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.

Google Analytics has added a new AI assistant which lets you track traffic that comes from multiple different chatbots, including ChatGPT, Gemini and Claude. Google will now label traffic sources from different chatbots. This allows users to see which Chatbots they are performing well with and adjust their strategy accordingly.

Why this is important for Marketers

Chatbot traffic has grown exponentially in the last few years, although it still only accounts for around 3% of search engine traffic. This is still a huge number of people, regularly exceeding 5 billion searches monthly, and with many users now using it as their primary source of information online. For website owners, it is extremely important to understand how and why your traffic is coming to you, so you can maximise the potential traffic down the line.

For example, if your site were an information-based site, many users now use AI Overviews, ChatGPT, etc. for informational-based searches, 70% of users now use AI, while some search types, such as E-Commerce, still rely heavily on advertising and users entering websites as opposed to AI. For this information-based site, you would want to make sure you are structuring your content in a way that makes it visible for search engines to use your information. While the conversion rate is lower on AI tools compared to traditional search, being seen in Chatbots may still often lead to people coming to your website and converting.

What users should track in GA4?

Now that AI Assistant traffic is available in GA, there are a number of different ways you can track AI traffic in relation to your site. These help you to understand what content performs best on AI inside AI tools.

Different metrics to track include:

• Conversion rate from AI traffic – Making sure that your traffic is converting well is a priority. If the traffic coming from AI searches is low-quality, then you want to adjust your strategy for how you’re appearing in AI to target more high-value customers.

• Average Engagement Time – Related to the last point, once again linked to low-value traffic, if the traffic you’re receiving from AI is not engaging with your content for long periods of time, you want to try and adjust your strategy to target different kinds of traffic that offer more value

• Landing Pages from AI traffic – This is important because you can see which pages on your site are already performing well in AI and which ones aren’t. This allows you to analyse your content and draw conclusions on what types of content are working well in AI, helping you adjust future content plans to scale your site.

Tips for Improving AI Conversion

While making sure you know what content performs well in AI is important, and you are on top of tracking all the right data, knowing how to adjust your content to improve it for AI performance is also important. Here are a few quick tips to make sure your content is AI-ready. Ask yourself these questions. Does your content:

• Clearly answer specific questions
• Use concise headings and subheadings
• Includes factual and well-structured information
• Demonstrates expertise and authority
• Uses schema markup and clear hierarchies

These GA4 analytics metrics are a step forward for users in being able to understand and analyse their AI traffic, helping to stay on top of the ever-evolving industry and making the most of users’ changing search query habits.

Google confirmed last week that it will no longer support FAQ rich results in search. The expandable question-and-answer panels that appeared directly beneath certain search listings are being removed, along with the Search Console features that allowed webmasters to monitor their performance. For any business that invested time in implementing the FAQ schema, this is worth understanding clearly: what is changing, what it means in practice, and whether any of that investment needs to be redirected.

The short answer is that this change is less damaging than it might initially sound. But there are some useful lessons in it about how to think about structured data more broadly as Google continues to adjust what it surfaces and how.

What FAQ rich results actually were

FAQ rich results were the expandable accordions that appeared below a search listing, showing individual question-and-answer pairs pulled directly from a page’s structured data. When implemented correctly using FAQ schema markup, they could significantly expand a search listing’s visual footprint on the page, making it more prominent without requiring a higher ranking.

Google started restricting FAQ rich results in 2023, limiting them to government and health websites. The full removal confirmed last week completes that process. The associated Search Console report, which showed impressions and clicks from FAQ rich results, will be deprecated alongside the feature itself.

How much does this actually matter?

For most businesses, the honest answer is: less than it might appear. By 2023, Google had already restricted FAQ rich results to a narrow category of sites, which means the vast majority of businesses had not been benefiting from them in search results for some time. The visual expansion of a listing was a genuine competitive advantage when the feature was widely available, but that window closed a few years ago.

What is changing now is the formal retirement of a feature that was already largely inactive for commercial websites. The Search Console report being removed is a minor practical inconvenience if you were still tracking FAQ impressions, but the disappearance of the report does not reflect a loss of current traffic or visibility for most sites.

Should you remove your FAQ schema?

Not necessarily, and in many cases the answer is no. This is an important distinction. FAQ schema no longer produces rich result panels in standard Google Search, but structured data serves multiple purposes beyond generating visual enhancements in the search results page.

As we covered recently when looking at the tools worth using for AEO, structured data is one of the cleaner signals available for AI retrieval. FAQ schema specifically provides AI systems with a machine-readable layer of question-and-answer content that can inform how those systems cite and use your content in AI-generated answers, entirely separate from whether Google renders it as a rich result in traditional search.

The argument for keeping well-implemented FAQ schema in place is straightforward. It costs nothing to maintain, it provides clarity to search engines and AI systems about the purpose and structure of your content, and removing it does not guarantee any benefit. Unless your FAQ schema is technically broken or generating errors in Search Console, the case for removing it is weak.

What to redirect your structured data efforts towards

If this news prompts a review of how structured data is implemented on your site, that is a worthwhile exercise. The specific types of schema that continue to produce rich results in Google Search and that are increasingly relevant for AI retrieval are worth prioritising. The most consistently valuable include:

• Review schema. Star ratings in search results remain one of the highest-impact visual enhancements available and are directly relevant to businesses in sectors such as healthcare, dental, aesthetics and professional services where social proof influences decisions.
• Local business schema. For businesses with physical locations, accurate and complete local business schema reinforces the information shown in Google Business Profiles and supports consistent citation across AI search systems. This is particularly important for multi-location businesses and sectors where local search intent is high.
• Product schema. For e-commerce businesses, product schema supporting price, availability and review information continues to produce rich results in both standard search and Shopping. This remains one of the most directly commercial schema types available.
• Article and breadcrumb schema. These support how content pages are understood and indexed, contributing to cleaner crawling and more accurate representation in search results, and remain relevant for content-heavy sites.
• How-to and event schema. Both continue to produce rich results for relevant content types and are worth implementing where the content justifies it.

The broader pattern is worth noting

The removal of FAQ rich results is part of a longer pattern. Google has been progressively reducing the variety of rich result types it surfaces in standard search as AI Overviews and AI Mode take up more of the results page. The shift towards AI-generated answers changes what it means to be visible in search. The visual real estate previously occupied by FAQ panels, knowledge panels and similar features is increasingly being consumed by AI-generated content instead.

This does not mean structured data is becoming less important. If anything, the opposite is true. As AI systems take a more active role in assembling answers from multiple sources, the clarity and accuracy of the signals you provide about your content become more valuable, not less. The mechanism by which those signals produce a visible result in search is changing. The underlying importance of giving search engines and AI systems well-structured, accurate, machine-readable information is not.

What to do now

A few practical steps are worth taking in light of this change.

• Check your Search Console for any FAQ rich result errors or warnings. These will stop being reported when the feature is deprecated, but addressing any existing errors is good practice before the report disappears.
• Audit your current structured data implementation more broadly. If FAQ schema was the only schema type in use on your site, now is a good time to review whether review, local business or product schema should be added where applicable.
• Do not remove FAQ schema solely because of this change. If your FAQ schema is well-implemented and error-free, leave it in place. Its value in an AI retrieval context is not affected by Google’s decision to stop rendering it as a rich result.
• Focus new structured data investment on the types that continue to produce visible results: review, local business, product and event schema, depending on what is relevant to your business.

The disappearance of FAQ rich results is worth knowing about, but it is not a reason to panic or make sweeping changes. It is a reminder that search is a changing environment and that the value of any single technical feature is always temporary. The underlying principle, making your content clear, accurate and well-structured, remains constant regardless of which specific features Google chooses to support at any given time.

Google has announced that citations within AI Overviews and AI Mode will now have new formats and displays which will hopefully boost CTR, making AEO even more crucial for business and their digital presence moving forward. In this article we’ll go through the 5 different ways Google has announced they will display links in 2026.

Further Exploration

Google have introduced a “further exploration” section to AI Overviews that will appear at the bottom of AI responses that encourages further reading on websites that are topical authorities on a specific subject. This is one of the first indications of AI Overviews being used to truly being used to platform links rather than use them subtly like in previous iterations of AI Overviews. Getting into these further exploration links could be crucial for business’ as they are the most clearly displayed links to date yet in AI Overviews and if they appear often, business will be pushing to ramp up their AEO

Subscriptions Links

Subscription linking allows readers who pay to read your content link their subscriptions to their Google account. These subscriptions will then appear in AI Overviews, as seen below, so people can see if information has come from one of their trusted sources, and improving the chances of CTR. This is mainly going to be a good feature for news publications that use subscription models to allow people access to their content.

Community Advice

A lot of people, when searching online, especially when asking question based searches want to seek out advice from others who have shared similar experiences. Attaching “reddit” to the end of searches, is a tactic from users to often seek out advice from others.

Now AI Overviews will include previews from of public perspectives and online discussion from communities for certain, question based searches. These new forms of links will help community based cites and forums get more links in AI overviews.

Expanded Links within Content

AI Overviews already includes links throughout content so you can dig further into research as your reading through AI content. The latest update still allows for seeing links directly where you need them but now these links will just be more prevalent in AI searches.

Website Hover

When you hover over an inline link the website that the information is being pulled from will now appear with their web icon, page and website name attached. Once again this feature is just building out on existing linking features to make websites more obvious in AI Overviews and improve CTR.

A lot of these new features being added into AI Overviews and AI Mode will improve click-through-rate to websites, which means being seen and recognised as a topical authority by Google and appearing in AI Overviews will be more important than ever going forward.

For years, Google has dominated the digital advertising world. But according to recent forecasts from EMARKETER, 2026 could mark a major turning point: Meta is expected to surpass Google in digital ad revenue for the first time.

At Intelligency, we see this as more than just an industry headline. It reflects how consumer behaviour, AI, and advertising performance are evolving and why brands need to rethink where they invest their marketing budgets.

Why Meta Is Growing So Quickly

Meta’s projected ad revenue is expected to reach $243.46 billion in 2026, slightly ahead of Google’s forecasted $239.54 billion.

Platforms like Facebook and Instagram are becoming increasingly effective at helping advertisers reach the right audiences at the right time. Meta’s AI-powered tools, including Advantage+ and automated creative optimisation, are making campaigns easier to manage while improving results. Reels has also become a major driver of engagement and ad growth.

In simple terms, advertisers are seeing stronger returns from Meta’s ecosystem, and they’re increasing spend accordingly.

What This Means for Businesses

This shift doesn’t mean Google advertising is disappearing. Search ads still play a critical role, especially when users are actively looking for products or services.

However, Meta’s rise highlights an important trend: discovery-based advertising is becoming just as valuable as intent-based advertising. Consumers are increasingly discovering brands through social content rather than traditional search alone.

For businesses, that means successful digital marketing strategies can no longer rely on one channel. Brands need a balanced approach that combines search, social, video, and AI-driven targeting.

The Bigger Picture

Another important takeaway is market consolidation. EMARKETER predicts Meta, Google, and Amazon will collectively control over 62% of global digital ad spend in 2026.

For marketers, this creates both opportunity and competition. The platforms are becoming more sophisticated, but standing out requires stronger creativity, smarter targeting, and continuous optimisation.

At Intelligency, we help brands navigate exactly this kind of change, turning industry shifts into practical growth strategies that deliver measurable results.