For as long as I’ve worked in search marketing, I’ve viewed the path to purchase as a simple sequence: search query → click → buy.
I’ve approached SEO through much the same model, using organic traffic, impressions, and click-through rate (CTR) as the primary measures of success.
Google’s Universal Commerce Protocol (UCP) tells me that this familiar path is changing. Google is evolving from a discovery engine into a transaction layer where searching and buying can happen inside the same experience.
With the rise of “agentic commerce,” I’m seeing Google gain the ability to discover, evaluate, compare, and purchase products on a user’s behalf within AI-powered experiences such as AI Mode, Gemini, YouTube, and Gmail.
I believe the SEO implications are substantial. Instead of optimizing only for clicks, I now need to think about optimizing for AI-assisted transactions. If a brand cannot communicate through UCP and the product data that supports it, it risks becoming invisible to the next generation of shoppers.
Here’s how I understand UCP, why I think it will reshape digital marketing, and what I recommend doing now to prepare an SEO strategy for agentic commerce.
UCP: The infrastructure behind AI transactions
I think of UCP as an open-source, vendor-agnostic standard that supports the entire commerce lifecycle inside an AI interface. That lifecycle can extend from product discovery and cart creation through checkout, fulfillment, and post-purchase tracking.
Google co-developed UCP with Shopify, Walmart, Target, Wayfair, Etsy, and other commerce leaders. From my perspective, it acts as a universal translator between AI shopping agents and the systems merchants use to operate their online stores.
The clearest analogy I can make is that UCP may become the ecommerce equivalent of HTTPS. HTTPS standardizes secure communication between browsers and servers; UCP standardizes how AI agents interact with online stores. Instead of building a custom one-to-one integration for every merchant, an AI agent can use a shared framework to browse inventory securely and complete purchases across many stores.
How I see AI transactions flowing through UCP
Imagine I ask AI Mode to “find and order a replacement water filter for a 2021 Samsung French-door fridge with the fastest shipping.” UCP can coordinate that transaction through a structured workflow.
Capability publication
First, I expect the merchant to publish the capabilities its store supports, including product search, live pricing, fulfillment options, and accepted payment methods. This gives the AI agent a clear picture of what it can request and complete.
From product discovery to payment and confirmation, this mobile shopping sequence shows a Monos suitcase purchase completed with Google Pay through Google’s Universal Commerce Protocol.
Handshake
Next, the AI agent reads the merchant’s profile, compares those capabilities with its own, and establishes a secure path forward. I see this step as the point where the systems can align on details such as loyalty programs and supported digital wallets.
Action execution
Once the systems are aligned, 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 the transaction needs my input—perhaps to select a delivery window or confirm a shipping address—UCP can pause the process and prompt me. After I respond, control returns to the AI so it can finish the workflow.
I don’t see UCP as merely a technical update. I see it changing the way AI discovers, evaluates, and purchases products—and that makes it directly relevant to SEO.
1. I’m shifting from click-throughs to buy-throughs
In an agentic search environment, I can no longer treat website traffic as the only measure of business value. Features such as Universal Cart can let shoppers add products from multiple retailers to one Google cart and check out with Google Wallet, dramatically shortening the buying journey.
A shopper may never visit my homepage, category page, or product detail page. That changes my SEO objective: I need to earn product selection within the AI recommendation layer so a search query can become a sale even when it generates no intermediate website visit.
2. I’m planning for hyper-personalized queries
I’m also rethinking keyword research. Shoppers are moving beyond broad searches such as “men’s running shoes” and using detailed, situational prompts like “Best running shoes for flat feet under $150 that can arrive by Friday.”
To match a request that specific, I know a search engine needs more than polished on-page copy. It needs rich, structured, and queryable product attributes. UCP helps bridge that gap by giving AI agents a way to match merchant inventory with a shopper’s precise requirements.
3. I expect less checkout friction
I continue to see cart abandonment as a major ecommerce challenge, especially when shoppers encounter long forms, broken checkout flows, or unexpected shipping costs. Because UCP can work with secure digital wallets and automatically pass verified user data, I expect it to eliminate many of those friction points.
As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.
For high-intent, urgent, or repeat purchases, I believe merchants that support UCP may capture more conversions than competitors that send every shopper to a separate checkout experience.
4. I can retain brand control and customer ownership
One detail I consider especially important is that the merchant remains the Merchant of Record when a transaction takes place through UCP. I can still control pricing, fulfillment, and return policies while retaining the customer relationship and first-party data. UCP provides the transactional infrastructure without replacing the merchant’s role.
If I limit an SEO strategy to blog articles and meta descriptions, I overlook the technical infrastructure that powers AI commerce. To make products eligible for UCP-powered experiences, I recommend focusing on the following priorities.
I would optimize the Merchant Center feed
I no longer view Google Merchant Center (GMC) as a tool used only for Shopping ads. I see it becoming a primary source of product information for AI discovery, which makes feed quality central to both visibility and transaction eligibility.
Enable the native_commerce attribute: To opt into UCP-powered checkouts, I would add the native_commerce attribute to the product feed. Google recommends using supplemental feeds to apply it at the product level without changing the primary feed.
Map product identifiers: I would make sure every product ID in the GMC feed maps one-to-one with the corresponding ID in the internal checkout API. If the identifiers differ, I would use the merchant_item_id attribute to align them.
Complete policy data: I would keep returns, shipping, and customer-support information complete and current. Clear policy data gives an AI agent the details it needs to evaluate a merchant confidently.
I would align structured data with the product feed
Because AI search depends on consistent information, I would keep the Product, Offer, and Review schema on the website synchronized with the Merchant Center feed. If the price, availability, identifiers, or other details conflict, validation problems could make a product ineligible for AI-powered checkout.
I would prepare for conversational attributes
As Google introduces semantic attributes designed for conversational AI search, I would prepare inventory and product-information systems to supply richer answers. In particular, I would prioritize:
Real-time inventory availability.
Direct answers to product FAQs, such as “Is this jacket machine washable?”
Detailed compatibility information, including accessory pairings, sizing guides, and model-specific replacements.
I would treat these details as more than feed enhancements. They are the signals that help an AI agent decide whether a product satisfies a nuanced request involving price, fit, compatibility, delivery speed, or another real-world constraint.
Beyond clicks: The next SEO opportunity I see
To me, the Universal Commerce Protocol reflects a broader transformation in search. It expands the role of SEO beyond generating traffic and brings product data, inventory systems, checkout infrastructure, and conversion readiness into the search conversation.
By prioritizing structured product data, reliable commerce information, and readiness for agentic transactions, I can position a brand to capture demand at the exact moment a shopper expresses intent.
I don’t believe the future of search will be only about getting found. Increasingly, it will be about making sure the products I represent can be evaluated, selected, and bought.
I see Google’s unveiling of Gemini Intelligence at the May 12 Android Show as a significant step toward an agent-powered future. Announced alongside a new laptop called the Googlebook, Gemini Intelligence is designed as an underlying layer that works across the Android operating system on laptops, phones, watches, and glasses.
The Googlebook makes that vision tangible to me. Built from the ground up around an AI agent, it can understand what is on the screen and act on it. I could point to a date in an email and have the agent schedule a meeting, or select furniture in an app and see how those pieces might look in my living room.
I believe this ability to complete tasks without requiring someone to open a webpage will fundamentally change how people search, discover information, and conduct commerce. Here is how I expect that shift to affect the search industry.
What the shift to an agentic operating system means
Until now, I have viewed search as a familiar sequence: someone has a question or intent, enters it into a search engine, receives a list of links, and chooses one. Earning a prominent position on that list was the prize, and much of the SEO industry was built around winning that click.
Gemini Intelligence starts from a very different assumption. Search intent still exists, but an AI agent can handle the steps between the request and the outcome. It can read pages, complete forms, and increasingly finish the entire task. Instead of visiting a website myself, I may have an agent visit and use it on my behalf.
When I look for an early example, Chrome Auto Browse stands out. Launched in January and built on Gemini 3, it can manage multistep tasks such as researching flights, filling out forms, scheduling appointments, and managing subscriptions. It then pauses for approval before making a purchase.
A 2025 preprint supports this view. Researchers evaluated the declared-tools approach across online shopping, authentication, and content management. They found that giving an agent pre-structured interaction data reduced processing requirements by 67.6% and lowered costs by 34% to 63% compared with parsing a complete HTML document. Task success declined only slightly, from 98.8% with the traditional method to 97.9%.
The architecture behind Gemini Intelligence
To me, the architecture is as important as the interface. AI agents naturally favor websites they can interact with cleanly and efficiently, and Gemini Intelligence can only deliver on its promise if those agents can perform tasks reliably.
I see two protocols as central to making that possible. WebMCP turns a website’s actions into callable tools, while the Universal Commerce Protocol (UCP) allows an agent to complete a sale. Together, they enable an agent to finish a task without requiring a person to load and navigate the underlying webpage.
As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.
WebMCP
I think of WebMCP as a labeled menu for AI agents. The API allows a website to declare functions as structured tools an agent can call, including searching inventory, beginning checkout, or submitting a support request.
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)
I see UCP as the transactional counterpart to WebMCP. It gives AI agents a shared language for discovering products, building a cart, completing checkout, and managing orders without requiring someone to visit the merchant’s website.
Google also offers a consumer-facing layer called Universal Cart. It can collect items as I move across Search, Gemini, YouTube, and Gmail, creating a more connected shopping experience across Google’s products.
The range of companies behind UCP shows me how seriously the industry is taking this shift. Google, Shopify, Walmart, Target, Etsy, Wayfair, PayPal, and Stripe co-developed the protocol, which launched in January.
How I would prepare for agentic AI
My main takeaway is that websites are rapidly evolving from destinations into backends—from places people actively visit into systems agents quietly use. As the operating system becomes a search and action layer, I no longer think ranking is the only question that matters. I also need to ask whether an agent can actually use the site.
To prepare, I would begin by auditing the site’s most valuable actions, whether that means submitting a lead form, completing a booking flow, or reaching checkout. I would determine whether an agent could complete each action reliably and check the site’s Lighthouse Agentic Browsing score much as I would review Core Web Vitals. The goal is to understand whether an agent can use the site, not merely read it.
If I ran an ecommerce business, I would confirm whether the checkout process is accessible through UCP or ACP. I would also continue investing in retrieval and visibility because an agent still needs to find and trust the business before it can act on anyone’s behalf.
I rely on Google Search Console because it is excellent at collecting search data. The challenge is that it still does not make interpretation easy.
When I open almost any property, I usually find thousands of queries, landing pages, impressions, clicks, rankings, and click-through rates. That volume is useful, but it can quickly become overwhelming when I am trying to answer one simple question: what should I do next?
For years, my workflow was familiar: export the data into Excel or Google Sheets, build pivot tables, apply filters, and start digging for patterns. That approach works, but it is slow. More often than not, I am searching for insights without knowing exactly what I am looking for.
That is where AI makes the workflow more useful. I use it to speed up the hardest part of Search Console analysis: finding meaningful patterns hidden across thousands of rows of search data.
I think of Google Search Console as my source of truth and AI, whether ChatGPT or Claude, as the analyst sitting beside me. GSC shows me what happened. AI helps me explore why it happened, uncover opportunities I might miss, and organize messy data into decisions I can act on.
A quick note on regex
Most of the examples I use start in the same place inside Google Search Console: Performance → Queries → + Add Filter → Query → Custom (regex).
From there, I enter a regular expression to filter query data before exporting it for analysis.
The useful part is that I no longer need to memorize regex syntax. I can ask ChatGPT to write it for me. For example, I might prompt: Create a regex for Google Search Console that matches queries beginning with question words.
ChatGPT may return something like (?i)^(who|what|why|how|can|does|will|should)b.
If I need something more specific, I simply describe the pattern I want. I might ask for a regex that matches queries containing five or more words, identifies comparison searches, or finds branded queries that include product names.
The better I describe the pattern, the better the regex usually becomes.
Here are seven practical ways I combine Google Search Console with AI so I can spend less time sifting through data and more time making decisions.
1. I stop looking only at queries and start looking at intent
Most Search Console analysis still happens at the keyword level. The problem is that people do not really search by keyword. They search with intent.
Instead of reviewing thousands of individual queries one by one, I use regex to isolate investigation-focused searches before exporting the data.
One useful regex is (?i)^(best|top|vs|review|reviews|compare|comparison).
After exporting the filtered query data, I ask Claude or ChatGPT to classify intent. My prompt is usually something like: Categorize these queries into informational, navigational, investigation, transactional, and local intent. Return a CSV with classifications and confidence scores.
This helps me spot patterns that are difficult to see keyword by keyword. Informational traffic may be growing while commercial investigation queries are declining. Transactional queries may rank well but earn weak click-through rates. Comparison searches may be driving impressions without having dedicated content to support them.
When I segment by intent, the next steps become much clearer.
2. I discover questions my audience is already asking
Question-based keyword research is not new, but AI helps me identify themes across hundreds of question-oriented searches much faster.
I start with a regex like (?i)^(who|what|where|when|why|how|can|does|should|will)b.
Then I export the results and ask Claude or ChatGPT: Group these questions into common themes and identify unanswered topics.
A Google Search Console query filter highlights how regex can narrow SEO performance data, helping marketers turn thousands of search terms into focused insights.
Instead of manually reviewing hundreds of questions, I can quickly see broader patterns around pricing concerns, product comparisons, implementation challenges, and industry-specific use cases.
This becomes more than a content exercise. I can use these themes to improve FAQs, support resources, sales enablement materials, and AI Overview optimization.
The best opportunities are often not hidden in one query. They are hidden in clusters of related questions.
3. I find queries likely to trigger AI Overviews
Google does not give me a filter for queries likely to trigger AI Overviews, but I can build a useful approximation.
I start by isolating common informational and comparison patterns with a regex like (?i)^(what is|how to|best|vs|difference between|guide to).
Then I export the matching queries and ask Claude or ChatGPT: Review these queries and group them by the content format needed to answer them effectively.
The themes often fall into definitions, tutorials, comparisons, or expert recommendations.
This helps me see where my content may need to shift from simply ranking for keywords to becoming the best available answer. Increasingly, those are not always the same thing.
4. I track emerging trends earlier
Traditional keyword research can be reactive. By the time a trend is obvious in keyword tools, competitors may already be building content around it.
Google Search Console can help me identify shifts earlier, as long as I know how to look for them.
Instead of searching for individual keywords, I use ChatGPT to build regex around broader concepts. For example, I might prompt: Create a Google Search Console regex to identify searches related to AI agents, copilots, assistants, automation, and autonomous workflows.
The output may look like (?i)(ai agent|agentic|copilot|assistant|automation).
This same approach works for new technologies, product categories, competitors, industry buzzwords, and changing customer concerns.
Once I filter and export the data, I let AI look for emerging themes. A prompt I like is: Review these queries and identify emerging themes, new terminology, and shifts in search behavior. Highlight which topics appear to be gaining traction, recommend whether they deserve a new content asset or an update to an existing page, and identify any patterns that could influence our content strategy.
Instead of only confirming that a trend exists, AI helps me decide whether the trend is meaningful enough to act on and what the next move should be.
5. I surface conversion intent inside informational traffic
One of the most overlooked opportunities in Search Console is finding bottom-of-funnel signals inside queries that appear informational at first glance.
I might ask ChatGPT: Create a regex for searches that indicate evaluation, comparison, pricing, alternatives, migration, implementation, or vendor selection intent.
An example output is (?i)(cost|pricing|price|vs|alternative|compare|implementation|migration).
I apply that regex to the query report, export the filtered data, and then ask Claude or ChatGPT to analyze it.
My prompt usually looks like this: Review these Google Search Console queries and identify recurring buying signals. Group them into themes such as pricing, comparisons, implementation, and vendor evaluation. Recommend which existing pages should better address this intent, and identify opportunities to improve content through stronger CTAs, internal links, comparison tables, FAQs, or supporting resources.
A visual metaphor for AI turning messy Google Search Console queries into clear SEO decisions, separating qualified intent from irrelevant traffic signals.
I often find that pages created for top-of-funnel education are already attracting visitors who are evaluating solutions. In that case, the best opportunity may not be creating a new page. It may be improving the page that already earns the visit, so users can take the next step without breaking the informational experience.
Sometimes the biggest content opportunity is recognizing the conversion intent already reaching the pages I have.
6. I find audience-specific opportunities
One of my favorite ways to uncover new content opportunities is filtering queries by industry, audience, or customer segment. It quickly shows me whether my content is resonating with the audiences I intended to reach or revealing opportunities I had not considered.
I start by asking ChatGPT to create a regex based on the audience segments that matter most to the business.
For example, I might prompt: Create a Google Search Console regex that identifies queries related to healthcare, manufacturing, retail, education, financial services, government, and nonprofit organizations.
An example output is (?i)(healthcare|hospital|medical|manufacturing|factory|retail|education|school|financial|bank|government|public sector|nonprofit).
After applying the filter and exporting the results, I ask Claude or ChatGPT: Analyze these queries and group them by audience segment. Identify which industries show the strongest search demand, what recurring questions or pain points each audience has, and recommend opportunities for new content, landing pages, case studies, or internal linking that would better serve those audiences.
The differences can be valuable. Healthcare searches may consistently focus on compliance, while manufacturing queries may revolve around implementation. Retail searches may reveal entirely different use cases than financial services searches.
7. I uncover striking-distance opportunities at scale
Every SEO knows the classic advice: look at keywords ranking in positions 5-15 to identify opportunities within striking distance.
The challenge is doing that at scale. A report with hundreds of queries where a site is close to stronger rankings can become overwhelming fast.
I take the regex patterns above a step further. I apply the filters that match my goals, then narrow the report to positions 5-15 before exporting the queries.
Then I ask my AI analyst: Identify recurring themes across these queries and recommend page-level optimizations rather than keyword-level optimizations.
Instead of getting tiny recommendations for individual keywords, I often uncover larger opportunities. A page may be missing subtopics, comparison details, stronger internal links, or use cases that would make it more complete.
The result is usually fewer optimizations, but more meaningful ones.
Turning Search Console data into decisions
As an SEO, I do not have a data shortage. I have a prioritization problem.
Google Search Console remains one of the richest sources of insight into how people discover a business. The difficult part is turning thousands of rows into something actionable.
That is where AI fits into my workflow. It helps me uncover patterns, organize information, and surface opportunities I might otherwise miss. It is not a replacement for SEO strategy, experience, or critical thinking.
The real advantage is not writing better regex or exporting cleaner spreadsheets. It is spending less time searching for insights and more time acting on them.
Because data does not improve SEO. Better decisions do.
I see the next major battleground for brands being shaped by AI. Every day, AI engines and autonomous agents decide which brands to recommend, compare, cite, and transact with on behalf of consumers. To compete, I have to make my brand the trusted choice AI selects.
This shift is already underway. Adobe data shows that AI-referred traffic to U.S. retail websites grew 4,700% year over year through mid-2025. Salesforce reports that AI and autonomous agents influenced one in five online orders globally during Cyber Week, driving an estimated $67 billion in sales.
As AI becomes the interface between consumers and brands across discovery, evaluation, and purchase, I need to think beyond traditional rankings. A new competitive layer is emerging: the AI decision layer. This is where AI systems evaluate trust, relevance, authority, and transaction readiness before deciding which brands make the shortlist.
If I fail to influence this layer, my brand may be excluded before a customer ever sees it. That makes AI visibility, credibility, and actionability core parts of modern search strategy.
How I take a brand from found to actioned
Agentic commerce readiness follows a clear sequence. I start by making sure AI engines can find my brand, then I move through the remaining stages until AI agents can understand, trust, recommend, and transact with it.
Step 1: I get found by enabling AI discovery and access
Machine accessibility is the foundation of AI visibility. If I want AI systems to discover and access my brand, I have to prioritize technical hygiene and token efficiency.
I start by allowing the right crawlers on my website. Google, OpenAI, Anthropic, and Bing need to reach my content without unintended restrictions.
Then I get the basics right. I set up XML sitemaps and robots.txt, fix crawl errors, add canonical tags, and maintain strong Core Web Vitals. I also make sure my website content is rendered server-side so agents can reliably navigate and reason over my pages.
I also pay close attention to token efficiency. Bloated HTML wastes valuable tokens that AI systems could otherwise use to understand my content, products, and brand.
To make my site more AI-ready, I publish assets that help large language model crawlers process my content more efficiently. An llms.txt file can give LLM crawlers a concise map of my website, while Markdown versions of key content can reduce token consumption and improve machine understanding.
Between consumers and brands, AI agents now act as the decision layer, handling discovery, evaluation, trust signals, and transactions before products reach the shortlist.
Step 2: I become understood by building semantic clarity
To be understood by AI engines, I need to build entity authority. This helps AI interpret who I am, what I offer, and why my brand matters.
Structured data turns my web pages into machine-readable knowledge that AI systems can understand, trust, and use. I strengthen my entity graph with comprehensive schema, trusted citations, and linked references.
I also deliver clean, server-rendered HTML that AI can access without friction. Semantic HTML, structured @graph IDs, and consistent naming help AI engines connect the right context to my brand.
Step 3: I get retrieved by structuring content for AI extraction
Traditional search ranks pages, but AI search retrieves and cites passages. That means I win on relevance, clarity, authority, and freshness rather than length alone. Original expertise, proprietary data, and real-world experience give my content a stronger chance of being selected.
To structure my content for retrieval, I use a clear heading hierarchy with H1, H2, and H3 tags. Under each heading, I create descriptive, self-contained sections that can stand on their own.
I build interconnected topic clusters instead of isolated pages because AI needs enough context to assemble complete answers.
I also front-load every section. I put the core answer and the most important metrics in the opening sentence before a model hits its token limit.
Step 4: I build trust with authority and grounding signals
Just because AI engines retrieve my content does not mean they will recommend my brand. Retrieval is only one step. Trust is what moves a brand closer to selection.
AI systems prioritize sources they can trust, so authority and credibility become decisive. Google’s experience, expertise, authoritativeness, and trustworthiness principles, known as E-E-A-T, remain some of the strongest signals influencing whether a brand is cited, referenced, or selected.
A visual roadmap for becoming the brand AI selects: first be found and understood, then retrieved, trusted, chosen and finally actioned by autonomous assistants.
Trust extends far beyond my website. AI evaluates review sentiment, location accuracy, pricing consistency, product availability, and entity alignment across the web. When those signals conflict, AI confidence decreases.
Credibility is now computational. Grounding, the process of validating responses against trusted evidence, is the bridge between visibility and recommendation.
To earn computational trust, I create original, expert-driven content that shows real experience and unique value. Then I align every external signal so reviews, listings, maps, and directories all tell one consistent story about my brand.
Step 5: I get chosen by earning machine and human preference
AI agents parse attributes, verify claims, and score confidence in milliseconds. If I cannot make my value clear to AI, my brand becomes invisible at the decision point.
But emotional preference still matters. Consumers may delegate routine purchases, yet they hold tightly to choices tied to identity. The strongest brands optimize for both machine readability and human resonance.
To earn AI recommendations, I measure AI visibility, citation, and recommendation rates through query fan-out testing. I keep brand, product, and location data consistent across every channel. I also work to earn trusted mentions and references that strengthen AI confidence in my brand.
Recommendation is no longer the finish line for AI search. Discovery, selection, and checkout can now happen inside an AI assistant without the customer ever visiting my site.
An agentic website is designed for AI agents to discover information, retrieve answers, and perform actions on behalf of users. NLWeb helps make website content conversational and machine-readable, improving how AI systems find and understand the site.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Web Model Context Protocol, or MCP, extends this capability by giving AI agents a standardized way to interact with website functions. That can include retrieving data, initiating workflows, and submitting forms.
Agentic commerce moves the full transaction inside the assistant. Google’s Universal Commerce Protocol, or UCP, enables chat-based bookings. OpenAI and Stripe’s Agentic Commerce Protocol, or ACP, pushes inventory so AI systems can surface it more easily. Agent Payments Protocol, or AP2, then lets the agent pay.
Underneath these capabilities is MCP, which enables an LLM to read products, content, and live data. This changes my website from a destination into a source of truth. It supplies the inventory, pricing, and signals that drive every agent journey.
How I measure performance in the AI decision layer
I still track traditional search metrics like rankings, sessions, and clicks. They remain useful, but they are no longer enough to measure success in AI search and agentic commerce.
For visibility, I track AI presence rate, AI share of voice, citation frequency, and agent recommendation rate.
For commerce, I track AI-influenced revenue, agent conversion rate, autonomous transaction volume, and agentic wallet share.
I also expect traffic patterns to change. Direct visits may decline as agents handle discovery, but AI-influenced transactions through machine-readable layers like WebMCP and schema endpoints can offset that loss and create new revenue paths.
With these changes in place, my website can become the trusted source AI systems rely on for both information and action.
From SEO to decision architecture
SEO remains the foundation for winning search, but a deeper shift became concrete at Google I/O 2026. AI agents now parse raw HTML, distill the browser’s native accessibility tree, and capture visual screenshots through vision models.
Together, these three paths determine whether a site is truly actionable for AI. My page can be technically flawless and still fail if its structure, semantics, or user experience breaks the chain. If I miss any stage, trust and transaction readiness suffer.
When I get these pieces right, my brand becomes discoverable, understandable, trusted, and transactable when AI agents make decisions. The brands that build these capabilities today will be the brands AI surfaces, trusts, and recommends tomorrow.
I see these two new analyses as an important reminder that ChatGPT citations are not as fixed or transparent as they may look. The sources shown in an answer can change when ChatGPT routes search traffic through different hidden retrieval pipelines.
Research from Chris Green and Suganthan Mohanadasan adds a new wrinkle to AI visibility tracking: the final answer does not reveal how ChatGPT selected its sources. Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP, but those labels sit behind the answer rather than inside the citation cards users see.
Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. In most cases, prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.
What stands out to me is that 11.6% of prompts changed their primary search source across repeated runs. When that happened, URL overlap dropped from 0.273 to 0.149, and domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.
Mohanadasan looked at the issue from another angle. He inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches. He found a result_source field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.
He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results. While Green’s repeated-prompt test found Labrador dominating his dataset, Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.
I also think the skipped-search finding matters. Mohanadasan found that ChatGPT classified some queries before searching, using a turn_use_case field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could branch into many searches, including site: probes, pricing checks, and searches for unnamed competitors. That changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.
Another useful distinction is that fetched does not always mean cited. Mohanadasan separated three outcomes: fetched, cited, and mentioned. A page can be pulled into ChatGPT’s context without being shown to users, cited as support for a specific sentence, or skipped as a source even when a brand is mentioned in the answer.
In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed that gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts. Vendor pages were cited for their own facts, such as prices and specs, while third-party pages were more likely to support broader recommendation claims.
The practical takeaway for me is that there is no single ChatGPT visibility result to measure. A page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.
Both analyses also point back to readability. ChatGPT’s source selection depends partly on what it can retrieve and understand. Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.
Green’s results showed that source routing can change which URLs and domains enter the answer set. That makes plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages more important when source selection depends on retrieval and readability.
In Part 1, I looked at the third-party citation signals that matter so much for AI visibility. In Part 2, I made the case for publishing original data, because it is the strongest single predictor of page originality, and the barrier to earning visibility and authority through this approach is still surprisingly low.
Now I have more evidence for why proprietary data should be part of content creation.
Publishing a number matters, but the number itself is not always what gets cited. I looked at Gauge’s citation data to understand what AI systems actually reward when brands publish first-party data. The answer is narrower, sharper, and more useful than simply saying, “original data wins.” Original data does win, but only when it is packaged in the right way.
The format AI rewards most is the benchmark that answers a clear commercial question: which option is best?
First-party research is scarce and punches above its weight
I worked from Gauge’s cited-URL set: 301 live pages cited by AI systems across 316 unique prompts and 7 verticals. Together, those pages carried 1,075 citations.
After auditing the URLs, I found that only 8 of the 301 pages qualified as primary research. To count, the page had to include the original source of the data and its methodology, rather than simply writing about someone else’s numbers.
That means primary research made up just 2.7% of the cited set. But those same 8 pages earned 90 of the 1,075 citations, or 8.4% of the total citation volume. In other words, first-party research appeared rarely, but when it appeared, it over-indexed by roughly 3x on citation share.
The cleaner way I see this is citation density.
Primary research averaged 11.3 citations per page. Everything else averaged 3.4 citations per page. A primary-research page was 3.3x as citation-dense as a non-primary page.
Primary research is rare, but this Gauge analysis shows it punches above its weight: cited pages with original research averaged 3.3x more citations than everything else.
That is the compounding effect of primary research.
There, original data correlated with page originality more strongly than any other trait. Here, original data correlates with citation density. Both findings point in the same direction: the number only you can produce is the lever.
Original research wins when the question has a benchmark
This is where the “original data wins” idea needs more precision.
The 90 primary-research citations were not distributed evenly across the 8 pages. They were not distributed evenly across topics either.
Of those 90 citations, 75 came from one cluster: cloud data warehouse benchmarks. Fivetran’s warehouse benchmark alone earned 44 citations, which was just under half of every primary-research citation in the set.
Once I strip out the benchmark cluster, first-party research barely registers in the citation set. The win is not simply, “I published original data.”
The real win is, “I published a benchmark that answers a buying comparison,” and almost nobody builds those well. By benchmark, I mean a page that measures a set of named things against each other on a specific yardstick and publishes the results as numbers.
A striking citation split: cloud data warehouse benchmarks dominated AI-cited primary research, with Fivetran’s benchmark alone pulling 44 citations from the 90-citation set.
Original research is most powerful when it directly answers commercial comparison queries.
This is also what Google is pushing toward with non-commodity content: new, helpful information that is hard to get elsewhere.
The primary-research citations clustered where prompts asked AI to compare options on measurable specs such as speed, cost, latency, yield, or performance.
That explains the warehouse benchmark spike. The “HR Tech / Compensation” label was noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.
Crypto / Solana showed the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need firsthand ecosystem data, not generic explainers.
The pattern disappeared in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning the data, I found no cited primary-research page in those topics.
A closer look at the content that held 44 citations
Fivetran’s warehouse benchmark took 44 citations from this dataset on its own. Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations. So I wanted to understand why.
The page was published in 2022, but when I examine it, it is easy to see why LLMs still prefer it.
Primary-source visibility is highly concentrated: benchmark-driven topics like HR tech and crypto attract far more AI citations than explainers or listicles.
It answers a measurable comparison head-on. The page names BigQuery, Redshift, Snowflake, and Databricks, then ranks them on speed and cost. It is entity-rich and willing to name the major players directly.
It runs on real first-party data. Fivetran tested against actual customer usage rather than relying on synthetic assumptions, and the page calls that choice out clearly.
It shows the method step by step. The page walks through what data was queried, which queries were used, and how each warehouse was configured and tuned. A reader, or a model, can see how the numbers were produced.
The structure is easy to lift. Descriptive headings such as “Results,” “How much did performance improve?,” and “Why are our results different from previous benchmarks?” help AI map a question to the exact passage that answers it.
It links to raw data and sources. The page footnotes references, including the C-Store paper, and points to the underlying data. That makes the claims verifiable. Few brands put that much work into a data-backed content piece, and even fewer share the full dataset for transparency.
It shows its limits. Dated correction notes from December 2022, named qualitative limitations, and an honest “performance floor” caveat make the claims more credible, not less. The corrections also show care.
The URL never moved. A page from 2022 is still earning citations in 2026 because it stayed live at one canonical address.
The data behind a page like this is easier to pull and analyze than it has ever been. The hard part is everything around the data: the clean method, linked sources, corrections, navigable structure, and willingness to say what the numbers do not prove. That is the craft, and that is the moat.
Fivetran's 2022 benchmark page shows why clear, comparison-led research can become a lasting citation source for AI and search visibility.
This kind of first-party data content is not a thin press release with a few loosely pulled numbers. It requires real work, and it can hold authority for years. My takeaway is simple: AI does not reward “original data” by default. It rewards first-party research when the page gives a clear answer to a measurable comparison and signals depth, expertise, and trust.
The opportunity is to publish a retrievable dataset for a buyer question where AI does not yet have a clean benchmark source. That connects directly to the unanswered-questions finding from Part 2. The opening exists, but in many verticals, nobody has walked through it with a real dataset.
Original data needs a citation-ready package
Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the user’s question.
That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists, but the citation never happens.
The pages that won in this dataset had both ingredients: original numbers and a clean citation shape. They had stable URLs, clear methods, named comparisons, and results that answered buyer questions directly.
Who wins: brands with proprietary product, usage, or pricing data that package it into a comparison a buyer can act on, especially one that can inform LLM-generated recommendations.
Who loses: brands that publish original numbers inside dense narratives, on slow or unstable pages, with no clear comparison frame for AI to retrieve and reuse.
When I think about a citation-ready research page, I look for four parts.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
Lead with the comparison result. The headline finding, such as “X is fastest” or “Y is cheapest at scale,” should appear in the first 30% of the page. Lead with the result, then explain the method and nuance.
Box the methodology. Show the sample, time window, what was measured, and how the measurement happened. Attribution confidence is part of what makes a number citable, so the method needs to be clear on the page.
Frame the comparison explicitly. AI reaches for benchmarks when prompts ask “which is best.” A table comparing named options on named specs is the format most likely to be lifted.
Keep the URL stable. Use one canonical page and keep it live. Do not migrate it or rename it every redesign. The citation earned this quarter only compounds if the page is still there next quarter. In this dataset, 64 of 365 cited URLs were dead, redirected, or otherwise broken, taking 203 citations down with them.
This is the work behind a citable benchmark, and it is more involved than it looks.
HockeyStack documented its own version in a playbook on launching research reports. The company published 18 original reports built entirely on anonymized first-party customer data, the kind of data no competitor could replicate.
Its process includes the same steps the Fivetran page demonstrates: list the data points needed, have a teammate pull them with SQL, define and document the method so the numbers can withstand scrutiny, and structure the report around a real ICP question. HockeyStack calls methodology non-negotiable because without it, someone will always dispute the data.
With AI analysis, pulling the data is often the easier part now. Building the content into something citable, trustworthy, and durable enough to keep earning visibility for commercial queries years later is where the harder work sits.
What sites are already trusted for your topic? When a benchmark you did not publish is earning the citations in your category, the Citation Source Mapper can map that trusted set into a ranked, pitchable target list. It is available in the premium library.
This post first appeared on the author’s website and is republished here with permission.
I can publish consistently, follow SEO best practices, and still watch competitors outrank me. When that happens, I usually find that the issue is not content quality alone. It is content coverage. Competitors are answering questions my audience is already asking, while my site is not fully part of that conversation yet.
That is where I use content gap analysis. It helps me identify the topics competitors rank for that I do not, then decide which opportunities are actually worth pursuing.
Finding gaps is rarely the hard part. SEO tools make that fairly easy. The real challenge is making sense of thousands of keywords across several reports and deciding what deserves attention first.
My workflow combines competitor data, first-party search data, and AI so I can prioritize content opportunities around business impact instead of search volume alone.
I bring my SEO data together before analyzing it
In this workflow, I use Semrush to identify competitive opportunities, Google Search Console to validate where my site already shows signs of authority, and Google Analytics to add business context. Then I use Claude to bring those datasets together, group related opportunities, identify patterns, and help me decide what belongs on the content roadmap.
I follow this process in one of two ways.
I export reports directly from the platforms and upload them to Claude.
If I have connected those platforms through MCP (Model Context Protocol, a standard that allows AI models to connect securely to data sources), I let Claude pull the data directly without manual exports. The workflow changes, but the analysis does not.
Here is the process I use to turn a pile of SEO data into a prioritized content plan.
Step 1: I choose the right competitors
A content gap analysis is only as useful as the competitors I compare myself against. That sounds obvious, but it is one of the easiest places to go wrong.
If I compare my site to Amazon, Reddit, or Wikipedia, I will end up with thousands of keyword “opportunities” that were never realistic in the first place. My goal is not to find every site ranking for my target keywords. My goal is to find businesses competing for the same audience.
I usually start with Semrush’s Organic Competitors report. Instead of relying only on a list of known competitors, I use this report to find domains that compete across many of the same keywords. From there, I narrow the list to three to five sites that closely match the business and target audience I am analyzing.
I do not worry if a few familiar names do not make the cut. Business competitors and organic search competitors are not always the same.
I also filter out sites that can distort the analysis, including large marketplaces like Amazon, community-driven sites like Reddit or Quora, reference sites like Wikipedia, local directories, review sites, and publishers that do not directly compete with the business.
There are exceptions. If I am analyzing a publisher, comparing against other editorial sites makes sense. The key is choosing competitors that create the type of content I am realistically trying to outperform.
A Semrush competitor analysis view turns organic search data into a clear map of rival domains, traffic potential, keyword overlap, and content gap opportunities.
Before I move forward, I sanity-check the competitor list with stakeholders. Sales or product teams may know about newer competitors or strategically important niches that do not yet show up clearly in Semrush.
Once I have settled on the right competitors, I am ready to find the gaps that matter most.
Step 2: I gather and prepare the data
With the competitor list finalized, I collect the data Claude will analyze. Whether I upload exports or connect through MCP, the goal is the same: bring together competitive rankings, my site’s search performance, and engagement data so I can separate meaningful opportunities from noisy keyword lists.
I like to pull data from three core sources.
Semrush: I find the gaps
I start with Semrush’s Keyword Gap tool using the competitors selected in Step 1.
From there, I pay close attention to three buckets: keywords competitors rank for and I do not, keywords where I rank but competitors rank higher, and keywords where I rank but competitors do not.
The first bucket often points to missing topics or content hubs. The second bucket can reveal quicker wins, especially when my site already appears on Page 1 or Page 2. The third bucket shows existing strengths that I should protect and continue building around.
Google Search Console: I validate the opportunity
Next, I check Google Search Console before assuming every missing keyword deserves a new page.
For example, Semrush may show that I do not rank for a keyword, but GSC might reveal that I already receive impressions for closely related queries. That tells me Google has started associating my site with the topic, even if rankings have not caught up yet.
Those “almost there” topics often deserve a higher priority than topics where I would be starting from scratch.
In GSC, I look for queries with high impressions and average positions between 8 and 20, existing pages ranking for related terms, and long-tail queries that reveal additional search intent.
Google Analytics: I add business context
Search volume is only part of the story. Engagement metrics help me answer a more important question: if I improve visibility for this topic, is it likely to support business goals?
A Semrush content gap analysis view reveals where a competitor ranks and the analyzed site does not, turning keyword overlap data into a practical roadmap for SEO content opportunities.
I review metrics such as organic sessions, engagement rate, average engagement time, key events or conversions, and landing page performance.
If a related content hub already drives engaged visitors or conversions, expanding that topic may be a smarter investment than chasing a completely new keyword with higher search volume.
I clean the data before handing it to Claude
If I am manually downloading the data and uploading it to Claude, I clean it first. Claude is excellent at finding patterns, but it can only work with the data I provide. Cleaner data leads to cleaner topic clusters and better recommendations.
I remove duplicate keywords, competitor-branded terms, careers queries, login queries, support queries, locations or product lines outside the business, keywords with clearly different search intent, and high-intent commercial keywords that are too broad to compete for.
For a manual workflow, I export Keyword Gap data from Semrush, query data from Google Search Console, and landing page performance data from Google Analytics, then upload the files to Claude. For a connected MCP workflow, I ask Claude to retrieve the Keyword Gap report, GSC query data, and GA4 landing page metrics directly from connected accounts.
Step 3: I ask Claude to find the story in the data
At this point, I should have a clean dataset that combines competitive keyword gaps, Search Console performance, and Google Analytics data.
This is where the workflow becomes much more useful. Instead of scrolling through thousands of rows looking for patterns, I ask Claude to organize the data into something I can actually build a strategy around.
The mistake I see most often is asking AI to “cluster these keywords.” That usually produces clusters based on keyword similarity alone. That can be useful, but it does not tell me what to do next.
Instead, I ask Claude to think like an SEO strategist. I give it context about the business, including products or services, target audience, primary business goals, content priorities or constraints, and the exported or connected data from Semrush, GSC, and Google Analytics.
Then I ask Claude to organize opportunities by search intent, funnel stage, business relevance, existing authority signals from GSC, user engagement from GA4, recommended content format, and internal linking opportunities.
Rather than returning a spreadsheet of grouped keywords, I want Claude to produce topic clusters with a clear recommendation for each one.
For example, one cluster might be labeled Technical SEO Audits and include supporting keywords, estimated opportunity, existing pages that could be updated, whether a new page is needed, internal linking recommendations, a priority score, and the reasoning behind the recommendation.
A content gap workflow turns scattered SEO signals into topical clusters, showing where AI search visibility, privacy-first analytics, and technical SEO need deeper coverage.
Another cluster might reveal that several competitor keywords can be addressed by expanding an existing guide instead of publishing three separate articles. That is the kind of insight that is hard to spot manually but much easier for AI to surface.
I separate quick wins from long-term investments
Not every opportunity belongs on the same roadmap. As part of my prompt, I ask Claude to classify each cluster into quick wins, new content opportunities, and authority plays.
Quick wins are existing pages that can be refreshed, expanded, or better optimized. New content opportunities are topics that deserve dedicated content because the site has little or no visibility. Authority plays are larger subject areas that may require multiple pieces of content and ongoing investment to compete effectively.
This simple step helps me move from an overwhelming keyword list to a roadmap with both short-term wins and long-term initiatives.
I do not skip the human review
Claude can organize information remarkably well, but it does not know the business the way I do.
Before moving on, I ask whether the topic supports business goals, whether multiple search intents are being combined into one cluster, whether existing content could already satisfy the need, whether the opportunity is realistic given authority and resources, and whether I would actually assign the topic to a writer.
If the answer is no, I refine the cluster or remove it.
The goal is not to accept every AI recommendation. The goal is to spend less time organizing data and more time making strategic decisions.
The biggest prompt lesson is simple: I do not ask Claude to organize keywords. I ask it to recommend what my content strategy should be based on the data I have provided.
Step 4: I score and prioritize the opportunities
Once Claude has grouped the keywords into topic clusters, the next step is deciding what deserves attention first.
This is where many content gap analyses fall apart. Teams naturally gravitate toward the biggest search volumes, but volume is only one piece of the puzzle. A topic that attracts qualified visitors and supports business goals is often a better investment than a high-volume keyword that is difficult to rank for or unlikely to convert.
I score each opportunity across several criteria before I build a roadmap.
A prioritized content gap roadmap turns scattered SEO data into clear next moves, ranking quick wins by impact, effort and AI visibility.
Business relevance
I start with a simple question: if this content performs well, does it help the business?
Topics aligned with products, services, or the customer journey should carry more weight than informational topics with little commercial value.
Existing authority
Next, I look at signals from Google Search Console. If my site already earns impressions or ranks on the second page for related queries, Google has likely established some level of topical authority.
In those cases, improving an existing page or expanding a content hub may produce results much faster than starting from scratch.
Search demand
Search volume matters, but I do not let it dominate the scoring model.
A collection of related long-tail queries with moderate demand can sometimes generate more qualified traffic than one broad keyword.
Ranking difficulty
I review the current search results before committing to a topic. I look at whether authoritative brands dominate the first page, whether the intent is informational, commercial, or transactional, what types of content are ranking, and whether I can realistically create something more useful or complete.
This quick reality check keeps me from chasing opportunities that are not practical.
Estimated effort
Finally, I consider the work involved. Some opportunities require a light refresh of an existing article. Others call for a new content hub supported by multiple pages.
Both can be worthwhile, but they should not carry the same priority when resources are limited.
I let Claude apply the framework
Once I define the scoring criteria, Claude can evaluate every topic cluster consistently.
For example, I may ask Claude to score each opportunity on a five-point scale for business relevance, existing authority, search demand, ranking difficulty, and content effort. Then I ask it to calculate an overall priority score and explain why each recommendation received that score.
A tactical SEO refresh brief turns AI-assisted content gap analysis into page-level priorities, surfacing validation lessons, effort estimates, and the biggest opportunities.
The explanation is just as valuable as the number. If I disagree with a recommendation, I can adjust the weighting, add more business context, and ask Claude to score the opportunities again.
By the end of this step, I have more than a list of content ideas. I have a prioritized content strategy that shows what to tackle next, what can wait, and what is not worth pursuing.
Step 5: I turn priorities into page-level recommendations
Once I have prioritized the opportunities, the next step is figuring out exactly what to change.
Rather than handing a team a ranked list of topics, I ask Claude to generate page-level recommendations for the highest-priority opportunities. This is where connected data becomes especially valuable.
Because Claude has access to Semrush research, Google Search Console performance, Google Analytics metrics, and my prioritization framework, it can evaluate each page in context instead of treating every recommendation the same.
For each priority page, I ask Claude to produce a recommendation that explains why the page was selected, the primary keyword cluster, current rankings and impression data, supporting evidence from GSC and competitor research, recommended updates, estimated effort, expected impact, and priority level.
One of the biggest advantages of this approach is validation.
Before recommending a refresh, Claude can compare URL-level Search Console data against the original analysis. Sometimes what looks like a strong opportunity turns out to be misleading. A keyword may have inflated impression counts, a URL could have been mislabeled in an export, or the page may not be as close to ranking as it first appeared.
Catching those issues before assigning work can save hours of unnecessary effort.
The recommendations also make stakeholder conversations easier. Instead of saying, “I think we should update this page,” I can point to the supporting data, explain why it is a priority, estimate the effort involved, and tie the recommendation back to the larger content strategy.
I treat these recommendations as implementation plans rather than full content briefs. They help SEO and content teams understand what should change, why it matters, and where to focus first. Writers can then use those recommendations to create or update content with confidence.
Step 6: I measure whether the gap is closing
Publishing the content is not the finish line. It is the start of the next round of analysis.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
I begin with Google Search Console, tracking whether target queries are gaining impressions, improving in average position, and generating more clicks. When I refresh an existing page, I compare performance before and after the update to see whether the changes actually moved the needle.
Next, I look at Google Analytics. Better rankings do not always translate into better business outcomes, so I review organic traffic alongside engagement and conversion metrics. If an updated page attracts more visitors but fails to keep them engaged or contribute to conversions, I know it is time for another round of optimization.
If I am using Claude through MCP, I can also ask it to compare performance over time and summarize what changed. I might ask which refreshed pages improved the most, which content clusters gained the most visibility, which recommendations drove the strongest business results, and which opportunities still need attention.
Instead of comparing reports month after month, Claude can quickly surface significant changes and point me toward the pages that deserve attention.
I do not treat content gap analysis as a one-time exercise. Competitors publish new content, search behavior shifts, and my own site authority evolves. I like to repeat this workflow every quarter, or more often in fast-moving industries, so I can keep finding new opportunities and stay ahead of competitors.
The tools will continue to improve, but the repeatable workflow is what creates the advantage.
I build a repeatable content gap analysis process
A content gap analysis helps me prioritize opportunities worth pursuing instead of chasing every possible keyword.
Semrush helps me uncover competitive gaps. Google Search Console shows where I already have momentum. Google Analytics adds the business context that rankings alone cannot provide. Claude brings those datasets together, helping me identify patterns, prioritize opportunities, and create actionable recommendations in a fraction of the time it would take manually.
Whether I upload reports or connect my tools through MCP, the workflow stays the same. I gather the right data, validate the opportunities, let AI organize the information, and apply my own expertise to decide what comes next. That is the part AI cannot replace.
The biggest advantage is not simply having better prompts or faster analysis. It is having a repeatable process that helps a team make smarter content decisions every quarter.
Prompt template: My prioritized content gap roadmap
Here is the prompt I use after I have gathered the data, whether I have uploaded exports from Semrush, Google Search Console, and Google Analytics or connected those tools to Claude through MCP.
“You are an experienced SEO strategist helping me perform a content gap analysis.
I’ll either provide exported reports from Semrush, Google Search Console, and Google Analytics, or you’ll access those tools through connected MCP integrations.
My goal is to identify the highest-impact content opportunities based on competitor visibility, existing authority, business value, and implementation effort.
Here’s my business context:
– Company: – Industry: – Products/services: – Target audience: – Primary business goals: – Geographic focus: – Any strategic priorities or constraints: – Tone of voice: [Insert brand voice adjectives here (e.g., authoritative, conversational, technical)].
Using the available data, complete the following tasks.
1. Identify content gaps
Organize keywords into these categories: – Competitors rank and we don’t. – We rank below competitors. – We rank and competitors don’t.
Highlight any content gaps, opportunities to consolidate pages, or keyword cannibalization issues.
2. Validate the opportunities
Use Google Search Console data to determine: – Which topics already receive impressions. – Which pages rank between positions 8 and 20. – Which existing URLs have the strongest chance of improving with optimization.
Use Google Analytics data to determine: – Which pages drive meaningful engagement. – Which pages contribute to conversions. – Which content hubs are worth expanding.
3. Create strategic topic clusters
Group related opportunities by: – Search intent – Business relevance – Funnel stage – Recommended content type – Internal linking opportunities
Don’t cluster based only on keyword similarity. Focus on topics that should become part of the same content strategy.
4. Prioritize every opportunity
Score each topic cluster using: – Business relevance – Existing authority – Search demand – Ranking difficulty – Estimated effort
Assign each opportunity a priority (High, Medium, Low) and explain why.
Separate recommendations into: – Quick wins – New content opportunities – Long-term authority investments
5. Recommend next steps
For every high-priority opportunity, recommend whether we should: – Refresh an existing page – Consolidate multiple pages – Create a new page – Build a pillar page with supporting content
Include supporting evidence for every recommendation.
6. Deliver the results
Create: – An executive summary – Prioritized topic clusters – A scored opportunity table – Page-level recommendations for the highest-priority URLs – A phased implementation roadmap (30, 60, and 90+ days)
If you find conflicting data between Semrush, Google Search Console, and Google Analytics, explain the discrepancy and recommend which source should guide the decision. The output should both be HTML and a Google Sheet.
Before presenting your final recommendations, validate your own analysis. If reviewing Search Console or Analytics data changes your original recommendation, explain why and update your prioritization accordingly.”
This prompt is only a starting point. I add business context, editorial guidelines, and scoring criteria that are unique to the organization I am analyzing. The more context I give Claude, the more useful and actionable its recommendations become.
For most of the past decade, I treated organic marketing as a visibility game. I wanted brands on Page 1, inside featured snippets, and in front of the people already searching.
That north star has moved.
When I spoke at SMX Advanced on June 5, the question I put to the room was not simply, “How do I get a brand found?” The harder question was, “How do I get that brand chosen?”
In 2026, those answers are no longer the same. The distance between being discovered and being selected is where I see many brands losing ground.
In AI search, my reputation shows up first
The old user journey was messy and multi-step. People explored, compared, checked reviews, read Reddit threads, visited comparison sites, and moved toward a decision over time. Now, a single AI prompt can compress much of that process into one synthesized answer.
AI search does not reward the brand that shouts the loudest in paid media or stuffs the most keywords into metadata. I see it rewarding the brand with the strongest reputation in the places that matter. Reddit discussions, review sites, comparison pages, expert commentary, forums, and editorial coverage are all being absorbed by large language models and blended into recommendations.
In other words, my brand is no longer defined only by what I say about it. It is shaped by how AI understands it, and AI is reading what everyone else has said, too.
Owned content on websites and social channels will always carry a promotional bias. AI systems look for outside validation to support, challenge, or clarify those claims.
That changes the work of organic marketing. I can no longer stop at visibility. I have to build a brand that is found, correctly understood, and ultimately chosen. Those are three separate challenges, and I need a strategy for each one.
Found: I need to appear where my audience actually looks
The first challenge is still discoverability, but the canvas is much wider than Google. People now discover brands through ChatGPT, Reddit, YouTube, TikTok, Google, Quora, LinkedIn, and word of mouth. I have to understand which of those entry points matter most to the specific audience I want to reach.
That starts with mapping the sources my audience genuinely trusts: the publications, platforms, communities, creators, analysts, newsletters, and peer groups that influence their decisions. The intersection of semantic relevance, domain authority, and audience affinity tells me which third-party properties are worth pursuing.
For one B2B audience, that might mean Wired, Tom’s Guide, or an active LinkedIn group where buyers discuss vendors in a specific vertical. For another, it might be r/smallbusiness or a Substack newsletter with 40,000 engaged subscribers.
Once I know where the audience spends time, I can create useful content, earn credible mentions, and participate in the conversations already shaping decisions. This is audience-first, performance-driven PR and organic strategy, not generic brand awareness.
AI search leans heavily on outside validation: this chart shows third-party communities, reviews, and earned media driving 93% of citations versus 7% from owned channels.
The data makes the case even stronger. Across the top commercial sectors analyzed, 93% of AI search citations came from third-party sources. If I only invest in content on my own domain, I risk being invisible to the systems now doing much of the brand discovery work.
Understood: I need consistent signals everywhere
Getting found matters, but it is not enough on its own. If machines are surfacing my brand, they also need to understand it accurately.
LLMs do more than crawl my website. They build a consensus picture from everything available online: reviews, Reddit discussions, press coverage, YouTube commentary, Trustpilot ratings, forum threads, and more. If those signals conflict with the story I am telling about myself, I have a real problem.
If I claim premium positioning while thousands of articles question whether the brand is truly luxury, heavy discounting is part of the public record, and review scores are poor, AI is unlikely to recommend that brand as a premium option. The model has read the broader story, not just the homepage copy.
That is why brand messaging consistency has become an SEO issue. Owned, earned, and paid content all need to reinforce the same core associations. Conflicting signals do not just confuse customers; they can weaken AI visibility.
Digital PR plays a critical role here because it helps shape the external narrative. Through strategic media placements, expert commentary, and search-informed coverage, I can influence what journalists write, what audiences remember, and what models learn.
I also have to think beyond one obvious keyword. The query fan-out, or the range of prompts a potential customer might use, requires positive and consistent answers across every touchpoint an LLM might evaluate.
Chosen: I need trust signals that influence the decision
The third challenge is the hardest and probably the most important. Trust has always been an SEO currency, but as clicks decline and zero-click search becomes more common, trust matters even more.
According to an Ahrefs study, brand appearance in AI Overviews is most strongly correlated with branded web mentions. In practical terms, that means the number of times a brand is positively named across authoritative third-party sources is becoming one of the most powerful signals organic marketers can influence.
That is also the core output of strong digital PR. Based on the last 4,000 pieces of U.S.- and U.K.-based coverage driven for clients, 91% of AI search citations included expert insight rather than branded content or product pages.
That tells me expert-backed, editorially independent coverage is critical. Internal experts are now one of the most valuable assets a brand has. Brands that invest in real thought leadership, original research, and data-backed studies are giving both people and AI systems stronger reasons to trust them.
The three content formats I see consistently supporting LLM inclusion are product roundups and listicles that place a brand inside trusted “best of” editorials, reliable data-backed research that journalists and LLMs can cite, and expert thought leadership that positions real people as credible voices in their category.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
What does not work is chasing inauthentic mentions through artificial link schemes, fake expert personas, or manufactured coverage. Google has already flagged these kinds of tactics in its GEO guidance, and models are getting better at distinguishing genuine authority from manipulated signals.
The reputational risk is also high. If I try to manufacture authority and get caught, I do not just lose visibility. I damage the trust I was trying to build.
This cannot be a one-time effort. Multiple studies, including research from Waseda University, have identified a correlation between AI brand visibility and content recency.
Brands that maintain a steady flow of credible, expert-backed third-party coverage do not just appear more often in AI responses. They appear with more confidence.
Frequency and freshness both matter. A one-off PR campaign is not enough. I need to treat credible external validation as an always-on strategic investment.
The framework I use in practice
When I think about brand discovery in 2026, I come back to three words: found, understood, and chosen.
Found: I map the audience’s real sources of influence and make sure the brand is credibly present across the fragmented ecosystem where discovery now happens.
Understood: I work to make sure everything said about the brand tells a consistent story, matches the desired positioning, and reinforces the associations that drive preference.
Chosen: I continuously build genuine trust signals through earned coverage, expert commentary, and third-party validation, so that when a person or machine compares the brand with a competitor, credible external evidence tips the decision in my favor.
The brands winning in organic search right now have not unlocked some secret technical trick. They have built reputations worth recommending, and they have made sure machines can understand those reputations clearly.
That is where I believe organic marketing has to go next. Instead of chasing the algorithm, I need to build something worth finding, worth understanding, and worth choosing.
I’m seeing traditional Google rankings deliver less predictable value than they once did. Ads, AI Overviews, and other search engine results page features are pushing organic links farther down the page, which means visibility no longer depends only on where a brand ranks in the classic blue-link results.
As search keeps shifting, I believe brands need to ask a more practical question: how do I make sure my brand is represented accurately inside AI-powered answers?
The more I understand how AI engines use brand information and when they cite it, the easier it becomes to build a real AI visibility strategy. This moves the conversation beyond whether an AI model “knows” a brand and toward how that brand can earn presence, trust, and discoverability in AI search.
The click economy is shrinking
I think most brands should start learning AI search and building an AI SEO strategy now. A full shift from organic search to AI search may still be years away, but the direction is clear enough that waiting creates risk.
Google is already leaning hard into AI search. In an April article from The Verge, CEO Sundar Pichai said that search had a strong quarter, with AI experiences driving usage, queries reaching an all-time high, and revenue growing 19%.
Users are changing their behavior too. A Pew Research study found that when people see an AI-powered summary in search results, they click a blue link only 8% of the time. When no AI summary appears, that click rate rises to 15%.
AI search traffic may still be smaller than organic traffic, but I would not dismiss it. According to Similarweb, AI traffic converted at 11.4%, compared with 5.3% for organic search traffic. That makes AI visibility worth tracking even before it becomes the dominant traffic source.
How I separate AI usage from AI citation
I think about brand presence in AI systems in two main ways: usage and citation.
Usage happens when an AI engine ingests information about a brand and draws on that information when answering a query. In some ways, this reminds me of how Google traditionally indexed pages before ranking and serving them in search results.
When an AI engine uses brand content, it may mention the brand without linking to it. Even an unlinked mention can matter because it can create discovery, influence perception, and prompt users to search for the brand directly.
Ahrefs data shows most Google AI Overview citations still come from high-ranking organic pages, with 76.10% in the top 10 and a smaller share outside the top 100.
Citation is different. A citation happens when an AI engine directly references a brand as a source of information. That reference might be a link to a web page, a social profile, or even a clickable phone link that lets someone contact the business.
Within OpenAI, usage and citation appear to depend on separate technical systems. As OpenAI’s documentation explains, OAI-SearchBot and GPTBot are deployed separately among four distinct user agents. Other AI systems have their own controls, but the same broader distinction still applies.
Why citations do not tell the whole story
I do not see citations as the full AI visibility picture. AI engines often answer questions directly without citing web sources, and this pattern is not entirely new. Before AI Overviews, Google was already moving in that direction with featured snippets.
Ahrefs found that ChatGPT retrieves almost the exact same number of cited and uncited URLs to generate an average response: about 16.57 cited URLs and 16.58 uncited URLs. But Reddit made up 67.8% of uncited URLs, which means comparing cited and uncited URLs is often really a comparison between search results and Reddit API output.
That matters because AI systems are not neutral in the uncited information they surface. Some platforms and websites are simply more influential than others. If I try to push a brand into AI answers without understanding where the model gets its information, I am working at a disadvantage.
How I would improve brand usage and citation
I would start by tracking the brand’s current AI visibility and monitoring progress over time. That means running a representative set of prompts through an AI visibility platform, reviewing the sources that get cited, and asking what those sources reveal about the model’s preferences.
There are already many AI citation tracking tools available, and established platforms like Semrush and Ahrefs have added AI tracking features as well. I would choose a tool based on the prompts, markets, and engines that matter most to the brand.
I would also scale tracking and research as much as budget allows. AI prompt tracking often depends on API calls, so it can cost more than traditional rank tracking. Still, the data is usually richer, even when the sample size is smaller.
As long as the prompt sample is broadly representative, most platforms can pull multiple responses and calculate an average. That gives me a more useful view of recurring patterns instead of relying on one-off answers.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
I would keep reading studies from AI platforms, SEO vendors, and data providers too. Those reports are valuable because they show which sources AI engines rely on and where brands may have the best chance to appear.
The key is continual monitoring. Over time, I can work to place a brand inside the sources AI engines already trust and use most heavily.
Why I still care about traditional rankings
Yes, I still think traditional search rankings matter, but not for the same reasons they used to. The relationship between organic position and business performance is less direct now, especially as SERP features and AI answers absorb more user attention.
At the same time, Ahrefs research suggests a relationship between AI citations and Google rankings, at least inside Google AI Overviews. A July 2025 study found that 76.1% of pages cited in AI Overviews ranked in Google’s top 10 organic results. If AI Overviews become a dominant AI search experience, traditional rankings will still influence visibility.
I also pay attention to content quality. Semrush found that AI engines rarely cite generic content that simply repeats what other sources already say. The content that earns citations usually contributes something distinct.
That fits closely with Google’s helpful content guidance, which rewards original information and useful perspective. In my view, content with trusted data, original insight, and a clear point of view can support both Google rankings and AI citations.
Because many classic SEO tactics can also support AI citations, I would not abandon traditional SEO. I would treat it as part of a broader visibility strategy that now includes AI usage, AI citations, and brand presence across trusted third-party sources.
Where I think AI visibility is heading
Both usage and citation need ongoing tracking and analysis. If I want AI engines to use a brand’s knowledge and content, I need to understand which sources each model relies on and help the brand appear in those places. If I want citations, I need the brand’s content to stay crawlable, rank well, and say something original.
Classic SEO still earns its place because the same work that improves organic visibility can often improve AI visibility too. But returns from traditional rankings are changing, and AI SEO may eventually become the primary discipline. For now, I would keep ranking, start tracking, and build for both usage and citation.
I use Conductor’s MCP Server to ground the AI tools my team already relies on in verified AEO and SEO intelligence, instead of depending on a stale snapshot of the web.
A bold launch visual introduces an AEO and SEO Intelligence Layer, framing verified search and AI visibility data as a modern layer for marketing teams.
