That was phase one.

Phase two is already underway, and the stakes are higher. AI agents, tools like OpenAI’s Operator, Google’s Project Mariner, and Perplexity’s shopping assistant, are no longer just recommending products. They are completing purchases on behalf of users. They browse, compare, add to cart, and check out. The user may not even open a browser.

This changes everything about what it means to be visible online.

The question for ecommerce brands is no longer only “can AI find me?” It is “can AI buy from me?” If your site is not built to support agentic transactions, you will not just lose rankings. You will lose sales at the moment of intent, to competitors whose infrastructure is agent-ready.

Here is what is happening, why it matters, and what your ecommerce team needs to do about it.

From Discovery to Action: What AI Agents Actually Do

An AI agent is not a search engine. It is a task executor. When a user tells OpenAI’s Operator “order me a pair of trail running shoes under AED 400,” the agent does not return a list of links. It visits product pages, reads specifications, compares options, and initiates the purchase.

This is a fundamentally different type of site visit. There is no browsing session. There is no emotional engagement with your homepage photography. There is no window shopping. The agent arrives with a brief, evaluates whether your product and site meet its requirements, and either transacts or moves on.

Several of these agents are already live and being used by consumers today:

• OpenAI Operator: a browser-use agent that can fill forms, click through checkout flows, and complete purchases across standard ecommerce sites
• Perplexity Shopping: a discovery and transaction layer that pulls structured product data and enables one-click purchasing from within the Perplexity interface
• Google Project Mariner: Google’s browser agent, currently in research preview, designed to navigate and transact on behalf of users
• Anthropic Claude with computer use: capable of navigating browser-based interfaces to execute multi-step tasks including ecommerce flows

These are not concepts. They are tools your customers are already using. And right now, most ecommerce sites are not built to handle them well.

What an AI Agent Needs to Complete a Purchase

To understand the gap between current ecommerce infrastructure and agent-readiness, it helps to map exactly what an agent needs at each stage of the purchase journey.

1. Structured Product Data It Can Actually Read

Agents read data the way search crawlers do, but they need more of it and they need it to be precise. Schema.org Product markup is the foundation. Every product page should include:

• Name, SKU, brand, and category
• Current price and availability (updated in real time, not cached)
• Aggregate review rating and review count
• Return and shipping policy markup where available
• Variant-level data (size, colour, stock per variant) as distinct structured entries

If this data is missing, inconsistent, or buried in JavaScript that the agent cannot parse, your product will not meet the evaluation criteria. The agent will not guess. It will move to a site that gives it what it needs.

2. A Checkout Flow the Agent Can Navigate

Most ecommerce checkouts are designed for human browsing: multi-step flows, pop-up banners, login walls, CAPTCHA gates, and session-dependent cart logic. Agents struggle with all of these.

The key friction points to address:

• Guest checkout must be a clear, uninterrupted path. If your site forces account creation or uses dark patterns to obscure guest checkout, agents will fail at this step.
• CAPTCHA and bot detection should distinguish between malicious bots and legitimate agent traffic. Shopify’s commerce APIs, for instance, offer token-based authentication that allows verified agents to transact without triggering fraud blocks.
• Forms must use semantic HTML with correct field labelling. An input field for “email” that is only identifiable visually (not by name or label attributes) cannot be reliably filled by an agent.
• Payment flows that rely on redirect-heavy third-party processors or non-standard iframe implementations can break agent journeys mid-checkout.

Shopify merchants are ahead here. Shopify has been actively building agent-compatible commerce infrastructure, including its Storefront API and Buy SDK, which are already used by agentic integrations. If you are on a legacy or heavily customised platform, this is worth auditing now.

3. Trust and Confidence Signals

Before an agent transacts, it needs confidence that the merchant is legitimate and the product data is accurate. This is the trust layer. It includes:

• Price consistency across your site, Google Shopping, and any feeds you push to third-party platforms. Agents will flag discrepancies.
• Review data that is indexable. Reviews locked inside JavaScript widgets or loaded via non-standard plugins may not be visible to agents. Structured review markup in JSON-LD is the safest approach.
• Return and refund policies written in plain language and marked up with schema where possible. Policies buried in PDF documents or accessible only through modal pop-ups are effectively invisible to agents.
• Clear merchant identity signals: About pages, contact information, business registration details, and HTTPS security are basic requirements that agents use as trust indicators.

4. API Access and Agent-Specific Protocols

The most forward-looking brands are not waiting for agents to scrape their product pages. They are publishing structured data feeds and opening headless commerce APIs that agents can query directly.

One emerging standard worth watching is llms.txt, a protocol (modelled on robots.txt) that allows sites to communicate directly with AI models and agents about what data is available and how it should be accessed. It is early, but adoption is growing among sites that are positioning themselves as AI-native.

For larger ecommerce operations, investing in a headless commerce layer with documented APIs is the most durable infrastructure play. It future-proofs you against whatever the next generation of agent protocols looks like.

Agent-Ready vs. Agent-Hostile: Where Most Sites Stand Today

Most ecommerce sites were built for human browsing, not machine execution. That is not a criticism. It is simply a reflection of how the industry evolved. But the gap between where most sites are and where agent-ready infrastructure sits is significant. Here is a direct comparison:

For the last two years, the conversation around AI visibility has been a discovery problem. Brands wanted to show up in ChatGPT responses, get cited in Perplexity, and appear in Google’s AI Overviews. The goal was to be mentioned. To be found.

That was phase one.

Phase two is already underway, and the stakes are higher. AI agents, tools like OpenAI’s Operator, Google’s Project Mariner, and Perplexity’s shopping assistant, are no longer just recommending products. They are completing purchases on behalf of users. They browse, compare, add to cart, and check out. The user may not even open a browser.

This changes everything about what it means to be visible online.

The question for ecommerce brands is no longer only “can AI find me?” It is “can AI buy from me?” If your site is not built to support agentic transactions, you will not just lose rankings. You will lose sales at the moment of intent, to competitors whose infrastructure is agent-ready.

Here is what is happening, why it matters, and what your ecommerce team needs to do about it.

From Discovery to Action: What AI Agents Actually Do

An AI agent is not a search engine. It is a task executor. When a user tells OpenAI’s Operator “order me a pair of trail running shoes under AED 400,” the agent does not return a list of links. It visits product pages, reads specifications, compares options, and initiates the purchase.

This is a fundamentally different type of site visit. There is no browsing session. There is no emotional engagement with your homepage photography. There is no window shopping. The agent arrives with a brief, evaluates whether your product and site meet its requirements, and either transacts or moves on.

Several of these agents are already live and being used by consumers today:

• OpenAI Operator: a browser-use agent that can fill forms, click through checkout flows, and complete purchases across standard ecommerce sites
• Perplexity Shopping: a discovery and transaction layer that pulls structured product data and enables one-click purchasing from within the Perplexity interface
• Google Project Mariner: Google’s browser agent, currently in research preview, designed to navigate and transact on behalf of users
• Anthropic Claude with computer use: capable of navigating browser-based interfaces to execute multi-step tasks including ecommerce flows

These are not concepts. They are tools your customers are already using. And right now, most ecommerce sites are not built to handle them well.

What an AI Agent Needs to Complete a Purchase

To understand the gap between current ecommerce infrastructure and agent-readiness, it helps to map exactly what an agent needs at each stage of the purchase journey.

1. Structured Product Data It Can Actually Read

Agents read data the way search crawlers do, but they need more of it and they need it to be precise. Schema.org Product markup is the foundation. Every product page should include:

• Name, SKU, brand, and category
• Current price and availability (updated in real time, not cached)
• Aggregate review rating and review count
• Return and shipping policy markup where available
• Variant-level data (size, colour, stock per variant) as distinct structured entries

If this data is missing, inconsistent, or buried in JavaScript that the agent cannot parse, your product will not meet the evaluation criteria. The agent will not guess. It will move to a site that gives it what it needs.

2. A Checkout Flow the Agent Can Navigate

Most ecommerce checkouts are designed for human browsing: multi-step flows, pop-up banners, login walls, CAPTCHA gates, and session-dependent cart logic. Agents struggle with all of these.

The key friction points to address:

• Guest checkout must be a clear, uninterrupted path. If your site forces account creation or uses dark patterns to obscure guest checkout, agents will fail at this step.
• CAPTCHA and bot detection should distinguish between malicious bots and legitimate agent traffic. Shopify’s commerce APIs, for instance, offer token-based authentication that allows verified agents to transact without triggering fraud blocks.
• Forms must use semantic HTML with correct field labelling. An input field for “email” that is only identifiable visually (not by name or label attributes) cannot be reliably filled by an agent.
• Payment flows that rely on redirect-heavy third-party processors or non-standard iframe implementations can break agent journeys mid-checkout.

Shopify merchants are ahead here. Shopify has been actively building agent-compatible commerce infrastructure, including its Storefront API and Buy SDK, which are already used by agentic integrations. If you are on a legacy or heavily customised platform, this is worth auditing now.

3. Trust and Confidence Signals

Before an agent transacts, it needs confidence that the merchant is legitimate and the product data is accurate. This is the trust layer. It includes:

• Price consistency across your site, Google Shopping, and any feeds you push to third-party platforms. Agents will flag discrepancies.
• Review data that is indexable. Reviews locked inside JavaScript widgets or loaded via non-standard plugins may not be visible to agents. Structured review markup in JSON-LD is the safest approach.
• Return and refund policies written in plain language and marked up with schema where possible. Policies buried in PDF documents or accessible only through modal pop-ups are effectively invisible to agents.
• Clear merchant identity signals: About pages, contact information, business registration details, and HTTPS security are basic requirements that agents use as trust indicators.

4. API Access and Agent-Specific Protocols

The most forward-looking brands are not waiting for agents to scrape their product pages. They are publishing structured data feeds and opening headless commerce APIs that agents can query directly.

One emerging standard worth watching is llms.txt, a protocol (modelled on robots.txt) that allows sites to communicate directly with AI models and agents about what data is available and how it should be accessed. It is early, but adoption is growing among sites that are positioning themselves as AI-native.

For larger ecommerce operations, investing in a headless commerce layer with documented APIs is the most durable infrastructure play. It future-proofs you against whatever the next generation of agent protocols looks like.

Agent-Ready vs. Agent-Hostile: Where Most Sites Stand Today

Most ecommerce sites were built for human browsing, not machine execution. That is not a criticism. It is simply a reflection of how the industry evolved. But the gap between where most sites are and where agent-ready infrastructure sits is significant.

Here is a direct comparison:

The SEO and GEO Connection

It is worth being direct about how agentic readiness connects to everything you have already built around SEO and generative engine optimisation.

The signals that make you citation-eligible in ChatGPT or AI Overviews (authoritative content, structured markup, trustworthy merchant signals, clean technical infrastructure) are largely the same signals that make you transaction-eligible when an agent arrives with a purchase intent.

The difference is execution depth. GEO optimisation for discovery needs your content to be readable and trustworthy. Agentic optimisation for transaction needs your entire site infrastructure to support machine-driven action: from the product data layer through to the confirmation email.

Brands that have invested in technical SEO fundamentals (schema, crawlability, site speed, clean URL structures) are closer to agent-ready than they might think. The gap is typically in the checkout and trust layers, not in the content.

A Prioritised Action Plan for Ecommerce Teams

If you want to make your ecommerce site agent-ready, here is where to start. These are sequenced by impact and effort.

Priority 1: Schema audit and fix

Run a full schema.org audit across your product pages. Use Google’s Rich Results Test and a structured data crawler to identify gaps. Every product page needs complete Product schema with live pricing, availability, and review data. This is the single highest-impact change you can make.

Priority 2: Checkout flow audit

Walk through your checkout as a non-logged-in user. Document every friction point: login prompts, CAPTCHAs, unclear form labelling, payment redirect flows. Prioritise clearing the guest checkout path first. Then review your form field semantics with your development team.

Priority 3: Policy and trust page visibility

Move your return and shipping policies onto indexable, on-page HTML. Replace PDF downloads and modals with clean text pages that are linked from product pages and the checkout. Mark them up with schema where available.

Priority 4: Review data accessibility

If your reviews are loaded via a third-party widget that renders in JavaScript, talk to your development team about adding JSON-LD review markup to your page source. Agents cannot reliably access JavaScript-rendered content.

Priority 5: Publish an llms.txt file

This takes less than an hour and signals to agents and AI models that you have structured your site intentionally for machine access. It will not make your site agent-ready on its own, but it is a clear marker of intent and early adoption.

Priority 6: Evaluate your commerce platform’s API capabilities

If you are on Shopify, you likely already have access to the Storefront API. Understand what is available and whether your current setup exposes it to external consumers. If you are on a legacy platform, this is a longer-term infrastructure conversation worth starting now.

The Window to Act Is Now

Agentic commerce is not a 2028 problem. OpenAI Operator has been in public release since early 2025. Perplexity’s shopping features are live and actively used. Google’s agent layer is in advanced testing. The brands that build agent-ready infrastructure now will have a meaningful head start before this becomes table stakes.

The good news is that if you have been investing in technical SEO and GEO, you are not starting from zero. The foundations are largely in place. What is needed now is a deliberate audit of your checkout flow, your data layer, and your trust signals, viewed through the lens of machine execution rather than human browsing.

The brands that get this right will not just appear in AI responses. They will be the ones AI agents choose to buy from.

The post Can AI Agents Shop on Your Website? appeared first on Sandstorm Digital® Worldwide.

If your site experienced traffic swings in late May or early June, this update is almost certainly the primary cause. Here is everything you need to know about what happened, how to read the data accurately, and what actions to prioritise in the weeks ahead.

The Official Timeline: Key Dates and Rollout Details

Google released the May 2026 core update on May 21 at 8:40 AM PDT, flagging it on the Search Status Dashboard with the standard note that the rollout may take up to two weeks to complete. It wrapped on June 2 at 5:40 AM PDT, a total window of 11 days and 21 hours.

For context, here is how 2026 updates have stacked up so far:

• May 2026 core update: 12 days (May 21 to June 2)
• March 2026 core update: 12 days (March 27 to April 8)
• March 2026 spam update: under 20 hours (March 24 to March 25)
• February 2026 Discover core update: 22 days (February 5 to February 27)
• December 2025 core update: 18 days (December 11 to December 29)

This is the fourth confirmed ranking-related update Google has listed on its Search Status Dashboard in 2026 and the second broad core update of the year. Only six weeks separated the March update’s completion and the May launch.

A Significant Timing Coincidence: Google I/O and Gemini 3.5 Flash

The launch date was not a random choice. May 21 was also the opening day of Google I/O 2026, where Google announced that Gemini 3.5 Flash would now power its AI Search features, including AI Overviews. It has been noted that the core update appeared to align with the model powering AI Search features being upgraded.

This matters for digital marketers because it means the update’s impact on AI Overviews, featured snippets, and other Search features may be harder to separate from its effect on standard organic rankings. Sites that saw AI Overview visibility shift during the rollout window may be dealing with both a traditional ranking recalibration and changes driven by the new AI model simultaneously.

Observation: Heavy Volatility Across Verticals

Unlike the March update, which many SEO professionals described as relatively mild, the May update generated substantial movement from early in its rollout. Third-party tracking platforms including Semrush and Advanced Web Ranking recorded peak SERP volatility on the weekends of May 23 and May 30. A further surge in tracking metrics was reported in the final 24 hours before the update was marked complete.

Some reported impact across verticals and countries within the first weekend, adding that the May update was significantly more powerful than March, describing it as closer to what the industry typically associates with a major broad core update.

Others observed notable movement over the weekend of May 30 to June 1, with a number of sites recording large traffic surges during that window.

The consensus across the professional SEO community is that this update carried considerably more weight than its predecessor.

What Google’s Core Updates Actually Evaluate

Google’s broad core updates are not targeted penalties. They are comprehensive re-evaluations of how its core ranking systems assess content quality across the entire index. Every site in Google Search is technically eligible to be affected, in either direction.

Core updates operate by reassessing how well content demonstrates Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) across Google’s quality evaluator guidelines. The May 2026 update appears to have placed particular weight on:

• Firsthand experience and original insight: Content that demonstrates genuine knowledge of a topic rather than aggregating or rephrasing what already exists elsewhere.
• Content that matches user intent accurately: Pages that align with the format, depth, and information type users actually need for a given query.
• Trustworthiness signals: Clear authorship, verifiable credentials, transparent sourcing, and site-level reputation factors.
• Helpful, people-first content: Pages built primarily to answer user questions, not to capture rankings through keyword volume alone.
• AI-generated content quality: AI-assisted writing itself is not a disqualifier, but content that lacks expertise signals, original analysis, or real value is increasingly identifiable and vulnerable.

Sites that lost rankings during this update were not necessarily penalised. In most cases, Google reassessed other pages as a stronger match for certain queries. Recovery typically requires addressing content quality and intent signals rather than technical fixes.

Why This Update Is Harder to Read Than Most

One of the defining characteristics of the May 2026 update is that volatility did not occur in a single wave. Rankings shifted at multiple distinct points throughout the 12-day rollout window, including at the start, across both weekends, and again in the final hours before completion.

This creates a significant measurement challenge. A site that moved on May 24 may be responding to a different quality signal evaluation than a site that moved on June 1. Single-day comparisons or spot-checking rankings mid-rollout will produce misleading conclusions.

Adding further complexity, the simultaneous launch of Gemini 3.5 Flash as the AI Search model means that changes to AI Overview appearances, featured snippet selections, and other SERP features may reflect model-level decisions rather than traditional ranking signals. Separating these two forces in your data requires careful analysis.

When and How to Analyse Your Data

Google’s own core update documentation is clear: do not begin analysing Search Console data until at least one full week after the update has completed. With the May update finishing on June 2, the earliest meaningful analysis window opens around June 9.

When you do analyse, Google recommends comparing the post-update week against the week before the rollout began (prior to May 21). The dimensions to examine:

• Pages: Which specific URLs gained or lost impressions and clicks?
• Queries: Which search terms drove the change? Are they informational, navigational, or transactional?
• Countries: Is the impact concentrated in specific markets or global?
• Devices: Are mobile and desktop performance diverging?
• Search types: Did Discover, Image, or Video performance shift independently of web results?
• Snippets and features: Did you lose or gain featured snippets, AI Overview appearances, or other SERP features?

Avoid drawing conclusions from a single day’s data. Look for consistent patterns across at least seven days post-completion to distinguish genuine ranking shifts from the residual volatility that typically lingers in the immediate aftermath of a major update.

The Path Forward: What to Prioritise Now

If your site saw rankings decline during this update, the response is a content quality and relevance assessment, not a penalty recovery process. Here is how to approach it:

1. Audit your most affected pages through a quality lens

For each page that lost significant impressions, ask: Does this page provide genuine first-hand expertise or unique insight? Does it accurately match the intent behind the queries it was ranking for? Does it clearly demonstrate who created it and why they are qualified to do so? Is it built to help a reader, or built to rank?

2. Review intent alignment for your target queries

Run the queries your affected pages were targeting and assess what is now ranking. If Google has shifted toward a different content format or depth level, your content may need to be repositioned to match the updated understanding of user intent for that query.

3. Strengthen E-E-A-T signals across your site

Named authors with visible credentials and real industry presence, transparent sourcing, and evidence of firsthand experience are increasingly important signals. If your content lacks these, strengthening them is a foundational step in building long-term resilience.

4. Do not act on mid-rollout data

If you made significant site changes between May 21 and June 2, it is worth reviewing whether those changes were a response to temporary volatility rather than stable update impact. Acting on mid-rollout signals often leads to unnecessary changes that address fluctuations rather than genuine quality gaps.

5. Investigate unexpected gains with the same rigour

Sites that gained rankings during this update hold valuable information about what Google is currently rewarding. Understanding exactly why those pages improved can help you intentionally replicate those signals across underperforming content.

The Bigger Picture: Where Google Search Is Heading

The May 2026 core update does not exist in isolation. It lands at a moment when the entire search landscape is being restructured by AI-generated overviews, conversational search, and multi-surface discovery. Position one organic click-through rates have already declined significantly on queries where AI features appear, as users get answers directly in the SERP rather than clicking through to source content.

Traditional keyword-based ranking strategies, built around optimising for a single search surface, are becoming less sufficient on their own. Brands that maintain visibility across multiple discovery channels, including organic search, AI Overviews, and cited sources in AI-generated answers, are better positioned to sustain traffic as these shifts continue.

This update reinforces a direction Google has been moving toward consistently: content that earns authority over time through demonstrated expertise, genuine usefulness, and clear trustworthiness will continue to perform. Content built primarily to capture rankings through volume and keyword density will face increasing headwinds.

Navigating Google core updates requires more than a reactive response. At Sandstorm Digital, we help brands across the region build content strategies grounded in E-E-A-T, search intent, and AI-era visibility. If you want an expert review of how this update has affected your site and a clear roadmap for what to do next, get in touch with our SEO team.

The post Google’s May 2026 Core Update appeared first on Sandstorm Digital® Worldwide.

A leading Destination Management Company based in Dubai, Rayna Tours has built a reputation for delivering holiday packages, tours, visas, and excursions across a wide portfolio of global destinations, with a footprint that spans the UAE and international markets.

As search behavior continues to shift, travel brands face a growing challenge: being discoverable not just on Google, but across AI-powered search engines, answer engines, and generative platforms that are increasingly shaping how people plan and book trips. That’s where this partnership comes in.

Sandstorm Digital will be working with Rayna Tours across three core areas: SEO to strengthen organic visibility and capture high-intent travel queries, GEO (Generative Engine Optimization) to ensure Rayna Tours surfaces in AI-driven search results and recommendations, and AI search strategy to position the brand for the way travelers are searching today and where that search is heading.

The travel category is one of the most competitive spaces in search. It’s also one of the most exciting to work in, because the stakes are real and the margin between being found and being overlooked is significant. We’re looking forward to getting to work.

Welcome to the Sandstorm family, Rayna Tours.

The post Sandstorm Digital Welcomes Rayna Tours to Its Roster appeared first on Sandstorm Digital® Worldwide.

This is not a hypothetical. It is the structural reality of how AI-mediated discovery works across every major platform right now, from Google’s AI Overviews and Information Agents, to ChatGPT’s browsing capabilities, to Perplexity’s answer engine, to Microsoft Copilot embedded in the tools your customers use every day at work. The surface area of AI-driven content consumption is expanding rapidly and consistently across every channel. The measurement infrastructure most brands rely on is capturing almost none of it.

The visit did not happen. The influence did. And that gap is growing every quarter.

The Scale of What Is Already Happening

AI-mediated content discovery is not an emerging trend. It is the current reality of how a significant and growing portion of your potential audience encounters information, compares options, and narrows down decisions before they ever visit a website or open a search results page.

ChatGPT alone crossed 500 million weekly active users in early 2026 and continues to grow. Perplexity has established itself as the default research tool for a fast-growing segment of professional and high-intent users. Microsoft Copilot is embedded across the Office 365 suite, meaning it sits inside the working environment of hundreds of millions of people globally. Google’s AI Overviews now reach over 2.5 billion monthly users, and the newly announced Information Agents will run continuously in the background on behalf of subscribers, monitoring topics and synthesising content without any active search behaviour from the user at all.

Every one of these platforms reads content from the web, synthesises it, and delivers answers to users without those users visiting the source. Every one of them is doing this at a scale that dwarfs what any single brand’s owned analytics can detect.

Why Your Analytics Cannot See It

The measurement problem sits at the core of this issue and it is more structural than most teams realise.

When ChatGPT browses the web to answer a question, or when Perplexity synthesises multiple sources into a response, or when Google’s AI Overviews draws on your content to answer a query, no session is recorded in your analytics. No click appears in Search Console. No impression is attributed. The content was consumed. The brand was assessed. The user moved forward in their decision process. Your dashboard shows nothing.

Google’s own tooling illustrates the gap clearly. Search Console currently has no filter that separates AI Mode or AI Overview traffic from standard organic results. You can see total impressions and total clicks, but you cannot isolate how much of your content’s reach is happening through AI surfaces versus traditional ranked results. The May 2026 GA4 update added a native AI Assistant channel group that captures direct referral traffic from recognised AI platforms. That is progress, but it only captures the cases where the user actually clicked through. It does nothing for the far larger volume of interactions where the AI delivered the answer and the user never needed to visit.

For Perplexity and ChatGPT, third-party analytics tools offer partial visibility into referral traffic when a click does happen. But the ratio of AI-mediated content consumption to AI-driven clicks is not one to one. It is not close. The visits you can count are a fraction of the influence that is actually happening.

If your content strategy is being evaluated on sessions and clicks alone, you are measuring a fraction of its actual reach and impact.

The Numbers That Frame the Opportunity

Despite the measurement gap, the data that does exist paints a clear picture of both the scale of the shift and the opportunity it contains.

58.5%  of all Google searches now end without a single outbound click, according to SparkToro and Datos.

11%  is the current position-one click-through rate on queries where AI features appear, down from 27%.

500M+  weekly active users on ChatGPT as of early 2026, the majority using it for research and information tasks.

35%  more organic clicks earned by brands cited inside AI Overviews compared to non-cited competitors on the same queries.

That last figure is the most strategically important one. Brands that are being named inside AI responses are not just gaining invisible exposure. They are earning measurably more traffic on the queries where clicks do happen. Being cited creates a compounding advantage: AI-surface visibility increases brand familiarity, which increases click likelihood when a user does reach a results page or follow-up prompt.

There is a second data point worth holding alongside this. Research from Ahrefs found that traffic arriving directly from AI assistants converts at dramatically higher rates than standard organic traffic, in documented cases generating around 12% of signups from less than 1% of total traffic volume. The users who arrive from AI platforms are further along in their decision process. They are not browsing. They have already evaluated options and they are ready to act.

What Gets You Named Across AI Platforms

Citation behaviour varies across platforms but the underlying signals that drive it are more consistent than they appear. Whether the system is Google’s AI Overview, Perplexity’s answer engine, or ChatGPT’s browsing layer, the content being cited shares identifiable characteristics.

Genuine expertise and a clear point of view

AI systems are trained to identify and prioritise sources that demonstrate real subject matter authority. Content that hedges, that reads as generic, or that could have been written without domain knowledge is less likely to be cited than content that takes a clear position, draws on specific experience, and offers something that cannot be found in fifty other places. This is the most direct operationalisation of E-E-A-T in an AI-native search environment.

Structural clarity that works for machines

AI systems synthesise content most effectively when it is clearly structured. Logical heading hierarchies, explicit answers to implicit questions, and conclusions that do not require reading three paragraphs of context first all make content easier to extract and attribute. The standard for good content structure has not changed. The system evaluating that structure has become far more automated and far less forgiving of ambiguity.

Schema and machine-readable signals

Structured data communicates the nature, authorship, and context of content in a format optimised for machine consumption. Article schema, author markup, FAQ schema, and organisation schema all contribute to how confidently an AI system can identify and attribute a source. Across platforms that index the open web, this layer of technical signalling matters more than it did when the only system reading it was a traditional crawler.

Consistent topical authority across a content ecosystem

No AI platform cites a source based on a single page. Citation confidence is built through consistent, depth-first publishing around a topic over time. Brands that have a recognisable point of view on a specific domain, supported by a body of content that demonstrates genuine engagement with that domain, are the ones whose names appear when AI systems are deciding which source to attribute an answer to.

Freshness and factual reliability

AI systems are sensitive to content recency on fast-moving topics, and they are increasingly capable of cross-referencing factual claims against other sources. Content that is regularly updated, clearly dated, and accurate on verifiable points is more likely to be cited in contexts where the user’s question demands a trustworthy answer. For brands in regulated industries or categories where information changes frequently, this is both a risk to manage and an advantage to build.

The Arab World Dimension

The platforms driving this shift are global, but their regional penetration and the content ecosystems they draw on are uneven in ways that create specific opportunities for brands operating across the GCC.

ChatGPT, Perplexity, and Copilot are all actively used across the region and their reach is growing. Google’s AI features are US-first at launch but regional rollout is a matter of timing rather than if. The brands building for AI citation now, before these features reach full regional deployment, will hold structural advantages that are difficult to close once the shift happens.

The Arabic-language opportunity is the most underexploited part of this picture. AI platforms are significantly better at synthesising well-structured, entity-rich Arabic content than they were eighteen months ago. But the supply of that content remains limited relative to English. Across virtually every category, there is far less Arabic content with the structural clarity and topical depth required for confident AI citation. Brands that build this now are not just optimising for today’s traffic. They are building a citation footprint in a space where competition is still low and the compounding returns will be significant.

Updating the Measurement Conversation

One of the most important practical implications of the invisible traffic problem is the conversation brands and agencies need to have about how content performance is evaluated and reported.

If a brand’s content is being cited by AI systems at scale across Google, ChatGPT, and Perplexity, but the measurement model is built around sessions and clicks, the investment in content looks less productive than it actually is. Teams making budget decisions based on that data are working from an incomplete picture. In some cases, content that is performing exceptionally well in terms of AI-surface citation and brand influence will appear to be underperforming on traditional metrics.

The frameworks that address this gap are still developing across the industry, but the starting points are practical. Share of Voice in AI responses can be tracked manually across major platforms for high-priority queries. GA4’s AI Assistant channel group provides baseline data on AI-referred click traffic. Brand search volume trends can serve as a proxy for the cumulative effect of AI-surface exposure even when direct attribution is not available. And conversion quality analysis on AI-referred sessions will consistently show a different profile to standard organic traffic.

Three Things to Do This Quarter

• Audit your highest-value content for citability, not just rankability. Review the pages driving the most organic value and assess whether they are structured in a way that allows an AI system to extract a clear, attributable answer. Ambiguous conclusions, buried key points, and content that requires full sequential reading are structural barriers to AI citation across every platform.
• Implement and audit structured data consistently across your content properties. Author markup, article schema, FAQ schema, and organisation markup are the machine-readable signals that help AI systems attribute content with confidence. If these are inconsistent or missing, they represent the highest-return technical fix available in the current environment.
• Build a supplementary measurement framework alongside your standard analytics. Track brand citation in AI responses for your most important query categories, monitor GA4’s AI Assistant channel for conversion quality signals, and use brand search volume as a proxy for cumulative AI-surface influence. The brands that develop this visibility now will have a significantly more accurate picture of content ROI than those still relying on session data alone.

The invisible traffic problem is not a reason to pull back on content investment. It is a reason to redirect it toward content built for citation across every AI platform, structured for machines as well as humans, and measured against influence rather than sessions alone. The brands that make that shift now will be the ones whose content is doing the most work when AI-mediated discovery reaches full scale across the region.

The post The Invisible Traffic Problem Nobody Is Talking About appeared first on Sandstorm Digital® Worldwide.

Here is my read on the announcements that matter most for organic search strategy, and what we are already doing about them at Sandstorm Digital.

Google Search Is Now AI Search. Full Stop.

The clearest signal from I/O 2026 was not a feature announcement. It was a positioning statement: Google Search is AI Search. AI Overviews and AI Mode are being merged into a single unified experience. The search box has been redesigned for the first time in over 25 years, now accepting images, files, videos, and Chrome tabs as query inputs alongside text.

Sundar Pichai described this as the biggest upgrade to the search box in over 25 years. That framing matters. Google is not adding AI to search. Google is replacing the traditional search interface with an AI-native one. For SEO practitioners, the mental model of optimising for a ranked list of links is becoming structurally obsolete.

When the interface changes, the optimisation strategy must change with it. The question is no longer just ‘can Google crawl and rank this page?’ It is ‘will Google’s AI surface this content when a user, or an agent acting on their behalf, asks a relevant question?’

Generative UI in Search: The New SERP Nobody Is Talking About

Buried in the search announcements was something that should be front of mind for every SEO team: generative UI in Search. Using the agentic coding capabilities of Gemini 3.5 Flash and the Google Antigravity platform, Search can now build custom response layouts on the fly. We are talking about interactive visuals, simulations, tables, and dynamic components assembled in real time, tailored to the specific query.

These generative UI capabilities will be available to all users in Search this summer, free of charge. That is not a beta. That is a mainstream deployment timeline.

What this means in practice is that for complex, high-intent queries, the results page may no longer look like a results page at all. It may look like a custom tool built for that specific question. If your content is not structured in a way that feeds cleanly into this kind of AI-assembled response, your visibility in these moments will be limited regardless of your traditional ranking signals.

Information Agents: SEO for a Zero-Click-Before-the-Click World

Information Agents in Search are personalised AI agents that users can configure to run continuously in the background, surfacing relevant content, monitoring topics, and taking action on a user’s behalf. They are rolling out this summer, starting with Google AI Pro and Ultra subscribers.

Think about what this means for the top of the funnel. A user does not need to search for your content at the moment they encounter a question. An Information Agent may have already surfaced competing answers hours earlier. The discovery moment for your content may happen when the user is not even looking at a screen.

This is not the death of organic search. But it does mean that traditional SEO metrics like impressions and clicks will increasingly undercount actual AI-driven content exposure. We need new frameworks for understanding how content performs in an agent-mediated world.

What actually gets surfaced by Information Agents?

Based on what Google has disclosed, these agents use Gemini 3.5 and are designed to deliver personalised, contextually relevant content. The signals that matter are the same signals that have always made content authoritative: clear structure, genuine expertise, consistent publishing, and schema that communicates what your content is about at a machine-readable level. What changes is the weight and timing of those signals.

Gemini Omni and the Video Content Equation

Google introduced Gemini Omni at I/O, a multimodal model that generates, edits, and transforms video from virtually any input type. Text, images, audio, and existing video clips can all serve as source material. All Omni-generated content will carry SynthID watermarks.

For SEO teams, Gemini Omni matters because it accelerates the volume and variety of video content on the internet. YouTube’s Ask YouTube feature, also announced at I/O, allows users to query video content conversationally, with Gemini powering the responses. Your video content strategy is now also a discoverability strategy for AI-mediated video search.

If your brand is not producing structured, searchable video content, you are missing an increasingly significant surface area for AI-driven organic visibility.

SynthID and Content Credentials: The Coming Transparency Layer

SynthID has now marked over 100 billion pieces of AI-generated content. C2PA Content Credentials verification is rolling out in Search and Chrome over the coming months, with major partners including NVIDIA, OpenAI, and Kakao participating. Users will be able to verify the provenance of content they encounter across Google surfaces.

For SEO, this introduces a new dimension of content quality signalling. Google has long rewarded E-E-A-T: Experience, Expertise, Authoritativeness, and Trustworthiness. Content Credentials operationalise a new layer of trust verification. Brands producing AI-assisted content that is clearly attributed, properly structured, and demonstrably authored will be better positioned than those relying on anonymous, unattributed AI output.

At Sandstorm Digital, we have been advising clients to treat AI content disclosure not as a liability but as a differentiator. I/O 2026 confirms that direction.

The SEO Opportunity in an Agentic Search World

Several of the features announced at I/O are US-first at launch. Information Agents, Gemini Spark, and the generative UI in Search are all rolling out domestically before reaching international markets. For GCC brands, this is a window.

The brands and agencies that build for agentic discoverability now, before these features reach mainstream adoption in the region, will hold structural advantages when the rollout happens. That means investing in content architecture, schema implementation, and AI-legible content structures today rather than retrofitting them under pressure.

Arabic-language AI discoverability adds another layer. Google’s multilingual AI capabilities are improving rapidly, but the Arabic web remains significantly underoptimised for the kind of structured, entity-rich content that AI systems surface confidently. Brands that address this gap proactively will benefit disproportionately when AI-native search reaches full deployment across the region.

What Our SEO Team Is Doing Right Now

• Auditing content architecture for AI-legibility. Are headings, entities, and structured data communicating meaning clearly to a machine reading context, not just a human scanning a page?
• Building generative UI-ready content formats. Long-form, structured content that can be assembled into dynamic response layouts has a different profile than traditional blog posts. We are developing templates that serve both.
• Developing an Arabic AI discoverability framework for GCC clients, combining entity optimisation, schema implementation, and Arabic-first content structures designed for AI-mediated search surfaces.
• Revising E-E-A-T guidance in light of Content Credentials. Attribution, authorship signals, and transparent AI use are becoming technical SEO considerations, not just editorial ones.

The core principle of SEO has not changed: be genuinely useful, be clearly structured, be trustworthy. What Google I/O 2026 confirms is that the systems evaluating those qualities are now far more sophisticated, and far more autonomous, than they were twelve months ago.

The post Google Just Rewrote the Rules of Search. Again. appeared first on Sandstorm Digital® Worldwide.

Sandstorm Digital was in attendance, virtually, at Google’s annual developer conference, Google I/O 2026, held on May 19 and 20. What we witnessed was not an incremental update to familiar tools. It was Google laying the groundwork for a fundamentally different internet, one where AI agents do not just assist users but act on their behalf. For performance marketers operating across the GCC and Middle East region, the implications are significant and the timeline is now.

Setting the Stage: Google’s Agentic Shift

Google CEO Sundar Pichai opened the keynote with a clear declaration: we are now in the part of the AI cycle where people want to see real value in the products they use every day. Ten years after pivoting the company to be AI-first, Google’s 2026 announcements represent the most consequential shift in how users will interact with search, content, and commerce since the smartphone era.

The transition from AI that simply assists users to agents that can independently navigate complex tasks across an entire workflow is not a future scenario. It is what Google announced at I/O 2026.

Two new model families anchor everything: Gemini 3.5 Flash and Gemini Omni. Both feed directly into the agentic experiences rolling out across Search, Chrome, YouTube, and Android over the coming months.

The Announcements That Matter Most for Paid Media

1. AI Search Is Now Just Search

Google made its position explicit at I/O: Google Search is AI Search. AI Overviews and AI Mode are being merged into a single unified interface, powered by Gemini 3.5 Flash. The search box itself has been redesigned for the first time in over 25 years, now accepting images, files, videos, and Chrome tabs as inputs alongside text.

This is not a feature toggle. This is the default experience. For paid advertisers, it raises an urgent question: how do your campaigns perform when the results page is no longer a list of ten blue links but a dynamically generated AI response with generative UI, interactive visuals, and built-in commerce functionality?

2. Information Agents in Search

Google announced Information Agents: personalised AI agents running in the background 24/7, set up by users to monitor, research, and surface information on their behalf. These agents can find it, book it, and search it autonomously. For select service categories, Search can now call businesses on a user’s behalf.

For paid advertisers, this changes the nature of intent. A user no longer needs to search for your business at the moment they want to act. An agent may be doing the groundwork hours or days in advance, filtering options before the user ever sees a result. Being visible to these agents requires a fundamentally different approach to how you structure campaigns, ad copy, and landing page signals.

3. Agentic Commerce and the Universal Cart

One of the most consequential announcements for eCommerce advertisers was the Universal Cart, rolling out across Search and the Gemini app this summer, with YouTube and Gmail to follow. Users can add items to their cart while reading email, watching YouTube, or browsing the web, then check out directly on Google via the Universal Commerce Protocol.

Google also introduced an Agents Payment Protocol that allows AI agents to complete purchases autonomously, within parameters set by the user, including preferred brands and maximum prices. Gemini Spark, Google’s new personal AI agent, will gain these purchase capabilities later this year.

This is the agentic commerce model we have been tracking closely at Sandstorm Digital. The layer between user intent and final purchase is now potentially occupied by an AI agent. Advertisers who are not optimising for agent-legible signals risk being filtered out before a human ever makes a decision.

4. Gemini Spark: Your Users Now Have Personal AI Agents

Gemini Spark is a 24/7 personal AI agent built into the Gemini app. It runs on dedicated infrastructure, operates in the background even when a user’s device is off, and will integrate with third-party tools via MCP later this summer. It is powered by Gemini 3.5 and the Google Antigravity platform.

Spark is launching first for Google AI Ultra subscribers in the US, at a new $100/month tier. The GCC rollout timeline is not yet confirmed, but given Google’s regional investment trajectory, it is a matter of when, not if. Brands that wait to adapt will find themselves behind audiences who have already handed significant decision-making to their AI agents.

5. Ask YouTube: Attention in a New Format

YouTube is getting an Ask YouTube feature powered by Gemini, allowing users to ask questions about content, receive answers with follow-up context, and navigate the platform conversationally. For brands investing in YouTube as a paid channel, this signals a shift in how users interact with video content and what it means for a viewer to be genuinely engaged.

6. SynthID and AI Content Transparency

Google’s SynthID watermarking system, now embedded in over 100 billion pieces of AI-generated content, is expanding to Search and Chrome through C2PA Content Credentials. Users will soon be able to right-click any image in Chrome to check whether it was generated by AI. Partners including NVIDIA, OpenAI, Kakao, and Eleven Labs have signed on to this standard.

For advertisers producing creative at scale using AI tools, this is both a compliance consideration and a trust signal opportunity. The brands that lean into transparency early will be better positioned as audiences become more discerning about AI-generated media.

What This Means for Advertisers

The GCC  and the middle east markets sit at an interesting intersection. Mobile-first behaviour, high digital adoption rates, and a consumer base that embraces new technology quickly mean that agentic AI features will find receptive ground here. At the same time, the regional rollout of features like Spark and Information Agents will lag the US market, giving brands a window to prepare.

At Sandstorm Digital, we are already working with clients to assess how agentic search will affect campaign structure, audience targeting, and conversion path optimisation. The brands that begin this work now will have a measurable advantage when these features reach full deployment across the region.

Three Things to Do Before the End of Q2

• Audit your campaign structure for agent-legibility. Are your product feeds, structured data, and landing pages communicating the right signals to AI systems, not just human reviewers?
• Review your creative production workflows. With SynthID and Content Credentials rolling into mainstream Google surfaces, AI-generated ad creative will carry new levels of visibility. Have a clear position on how you use and disclose AI in your creative process.
• Map the agentic commerce journey for your category. Where does your brand sit in the chain between user intent and AI-assisted purchase? The Universal Cart and Agents Payment Protocol will reshape that journey for eCommerce clients across every vertical.

The post The Age of the AI Agent Is Here appeared first on Sandstorm Digital® Worldwide.

The Assistant Is Now Available Globally

As of April 2026, Meta has rolled out its AI Business Assistant to all advertisers globally, across the US, EMEA, Asia-Pacific, and Latin America. It lives directly inside Ads Manager and Meta Business Suite, at no extra cost. Advertising and agency partners can access it within Meta Ads Manager, Meta Business Suite, and Business Support Home.

Meta is currently offering these business AI tools for free to help smaller businesses achieve scale, though CEO Mark Zuckerberg has hinted that a longer-term monetization model is likely on the horizon.

What It Actually Does

The assistant is not a general chatbot. It is built around your ad account data specifically, and its value comes from three core functions.

Performance insights on demand

Instead of manually digging through Ads Manager, you can ask the assistant to surface trends, generate charts, and identify what is working or underperforming across your campaigns. It pulls from your current and historical data, so the output is specific to your account, not generic advice.

Industry benchmarking

This is one of the more practically useful features. The assistant benchmarks your CPR, CTR, and ROAS against an aggregated cohort of advertisers with similar characteristics, including spend level, optimization goal, and advertiser vertical. If your strategy shifts, the benchmark cohort adjusts automatically, so the comparison stays relevant.

Opportunity Score recommendations

The assistant surfaces Opportunity Score suggestions with detailed reasoning, not just a number. The headline stat Meta is citing is a 12% median decrease in cost per result for small business advertisers who applied Opportunity Score recommendations. That is not a guarantee, but it is a directionally meaningful data point.

Issue resolution

This one is underrated. You can describe an account issue in plain language and the assistant will work to resolve it: restricted accounts, spend limit increases, delivery errors. The tool targets a 20% improvement in issue resolution rates, and for small businesses this is particularly useful since it reduces dependence on Meta support queues.

Where It Fits in Your Workflow

The assistant is most effective when you have at least four to six weeks of campaign history. Without historical data, the benchmarking and performance analysis features have limited value. It is also worth being clear about what it does not replace: campaign strategy, creative direction, budget decisions, and conversion tracking setup all still require human judgment. The assistant recommends, you decide.

Throughout 2026, Meta has indicated it plans to expand capabilities into campaign planning and creation. That would represent a significant jump in scope from where the tool sits today.

What This Means for Performance Marketers

The practical shift here is speed. Insights that previously required manual reporting or an analyst pulling data can now surface in seconds. For agencies managing multiple clients or for in-house teams without deep data resources, that compression of time is real. The benchmarking feature is also worth paying attention to, particularly for clients in competitive verticals who want a reference point beyond their own historical performance.

Meta’s business AI is now handling 10 million conversations per week, which gives a sense of how fast adoption is moving. The tool is still in beta and being refined, but the trajectory is clear: AI is becoming the interface layer between advertisers and their ad accounts.

If you are running Meta campaigns and have not explored the assistant yet, it is worth a look, especially before the feature set expands and the learning curve grows with it.

The post Meta’s AI Business Assistant, Explained appeared first on Sandstorm Digital® Worldwide.

Named after the Arabic word for journey, Siyate has built something genuinely distinct in the UAE fragrance market. Their collections span Eau de Parfum lines like the culturally rooted Echoes of Heritage and the indulgent A Feast for Your Senses, the alcohol-free Rooted Mirage crafted for sensitive skin and children, and the collector-grade Alpine Prive Extrait de Parfum. Beyond fragrance, their offering extends into wellness oils and elixirs, with a gifting and corporate partnerships vertical that positions them well for B2B growth across the GCC.

Siyate runs on Shopify, and the brand has clear ambitions to scale. We will be partnering with them on two fronts: SEO to build organic visibility across high-intent search terms in the UAE and wider GCC market, and a full website template migration to elevate the digital experience to match the quality of the product itself.

On the ecommerce side, there is real work to do and real opportunity to unlock. A niche fragrance brand lives and dies by how well it converts browsers into buyers, and that means everything from site architecture and page speed to product page structure, collection hierarchy, and the signals search engines use to understand what a store sells and who it sells to. The migration gives us a clean foundation to build that properly from day one.

We are looking forward to helping Siyate grow its digital footprint and reach the customers who are already looking for exactly what they offer.

The post Sandstorm Digital Welcomes Siyate: Dubai’s Fine Fragrance House appeared first on Sandstorm Digital® Worldwide.

We read it so you don’t have to. Here’s the honest breakdown.

First: Is SEO Dead (Again)?

No. Stop asking.

Google is very clear on this. Their generative AI features are built on top of the same core ranking and quality systems that have always powered Search. The AI is not operating in some parallel universe with different rules. It is pulling from the Search index, using retrieval-augmented generation (RAG) to ground its responses in real, crawlable web pages, and applying the same quality signals it always has.

So if you have been doing solid SEO work, you are already most of the way there. That is not a consolation prize. That is the actual answer.

The One Thing That Matters More Than Everything Else

Google says it plainly: create content that is unique, compelling, and useful, and it will likely influence your visibility in generative AI search more than any other tactic.

They even give you a useful frame to test your content against. The difference between commodity content and non-commodity content.

Commodity content is “7 Tips for First-Time Homebuyers.” It is generic. It could have been written by anyone, or frankly, by a chatbot in 30 seconds. It adds nothing new to the conversation.

Non-commodity content is something like “Why We Waived the Inspection and Saved Money: A Look Inside the Sewer Line.” That is specific. That is experience. That is the kind of thing an AI system looks for when it is trying to give a user a genuinely useful answer, not just a competent-sounding one.

The question to ask yourself before publishing anything: Is this content my visitors would find satisfying? If the honest answer is no, do not publish it.

The Technical Stuff Still Matters (But Not More Than It Did)

Google is not asking you to do anything new on the technical side. The checklist looks familiar: your pages need to be indexed and crawlable, your site needs to render properly including JavaScript, duplicate content should be kept in check, and page experience signals like speed and mobile display still count.

One thing worth calling out: semantic HTML. Google specifically mentions it helps AI agents and accessibility tools navigate your content. It does not have to be perfect (the web is not valid HTML and never has been), but it is worth caring about.

Search Console remains your best diagnostic tool. If you are not verified, fix that first.

For Ecommerce and Local Businesses: Do Not Skip This

Google explicitly flags that generative AI responses can include product listings and local business information. If you are running ecommerce or a service business, Merchant Center feeds and Google Business Profile are now AI discoverability tools, not just nice-to-haves. Products that live in your Merchant Center feed have a path into AI-generated answers. Products that do not, do not.

There is also a mention of something called Business Agent, a conversational experience that lets customers chat with your brand directly in Search. Worth keeping an eye on as this matures.

The Myth-Busting Section Is the Most Valuable Part of the Guide

This is where Google earns real respect. They directly call out tactics that have been circulating in the industry as either unnecessary or outright useless for Google Search specifically.

llms.txt files. Do not bother creating one for Google Search ranking purposes. Google may crawl it. It does not treat it as special. The file has value in other contexts (some AI tools and crawlers do reference it), but for Google AI Overviews and AI Mode, it changes nothing.

Content chunking. There is no requirement to break your content into small chunks so AI can understand it better. Google’s systems can handle nuanced, multi-topic pages. Write for your audience, not for a hypothetical AI chunk parser.

Rewriting content to match AI phrasing. You do not need to stuff in long-tail keyword variations or write in a particular way because you think AI systems prefer it. Google’s AI understands synonyms and intent. Write like a human expert.

Buying or engineering inauthentic mentions. This one is important. There is a growing cottage industry around getting your brand mentioned across blogs, forums, and other sites to appear more prominent in AI responses. Google calls this out directly. Their spam systems catch it. Their quality systems discount it. It is the link scheme problem, restaged for the AI era.

Over-investing in structured data for AI. Structured data is still worth using for rich results. But Google is clear: there is no special schema markup that gets you into AI Overviews. Do not let anyone sell you on a proprietary schema strategy for AI visibility.

The Agentic Search Section Is a Preview of What Is Coming

This is the part of the guide that is furthest from current reality but closest to what everyone should be thinking about.

AI agents can visit your website, take screenshots, read your DOM, interpret your accessibility tree, and complete tasks on behalf of users. Think booking a reservation. Comparing product specs. Submitting a form. These are not search behaviors. These are interaction behaviors. And they are already happening in early form.

Google points to a separate guide on agent-friendly website best practices and mentions the Universal Commerce Protocol (UCP) as an emerging standard for agent-based commerce interactions.

If your website is a mess to navigate for humans, it will be a mess for agents too. Accessible, clean, well-structured sites have a real advantage here as this layer of AI interaction develops.

What This Means for Your Strategy

Pull back the complexity and you get a clean picture of what Google is telling you to do:

The brands that will win in AI search are the ones with genuine expertise, clean sites, and content that actually earns trust. Not the ones with the cleverest technical workarounds or the newest AI file format.

The rules did not change. The stakes just got higher.

If you want to pressure-test how your content and technical setup perform in AI search environments, that is exactly the kind of work we do at Sandstorm Digital. Get in touch.

The post Google Published Its AI Search Guide appeared first on Sandstorm Digital® Worldwide.

That window is getting a little murkier.

Google has quietly updated its official documentation on ad group and asset group prioritization to include a notable clarification around AI-powered search experiences. Under what Google describes as “advanced search experiences,” which covers AI Mode, AI Overviews, Google Lens, and autocomplete searches, the search term shown in your reporting now represents the best approximation of the user’s intent rather than the literal query entered.

In other words: what you see in the report may not be what the user typed.

Why This Matters for Advertisers

For years, the Search Terms Report has operated on a relatively straightforward premise. A user enters a query, a keyword matches it, and that query surfaces in your report. Optimization decisions, negative keyword strategies, and client-facing insights have all been built on that foundation.

AI-powered search experiences break that model. A user interacting with AI Mode may refine a query across multiple conversational turns. A Lens search involves an image, not a typed phrase. Autocomplete can shape a query before a user even finishes forming their intent. None of these interactions map cleanly to a traditional keyword match, which means Google has to make some interpretive decisions before surfacing anything in your reporting.

The documentation confirms that when this happens, AI-based ad group prioritization steps in. Rather than matching a literal keyword, Google ensures that the most relevant ad groups or asset groups are selected to match the user’s overall intent. The reported search term reflects that interpreted intent, not necessarily the original user input.

The Transparency Question

This is where it gets more complicated for performance marketers.

Advertisers currently have no way of knowing which reported search terms reflect a direct user query and which reflect Google’s interpretation of one. There is no flag, no label, no separate column. The report looks the same either way.

This creates real friction in several areas:

Compliance-sensitive industries, such as finance, healthcare, and legal, use search term data to ensure ads are appearing in appropriate contexts. If some of those reported terms are approximations rather than actual queries, the review process becomes less reliable.

Negative keyword management becomes less precise. If you are excluding terms based on what you believe users are typing, and some of those terms are interpretations, your exclusions may not behave the way you expect.

Client reporting takes on added nuance. Presenting search term insights to clients or internal stakeholders as direct user language is now a claim that requires a qualification.

What This Reflects About the Direction of Google Ads

This update is not happening in isolation. It is part of a steady shift in how Google Ads operates as AI becomes more central to the search experience. Broad match behavior, Smart Bidding, Performance Max, AI Max, and now interpreted search term reporting all point in the same direction: Google is taking on more of the decision-making, and advertisers are working increasingly with signals and approximations rather than raw data.

That is not inherently a problem. AI-powered matching can surface relevant audiences that tightly structured campaigns would miss. But it does require a shift in how advertisers think about their data and how they communicate about it.

What to Do About It

The practical response is to update how you interpret and present search term data, rather than treat it as ground truth.

Continue using the Search Terms Report for directional insight. It still reveals intent patterns, category themes, and optimization opportunities. But treat it as a layer of inference rather than a verbatim record of user language, particularly for accounts running against AI Mode or heavy autocomplete traffic.

Build stronger anchors elsewhere. First-party data, landing page performance, conversion quality, and audience signals all remain less affected by how Google interprets queries. These should carry more weight in optimization decisions as reporting transparency continues to evolve.

Review negative keyword strategies with this in mind. If certain exclusions are not performing as expected, interpreted search terms may be part of the explanation.

The Bigger Picture for GCC Advertisers

For markets like Saudi Arabia, the UAE, and the wider GCC region, this has particular relevance. AI-powered search adoption is growing quickly, and search behavior in Arabic is increasingly shaped by conversational patterns and multi-step refinements that do not map neatly to traditional keyword structures. As AI Mode and AI Overviews expand in regional markets, the gap between what users input and what surfaces in reporting is likely to widen.

Staying ahead of that gap means building campaign structures and reporting frameworks that account for interpretation, not just matching.

The Search Terms Report is not broken. But it is evolving, and the advertisers who adjust their expectations and strategies accordingly will be better positioned as AI becomes the default search experience.

The post What Google’s Search Terms Update Means for Your PPC Campaigns appeared first on Sandstorm Digital® Worldwide.