Tag: SEO

Unlocking AI Search: Making Your Brand Truly Machine-Readable

As I delved into audits across Prince Edward Island, one issue stood out: businesses with significant expertise weren’t visible to AI systems because their knowledge wasn’t rendered into a machine-readable format.

Despite their leadership in biotech, manufacturing, and other sectors, critical business information was often trapped in PDFs, behind forms, or muddled in vague marketing copy. It was also disconnected from structured data systems that AI engines need for verification.

We’re living in a world where 88% of companies are integrating AI. Yet, McKinsey notes that 86% of leaders admit to being unprepared for its daily integration.

Many brands mistakenly equate AI visibility with being featured in a Gemini summary or a ChatGPT result, without solidifying the structured digital groundwork needed for ongoing visibility.

AI Visibility: The Basics Before the Buzz

If you’re only focusing on large language model (LLM) responses, you’re lagging. LLM visibility reflects authority—it doesn’t build it.

According to a study by Responsive, 22% of B2B buyers now use generative AI for vendor research. Traditional search use is expected to drop by 50% by 2028 as AI solutions become the go-to answer engines, as Gartner predicts.

Now, discovery happens through synthesizing answers rather than listing URLs. Until you’re part of the Knowledge Graph as a verified entity, your brand’s visibility will be inconsistent.

The Insights from 19 Case Studies: Expertise Powers AI Search

AI systems value concrete, structured data over descriptive text. Brands chasing fleeting AI mentions without anchoring their data won’t achieve lasting visibility, but those establishing structured data relationships will always be recognized.

Thus, SEO has evolved from simply creating content to becoming an information architect. As the case studies reveal, expertise remains a key signal that AI systems can interpret.

Case No.	Entity	Industry	The discovery	The SME solution
1	BioVectra	Biotech	Technical authority trapped in PDFs	Encoded cGMP data into facts
2	Wyman’s	Food manufacturing	Sustainability was a narrative	Structured supply chain schema
3	Murphy Hospitality Group	Hospitality	Invisible venue specifications	Constructed event logic
4	Invesco	FinTech	Opaque compliance data	Built regulatory ground truth
5	Sekisui Diagnostics	MedTech	Innovation lacked readability	Engineered diagnostic logic triples

Why SEOs Must Focus on Education

The main obstacle to AI readiness is the gap in education. We must evolve into information architects, comprehending our clients’ business logic deeply.

SEOs as Subject Matter Experts

Understanding is foundational. For instance, auditing a biotech firm requires a grasp of compliance as keen as their lead scientist’s.

AI relies on structured context for accurate answers. Vague marketing language feeds insufficient responses.

Clients Must Prepare Their Data

Data quality and governance activation equate to maximizing AI-driven value. SEOs must educate clients on digital presence impacting AI brand perception.

Focus on True AI Authority

Appearing in a ChatGPT reply isn’t the goal; becoming an authoritative node in the Knowledge Graph is. It ensures visibility across AI platforms like Gemini and Claude.

AI advancements will persist rapidly. SEOs and clients not prioritizing structured data will be left behind in AI discovery systems.

Inspired by this post on Search Engine Land.

May 22, 2026

Unveiling Google’s May 2026 Core Update: A New Era for SEO

Today, I’m excited to discuss the latest development in the world of search engines: Google has just rolled out the May 2026 core update. This follows the previous update we saw in March.

I learned that the announcement was made by Google through their official status page. It’s a significant moment as it marks the second core update of the year after March’s update and the earlier Discover update in February.

What Google is sharing. According to Google’s updated Search Status Dashboard, the rollout might take up to two weeks to complete. They also made a LinkedIn post explaining the aim is to enhance the visibility of relevant content.

Core updates like these occur several times yearly. They bring broad, impactful changes to Google’s algorithms, and though they often aren’t announced, this one is attracted due attention.

If you’ve noticed changes. Experiencing shifts in your site’s rankings? Google typically suggests focusing on producing quality content. Even if hit, it may not indicate problems with your pages.

For further guidance, consider reviewing the questions Google advises if affected.

The main takeaway? Prioritize creating authentic and helpful content; let these guiding principles lead your SEO endeavors.

For deeper insights, explore Google’s comprehensive documentation on core updates.

Reflection on past updates. Looking back, we’ve seen similar significant updates like the March 2026 and December 2025 rollouts, each influencing search result dynamics differently. Will this update continue that trend? Only time will tell.

Why this matters for us. Core updates can shake up the search engine landscape, causing noticeable volatility. It’s an opportunity for improved site visibility or a call to action to tweak your strategies if rankings dip. May this update bolster your SEO efforts, rewarding your dedication with increased organic traffic.

Inspired by this post on Search Engine Land.

May 21, 2026
Unveiling Reddit’s Impact on AI Search Dynamics
I often find myself explaining Reddit’s role in AI search. It’s frequently underestimated, yet its influence extends well beyond training data.

Clients frequently ask how AI training, licensed access, and retrieval systems can affect SEOs and AI strategies, particularly concerning Reddit.

Here are the typical questions I receive:
- Should I engage with Reddit to boost my brand visibility?
- Is advertising on Reddit beneficial if AI uses Reddit for training?
- Our CEO suggests creating a subreddit for each product. Is that wise?
- Why does Google’s AI reference a Reddit thread criticizing my product?
These inquiries often conflate three separate but interrelated concepts:
- Training data.
- Licensed or real-time access.
- Citation and retrieval systems.
Although connected, they serve different purposes. Understanding these distinctions impacts how we approach SEO and AI citations, especially as Reddit increasingly appears in AI-driven results.

Let’s demystify AI training, access, and citation. You might think, “ChatGPT was trained on Reddit,” means every post is directly stored in its memory—an incorrect assumption.

Training AI is akin to education. Kids learn concepts like using the Pythagorean theorem without remembering specific textbook answers. Similarly, AI learns conversational patterns, not individual Reddit posts.

AI doesn’t remember specific threads but discerns key discussion points from Reddit, like consumer preferences on r/RockTumbling.

Reddit partnerships with Google and OpenAI in 2024 enabled a transition from static datasets to ongoing access, allowing AI to stay updated on Reddit dialogs.

If AI training is like schooling, licensed access is a continuous flow of information akin to subscribing to a newspaper.

AI can cite Reddit, not because it’s preferential part of the training, but finds it useful for real-time querying, just like humans might refer to yesterday’s conversation.

Reddit’s prominence in AI results impacts my SEO strategy, yet it’s not only due to formal partnerships. Reddit’s depth in human experiences enhances its informational value.

Reddit offers what many websites lack: practical user insights and diverse opinions. Where official sites provide features, Reddit adds authentic experiences and user narratives.

Rather than mimicking Reddit, I focus on fostering authentic discussion by leveraging user insights from reviews, interviews, or forums, enhancing the context around my content.

I’ve realized that prioritizing nuanced details and showing reasoning can increase credibility, making my content more relatable in subjective decision-making scenarios.

Ultimately, integrating firsthand experiences and transparency can elevate content strategy, aiding systems that synthesize human input into AI insights.

Inspired by this post on Search Engine Land.
May 21, 2026
AI Search in Multilingual Regions: Lessons from Catalonia

When I think about AI search, I realize it’s more than just translating or localizing results. It’s about deciding which sources, narratives, and realities emerge on top. This complex system is incredibly fascinating to me, especially when I consider how multilingual regions like Catalonia challenge these AI search systems.

The unique geography of Catalonia, where Catalan and Spanish languages coexist, serves as an excellent stress test for AI technology. It’s intriguing to see the underlying patterns unfold when the same queries are entered in both languages across platforms like Google AI Overviews and ChatGPT.

In Catalonia, a query like Tradicions de Sant Jordi shows how AI systems can sometimes misidentify the language, often tagging Catalan as Occitan. This discovery was both surprising and revealing, shedding light on broader problems that transcend multilingual spaces.

Consider this: an AI system operating out of Barcelona with a local IP may choose the less prevalent language of Occitan over Catalan, a decision that feels bizarre given Catalonia’s linguistic and geographical context.

This issue isn’t isolated. In January 2023, Google acknowledged downgrading Catalan results in favor of Spanish, which sparked dissatisfaction among users. The subsequent updates improved things somewhat, but the root language-identification errors persist, affecting how AI synthesizes information today.

My journey into this topic has involved documenting AI search variations across Hispanic markets, observing how it often treats diverse Spanish-speaking regions as uniform, ignoring their unique contexts. However, in Catalonia, where geography remains constant, the retrieval patterns unfold in more distinct and educational ways.

For me, multilingual regions expose the foundational defaults in retrieval systems. Here, users can switch languages and observe firsthand how the system reallocates meaning, authority, and even the language of an answer.

The reality is, the same issues will likely emerge in seemingly monolingual markets, manifesting in different ways as AI technology advances.

Inspired by this post on Search Engine Land.

May 21, 2026
Discover Google Chrome Lighthouse’s New AI Scan Feature

I’ve recently discovered that Google has introduced a new feature in Chrome Lighthouse to check for llms.txt files. Though Google mentions that llms.txt isn’t necessary for AI search visibility, Lighthouse has started flagging sites based on their presence.

Google’s latest Lighthouse audits, under the “Agentic Browsing” category, now focus on a site’s usability for machine interaction. I find this interesting as it aligns with Google’s push towards better machine readability.

The new audits are part of Chrome’s evolving “Agentic Browsing” features, which analyze if sites are prepared for automated interaction. This concept came soon after Google issued guidance on AI search optimization, debunking the necessity of llms.txt files in their new guide on generative AI features.

What Lighthouse Evaluates Now. Lighthouse’s Agentic Browsing tests focus on how well my site is built for machine interactions, incorporating various deterministic audits as per Google’s documentation. These checks include:

– WebMCP integration.

– Accessibility tree integrity.

– Layout stability through CLS.

– Presence of an llms.txt file.

These audits help ensure that there’s a machine-readable summary at the site’s domain root. Google explains that without llms.txt, agents might take longer to understand a site’s main structure.

The impact of these audits doesn’t translate into a traditional Lighthouse score but into a fractional pass ratio related to agentic readiness signals.

The Tension. Interestingly, while these audits don’t directly affect SEO rankings, their mention in Google’s readiness checks could make SEOs reconsider their stance on llms.txt files.

Agentic Engine Optimization. Google’s approach aligns with insights shared by Addy Osmani from Google Cloud AI about Agentic Engine Optimization. Osmani emphasizes creating web content that is semantically structured, token-efficient, and easy for AI to process.

SEO vs. llms.txt. According to Google, creating llms.txt or similar files isn’t necessary for AI search success, as outlined in the guide on Mythbusting generative AI search. The AI systems can discover, crawl, and index a variety of file types encountered on the internet.

John Mueller from Google responded to concerns about the role of llms.txt in a discussion with Lily Ray on Bluesky, stating that the use of these files is more for functionality and not directly linked to search engine optimization.

Google’s Take on AI Agents. Besides llms.txt, Google’s Lighthouse guidelines place strong emphasis on accessibility and interface stability. The insight I gained is that AI agents heavily rely on the accessibility tree as their core data model, focusing on integrity and proper layout.

Ultimately, while Google indicates llms.txt isn’t needed for search, including such files might be beneficial for adapting to Google’s evolving tools that prioritize machine readability.

Further Exploration.

– Meet llms.txt, a proposed standard for AI website content crawling

– llms.txt isn’t robots.txt: It’s a treasure map for AI

– Does llms.txt matter? We tracked 10 sites to find out

Inspired by this post on Search Engine Land.

May 20, 2026
Unlocking the Power of Google Discover Publisher Profiles

I find it fascinating how Google Discover has evolved with the introduction of publisher profiles and follow features. These profiles have started making waves, yet they remain a bit enigmatic due to limited documentation.

More publishers, creators, and social-first accounts are now visible through these profiles. Let me take you through how these profiles work, how they connect with social accounts and the Knowledge Graph, and why some publishers already enjoy enhanced customization features.

As a technical SEO enthusiast, I’m quite accustomed to Google glossing over details in their documentation. And with Discover publisher profiles, that mystery deepens.

Google barely mentions these profiles in their official Discover documentation, though they seem to play an increasingly significant role in the visibility of publishers and creators.

It’s intriguing to see how Discover profiles let users manage the publishers they follow while gathering content from various websites and social platforms.

Because Google has been reticent about the inner workings of these profiles, I’ve taken upon myself to study their patterns across different accounts. Here’s what I’ve noticed about:

Google rolled out substantial updates to Discover in September 2025, vastly altering how we engage with content through publisher follows and profile pages.

The update granted publishers dedicated landing pages for content aggregation, offering users a streamlined way to interact with preferred publishers and seamlessly integrating social content into Discover.

The most eye-catching aspect of this update is how it empowers users to have greater control over publisher visibility while enabling brands to reach their audience more effectively.

Publishers can’t typically alter the layout of these pages, but some recently gained access to customize their profiles, an option part of a limited beta test.

Common to most publisher profiles are features like a profile photo, usually sourced from the Knowledge Graph or a YouTube profile, which also counts total social followers, and integrates various social media handles.

The social connections catered to include platforms like YouTube, TikTok, Instagram, Facebook, X, and LinkedIn. The ‘About’ section is succinct, often derived from a Wikipedia entry or something similar.

Some editable profiles offer additional features like customized banners, pinned posts, and external links that could direct users to apps or livestreams, further enhancing content reach.

There are two main types of Discover publisher profiles: ones for entities with websites and others solely focused on social media publishers.

Web-focused publishers’ profiles tend to be more comprehensive, often including the About section, logos, social accounts, and website links—although social links might sometimes need a manual push to be included.

On the other hand, profiles for social media publishers focus on prominent journalists, notable figures, and those solely identifiable through social media.

These profiles are generally less complete unless they are tied to a Knowledge Graph, missing elements like profile pictures or descriptions, frequently needing aid from connected YouTube accounts for better appearance.

Looking forward, I anticipate Google may broaden access to these editable profiles, though I suspect customization will remain selective, likely reserved for well-established publishers and creators.

Inspired by this post on Search Engine Land.

May 20, 2026
Is Zero Click Marketing Evolving with New AI Branded Links?

On May 7, 2026, something remarkable happened that completely shifted the landscape of AI-driven brand traffic. As I watched, ChatGPT quietly launched the most significant single-day transformation I’ve seen all year.
Overnight, the referrals from OpenAI to various brand sites practically doubled. It felt like each mention of a brand by ChatGPT was suddenly more valuable—because they turned into clickable referrals directly to the brands’ homepages.

Inspired by this post on Try Profound Blog.

May 19, 2026
Explore Google’s Enhanced Shopping Experience with Universal Cart

Imagine scrolling through Google Search and effortlessly collecting items from various retailers into one convenient Universal Cart. That’s exactly what Google is offering now, a seamless shopping experience that allows me to keep all my desired products in one place and check them out with a single click using Google Wallet.

Recently announced by Vidhya Srinivasan, VP/GM Ads & Commerce, Google’s Shopping Graph has reached an impressive 60 billion product listings, a significant jump from the 50 billion earlier this year. This growth reflects Google’s commitment to enhancing our online shopping experiences.

Universal Cart. With Universal Cart, I can add items from multiple stores while browsing Google Search, or even when I’m on YouTube and Gmail. It’s so liberating not to jump from site to site!

Here’s how it works: as I shop, Google helps me find the best deals and in-stock availability across different retailers. Then I simply choose my preferred store for checkout, leaving no room for the hassles generally associated with online shopping.

Google’s Universal Cart is smart too! Imagine you’re assembling a custom PC—your cart will alert you if any parts are incompatible and suggest compatible alternatives. Built on Google Wallet, it even recognizes payment perks and loyalty offers, revealing savings opportunities I might otherwise overlook.

Merchants. Google has partnered with renowned merchants like Nike, Sephora, Target, Ulta Beauty, Walmart, Wayfair, and Shopify sellers such as Fenty and Steve Madden. This wide array ensures I have plenty of shopping options!

Availability. This feature will roll out in the U.S. this summer, initially available on Google Search and the Gemini app, with plans to expand to YouTube and Gmail soon after.

UCP and AP2. Google is also extending the Universal Commerce Protocol to Canada and Australia soon, with plans for the U.K. The Agent Payments Protocol will support secure, accountable transactions by authorizing agents to shop on my behalf according to my specific criteria.

Moreover, Google’s innovative features are set to debut across Google products, starting with Gemini Spark. It’s an exciting time to be an online shopper!

Inspired by this post on Search Engine Land.

May 19, 2026
Discover Google’s Revolutionary Intelligent Search Box

Today, I want to share some exciting news. Google has unveiled its most significant change to the search box in 25 years. This new feature, known as the “Intelligent Search Box,” is designed to provide us with an easier way to access AI search capabilities.

This innovation is powered by the latest technology, the Gemini 3.5 Flash.

Here’s How It Looks. Google completely redesigned the search box to give us more space for longer and deeper queries. As I type my search, the box will expand, supported by an AI-powered suggestion tool that goes beyond simple autocomplete, according to Google’s Head of Search, Liz Reid.

What’s even more impressive is the ability to search with text, images, files, videos, and even my Chrome tabs. It’s truly versatile!

Let me show you what this looks like:

This innovation puts Google’s most powerful AI tools right at our fingertips, enabling us to ask questions more easily, as explained by Liz Reid from Google.

Seamless Transition to AI Mode. Google also made it easier to switch to AI Mode with their new AI Overviews feature, which is now available globally on both desktop and mobile. Initially launched to many in January, it’s now fully operational.

Here’s how it works:

Why It Matters to Us. The transformation of the Google Search Box impacts how we search and potentially changes the type of traffic Google sends our way. It may encourage more users like me to switch to AI Mode for deeper answers, possibly leading to fewer direct clicks on our websites.

While change can be challenging, it’s also inevitable. Google’s CEO Sundar Pichai emphasized how our expectations from Google Search evolve—from individual queries to ongoing conversations and now to agentic workflows. As the world’s most-used product, Google is determined to stay ahead of our needs.

Inspired by this post on Search Engine Land.

May 19, 2026
Create Custom SEO Reports with Ease Using Claude Code & GSC

I’ve always found SEO reporting to be a bit of a hassle. It used to mean spending hours exporting data from Google Search Console (GSC), tidying it up in spreadsheets, and then trying to make sense of it all in Data Studio.

Now, with AI tools like Claude Code, my workflow has completely changed. I can instantly create customized data visuals and reports in a fraction of the time it used to take.

Let me walk you through the journey of transforming GSC data into tailored reports, streamlining the entire process.

Using Claude Code is different from the standard Claude experience. While the regular Claude.ai acts like a chatbot, Claude Code functions as an AI coding assistant right on my computer. It’s capable of reading GSC CSV files, analyzing large datasets, and transforming raw data into clear, visual reports.

Initially, setting up Claude Code can be daunting, especially if you aren’t familiar with technical tasks. But don’t worry, the setup is a one-time effort. Once it’s up and running, generating reports takes just minutes.

The real magic happens after you connect Claude to GSC. Whether you’re in an enterprise environment or you’re an independent SEO consultant, having Claude Code set up is invaluable.

Starting your journey with Claude Code begins by creating an account on Claude.ai. Even without a paid subscription, I find the platform extremely helpful for generating SEO reports.

A crucial step in using Claude Code is installing Node.js on your machine. For this tutorial, I used a Mac, but it’s compatible with other operating systems too. Once Node.js is installed, I am able to install Claude Code and verify my setup through simple terminal commands.

After setting everything up, I navigated a series of prompts in Claude, choosing how to access GSC data and defining key parameters for my reporting.

Connecting Claude to GSC involves interacting with the Search Console API, albeit a bit technical. But Claude guides me through each step, ensuring a smooth setup.

The exciting part comes after the connection is established. I can now rapidly create focused reports, such as identifying top-performing pages or tracking keyword trends over time, tailor-made for my needs.

Overall, Claude Code redefines how I manage SEO reporting. It offers the perfect balance of speed, flexibility, and control. Once the groundwork is laid, it makes my reporting both dynamic and precise, adapting to the demands of my stakeholders with ease.

Inspired by this post on Search Engine Land.

May 19, 2026