple campaigns, that shift in how data is presented can make a meaningful practical difference.

A Smarter Starting Point in Google Analytics

The most immediately visible change is the addition of AI Overviews at the top of the Google Analytics homepage. Rather than landing in a dashboard full of raw data and having to work out what matters, you now see an instant AI-generated summary of the most important recent developments, covering traffic changes, sales activity and other key events. From there, you can ask follow-up questions in plain language to explore any of those highlights in more depth. It is a significant quality-of-life improvement for anyone who spends time in Analytics regularly, and particularly useful for business owners or marketing managers who need a quick read on performance without deep-diving into the data themselves.

Personalised Insights Built Into Google Ads

Google has also refreshed the Google Ads homepage to display AI-powered insight cards tailored to each individual account. Rather than a generic overview, you now see information relevant to your specific campaigns, along with a prompt box that allows you to generate custom insights based on what you actually want to know. Whether you are trying to understand how a competitor is affecting your impression share or identify trends that could improve campaign performance, the intent is to give you the context to make faster, more confident decisions. Google is also developing new dashboard visualisations that convert raw data into clear charts and summaries using simple text prompts, powered by its Gemini AI models.

Benchmarking Against the Competition

Perhaps the most strategically useful addition is a new benchmarking tool within Google Analytics, which will show how your performance compares to competitors in your category. For brands that have lacked a reliable way to contextualise their numbers, this fills a genuine gap. Knowing that your conversion rate is above or below the industry average is far more actionable than seeing the figure in isolation. Taken together, these updates reflect Google’s broader push to make its advertising platforms more accessible and more useful, reducing the gap between the data available and the decisions that data should be driving.

Anthropic announced this week that it is adding invisible, machine-readable watermarks to text generated by new Claude models. The rollout is global, covering Claude, the Claude API, Claude Code, Claude Cowork and Claude Tag. The trigger is the European Union’s AI Act transparency code, but Anthropic has confirmed it is not limiting the feature to Europe – it is being enabled everywhere Claude is available.

For anyone using Claude as part of their content workflow, whether to draft, edit, refine or produce marketing material, this is worth understanding. It does not change what the tool does or how it reads. But it changes how that content can be identified, and that has implications that are already beginning to ripple through discussions about disclosure, authenticity and AI detection.

What the watermark actually is

The watermark Anthropic is embedding is not a visible label or a footer. It is an imperceptible, machine-readable signal woven into the text itself. Anthropic says it does not change the meaning, quality or readability of the output. To the person reading it, nothing looks different. The mark is invisible to the human eye.

What the watermark does is allow a detection tool to confirm, with high probability, whether a given piece of text was generated by a supported Claude model. Think of it as a fingerprint in the language patterns rather than a stamp on the page.

Anthropic is also working to apply watermarking retrospectively to Claude models released before 2 August 2026, during the AI Act’s transition period. So this is not just about new models going forward – it is being applied more broadly over time.

What it does not do

Two caveats Anthropic has been explicit about are worth flagging because they change the practical implications considerably.

• It does not prove authorship of ideas. The watermark confirms that a Claude model generated the text. It does not mean Claude originated the ideas, research or creative direction. A human could have provided all of that and used Claude only to write up the output. The watermark captures the generation tool, not the intellectual source.
• Absence of a watermark is not proof of human authorship. Older models, heavily edited text and files with stripped metadata may not carry a detectable watermark. Someone could also remove or corrupt it deliberately. The absence of a watermark does not confirm that content was written by a person.

These two points matter because a lot of the commercial and legal conversation around AI content disclosure assumes a binary: AI-generated or human-generated. The reality is more layered. Most professional use of AI tools involves significant human input, direction and editing. The watermark detects the generation surface, not the level of human involvement.

Why this is happening now

The immediate driver is the EU AI Act, which came into force in stages through 2025 and 2026. The transparency code it establishes requires AI providers to make it technically possible to identify AI-generated content. Anthropic’s watermarking commitment is a direct fulfilment of that requirement for systems deployed in the EU.

The decision to roll it out globally rather than just in the EU is significant. It reflects a broader industry direction that has been building for several years. In 2023 major AI companies including OpenAI, Google, Meta and Amazon made voluntary commitments to develop watermarking and provenance technologies. Google ran a large-scale trial of its SynthID watermarking technology across 20 million Gemini responses in 2024. Research into text watermarking has been underway since the 1990s, but practical large-scale deployment has only become viable with the current generation of AI systems.

The timing also reflects growing pressure from publishers, regulators and educators who want tools to identify AI-generated content more reliably. AI detection tools have faced significant criticism for accuracy problems, including false positives that have wrongly flagged human-written text. Watermarking embedded at the generation stage is a more structurally reliable approach, though it still has the limitations Anthropic has acknowledged.

What this means if you use Claude for content

Claude is widely used across marketing, content production and communications. AI tools sit at the centre of most agency and in-house content workflows now, and Claude specifically is used for drafting, editing, tone adjustment, research summaries and strategy documents across the industry. The watermark does not change any of that. It does change the transparency landscape around it.

The practical implications depend on your context:

• For client-facing content. If a client, publication or platform uses a watermark detection tool and your content triggers it, you will want to have a clear position on how you use AI in your workflow. This is not a new conversation – disclosure expectations have been developing across industries for two years – but watermarking makes the question technically answerable in a way that was not previously possible at scale.
• For SEO and publishing. Google has been consistent that AI-generated content is not inherently penalised – quality and helpfulness are what matter. Watermarking does not change that position. But it does give Google, publishers and platforms more technical capability to understand the scale of AI content in any given context. How that capability is used over time is an open question.
• For edited and hybrid content. Anthropic explicitly notes that heavily edited text may not carry a detectable watermark. If your workflow involves significant human editing of AI drafts, the practical footprint of the watermark in your final output may be limited. This is consistent with the direction most good AI-assisted content workflows have been heading anyway – AI as a drafting and structuring tool, human judgment as the final layer.

The broader direction this points to

Anthropic’s move is the most significant practical step yet toward AI content provenance becoming a standard layer of the web. The question of how brands build recognition and trust online is increasingly bound up with questions of authenticity and transparency. Watermarking is one part of that picture.

The technology is not yet universal – other major AI providers are at different stages of deployment, and there is no single interoperable standard. But the direction is clear. Over the next two to three years, it is likely that most major AI-generated text will carry some form of machine-readable provenance signal, and that platforms, regulators and clients will increasingly have tools to detect it.

The most straightforward response for any business using AI in its content workflow is not to panic, and not to try to strip or circumvent watermarks – that path leads nowhere good. It is to be clear internally about how AI is used, at what stages and with how much human oversight, and to develop a consistent, honest position on disclosure that you would be comfortable defending if asked. That position should already exist. If it does not, the arrival of invisible watermarks in every Claude response is a reasonable prompt to develop one.