You pick up your phone to reply to a message. A few taps later, you’re watching a TikTok about a restaurant in Sicily, a boutique hotel in Copenhagen, or a local business you hadn’t heard of before.

Maybe you Google it right away. Maybe you don’t. But days or weeks later, someone mentions it, and you search for it. Just like that, discovery becomes search.

That’s happening more often than many businesses realize. People increasingly discover brands before they actively look for them, making Google less of a starting point and more of a place to validate decisions. That shift has important implications for SEO, local visibility, and content strategy.

Recommendation engines change the rules

TikTok’s recommendation engine is one of the most sophisticated consumer recommendation systems available today. Rather than waiting for users to type a query, it continuously learns from subtle behavioral signals, such as watch time, rewatches, pauses while scrolling, shares, and saves.

According to TikTok, recommendations are driven by a combination of user interactions, video information, and viewing behavior, not a single ranking signal.

If discovery increasingly happens before search, visibility strategies need to evolve.

The content that earns attention tends to share a few characteristics:

• A strong hook.
• Storytelling that keeps people watching.
• Fast-paced editing, visuals, and sound that feel native to the platform.

The shift is significant enough that even Google has acknowledged it. Google’s SVP Prabhakar Raghavan revealed that almost 40% of young people looking for somewhere to eat turn to TikTok or Instagram instead of Google Search or Google Maps.

Recommendation engines don’t wait for users to express intent through a search. They predict what people may find interesting before they even think to look for it.

Dig deeper: The SEO shift you can’t ignore: Video is becoming source material

Google understands intent. TikTok understands curiosity.

TikTok analyzes spoken language, captions, and on-screen text to understand what a video is about before deciding who should see it. It also reads text that appears within the video, considers location signals, and rewards content that generates meaningful conversations in the comments.

Experienced creators deliberately craft seamless loops in which the final seconds of a video naturally connect back to the beginning. Viewers often replay the video without realizing it, increasing completion rates and sending stronger retention signals that encourage wider distribution. Videos that continue generating saves and engagement over time are also more likely to keep being recommended.

Comments can be used to create conversations. Instead of ending the interaction with a simple answer, encourage the original commenter, or someone else, to reply again, even if the question has already been asked. For businesses, it’s also an opportunity to naturally reinforce important keywords by mentioning your hotel, restaurant, location, or services in your replies.

Every conversation adds another layer of semantic relevance, helping both users and TikTok better understand what your content is about.

Dig deeper: Why TikTok deserves a place in your SEO strategy

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TikTok as a local discovery engine

This behavioral shift is especially important for businesses where visual trust shapes purchasing decisions, including:

• Restaurants.
• Hotels.
• Beauty.
• Fitness.
• Retail.

Before visiting somewhere new, people increasingly want to experience it from their sofa. Short-form video makes that possible by instantly communicating atmosphere, context, and emotion.

More importantly, it dramatically reduces the cognitive effort required to make a decision. Instead of reading reviews, comparing ratings, opening photo galleries, and jumping between websites, people can evaluate an experience in seconds.

When planning your content strategy, ask yourself:

• What does the restaurant actually look like?
• What’s the atmosphere like?
• Does the food look as good as it tastes?

Within the first few seconds, viewers have often answered many of those questions.

The first decision is emotional. The research comes afterward.

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Discovery first, verification second

Google continues to dominate high-intent searches, Maps, local business information, and transactional queries.

What’s changing is where the customer journey begins.

In travel, hospitality, and lifestyle, discovery increasingly happens on platforms like TikTok, while Google becomes the place to validate those decisions.

Today’s customer journey is an interconnected discovery ecosystem. Every platform plays a different role, and brands that understand how they work together will earn both attention and conversions.

TikTok as a market research tool

Recommendation platforms aren’t just changing how people discover brands. They’re also changing how brands discover customer demand.

One of the biggest mistakes brands still make is creating content around what they want to communicate instead of what their audience actually wants to know.

TikTok’s Creator Search Insights is becoming one of the most valuable market research tools available because it reveals rising searches, unanswered questions, and content gaps directly from user behavior.

These insights help identify:

• Rising search topics.
• Unanswered questions.
• Seasonal demand.
• Content gaps.
• Emerging customer interests.

The findings should shape your SEO strategy, local landing pages, editorial planning, FAQs, and even product positioning. The most valuable keyword research may no longer begin inside a keyword tool. It may begin inside TikTok.

Dig deeper: Why social search visibility is the next evolution of discoverability

The search starts before Google

As search becomes increasingly AI-driven, earning attention before someone types a query may become one of the strongest competitive advantages a business can have.

The brands that succeed won’t start with channels. They’ll start with customer behavior.

The same person may search Google for a marketing course, browse TikTok for vacation inspiration, and turn to Instagram before buying a skincare product. Discovery doesn’t happen in the same place for every customer or every decision. It depends on the intent behind the search.

As AI-powered search increasingly answers questions without requiring a click, visibility will depend on far more than rankings alone. Brands will need to earn attention before the search, build trust throughout the journey, and be present wherever their audience chooses to discover, validate, and decide.

The brands that win won’t create content for algorithms. They’ll create content people genuinely choose to watch.

Ultimately, the future of SEO isn’t just about ranking when someone searches. It’s about becoming visible before they ever think to search.

Google Image Search has turned 25 years old and with that, Google has decided to completely revamp the Google Image Search home page at images.google.com from a clean search box, to a gallery of image collections.

“Today, we’re introducing a brand new browseable home for Google Images, featuring a dynamic, immersive gallery of images from across the web — updated in real time and intelligently tailored to your unique interests,” Brad Kellet, Senior Engineering Director, Search announced.

What it looks like. Here is a screenshot of the new Google Image Search homepage:

This is what the old Google Image Search home page looked like:

Features as well. Google is not only showing a gallery of images on its new image search home page but there are search features as well. The search box is at the top, where you can search by text, voice, by image and so forth.

You can also browse and save ideas to your collections on Google Image Search. Those images will appear as tabs above the main gallery, making it easy to jump back in and continue exploring based on what inspires you, Google explained.

Here are screenshots of how that works:

Availability. Google said this new Google Images home page will roll out over the coming weeks on desktop in the U.S. in English. You will need to sign in to your Google Account to try it out.

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Google will let you create images directly within AI Overviews in Google Search. “To help bring those unique ideas to life, we’re bringing image generation directly into AI Overviews in Search,” Google announced.

This uses Google’s latest Nano Banana AI model within AI Overviews to create these images.

Google said, “This update transforms a simple text prompt into a high-quality, custom visual made completely from scratch, seamlessly bridging the gap between imagination and reality.”

What it looks like. Here is a video of this in action:

Availability. Google will roll out this image generation feature within AI Overviews over the coming weeks in English, for all regions that currently support image creation in AI Mode.

Google also announced a redesign for Google Image Search, on its 25th anniversary of Google Image Search.

Why we care. This may have an impact on traffic to publishers, as it will add more AI-generated content (the images) to the AI Overview, potentially discouraging clicks from Google Search. Plus, if people get the image they want in the AI Overview, it might even discourage some use of Google Image Search – maybe?

In any event, it is wild to know that Google Image Search is now 25 years old.

If AI can’t find you, customers won’t either.

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AI shopping is changing what SEO needs to optimize. Structured data, product feeds, entity signals, and crawlable content no longer just influence rankings. They increasingly determine whether AI systems can understand, evaluate, and recommend your products.

The technical foundations haven’t changed. Their role has.

As AI becomes another path to product discovery and purchasing, brands need to strengthen the information AI relies on to make decisions.

AI shopping requires a broader view of brand knowledge infrastructure

For ecommerce and service brands, brand knowledge infrastructure has historically meant maintaining a Google Business Profile, keeping NAP data consistent, and ensuring core pages are crawlable. 

Those fundamentals still matter, but they’re now the floor, not the ceiling. Today, brand knowledge infrastructure has three layers.

The static layer 

Structured, agent-facing content, including clear return policies, shipping terms, and product differentiation in machine-readable formats. This information needs to be available in crawlable HTML, not hidden behind JavaScript or buried in PDFs. 

Agents evaluating whether to recommend your business for a booking or purchase will look for this information the same way a person would check your FAQ page. The difference is they’ll stop looking the moment they can’t parse it.

The real-time layer

Live product and inventory data that AI systems rely on for pricing, availability, and recommendations. 

Once a product is added, Universal Cart works in the background to monitor price drops, surface price history, and alert users when an item is back in stock, all powered by Gemini models. 

Agents pulling from this system need product data that’s accurate, up to date, and complete at the attribute level. A product listing with a missing shipping estimate or stale inventory count is unhelpful and untrustworthy to the machine making the recommendation.

The entity layer 

The signals that establish your brand as a trusted, machine-readable entity across the web. That includes:

• Consistent brand naming.
• A verified Google Business Profile.
• Organization schema with sameAs attributes pointing to authoritative sources.
• Accurate Knowledge Graph data. 

The entity markup that establishes your organization in Google’s Knowledge Graph is the highest-leverage schema implementation available in 2026. Its impact on AI Mode citations and Knowledge Panel accuracy is substantial and measurable, even though it doesn’t generate visible SERP features.

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What matters most for AI shopping

Traditional SEO asks whether people will click. AI shopping expands that to ask whether machines will trust your data enough to evaluate and recommend your products. These six priorities are where that trust is built or lost.

1. Product data quality

Complete, accurate, real-time product attributes, including titles, descriptions, pricing, inventory, and shipping information, are what AI systems evaluate first. The minimum data set for AI-ready product data includes:

• A title.
• Description.
• Price.
• Availability.
• Global Trade Item Number (GTIN) or Manufacturer Part Number (MPN). 
• Shipping speed and cost.
• Return policy.
• High-quality images. 

Stale or incomplete data creates a poor user experience and can prevent your products from appearing in AI-generated comparisons and recommendations before a person ever has a chance to see them.

Audit your product feeds the way you audit technical SEO: systematically, on a regular cadence, and with the assumption that every gap has a cost. 

Prioritize price and inventory accuracy first because those are the attributes AI systems verify most aggressively against real-time signals.

2. Machine-readable product information

JSON-LD Product markup, availability signals, pricing data, and shipping details make up the machine-readable layer AI systems parse before anything else. 

Implementation best practices haven’t fundamentally changed, but validation requirements have expanded to include AI Mode considerations that existing tools don’t directly measure. 

The current validation workflow requires two checks: Google’s Rich Results Test for traditional eligibility and a manual review of AI Mode citation behavior for your key queries.

Beyond Product schema, one of the most underused implementations is Organization schema with knowsAbout and sameAs properties. These establish your entity identity in Google’s Knowledge Graph and improve your chances of being selected as a cited source in AI Mode responses. 

3. Structured content beyond schema

Schema markup tells AI systems what your data is. Structured content determines how that data is presented on the page. AI systems evaluate both independently.

In practice, this means three things:

• Product specifications should appear in HTML tables, not prose paragraphs. An AI system assembling a comparison interface needs clean, scannable attribute rows, such as material, dimensions, compatibility, and weight, not a sentence that happens to contain those facts.
• Policies that influence purchase decisions, including returns, shipping terms, and warranties, should be hosted in crawlable HTML at a stable, linkable URL, not in a JavaScript accordion, modal, or PDF.
• If you publish comparison content, such as “our product vs. competitors,” present it as tabular data. AI systems building real-time product comparisons can extract information from structured tables more reliably than from narrative copy making the same claims.

This is as much a content production and CMS decision as it is an SEO one, and it’s worth auditing separately from your schema implementation.

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4. Real-time product feeds

With Google’s Universal Cart and generative UI both pulling from live product data, the quality of your real-time feeds is no longer just a commerce operations problem. It’s an SEO problem. Feeds that update infrequently, omit key attributes, or contain stale inventory signals will underperform in AI-generated shopping experiences, much like slow page speed underperforms in traditional search.

If you use a feed management platform, audit the refresh rate and attribute completeness of your Google Merchant Center data. If you manage feeds manually, establish a regular QA process at the SKU level, not just the category level. AI systems building comparison tables or product simulations from live data will skip products they can’t fully populate.

5. AI-ready business information

For service businesses, such as home repair, beauty, and pet care, prepare for the possibility that Google’s AI will call your business on a customer’s behalf. 

That means your Google Business Profile services, hours, and pricing need to be accurate, complete, and consistent with what’s on your website. 

Your phone staff also need to be ready to answer agent-style queries: specific, structured, criteria-driven questions about availability, pricing, and service scope.

Assume the AI system will check three things before deciding whether to call your business or move on to a competitor: 

• Your Google Business Profile services list.
• Your website’s pricing and availability information.
• Your reviews. 

If any of these are incomplete or inconsistent, you risk being bypassed without ever knowing it.

6. CRM and transactional data

Consistent brand naming, structured product identifiers in transactional emails, and clean order confirmation data are signals AI systems can use to connect a user’s history to a current purchase decision. 

Audit your transactional email stack with this question: If Google’s AI reviewed every order confirmation your brand has sent, could it accurately identify your products, pricing history, and brand identity? If not, those inconsistencies are creating friction in a recommendation process you can’t see.

If AI can’t find you, customers won’t either.

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The organic window is open, but it won’t stay that way

AI shopping doesn’t replace traditional SEO. It changes what successful SEO looks like. The same technical foundations you’ve relied on for years, including structured data, product feeds, entity signals, and crawlable content, now do more than improve visibility. They help AI systems understand your business well enough to recommend it.

Historically, incomplete or inconsistent data might have meant lower rankings or fewer rich results. In AI shopping, it can mean your products never make it into the comparison, recommendation, or transaction in the first place.

That’s why the six priorities in this article aren’t new SEO tactics. They’re established best practices that now carry greater weight as AI becomes another way people discover and buy products.

Brands that strengthen their brand knowledge infrastructure now will be better positioned as AI shopping matures and competition for visibility inevitably increases.

As long as I’ve been in search marketing, the path has been simple: search query → click → buy.

SEO followed the same model, with organic traffic, impressions, and click-through rate (CTR) serving as its primary measures of success.

Google’s Universal Commerce Protocol (UCP) signals where search is headed, shifting from a discovery engine to a transaction layer.

Driven by the rise of “agentic commerce,” Google can now discover, evaluate, compare, and complete purchases entirely within its AI-powered experiences, including AI Mode, Gemini, YouTube, and Gmail.

The SEO implications are significant. We’re moving from optimizing for clicks to optimizing for AI transactions. If your brand doesn’t speak the language of UCP, you risk becoming invisible to the next generation of shoppers.

Here’s what UCP is, why it’s reshaping digital marketing, and how to adapt your SEO strategy.

UCP: The infrastructure behind AI transactions

UCP is an open-source, vendor-agnostic standard that enables the entire commerce lifecycle, from discovery and cart building to checkout and post-purchase tracking, within AI interfaces. 

Co-developed by Google with Shopify, Walmart, Target, Wayfair, Etsy, and other ecosystem leaders, UCP acts as a universal translator between AI shopping agents and merchants’ storefront backends.

Think of UCP as the ecommerce equivalent of HTTPS. Just as HTTPS standardizes secure communication between web browsers and servers, UCP standardizes how AI agents interact with online stores. Instead of requiring custom one-to-one integrations for every merchant, AI agents can securely browse inventory and complete purchases across millions of online stores.

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How AI transactions flow through UCP

When someone asks AI Mode to “find and order a replacement water filter for a 2021 Samsung French-door fridge with the fastest shipping,” UCP handles the transaction through a structured workflow.

Capability publication

The merchant publishes its merchant capabilities, including product search, live pricing, fulfillment options, and accepted payment methods.

Handshake

The AI agent reads the merchant profile, matches it with its own capabilities, and establishes a secure path forward, such as aligning on loyalty programs or supported digital wallets.

Action execution

The AI searches for the product, verifies real-time inventory, builds the cart, and uses the Agent Payments Protocol (AP2) to complete a secure, tokenized transaction.

Human escalation

If user input is required, such as selecting a delivery window or confirming a shipping address, UCP pauses the transaction, prompts the user, and then hands control back to the AI to complete the workflow.

Dig deeper: How Google’s Universal Commerce Protocol could reshape search conversions

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Why UCP matters for search and SEO

UCP isn’t just a technical update. It changes how AI discovers, evaluates, and purchases products. Here’s why it matters for SEO.

1. From click-throughs to buy-throughs

In an agentic search environment, website traffic is no longer the only measure of business value. As Google rolls out features like Universal Cart, allowing users to add products from multiple retailers to a single Google cart and check out with Google Wallet, the buying journey becomes much shorter. 

Shoppers may never visit your homepage, category page, or product detail page. Your SEO objective shifts to earning product selection within the AI recommendation layer, turning a search query into a sale without intermediate web traffic.

2. The rise of hyper-personalized, conversational queries

Keyword research is evolving. Shoppers are no longer searching for “men’s running shoes.” They’re using highly specific, situational prompts, such as “Best running shoes for flat feet under $150 that can arrive by Friday.” 

To match those queries, search engines need more than on-page copy. They need rich, queryable product attributes. UCP bridges that gap, allowing AI agents to match your inventory with highly specific user requests.

3. Less checkout friction

Cart abandonment remains a persistent ecommerce challenge, often caused by lengthy forms, broken checkout flows, or unexpected shipping costs. Because UCP integrates with secure digital wallets and passes verified user data automatically, it removes many of those friction points. 

For high-intent, urgent, or repeat purchases, merchants that support UCP can capture more conversions than competitors that send users to a separate checkout experience.

4. Merchants retain brand control and customer ownership

When a transaction happens through UCP, the merchant remains the Merchant of Record. Brands still control pricing, fulfillment, and return policies while retaining customer relationships and first-party data. UCP simply provides the infrastructure that enables AI-powered transactions.

Dig deeper: Winning the AI decision layer: From AI discovery to agentic commerce

How to prepare your brand for UCP

If your SEO strategy is limited to blog articles and meta descriptions, you’re overlooking the technical infrastructure behind AI-powered commerce. To make your products eligible for UCP-powered search experiences, focus on these priorities.

Optimize your Merchant Center feed

Your Google Merchant Center (GMC) account is no longer just for Shopping ads. It’s becoming the primary source of product data for AI discovery.

• Enable the native_commerce attribute: To opt into UCP-powered checkouts, add the native_commerce attribute to your product feed. Google recommends using supplemental feeds to apply it at the product level without affecting your primary feed.
• Map product identifiers: Ensure every product ID in your GMC feed maps one-to-one with your internal checkout API. If they don’t match, use the merchant_item_id attribute to align them.
• Complete your policy data: Keep your returns, shipping, and customer support information complete and up to date. AI agents prioritize merchants with clear policy data.

Align structured data with your product feed

AI search relies on consistent data across your website and Merchant Center. Keep your Product, Offer, and Review schema synchronized with your product feed. Differences between the two can trigger validation issues that make products ineligible for AI-powered checkout.

Prepare for conversational attributes

Google is introducing new semantic attributes designed for conversational AI search. Start preparing your inventory systems to provide:

• Real-time inventory availability.
• Direct answers to product FAQs, such as “Is this jacket machine washable?”
• Product compatibility data, including accessory pairings, sizing guides, and model-specific replacements.

If AI can’t find you, customers won’t either.

Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.

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Beyond clicks: SEO’s next opportunity

The Universal Commerce Protocol reflects a broader shift in search. For SEOs, that expands our role beyond driving traffic. 

By prioritizing structured product data, data readiness, and agentic commerce, you can position your brand to capture demand at the moment of intent.

The future of search isn’t just about getting found. It’s about getting bought.

Google unveiled Gemini Intelligence at the Android Show on May 12, alongside a new laptop called the Googlebook. The company describes Gemini Intelligence as a layer that runs beneath the Android operating system across laptops, phones, watches, and glasses.

The new Googlebook is built from the ground up around an AI agent that understands what’s on your screen and acts on it for you. Point at a date in an email, and it’ll set up a meeting. Select pieces of furniture in an app, and it’ll show you what they’d look like in your living room.

Now that an operating system can complete tasks without users even opening a webpage, how people search, discover, and conduct commerce will fundamentally change. Let’s look at how this will affect the search industry.

What the shift to an agentic operating system means

Up until now, a person had a question or intent, typed it into a search engine, received a list of links, and chose one. Getting your website to rank on that list was the prize, and the entire SEO industry was built around earning that click.

Gemini Intelligence assumes something completely different. A user still has search intent, but an AI agent now handles the middle steps — reading pages, filling out forms, and, increasingly, completing the task for you. Instead of you visiting a website, an AI agent visits it on your behalf.

One example is Chrome Auto Browse, launched in January and built on Gemini 3. It handles multistep tasks like researching flights, filling out forms, scheduling appointments, and managing subscriptions, then pauses to ask before making a purchase.

Ecommerce has a good reason to move toward agentic AI.

A 2025 preprint evaluated the declared-tools approach across online shopping, authentication, and content management. It found that handing an agent pre-structured interaction data cut processing requirements by 67.6% and reduced costs by 34% to 63%, compared with parsing the full HTML document. Task success was only slightly lower than with the traditional method: 97.9%, compared with 98.8%.

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The architecture behind Gemini Intelligence

AI agents prefer sites they can transact with cleanly because it’s more efficient. Gemini Intelligence only works if agents can reliably perform tasks on websites.

Two protocols make this possible: WebMCP makes a site’s actions callable, and the Universal Commerce Protocol (UCP) allows an agent to complete a sale. Together, they let an agent finish the job without a human having to load a page.

WebMCP

This API lets a website declare its functions as structured tools an agent can call, such as searching inventory, starting checkout, or submitting a support request. This effectively lets you hand an AI agent a labeled menu.

Google co-developed WebMCP with Microsoft. An origin trial is live in Chrome 149, Firefox has committed to the third quarter of 2026, and Safari is expected to follow in the fourth quarter.

Universal Commerce Protocol (UCP)

This protocol gives AI agents a common language to discover products, build a cart, complete checkout, and handle orders without a user visiting the site. Google also has a consumer-facing surface layer called Universal Cart, which collects items as you move across Search, Gemini, YouTube, and Gmail.

Google, Shopify, Walmart, Target, Etsy, Wayfair, PayPal, and Stripe co-developed UCP, which launched in January.

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How to prepare for agentic AI

Websites are rapidly changing from destinations to backends, from places people visit to places agents quietly use. The operating system is becoming the search layer. The question is no longer whether you rank, but whether an agent can use your site.

To prepare, audit your most valuable actions, whether that’s a lead form, booking flow, or checkout page, and ask whether an agent could complete them instead. Check your Lighthouse Agentic Browsing score the way you check Core Web Vitals to see whether an agent can use your site in addition to reading it.

If you run ecommerce, find out whether your checkout is reachable through UCP or ACP. Keep doing the retrieval work, because an agent still has to find and trust you before it can act on your behalf.

Dig deeper. Are we ready for the agentic web?

YouTube expanded Ask YouTube to signed-in U.S. desktop viewers 13 and older, moving its conversational search experience beyond a Premium-only test.

What is Ask YouTube? Ask YouTube lets users type natural-language questions into the YouTube search bar and receive responses that combine text, video clips, long-form videos, and Shorts. Users can also ask follow-up questions to refine results.

Access expands. When YouTube announced the test in April, Ask YouTube was limited to U.S. YouTube Premium members 18 and older who opted in through youtube.com/new. On July 6, YouTube expanded it to signed-in U.S. viewers 13 and older using English-language searches on desktop.

• Signed-out viewers and supervised accounts remain excluded.
• YouTube said it will roll out the feature to more devices, languages, and users worldwide in the coming months.

YouTube standard Search isn’t going away. Users can switch back to traditional video results by clicking All on an Ask YouTube results page or by returning to the Home page. Ask YouTube remains a separate search option rather than a replacement for standard YouTube Search.

Views count for creators. YouTube said videos featured in Ask YouTube responses give creators another way to be discovered.

• Views from Shorts, videos, and previews shown in Ask YouTube responses count toward total view metrics and YouTube Partner Program eligibility. Featured videos also display the video title and channel name.
• YouTube said creators can improve their chances of appearing by publishing unique, high-quality content with clear chapters and descriptive titles. Those signals help its systems match video segments to viewer questions.

Why we care. YouTube is putting conversational search in front of a much larger group of U.S. desktop users. Your videos may need clear titles, chapters, and segments that answer specific questions well enough to appear in Ask YouTube responses.

What it looks like. Here’s a GIF of Ask YouTube in action:

The announcement. Try a new conversational search experience with Ask YouTube