Google has quietly shipped one of the more useful additions to Search Console in recent memory. Platform properties, launched earlier this month, allow you to connect your Instagram, TikTok, X and YouTube accounts directly to Search Console and see how your content on those platforms is performing within Google Search. Not on those platforms. Within Google Search itself.

It sounds like a subtle distinction but it is a meaningful one. For any business that posts content on social or video platforms without knowing whether it is generating search visibility, this gives you the first direct window into that question. And given that recognition across multiple channels now drives downstream search behaviour in ways that standard analytics have never been able to capture, platform properties is a feature worth setting up properly.

What platform properties actually shows you

Once you have verified a platform property in Search Console, you get access to three types of reporting, each showing a different layer of how your social and video content surfaces in Google Search.

• Performance report. Total clicks, impressions and engagement metrics from your social content appearing in Google Search. You can filter by specific posts and sort by the search queries that are driving traffic to your accounts or content. The data is also exportable if you want to pull it into a separate reporting dashboard.
• Insights report. A higher-level view showing traffic trends, your top-performing posts and the mechanisms by which people discover your accounts in Google Search. Useful for a quick overview without drilling into individual query data.
• Achievements. Milestone tracking for growth in clicks from Google Search, measured in 28-day windows. Less analytical, but useful for demonstrating momentum to clients or stakeholders.

The rollout is gradual, so not every account will see platform properties immediately. Google has confirmed it is expanding access over the coming weeks. If you do not see the option yet, it is worth checking back rather than assuming it has been skipped.

How to set it up

Setting up platform properties requires you to verify each social account separately within Search Console. The process is the same for all four supported platforms and takes a few minutes per account.

• Open Search Console and go to the property selector dropdown, or navigate to the verification page directly.
• Click ‘Add property’ and select one of the four available platforms: Instagram, TikTok, X or YouTube.
• Follow the on-screen verification steps to authorise the connection. Google uses a secure authorisation flow rather than asking for login credentials.
• Once verified, the platform property appears alongside your standard web properties in Search Console and data begins populating, typically showing historical data going back several weeks.

For businesses managing multiple clients, you will need to verify each platform account separately. There is currently no bulk verification option, and each platform property sits in its own reporting view rather than being rolled up into a combined dashboard.

Why this matters more than it might initially appear

The reason this is genuinely useful rather than just a nice-to-have is that it closes a specific and long-standing measurement gap. Until now, if a business posted a video on YouTube or a set of stories on Instagram, there was no reliable way to know whether that content was generating impressions or clicks within Google Search. You could track traffic from Google to your social profiles in some analytics tools, but you could not see what search queries were triggering those results or which pieces of content were responsible.

Platform properties flips that. You are now looking at the data from the Google Search side rather than from the social platform side. That means you can see which search terms are surfacing your YouTube videos, which Instagram posts are appearing in Google Image Search, and which queries are driving traffic to your X profile from within Google. That is a different and more useful frame than social platform analytics provides on its own.

The timing of this feature is also worth noting. Recent data from Similarweb confirmed that more than half of the downstream traffic generated by AI recommendations arrives via branded search rather than direct AI referral clicks. Branded search lifts are increasingly a downstream signal of visibility across multiple channels, not just traditional SEO. Platform properties gives you the first standardised tool for measuring whether your social content is contributing to that broader search signal.

The practical implications for content and reporting

For most marketing teams, the immediate value of platform properties falls into three areas:

• Proving the SEO value of social content. This has historically been difficult to demonstrate in a way that satisfies sceptical stakeholders. Platform properties gives you direct click and impression data from Google Search for content posted on social platforms. That is a credible, source-backed number rather than an inferred relationship.
• Understanding which queries drive social discovery. Seeing the specific search terms that are surfacing your YouTube videos or Instagram posts in Google Search tells you something useful about the intent of people finding you through that route. If a dental clinic’s treatment videos are appearing for ‘how long does teeth whitening last’ in Google Search, that is content worth making more of. Without platform properties, you would not know those searches were leading to your video.
• Informing content decisions with actual search data. The performance report shows which specific posts are generating clicks and impressions in Google Search. That gives you a direct signal about which content formats, topics and posting approaches are earning Google search visibility rather than just social engagement. The two do not always correlate.

What to check once you have connected your accounts

Once the platform property data starts populating, the most useful starting point is the performance report filtered by query. Look for search terms that are driving impressions to your social content that you are not yet targeting with your main website. These represent keyword opportunities where Google is already surfacing your brand through social content, and where a corresponding page or article on your website could strengthen that visibility further.

The insights report is worth reviewing monthly rather than weekly – it is a trend indicator rather than a granular data source. The most commercially useful data is in the performance report, particularly for video content on YouTube where the correlation between Google Search impressions and watch time can help inform both SEO and content production decisions.

For businesses running YouTube channels alongside a website, the combination of YouTube platform property data and standard Search Console data in the same interface starts to give a more complete picture of how their content performs across Google’s various surfaces. That has never been possible before within a single tool, and it is worth taking the time to set it up properly rather than treating it as a nice-to-have.

What is brand recognition?

Brand recognition is a step beyond brand awareness; it is about when people can easily identify a brand based on elements like brand colours, jingles, logos and campaigns.

Achieving brand recognition is no easy feat, so when customers and non-customers alike can recognise your brand, it shows the effectiveness and impact your brand-awareness campaigns have had.

A key example of brand recognition is when your business is the first thought brand for that particular product or service. Leading against competitors and demonstrating the overall commercial impact that’s been had.

What is the value of brand recognition?

Brand recognition is incredibly valuable because it tends to make people feel more comfortable investing in a brand. When trust and familiarity are established through recognition, it can help reduce the perceived risk around trying new services or products. This is favourable, especially when weighing up the risk, because it is an expensive and important investment on the customer’s part.

As brand recognition is strengthened, it can help build customer loyalty. Customers with a pre-existing positive experience are more likely to return to a brand. Rather than seeking out potential alternatives. Customer recognition also increases the chances of recommendations and referrals.

Another strategic advantage of becoming a recognised brand is the ability to improve the effectiveness of marketing campaigns. The more familiar a brand is, the less effort is required to convey that.

Above all, brand recognition offers a competitive advantage, allowing brands to stand out in over-saturated markets. Increasing the difficulty for competitors to capture their audience.

Building brand recognition

The most effective way for businesses to increase their brand awareness is to repeatedly and consistently create opportunities for exposure. The more a brand creates instances to familiarise audiences, the more memorable they become.

Some of the keyways to build up brand recognition include:

• Creating content that adds value to prospects and existing customers. This can come in several forms, whether that be publishing blogs, videos, social media content, podcasts or helpful guides. The aim is to entertain or educate whilst ensuring visibility remains consistent, all while demonstrating expertise.
• Developing a consistent brand identity. This means using fixed logos, colour palettes, typography, design styles, imagery, social media profiles and marketing materials.
• Developing a strong online presence can be a particularly effective method. Regular activity, SEO and well-optimised websites help keep the brand relevant and useful. This can also help with visibility while reinforcing recognition over time.
• Maintaining a clear brand voice from the moment of establishing a brand is essential; consistent communication creates moments of familiarity and reinforces cues on brand personality.
• Promoting customers’ stories, whether that’s in reviews, testimonials, UGC or referrals.

Overall consistency with the recommended methods is the key. Brand recognition isn’t established in one day; it takes time and repeated interactions from customers to reinforce this. Every aspect of a strategy should reinforce how people remember the brand, which eventually results in getting new and returning customers.

Brand recognition is far more than simply being recognised by name or logo. It reflects the trust, familiarity and credibility a business has built with its audience over time. When customers can instantly identify a brand and associate it with positive experiences, they’re more likely to choose it over competitors and recommend it to others.

While building brand recognition requires patience and consistency, the long-term rewards make the investment worthwhile. By delivering valuable content, maintaining a cohesive brand identity and creating meaningful customer experiences, businesses can establish a recognisable presence that supports sustainable growth, strengthens customer loyalty and helps them remain competitive in an increasingly crowded marketplace.

If your Google Ads campaigns use Dynamic Search Ads, there is an important date you need in your diary. Google has confirmed that all remaining DSA campaigns will be automatically upgraded to its newer AI Max format starting February 2027, giving advertisers several more months than originally planned to prepare. What looked like a race against a September 2026 deadline, right in the middle of the crucial pre-Christmas advertising period, has been extended following widespread feedback from the advertising community. That breathing room is welcome, but it should not be mistaken for an indefinite stay of execution.

What Is Actually Changing and When

Google is retiring Dynamic Search Ads entirely and replacing them with AI Max, its newer AI-driven approach to search campaign management. Rather than relying on keywords alone, DSA campaigns have long worked by automatically matching ads to relevant searches based on your website content, making them particularly useful for large or frequently changing product catalogues. The transition away from this format happens in two stages: from January 2027, you will no longer be able to create new DSA campaigns. From February 2027, any campaigns still running will be automatically migrated to AI Max on your behalf, whether you have prepared for it or not.

Why the Extension Was Granted

The delay was not granted out of generosity. It came directly in response to advertiser pushback. One recurring concern is that AI Max routes specific product searches to generic landing pages, something DSA handled more precisely by matching searchers to the most relevant page within your site. That distinction matters commercially, particularly for retailers with large or structured product ranges. Independent analysis has also shown AI Max delivering lower returns on ad spend in some campaign types, which is why treating this migration as a routine update would be a mistake.

What You Should Do Now

The February deadline may feel distant, but the window for proper preparation is shorter than it appears, especially for businesses with Q4 peak trading periods where any campaign disruption carries real commercial risk. The time to review your DSA campaigns is now, not in December. Start by auditing which campaigns are still running on DSA and how they are currently performing. Then build in time to test AI Max before the forced migration kicks in, so you can identify and resolve any issues on your own terms rather than Google’s. If you are unsure where to begin, this is exactly the kind of transition where working with a specialist pays for itself.

AI overviews, FAQ blocks and AI mode are each increasingly being displayed across the search engine results page. We have to ask the question: is it diluting the quality of content, and is it dominating the SERP, making organic content increasingly harder to find?

We see a plethora of AI uses, ranging from AI-generated search experiences (where AI creates or summarises information) and AI-assisted search features (where AI helps rank, organise, or present content).

Places AI is currently used on search engines

Google, Bing and Yahoo

Google is known for leading the race with a staggering 91.27% of traffic in comparison to other well-recognised search engines. Recognised for its cutting-edge updates, its fine-tuned algorithm and vast catalogue of websites and content to serve searchers’ queries.

In its journey to be the best and largest of search engines, AI has become a key and defining feature. With a strong presence across various search results features, it’s absolutely reshaping the way Google works.

We’ve observed the same evolution across other search engines, including Bing and Yahoo. Those features include:

The Impact of AI on organic content

AI-generated content doesn’t need to fight to be spotted; now, the first positions across the SERP are completely dominated by AI results, pushing organic content further down and reducing the chance of discoverability and clicks.

The AI Zero-clickopalypse

One of the most harmful impacts of AI-generated results is the zero-click epidemic, where AI content steals the valuable clicks from organic content on the SERP. When users don’t need to click into content, because AI has borrowed and displayed it at the top of the SERP, it directly steals traffic from those cited sites.

Ultimately, AI features are pushing the higher-ranking organic pieces of content below the fold, shrinking visible organic listings.

The consequence for a zero-click search varies. For more established brands and sites, reduced clicks may be inconsequential, while smaller brands building their content portfolio are more likely to suffer as a result, seeing the following metrics affected:

• Organic traffic  
• Click-through rates
• Page sessions
• Average duration of engagement

Shifting approaches from SEO to AEO

The entire strategy for SEO needs to adapt to AI Engine Optimisation (AEO). This changes the style, nature and focus of content, as well as the perspectives marketers use to produce it. The impact then falls on human users, who are increasingly likely to encounter more generic, factual content that doesn’t necessarily offer a human experience or perspective.

Rather than allowing users to research and draw their own conclusions, AI is serving cookie-cutter, generic content that reinforces confirmation bias, reduces searchers’ critical thinking, and stunts what was once curiosity, replacing it with quick, low-value information retrieval.

Algorithms creating personalised SERP’s

 Previously in SEO, when your content ranked, it would be a specific keyword it ranked for, and that was applied to all other sites using the same keyword. Now, personalisation breaks down that method of ranking, with results based on contextual clues like gender and location.

Content now tends to be displayed based on location, not a national or global ranking. Google also takes context from search history, inferring intent and resulting in more tailored responses, with historical searches influencing the type of results shown. Ranking has transitioned from a fixed metric to essentially a range in which you can get a general idea of performance.

So, is AI diluting the quality of content on the SERP?

The short answer is yes, but it’s not quite that simple. AI is reshaping the SERP in ways that make organic content harder to find, harder to click, and harder to rank consistently. The zero-click reality, the shift from SEO to AEO, and the fragmentation of personalised results all point in the same direction: the traditional organic search landscape is shrinking.

But dilution isn’t the same as destruction. The opportunity now sits with content that AI can’t easily replicate original research, genuine human perspective, authoritative expertise, and experiences that feel lived rather than generated. If AI serves the generic, the specific becomes more valuable.

The question isn’t whether AI is diluting quality. It’s whether we’re willing to create content that stands apart from what AI can summarise.

With the advances in AI search over the last few years, being cited in AI chatbots is now more paramount than ever to a business’s presence in this ever-evolving digital age. Robots.txt is a plain text file generated by a website that lets search engine crawlers know which of your content it can and can’t index for users to view. LLMs.txt is the same concept but for chatbots, pointing Chatbots to your content to allow them to know which of your content it can rank. Or at least this is how people in the industry are currently using it. In this article, we’ll explain all about LLMs.txt and if you really need to implement it on your site.

What is LLMs.txt

As we’ve mentioned, LLMs.txt is a text file that lives on your web server and is being used to try and make content more discoverable for AI chatbots. However, this isn’t really why it was designed. Unlike a traditional Robots.txt file, according to Google’s John Mueller, LLMs.txt doesn’t actually help you to rank in search engines at all, but was rather designed to help search engines who already know about your site to find out what else is here,

“So I talked with, I think, one of the people who created that proposal a while back. And the idea was really not to create something that makes it easier for search engines or LLM systems to discover all of your content, but almost more that if an LLM already knows about your site and wants to find out what else is here, then that might be an approach.

And I think the aspect of using this as a way to optimise for Discovery by AI systems or Discovery by search systems, that doesn’t make any sense at all.“

Many users at the minute think that using LLMs.txt will help them rank more on search engines, and this simply just isn’t the case.

How can you help your website rank in AI?

While using an LLMs.txt file won’t help you rank in AI, there are still steps you can take to make sure your website is more likely to rank in LLMs, which is going to be important moving forward as the amount of people using them is going to grow. Some steps you can take to help improve AI performance include:

• Question-based queries with direct answers
• Implement structured data
• Build brand authority
• Allow crawlers in robots.txt file

This last one is crucial: while LLMs.txt is largely irrelevant for ranking, Robots.txt is not. If, for whatever reason, you don’t permit LLM crawlers in your robots.txt file, then they won’t be able to crawl your site. Make sure you explicitly state that they can in your robots.txt file.

What this means for Marketers

An LLMs.txt file isn’t going to help improve your rankings in Chatbots, so if this is what your aim is in creating one, you don’t need to. Creating clear, structured, trustworthy content that is crawlable is going to do you way more favours in terms of improving your rankings.

Google Ads has announced three significant updates to the way it bids and manages budgets on your behalf. While the technical detail can get complex, the implications for your advertising spend are straightforward, and for business owners and marketing managers who rely on Google Ads to drive growth, understanding what’s changing matters more than ever. These updates began rolling out this month, with the most impactful change scheduled for 17th August.

Google’s AI Will Now Hunt for Customers You’re Currently Missing

The first update is the global expansion of a feature called Smart Bidding Exploration. In simple terms, Google’s AI will now actively look for additional customers you might be missing, people who are ready to buy but whose search behaviour falls slightly outside the patterns your campaigns normally target. Previously available only to a limited set of advertisers, this is now accessible to all, including those running Shopping campaigns. Crucially, it does this without requiring you to loosen your return-on-ad-spend targets, meaning Google is doing the exploratory work while still holding itself to your performance benchmarks. For businesses that feel they’ve hit a ceiling on their current campaigns, this is a meaningful new lever for growth.

Promotions and Sales Periods Just Got a Lot Less Manual

The second update introduces something called Promotion Mode, currently in beta. If you’ve ever run a product launch, a seasonal sale, or a flash promotion, you’ll know the manual effort involved in temporarily adjusting ad budgets and targets to match the spike in demand, then carefully resetting everything afterwards. Promotion Mode is designed to automate that process, giving Google permission to be more flexible during defined short windows without permanently changing your campaign settings. It’s an operational improvement as much as a strategic one, reducing the risk of human error during the moments when your campaigns matter most.

The August Update Every Advertiser Should Prepare For Now

The third update is the one that deserves the closest attention, because it will affect all campaigns that regularly run up against their daily budget limits, whether you actively choose to engage with it or not. From 17th August, Google will change how it optimises these campaigns behind the scenes, with the goal of delivering more predictable results when budgets are increased. Google will begin sending account notifications from 6th July and is recommending that advertisers review their cost-per-acquisition and return-on-ad-spend targets ahead of the rollout. Our advice: don’t wait for the notification. If you’re running budget-constrained campaigns, now is the right time to review your targets and ensure they still reflect your actual business goals, before Google’s changes trigger a recalibration that catches you off guard.

Anthropic’s latest AI model, Claude Fable 5, represents a significant step forward in autonomous reasoning, coding, and research capabilities. Unlike earlier generations of AI assistants, Fable 5 can handle complex, multi-step tasks with greater independence, helping businesses accelerate development, research, and operational workflows.

Coding

One of Fable 5’s best features is its ability to code to a more advanced level than other agentic AI agents that have come before it. Tasks that had previously taken software engineers days or even months can be done in a matter of hours, such as coding websites and developing web apps.

It can also work autonomously for longer periods of time than other AI models without the need for human input or prompts due to its advanced capabilities. Anthropic used a case study to test Fable 5’s software engineering capabilities before releasing it to the public, and here’s what they found.

“During early testing, Stripe reported that Fable 5 compressed months of engineering into days. In a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.”

This is invaluable for marketing businesses who would have to spend months of time and manpower towards development costs. It can also help digital marketers and web developers with other coding tasks in the industry such as:

• Assist in building websites
• Prototyping web apps
• Building landing pages from a brief
• Creating custom SEO tools and dashboards
• Integrating CRMs with marketing platforms

Research

The autonomous nature of Fable 5 means it is also capable of performing advanced research tasks and can even be used to help train users in complex tasks and new skills, including web development and SEO. It’s not only limited to training, though; it can also aid in complex digital marketing research tasks. Some SEO tasks Fable can help with include:

• Competitor analysis
• Content gap analysis
• Audience research
• Market trend identification
• Industry report summarisation
• Technical SEO audits

How does this affect Digital Marketers?

The ability to edit and build digital websites and web apps at scale allows marketers to be able to develop much more efficiently, while Fable’s enhanced research capabilities now make marketing tasks such as keyword research, SEO and data analysis so much less complex and streamlined if used in the right way.

For most of the past two decades, SEO success had a relatively simple definition: rank higher, get more traffic. Position one was the prize. Everything else was a means to that end. That model is now breaking down in ways that go beyond the usual search algorithm updates, and the businesses that are still measuring their digital marketing performance solely by keyword positions are at risk of misreading what is actually happening to their visibility.

The argument that recognition matters more than rankings is not a new one, but it has moved from theoretical to genuinely practical in 2026. AI systems are now doing much of the searching on behalf of users, assembling answers from multiple sources without users clicking through to individual sites. The question of where you rank in a list of ten blue links is becoming less relevant than whether your brand appears at all in the answers that AI systems generate.

Why rankings are a less reliable signal than they used to be

The mechanism by which rankings produced value was straightforward: rank well, appear near the top of the results page, get clicked. That chain of causation is weakening at multiple points. AI Overviews now absorb a significant proportion of queries that previously generated organic clicks. Zero-click search, where users get what they need without visiting any website, is no longer a niche concern. It has become the default for a growing category of queries.

At the same time, the buying journey for most products and services has become more fragmented. Someone considering a dental procedure, a culinary course or a care management platform might start with an AI-powered query, move through a Reddit thread or a YouTube comparison, check a review platform, and only then arrive at a branded search. The keyword at any single point in that journey is almost beside the point. What matters is whether the brand shows up meaningfully across the full arc of that journey, not just at the moment someone is ready to convert.

Rankings still matter. A page that ranks well organically is also more likely to be cited by AI systems, more likely to receive referral traffic from other sources, and more likely to appear in the kind of third-party contexts that build recognition over time. The point is not that ranking is irrelevant but that ranking is now a means to recognition, not an end in itself.

What recognition actually means in practice

Recognition, in the SEO context, means that your brand appears in meaningful contexts across the web beyond your own domain. It means you are cited when people write about your sector. It means AI systems draw on your content when assembling answers to relevant queries. It means your entity, the brand as a clearly defined and verifiable presence, is understood and trusted by the systems that shape modern search.

This shifts the emphasis of SEO work in some important ways. A single high-quality mention in a respected publication or industry resource is worth considerably more than fifty low-quality directory listings. Being cited accurately and consistently across multiple contexts, including forums, review platforms, sector publications and AI-generated content, builds the kind of signal that recognition-based SEO is trying to accumulate.

It also changes what you measure. Alongside keyword rankings and organic traffic, the metrics that tell you whether recognition is compounding into something commercially meaningful include unlinked brand mentions, referral traffic from third-party sources, direct traffic growth, and the frequency with which your brand appears in AI-generated answers for relevant queries. These are harder to track than a ranking report, but they reflect what is actually happening to your visibility more accurately.

Google Search Console has just made this more measurable

The timing of this shift is notable. Google announced at the start of June that Search Console is introducing AI performance reports: data showing how your content is appearing in AI-generated search features, alongside new controls that allow site owners to restrict or permit their content from being used in AI responses. This is a significant development. It means that AI visibility, which has been difficult to measure directly, is now becoming a reportable metric within the tools most marketing teams already use.

This builds on the tooling landscape we covered recently when looking at the platforms worth using for AEO work. The addition of native AI reporting in Search Console closes one of the most significant gaps in the measurement stack, and it means the argument for taking AI visibility seriously now comes with data to support it rather than just directional signals.

What this means for content and SEO strategy

The practical implications of a recognition-first approach are not dramatically different from what good SEO has always required. The fundamentals still hold. The shift is in emphasis and in how you evaluate whether the work is producing results.

• Quality of context over volume. A mention in a respected trade publication, a citation in an AI-generated answer, or a detailed review on a trusted platform does more for brand recognition than a high volume of thin directory entries. The emphasis should be on creating content and building relationships that generate substantive, contextually relevant references to your brand.
• Entity clarity. AI systems and search engines need to understand what your brand is, what it does, and how it relates to the topics you want to be known for. Structured data, consistent NAP information, clear about and contact pages, and a coherent presence across multiple authoritative sources all contribute to entity clarity. This is not glamorous work, but it underpins everything else.
• Content that answers specific questions. The content that AI systems draw from and that earns citations and mentions tends to be content that answers a specific question clearly and completely. Generic, broad content that covers a topic without taking a clear position or providing a definitive answer is increasingly unlikely to appear in AI-generated results.
• Branded intent as a commercial signal. When someone searches for your brand by name, that is recognition in action. A branded search that lands on a generic or poorly structured homepage is a wasted signal. Every branded intent visit should arrive at a page, answer or experience that closes the gap between recognition and conversion.

The framing of recognition over rankings is ultimately about accepting that the metric you have been optimising for is no longer the one that most reliably predicts commercial outcomes. Rankings are a proxy for visibility. Visibility is a proxy for recognition. Recognition is what actually drives the journey that leads to revenue.

That does not mean abandoning the work of ranking well. It means doing that work with a clearer understanding of what it is trying to achieve. A page that ranks well and is well-structured, authoritative and specific in what it answers is also a page that gets cited, shared, referenced by AI systems and remembered by people who encounter it. The tactics are largely the same. The goal is broader.

For businesses still measuring their digital marketing performance primarily by a monthly keyword ranking report, now is a good time to extend that view. Rankings tell you one thing about your position in one channel at one moment in time. Recognition tells you whether you are building something that compounds. In 2026, the latter is the one that matters more.

Everyone’s currently talking about the advantages of AI, how it can be a useful tool to improve workflow, making tasks that would have taken months take days, tasks that would have taken days take hours, but many people aren’t focusing on what tasks we should not be using AI to automate. In a world of automation, the human touch is now more important than ever.

Keyword Research

This one seems like it doesn’t make sense. Surely using AI for keyword research is great, right? Well, yes to a certain extent. AI can be incredibly useful for going through your site and picking out key topics, keyword groups and helping you create a framework for your keyword research, while making sure there are no gaps in your keyword strategy.

However, when you’re looking at which keywords to focus on to build out content, this is where it gets a little bit more tricky. For example, for a business that sells a service or product, you understand which keywords are going to generate more revenue and how they have intrinsically more value to your business than some other keywords that may have more search volume and a lower CPC that AI is telling you to focus on. Make sure you use AI to optimise but don’t let it dictate your keyword strategy. You need to take into account factors such as conversion and revenue when making these decisions.

Client Reporting

Similar to keyword research, of course we aren’t saying AI isn’t helpful at all when reporting to clients, but it’s not the be-all and end-all. AI can be used to help you create your physical report incorporating data, giving suggestions on different slides and angles you can tie into your reporting, helping you formulate talking points. All good stuff. But something AI can’t replace is client communication.

A lot of digital marketing is highly specialised and can get confusing quickly for clients who aren’t familiar with it. One of the skills of a marketer is to be able to convey these complex concepts to their clients in a presentable and succinct manner so that they can understand the value of your contribution and how important it is to their business.

By all means use AI to help you build out your client reports, but clients need interaction in order to be able to digest and understand a lot of the data that they’re seeing, and at the minute that’s still where the human element comes in.

Content Writing

This is one of the biggies. Over 50% of newly published articles and nearly 75% of newly created web pages now include AI-generated content. The problem that businesses face when doing this is they lose their brand voice and identity, but also, AI is being fed content from the internet to train and learn. Which means unless people are using AI to inform their content deliberately and writing it themselves, then the content becomes generic and regurgitated, offering nothing new. This is exactly the type of content Google hates. It wants content that is fresh, original and gives a unique perspective and voice on a topic from an authoritative figure in the space; this is what you should be aiming for. Too much AI Slop on the internet is a problem for Google, and they are penalising people who are using it in the wrong way.

Making sure you know when and how to use AI is crucial to building a successful website. If you’re using AI incorrectly, your site can be penalised by Google and negatively affect your rankings, but use it correctly, and your site will benefit from a boost in workflow productivity and efficiency.

If you run paid search campaigns, this week just got urgent. Google has begun deleting hourly, daily, and weekly reporting data older than 37 months, with the policy taking effect from 1 June 2026. This is what needs to be done over the coming days.

What’s Actually Changing and What Isn’t

It’s easy to panic, so let’s be precise. Granular reporting data, including hourly, daily, and weekly figures, will now only be retained for 37 months, while monthly, quarterly, and annual data remains accessible for 11 years. Once data passes its retention window, it will no longer be accessible through the Google Ads interface or APIs – meaning your dashboards, automated reports, and data warehouse pipelines could all be affected. If your agency holds years of campaign history for benchmarking or client reviews, that granular layer is now gone unless you already exported it.

Why This Hits Agencies the Hardest

For those of us managing multiple client accounts, the operational risk here is significant. Organisations running complex omnichannel reporting by blending data from multiple Google APIs need to audit their extraction workflows carefully to avoid serving clients incomplete performance dashboards. Think about seasonal trend analysis, day-of-week performance benchmarking, or long-term A/B test comparisons; all that granular intelligence becomes inaccessible without proactive data management.

Three Actions to Take This Week

Don’t wait for a client to ask why their historical chart has gaps. First, export all hourly, daily, and weekly data beyond 37 months immediately. Second, review any automated reporting workflows to confirm whether they store historical data independently or only query Google Ads on demand. The latter will now fail silently. Third, consider moving to a third-party data warehouse solution to ensure long-term retention sits outside Google’s control entirely.