Last week OpenAI launched “Instant Checkout” for ChatGPT, a feature allowing consumers to buy products without leaving the platform.
The feature, which utilizes Stripe’s Agentic Commerce Protocol to facilitate AI transactions, is available for Etsy merchants and soon for Shopify. An open-source version allows any merchant or developer to build custom integrations.
OpenAI’s application form is for merchants not on Etsy or Shopify who want to “1) integrate their products into ChatGPT Search results and 2) enable Instant Checkout in ChatGPT via the Agentic Commerce Protocol.”
AI ‘Rankings’
The shift to AI shopping is ominous. Ecommerce merchants who rely on traditional organic search traffic will almost certainly lose traffic. Merchants with clean, comprehensive product data that’s easily digested by AI agents could slow the decline, if not benefit.
Will ChatGPT prioritize products from merchants that have enabled Instant Checkout? OpenAI’s announcement seems to hint that it might:
When ranking multiple merchants that sell the same product, ChatGPT considers factors like availability, price, quality, whether a merchant is the primary seller, and whether Instant Checkout is enabled, to optimize the user experience.
Thus early ChatGPT merchants may have a competitive advantage.
How to optimize for generative engines? Product data alone may not elevate visibility. Remember that ChatGPT doesn’t rely solely on keywords. The context of conversations is key.
A prompt may not initially request product recommendations. For instance, a user may start by seeking solutions for ankle pain from running. The ensuing dialogue may include buying running shoes with better ankle support.
Other details may come up. Does the user live in a rainy state and thus require waterproof shoes? Does the user run on trails or flat surfaces?
Addressing every possible scenario via product data is seemingly impossible, yet merchants should address as many use cases as practical while encouraging off-site discussions in Reddit and elsewhere for context.
Product Feeds
ChatGPT’s product feed specifications allow 150 characters for the product’s title and 5,000 for its description.
Populate all product feed fields and available characters. The more info it has, the better ChatGPT can surface your product for various prompts. For example, a product’s “weight” field can elevate visibility when consumers seek lightweight goods.
ChatGPT’s feed specs include unique fields to keep in mind:
“related_product_ID” for “basket-building recommendations and cross-sell opportunities.” Instant Checkout allows only single-product purchases, but OpenAI says multiple-product buying is coming. The related products field could eventually help ChatGPT recommend more of your products and associate similar items.
“q_and_a.” This field has no character limit — seemingly perfect for additional information. In my testing, AI agents can easily fetch data from question-and-answer formats.
“popularity_score” can convey your most sought-after goods. ChatGPT does not explain the field’s impact. But it’s the Wild West for generative engine optimization, and who knows? An item’s popularity may help it stand out.
Search Atlas held an event last week to showcase new capabilities and improvements to their SEO platform which make it easier for digital marketer to scale SEO and take on more clients.
The new features enable marketers to more easily handle on-page and off-page SEO, paid search, impact and track LLM visibility, and scale Google Business Profile management, and that’s just a sample of all the new functionalities coming to the platform.
Auto PPC Retargeting
Search Atlas introduced a new new retargeting feature in Otto PPC. This new feature is designed for agencies and advertisers that are managing paid media. It simplifies campaign setup with a quick-start wizard that enables retargeting site visitors, which they claim can be launched in under 60 seconds.
Manick Bhan, founder of Search Atlas explained:
“The hardest thing about taking paid media business from a client is doing it justice, doing a good job, right? Because every time they get a click, they’re paying for it. The best way that you can show a client ROI on paid media is through retargeting. Run a retargeting campaign, retargeting the traffic that they already have on their website.
We wanted to be able to make this easy for you, so all you have to do is enable it inside Otto PPC, and you’re able to run retargeting campaigns now. So we have a wizard set up for you — just a couple clicks and you can launch a retargeting campaign in less than 60 seconds. It’s that easy.”
GBP Galactic
Search Atlas announced a feature for digital marketers who handle Google Business Profiles for clients. The GBP Galactic feature now has Service Area Business (SAB) support. GBP Galactic offers integration with social media auto-posting to Facebook and Instagram, with plans to add more social networks soon.
Bhan explained the social network autoposting:
“We’ve learned the LLMs they want to see your information not just on your website and GBP profile, they want to see your data in the social media platforms.. So what we can do now is, one time, build our GBP posts, and publish to all social networks, which will increase your visibility in the LLMs. And instead of having to use third-party tools to do this, it will be completely integrated.”
Bhan also shared about their citation network:
“We also added support for service area businesses in our citations product, so now you can even build aggregator network citations and put yourself into the aggregator networks for your service businesses… Because normally these aggregator networks, they want an address. We figured out how to do it so we can get you in without one. Pretty cool.
…ChatGPT, Claude, all the LLMs pay for the data from all the aggregator networks. So if you want to put your local business into the aggregators, as well as into all the websites, the aggregator networks are a shortcut to being able to do that and upload directly to ChatGPT.”
LLM Visibility
Another useful feature is LLM Visibility tracking and sentiment analysis. LLM visibility is now measurable directly in Search Atlas. It also tracks brand presence across ChatGPT, Claude, and other LLMs and is able to identify visibility trends beyond Google Search.
Expanded Press Release Network
Bhan announced that Signal Genesys, a press release company they acquired last year, has expanded their distribution to financial news and with a local news media network.
Bhan commented:
“The financial news network costs a whopping $10. And then the news media network costs about $20. So these are really cost-effective, especially for agencies. If you are working with clients and you need to keep prices low for yourselves, there’s a lot of margin in there for you.
And these networks in particular we found were indexed very well in ChatGPT.”
On-Page SEO
Interesting feature launched in their Otto product is a module called Domain Knowledge Network which assists users in building topical relevance with a semantic interface, just speak instructions to it and it will analyze the brand and suggest a content topic structure.
Revamped WordPress Plugin
Their WordPress plugin has been overhauled to make it more user-friendly. It now includes one-click installation to connect WordPress directly to Search Atlas, two-way synchronization that keeps Otto data and WordPress in sync in real time, and auto-publishing that enables SEO fixes generated in Otto to be deployed directly into WordPress.
Universal CMS Integration
Search Atlas is aiming to become CMS-agnostic, able to integrate with any website regardless of the CMS for publishing blog posts and landing pages in one click through their Content Genius feature. Right now Search Atlas can work with Drupal, HubSpot, Magento, Wix, and WordPress. They are also testing to integrate with Joomla, Shopify, and Webflow. Soon they’ll be able to integrate with ClickFunnels, Contentful, Duda, Ghost, and Salesforce.
Near Future: Otto Agent
Otto Agent represents the future of Search Atlas’s agentic revolution, replacing traditional UI-driven workflows with natural-language commands. It’s currently available as a beta program. Users can speak to the platform (via text or voice) to perform SEO actions directly. Otto Agent can execute end-to-end actions: site audits, fixes, title/meta/image optimization, GBP posts, and content generation.
Spending the day listening to their presentations, it became evident that Otto Agent typified Search Atlas’s approach toward developing an SEO platform that is useful. Having come from an SEO agency background, they understand what agencies need and aren’t waiting for competitors to do things first, they’re just moving forward with features that they feel agencies will find useful.
Otto Agent is an example of that forward-looking approach because it’s built on the idea that managing SEO will become agentic, conversational, and autonomous.
I didn’t know that much about Search Atlas before attending the event but now I have a better understanding of why so many agencies embrace Search Atlas.
Where is search going to develop? Is ChatGPT a threat or an opportunity? Is optimizing for large language models (LLMs) the same as optimizing for search engines? These are some of the critical questions that are top of mind for both SEOs and CMOs as we head into a multi-search world.
In Part 2 of this two-part interview series, I try to answer these questions based on data from our internal research to provide some clear direction and focus to help navigate considerable change. If you haven’t already, go back and read Part 1.
What you will learn in this Part 2:
Traditional Search Engine Results Page (SERP) Evolution: Why traditional search isn’t dying but fundamentally transforming, where it still excels, and how it is part of Google’s integrated approach to AI evolution.
Google AI Mode Strategy: How AI Mode and AI Overview operate as the same strategy at different thresholds, with AI Mode being 2.1x more likely to include brands while AI Overview remains highly selective.
Agentic AI Revolution: Why 33% of organic searches now come from AI agents browsing on behalf of users, creating real-time interactions that demand immediate content accessibility.
Search Funnel Transformation: How the customer journey has evolved from linear progression to unpredictable funnel-stage jumping, with AI handling research while conversion still happens through traditional organic channels.
The Three Pillars Framework: Why CMOs need reporting for early AI shift detection, automation for seamless AI-readiness, and strategic recommendations to influence how AI tells their brand’s story.
Do You Think There Is Any Future For Traditional SERP Search, Or Do You Think It Will Become Obsolete?
I think we’re witnessing more of an evolution than an extinction. Traditional SERP search has a future, but it’s going to look completely different.
According to our internal data, 92% of all searches happen here. And when it comes to meaningful actions, such as downloads, sign-ups, or purchases, 95% start on Google. Search volume hasn’t gone down – it’s actually grown 10% year-over-year. With AI Mode, Google is layering AI directly into the experience.
The takeaway is clear: AI hasn’t replaced traditional SERPs; it’s utilizing and aligning with them.
Image from author, September 2025
Where Traditional Search Still Excels
Traditional search still absolutely shines in certain areas. When you’re dealing with complex queries or personal searches, those traditional SERPs still provide something AI cannot: depth, discernment, and diverse perspectives. Ecommerce is a perfect example – when shopping, I still want to see those traditional listings to compare sources, read different reviews, and check various offers.
Traditional SERP’s And Google’s Integrated Approach
Google is handling this integration cleverly. They’re not replacing classic SERPs; they’re augmenting them. Google’s Gemini model powers AI Overviews that appear above traditional listings, creating comprehensive summaries from multiple sources. Classic SERPs provide the foundational data, and AI distills and presents it in new, user-centric ways.
For brands and CMOs, this creates a new optimization challenge. You’re not just thinking about traditional SEO anymore; you need to optimize for AI inclusion, too. If you get cited in an AI summary, your visibility increases dramatically. It’s an interesting paradox where fewer traditional listings appear, but cited sources gain more prominence.
We’re seeing conversational capabilities, multimodal search with images and video, and direct answers that go way beyond static blue links. Users can now ask follow-up questions, search with photos, or engage in natural language conversations – capabilities that would have been impossible with traditional link-based results.
When AI Search Meets Traditional SEO
The overlap between AI citations and traditional search results has grown 22.3% since 2024. However, this varies significantly by industry, making your vertical a key factor in strategy development.
The variation is substantial. Ecommerce saw minimal change at 0.6 percentage points, while Education increased by 53.2 percentage points. Your industry determines the approach you should take.
In YMYL sectors like Healthcare, Insurance, and Education, overlap reaches 68-75%. When trust is critical, Google tends to favor content that already performs well in traditional search rankings.
Ecommerce operates differently. Overlap remained flat, and AI Overview coverage actually decreased by 7.6 percentage points. Google appears to maintain separation between shopping queries and AI answers, likely to preserve the transactional flow that drives commerce.
Image from BrightEdge, September 2025
The Interconnected Search And AI Engine Ecosystem
What’s happening is that AI Overviews are acting as content curators, selecting which sources to reference and cite. This means your content needs to be clear, authoritative, and structured in ways that both humans and AI can easily understand and extract value from. The fundamentals of relevant content – quality, clarity, technical optimization – they’re more critical than ever.
The likes of ChatGPT and Perplexity tap into traditional search engines for factual grounding, so this interconnected ecosystem is becoming the norm. It’s not just about ranking on SERPs anymore; it’s about being discoverable across multiple channels: social search, AI interfaces, traditional SERPs, and whatever comes next.
The New Traditional CMO, SEO, And AI Reality
But those traditional foundations remain crucial – they just serve both humans and AI now. For straightforward, fact-based queries, AI can generate instant answers, removing the need to browse multiple results. But for anything complex, local, or transactional, those classic blue links still appear, sometimes as fallback options, or often as primary results depending on the query type.
However, it’s worth noting that AI Overview shares the screen with classic SERPs and ads. Still, your visibility may significantly increase when you get cited in an AI-generated summary, a paradox in which traditional results may decline, but referenced sources tend to become more prominent.
Keeping Pace With Change
The pace of change is also something CMOs need to prepare for. Google’s AI Mode is evolving incredibly quickly – features, user interface (UI) presentation, and citation logic change frequently. You need to invest in technology and teams that provide real-time insights into SERP and AI Mode visibility. Keep new AI entrants on your radar, and their experimentation and pilot projects, which are crucial for understanding what drives referenced visibility and conversions through AI sources.
The role of traditional SERPs is not dying. AI and traditional search work hand in hand; it’s now Google’s default approach, and both systems co-exist beautifully, serving diverse needs within the same search journey.
What Do You Think CMOs Should Consider About How Google AI Mode Might Change An Enterprise Approach?
This is one of the most significant strategic shifts CMOs are facing right now, and it’s happening fast. Google’s AI Mode is fundamentally changing how enterprise visibility, engagement, and measurement work across search and discovery channels.
Understanding Google’s AI Strategy: AI Overviews And AI Mode
Our recent analysis reveals that AI Mode and AI Overview are not distinct strategies. They’re the same strategy but operating at different thresholds.
Think of it this way: AI Mode acts as the broad discovery engine. It’s 2.1x more likely to include brands (compared to AI Overviews), surfaces more unique brands overall, and maintains pretty stable week-over-week patterns. When it shows sources, you’ll see fewer but more prominent source cards. It’s casting a wide net with lower barriers to entry.
AI Overview, on the other hand, is the dynamic curator. It’s much more selective – only including brands in 43% of responses – but shows significantly higher volatility, which tells us the algorithm is actively evolving.
AI Mode provides stable, broad discovery, whereas AI Overviews are where Google tests new ranking approaches with much higher selectivity. It’s clever – they’re serving different user needs while continuously refining their AI capabilities.
The Multi-Query Reality Of Google AI Search
An AI query is never just one search anymore. AI Mode runs dozens of queries on behalf of the user before showing an answer.
That one question – “What’s a good treadmill for beginners?” – becomes dozens of searches instantly. Google breaks it down into features, price comparisons, reviews, safety tips, compact options, and warranty information. The AI runs these searches in parallel, pulls results, and stitches them together into a single conversational answer.
It’s no longer about matching one keyword. You’re competing to be included across the entire web of related questions that the AI asks on the user’s behalf.
AI Mode And Living In The Browser
Think about how much time you spend in your browser every day. Now imagine if it could actually think alongside you. That’s exactly what’s happening with Google Chrome’s latest AI features, and honestly, it’s pretty mind-blowing.
Here’s what’s new: AI Mode lets you ask complex questions right in the address bar – no more opening countless tabs just to find answers. Planning a trip? Chrome’s multi-tab intelligence can now pull information from all your open tabs and create one coherent plan. And soon, agentic browsing will let Gemini handle the boring stuff like booking appointments while you focus on what actually matters.
The cool thing is, AI Mode isn’t replacing Google – it’s just giving us a smarter way to use it. Think conversational search, but built right into where you already spend most of your time.
For CMOs and marketing teams, this means rethinking how people will find and interact with your content. We’re not just optimizing for search anymore; we’re optimizing for conversation.
The CMO Content Strategy And Keeping Pace With Change
Your content strategy needs a complete rethink. AI Mode pulls directly from content to generate overviews and summaries, which means you can’t just optimize for traditional SEO anymore. Your content needs to serve both AI and human audiences simultaneously. The goal is not just to rank anymore; it’s also to be selected for AI-generated overviews.
CMOs need to prepare for the pace of change. Google’s AI Mode is advancing at a rapid pace, with frequent shifts in features, UI presentation, and citation logic. You need to invest in tools and teams that provide real-time insights into SERP and AI Mode visibility.
How Are Agentic AI Agents (Crawlers And Bots) Changing The Search Funnel? How Might These Changes Impact Roles On The CMO And The SEO Team?
We’re seeing a major shift in how content gets discovered and delivered, as new types of AI agents engage with websites and surface information in real-time conversations. AI agents are now browsing on behalf of users. Unlike classic crawlers, it’s not about indexing pages to be served up later; it’s real-time interactions. If you have a dead page, or it can’t interpret what your content is saying, you lose that moment.
The Rise Of AI Agent Website Interaction
They’re acting like digital assistants – researching, comparing, recommending. If your page is slow, or your content isn’t clear, they move on instantly. They are your future customers – potential new clients – arriving through AI. In the last month, we’ve seen visits from ChatGPT’s new Agent crawler double in visits to customer websites. 33% of all organic searches are from these agents. The growth is massive.
The AI Agent Preprocessing Layer
This creates a preprocessing layer that influences every subsequent customer interaction. Unlike traditional crawlers that simply index content, these systems navigate websites, submit forms, compare options, and make recommendations on behalf of the user in real-time. Each visit represents AI doing a search on your customer’s behalf, looking for content to help explain, recommend, and help your customers in a conversation.
How This Impacts The Evolution Of The Customer Journey
The awareness phase has evolved from user-driven discovery to “pre-aware” algorithmic surfacing where AI agents proactively recommend options based on context, preferences, and behavioral patterns – often before users consciously realize they need information. Modern buyer behavior no longer follows a straight-line progression. Instead, customers jump between funnel stages unpredictably, sometimes moving directly from initial awareness to making purchases, or cycling back to discovery phases for related products.
AI Search Users: Enter the funnel at the research and exploration stage, asking questions and gathering information to inform their decisions. They’re seeking understanding, not yet ready to transact.
Organic Search Users: Demonstrate clearer purchase intent, often searching for specific products, services, or solutions. They know what they want and are closer to conversion.
The Journey Dynamic: Many users begin with AI-powered research but ultimately convert through organic search or direct channels – making AI search valuable for top-of-funnel discovery despite its lack of direct conversions.
The Research Vs. Conversion Channel Reality
As AI search functions as a research channel, not a conversion channel, this confirms that AI systems are handling awareness and consideration stages, while conversion still requires traditional touchpoints. We found that 34% of AI citations come from PR-influenced sources and 10% from social platforms, demonstrating that traditional SEO concepts like E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) remain critical but must now work at machine scale across multiple platforms.
Immediate CMO Transformation Requirements
Foundation Strengthening: Companies must rapidly enhance SEO fundamentals – structured data, content authority, and technical excellence – that determine whether AI agents can find, understand, and cite their content. Brands not only need to keep the door open to agents, but they also need to embrace them, so they are not invisible to the AI agent processing layer I mentioned earlier.
New Measurement Frameworks: Marketing teams must develop new measurement frameworks that capture AI citation frequency, cross-platform visibility, and influence within AI responses, even when traffic attribution is impossible. Key metrics include brand visibility monitoring, AI presence testing, reference share analysis, and indirect conversion tracking.
CMO And Marketing Team Structure
The team structure evolution reflects a fundamental shift from departmentalized hierarchies to fluid, cross-functional pods. Technical teams become increasingly AI-augmented for scale, content teams shift from creation to curation and refinement, and new integration teams bridge SEO with data science and machine learning departments.
Concluding Thoughts: The CMO, SEO, And AI Reality Check
Here’s the critical takeaway: While you’re optimizing your funnel for AI discovery, remember that organic search is still where conversions happen. AI search serves as the research phase, helping users discover options and gather information.
But when they’re ready to take action – making a purchase, signing up, or downloading – they’re still turning to traditional organic search results. They recognize that AI discovery feeds into the organic funnel. Your SEO foundation becomes the conversion engine that AI discovery feeds into.
The smartest CMOs and marketers aren’t choosing between AI and organic search. They’re using proven SEO strategies as their foundation while adapting for AI discovery.
Pat Reinhart, VP of Services & Thought Leadership at Conductor, and Luiza Shahbazyan, Sr. R&D Product Manager at Conductor, will show you exactly how to win in the age of AI search. You’ll learn:
How to maximize your brand’s visibility across AI answer engines.
Key signalsthat influence AI citations, including content authority and digital PR.
Practical strategies to earn mentions and strengthen trust signals.
How to adapt your SEO workflows for Answer Engine Optimization (AEO).
Reserve Your Spot Today
Register now to get actionable tactics and data-backed insights that help your brand show up in AI results.
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Artificial intelligence is transforming how people search, discover, and act on information. For chief marketing officers and senior leaders, this is not a question of whether SEO is “dead” but of how to adapt to a new era where visibility spans AI-driven assistants, multimodal tools, and fragmented user journeys.
Two forces drive this disruption: rapid advances in technology and the accelerating adoption of new search behaviors by younger demographics.
As these forces converge, traditional measures of success such as rankings, traffic, and clicks are losing relevance.
What matters now is the ability to understand where visibility is shifting, how decisions are being shaped earlier in the funnel, and how to build adaptive strategies that secure brand presence across an expanding digital ecosystem.
The Disruption At Hand
The launch of ChatGPT marked a tipping point for digital marketing. Within months, generative AI became a mainstream tool, offering users new ways to answer questions, evaluate products, and plan decisions.
Gartner predicts that traditional search engine volumes will fall by roughly 25% as users increasingly turn to AI-powered platforms and assistants. While a 25% decline in a base as large as Google’s is still measured in trillions of searches, the shift is enough to destabilize established traffic models.
This does not spell the end of SEO. Instead, it signals a transformation of the internet itself. The way users seek and consume information is changing at the same pace as the technologies that enable it.
Why Visibility Is Changing Shape
Technology Drivers
Search is no longer confined to a search box. Google has introduced Circle to Search, Lens, AI Overviews and AI Mode. Perplexity and ChatGPT are establishing themselves as discovery platforms. Each of these represents a new entry point for user journeys, many of which bypass the traditional search results page altogether.
User Drivers
Younger demographics are accelerating the shift. At Google’s Search Central Live event in Bangkok, new data showed that Gen Z is not abandoning Google entirely in favor of TikTok or other alternatives, as commonly assumed. Instead, they are adopting AI-enabled features inside Google at a higher rate than any other age group. 1 in 10 Gen Z searches already begins with Circle or Lens, and one in five of those searches are commercial in nature.
The implication is clear: The next generation of consumers is interacting with the internet in ways that blend image recognition, voice, video, and AI assistance. Traditional keyword-driven search journeys are being replaced by multimodal, non-linear exploration.
AI intermediaries such as ChatGPT, Perplexity, or Google’s AI Overviews are now summarizing, curating, and interpreting information before users ever reach a brand-owned website. In many cases, research and decision-making occur entirely within these intermediaries.
At the same time, peer-generated content plays an outsized role. Reddit threads, product comparison lists, and third-party case studies are being pulled into AI-generated responses.
This ecosystem expands the number of sources that shape perception while reducing the likelihood that users visit a brand directly.
The result is a “dark funnel.” Purchase decisions are being made through fragmented, often opaque pathways that evade traditional tracking tools. For leaders, this means brand influence must extend beyond owned assets to encompass the broader ecosystem where AI models source their information.
Rethinking Organic Success Metrics
For nearly two decades, SEO success was measured through a narrow set of metrics such as keyword rankings, organic traffic, and click-through rates. In the AI-driven search environment, those measures are no longer sufficient.
Three shifts stand out:
Cross-Channel Lift: SEO is often the first point of exposure, even if it does not capture the last click. Google Analytics 4 now makes it possible to measure this by analyzing how many users first encounter a brand through organic search before returning directly, via social, or through paid channels. This reframes SEO as a driver of brand lift across the marketing mix.
Visibility In AI-Generated Citations: Being referenced in AI summaries does not always translate into immediate clicks, but it does influence perception and consideration. Success must account for brand presence within these outputs, even when user journeys bypass the website.
Topic-Level Visibility: AI search retrieves information at a thematic level rather than matching individual keywords. Tracking topic visibility, breadth of coverage, and the quality of source material is becoming more valuable than measuring a single keyword position.
Traditional measures such as “average position” in Google Search Console are increasingly unreliable. AI citations are often recorded as position one, regardless of context, creating a distorted picture of performance.
Strategic Imperatives For Leaders
The changes unfolding in AI-driven search are structural, not cyclical. Leaders cannot treat them as temporary turbulence. Instead, the task is to create resilience and adaptability in marketing organizations by pursuing five imperatives:
1. Audit AI-Driven Traffic And Visibility
Leaders must first establish a baseline of how AI is already affecting their businesses. While AI referrals are still a small share of overall traffic, they represent an emerging channel with unique characteristics.
Practical Step: Use GA4 or Looker Studio to segment traffic from platforms such as ChatGPT, Gemini, and Copilot. These sources typically appear under “referral” in analytics, but regex filters can separate them cleanly.
Why It Matters: Treating AI traffic as a distinct channel allows organizations to analyze landing pages, conversions, and revenue, rather than dismissing it as “miscellaneous.”
Leadership Lens: Framing AI traffic as a channel elevates its importance in boardroom discussions and positions the organization to justify future investments in tooling, content, or partnerships.
2. Track The Market, Not Just Internal Performance
A common misinterpretation is to view every decline in traffic as a failure of execution. In reality, shrinking demand in traditional search is often the root cause.
Practical Step: Compare organic and paid impressions for the same set of keywords. If both decline, the issue is demand-side, not execution-side. Layer this with Google Trends to visualize whether volumes are falling market-wide.
Why It Matters: This approach reframes the narrative from “our SEO team is underperforming” to “our market is shifting.” This distinction is crucial for maintaining stakeholder confidence.
Leadership Lens: CMOs who can separate market-driven shifts from operational gaps will have sharper conversations with the C-suite about resource allocation and risk.
3. Invest In Top-Of-Funnel Presence Across The Ecosystem
AI models increasingly draw from third-party sites, reviews, and community forums when generating responses. This widens the playing field for visibility beyond a brand’s own domain.
Practical Step: Build a program to secure mentions in authoritative third-party contexts such as industry directories, product comparison lists, peer forums, and niche communities.
Why It Matters: Being present in these external ecosystems ensures that when AI models summarize options, your brand is more likely to appear in the conversation even if the user never reaches your website.
Example: For a travel brand, this might mean appearing not only in “best hotel” lists on major sites, but also in Reddit threads, YouTube reviews, and AI-cited blogs.
Leadership Lens: Leaders must expand their definition of SEO from domain optimization to ecosystem visibility. This is not an incremental task but a fundamental shift in scope.
4. Rethink The Funnel And Customer Journey
The traditional linear funnel is breaking apart. Users now move through fragmented journeys that blend passive discovery (social, video, peer reviews) with AI-assisted evaluation.
Practical Step: Map how AI intermediaries are reshaping specific stages of your funnel. Identify which queries are being absorbed into AI summaries and where direct interaction with your brand is reduced.
Why It Matters: In some cases, entire query categories may be “lost” to AI intermediaries. Recognizing these blind spots early allows marketers to find alternative pathways such as social amplification, partnerships, or paid distribution.
Example: A B2B software vendor may find that “best CRM for mid-size companies” is increasingly answered by AI summaries citing analyst reports and third-party reviews. To remain visible, the vendor must prioritize those external references rather than relying solely on owned content.
Leadership Lens: CMOs must lead organizations to think less about protecting a single funnel and more about orchestrating presence across a patchwork of fragmented pathways.
5. Measure Indirect Value And Cross-Channel Lift
SEO has always influenced channels beyond the last click, but AI disruption makes quantifying that influence more important than ever.
Practical Step: Use GA4’s Explore feature to track first-touch organic sessions that later convert through direct, social, or paid channels. Create custom segments that isolate cross-channel lift.
Why It Matters: This evidence shows how SEO fuels the broader marketing mix, even if conversions are attributed elsewhere. It strengthens the business case for continued investment in visibility.
Example: A retailer may find that 40% of “direct” purchases were first initiated by an organic search session weeks earlier. Without quantifying this, the value of SEO would be understated.
Leadership Lens: Demonstrating indirect value reframes SEO from a cost center to a growth driver, positioning CMOs to argue for resources with greater authority.
Closing Note On Execution
These imperatives are not one-time actions. They are ongoing disciplines that must evolve alongside user behavior and technological change. Leaders who embed them into their operating rhythm will be better prepared to adapt strategies, justify investments, and maintain visibility in an AI-led digital economy.
The Leadership Agenda
Understand Your Risk Exposure
Your audience determines your level of risk. Organizations serving younger, consumer-facing segments are already seeing accelerated adoption of AI search tools. For B2B businesses with locked-down environments, the shift may be slower, but it is coming.
Scrutinize Vendor Claims
Acronyms proliferate in times of disruption. What matters is not whether a vendor calls their practice SEO, GEO, or another label, but whether they can demonstrate measurable strategies for sustaining visibility in AI-led ecosystems.
Be Ready To Be Agile
A 12-month static plan is no longer viable. AI search strategies must be adaptive, continuously informed by data, and responsive to new entrants and technologies.
Visibility Beyond Search Requires New Metrics
SEO is not dead. It is evolving into a broader discipline of experience visibility, where brand presence must extend across AI models, multimodal search tools, and fragmented user journeys.
For leaders, the challenge is not to hold onto the old metrics or frameworks, but to recognize how the internet is reshaping itself and to understand we’re starting to tread new ground, and with new ground comes uncertainty and risk.
Those who measure differently, broaden their presence, and align with user-driven change will not only withstand the disruption but also secure competitive advantage in the AI-led future.
Yoast announced the release of their Brand Insights tool, which helps track and monitor brand sentiment and visibility in AI platforms like ChatGPT. The new tool, currently in beta, is a new direction for Yoast because it’s not a plugin and doesn’t need CMS access. The complete tool is called Yoast SEO AI+.
The tool offers sentiment-tracking analysis by keywords, competitor rank benchmarking, citation analysis, and the ability to monitor specific brand questions.
The citation analysis is interesting because it tracks brand mentions. The sentiment analysis is also useful because it shows a graph based on keywords broken down by positive and negative sentiment.
Niko Körner, Senior Director of Product at Yoast explained:
“With Yoast AI Brand Insights, our customers can not only track their brand’s visibility, sentiment, and credibility in AI platforms like ChatGPT, but also see how they compare against the competition. As AI answers become a new starting point for customer journeys, this competitive perspective is crucial to staying ahead.
We worked hard to create a simplified KPI that truly reflects brand performance in the age of AI. Our AI Visibility Index combines sentiment, rank in LLM answers, brand mentions, and citations into one clear metric.
Soon, we will also be launching actionable recommendations to help businesses improve their AI visibility. This launch is only the beginning, and we are already working on improvements and expanding support for more large language models.”
The new Yoast tool is modestly priced, a sign that Yoast is focusing on providing SEO tools for SMBs who are interested in getting ahead in AI search.
OpenAI released the largest study to date on how users really use ChatGPT. I have painstakingly synthesized the ones you and I should pay heed to, so you don’t have to wade through the plethora of useful and pointless insights.
Asking (49%) and Doing (40%) queries dominate the market and are increasing in quality.
The top three use cases – Practical Guidance, Seeking Information, and Writing – account for 80% of all conversations.
Publishers need to build linkable assets that add value. It can’t just be about chasing traffic from articles anymore.
Image Credit: Harry Clarkson-Bennett
Chatbot 101
A chatbot is a statistical model trained to generate a text response given some text input. Monkey see, monkey do.
The more advanced chatbots have a two or more-stage training process. In stage one (less colloquially known as “pre-training”), LLMs are trained to predict the next word in a string.
Like the world’s best accountant, they are both predictable and boring. And that’s not necessarily a bad thing. I want my chefs fat, my pilots sober, and my money men so boring they’re next in line to lead the Green Party.
Stage two is where things get a little fancier. In the “post-training” phase, models are trained to generate “quality” responses to a prompt. They are fine-tuned on different strategies, like reinforcement learning, to help grade responses.
Over time, the LLMs, like Pavlov’s dog, are either rewarded or reprimanded based on the quality of their responses.
In phase one, the model “understands” (definitely in inverted commas) a latent representation of the world. In phase two, its knowledge is honed to generate the best quality response.
Without temperature settings, LLMs will generate exactly the same response time after time, as long as the training process is the same.
Higher temperatures (closer to 1.0) increase randomness and creativity. Lower temperatures (closer to 0) make the model(s) far more predictive and precise.
So, your use case determines the appropriate temperature settings. Coding should be set closer to zero. Creative, more content-focused tasks should be closer to one.
That LLMs are not a direct replacement for search. Not even that close IMO. This Semrush study highlighted that LLM super users increased the amount of traditional searches they were doing. The expansion theory seems to hold true.
But they have brought on a fundamental shift in how people access and interact with information. Conversational interfaces have incredible value. Particularly in a workplace format.
Who knew we were so lazy?
1. Guidance, Seeking Information, And Writing Dominate
These top three use cases account for 80% of all human-robot conversations. Practical guidance, seeking information, and please help me write something bland and lacking any kind of passion or insight, wondrous robot.
I will concede that the majority of Writing queries are for editing existing work. Still. If I read something written by AI, I will feel duped. And deception is not an attractive quality.
2. Non-Work-Related Usage Is Increasing
Non-work-related messages grew from 53% of all usage to more than 70% by July 2025.
LLMs have become habitual. Particularly when it comes to helping us make the right decisions. Both in and out of work.
3. Writing Is The Most Common Workplace Application
Writing is the most common work use case, accounting for 40% of work-related messages on average in June 2025.
About two-thirds of all Writing messages are requests to modify existing user text rather than create new text from scratch.
I know enough people that just use LLMs to help them write better emails. I almost feel sorry for the tech bros that the primary use cases for these tools are so lacking in creativity.
4. Less So Coding
Computer coding queries are a relatively small share, at only 4.2% of all messages.*
This feels very counterintuitive, but specialist bots like Claude or tools like Lovable are better alternatives.
This is a point of note. Specialist LLM usage will grow and will likely dominate specific industries because they will be able to develop better quality outputs. The specialized stage two style training makes for a far superior product.
It’s important to note that other studies have some very different takes on what people use LLMs for. So this isn’t as cut and dry as we think. I’m sure things will continue to change.
5. Men No Longer Dominate
Early adopters were disproportionately male (around 80% with typically masculine names).
That number declined to 48% by June 2025, with active users now slightly more likely to have typically feminine names.
Sure, us men have our flaws. Throughout history maybe we’ve been a tad quick to battle and a little dominating. But good to see parity.
89% of all queries are Asking and Doing related.
49% Asking and 40% Doing, with just 11% for Expressing.
Asking messages have grown faster than Doing messages over the last year, and are rated higher quality.
A ChatGPT-built table with examples of each query type – Asking, Doing, and Expressing (Image Credit: Harry Clarkson-Bennett)
7. Relationships And Personal Reflection Are Not Prominent
There have been a number of studies that state that LLMs have become personal therapists for people (see above).
However, relationships and personal reflection only account for 1.9% of total messages according to OpenAI.
8. The Bloody Youth (*Shakes Fist*)
Takeaways
I don’t think LLMs are a disaster for publishers. Sure, they don’t send any referral traffic and have started to remove citations outside of paid users (classic). But none of these tech-heads are going to give us anything.
It’s a race to the moon, and we’re the dog they sent on the test flight.
Even simple tools can still drive fantastic traffic and revenue (Image Credit: Harry Clarkson-Bennett)
SEO isn’t dead for publishers. But we do need to do more than just keep publishing content. There’s a lot to be said for espousing the values of AI, while keeping it at arm’s length.
Think BBC Verify. Content that can’t be synthesized by machines because it adds so much value. Tools and linkable assets. Real opinions from experts pushed to the fore.
But it’s hard to scale that quality. Programmatic SEO can drive amazing value. As can tools. Tools that answer users’ “Doing” queries time after time. We have to build things that add value outside of the existing corpus.
And if your audience is generally younger and more trusting, you’re going to have to lean into this more.
New research from BrightEdge offers insights into how Google’s AI Overviews ranks websites across different verticals, with implications for what SEOs and publishers should be focusing on.
AIO And Organic Search
The data shows that 54% of the AI Overviews citations matched the web pages ranked in the organic search results. This means that 46% of citations do not overlap with organic search results. Could this be an artifact of Google’s FastSearch algorithm?
Google’s FastSearch is based on ranking signals generated by the RankEmbed deep-learning model that is trained on search logs and third-party quality raters. The search logs consist of user behavior data, what Google terms “click and query data.” Click data teaches the RankEmbed model about what users mean when they search.
Click behavior is feedback about queries and relevant documents, similar to how the ratings submitted by the quality raters teach RankEmbed about quality. User clicks are a behavioral signal of which documents are relevant. So, as a hypothetical example, if people who search for “How to” tend to click on videos and tutorials, this teaches the model that videos and tutorials tend to satisfy those kinds of queries. RankEmbed “learns” that documents that are semantically similar to a tutorial are good matches for that kind of query. The models aren’t learning in a human sense; they are identifying patterns in the click data.
This doesn’t mean that the 54% of AIO-ranked sites are there because of traditional ranking factors. It could be that the FastSearch algorithm retrieves results that are similar to the regular search results 54% of the time.
Insight About Ranking Factors
BrightEdge’s data could be reflecting the complexity of Google’s FastSearch algorithm, which prioritizes speed and semantic matching of queries to documents without the use of traditional ranking signals like links. This is something that SEOs and publishers should stop and consider because it highlights the importance of content and also the importance of matching the type of content that users prefer to see.
So, if they’re querying about a product, they don’t expect to see a page with an essay about the product; they expect to see a page with the product.
Organic And AIO Overlap Evolved Over Time
When AIO launched, there was only about a 32% overlap between AIO and the classic organic search results. BrightEdge’s data shows that the overlap has grown over the sixteen months between the debut of AI Overviews and today.
Organic And AIO Match Depends On The Vertical
The 54/46 percentage split isn’t across the board. The percentage of AIO-ranked sites that match the organic search results varies according to the vertical.
Your Money Or Your Life (YMYL) content showed a higher rate of overlap between organic and AIO.
BrightEdge’s data shows:
Healthcare has a strong overlap: 75.3% overlap (began at 63.3%).
Education overlap has increased significantly: 72.6% overlap between organic and AIO, showing +53.2 percentage points growth, from 19.4% to 72.6%.
Insurance also experienced increased overlap: 68.6%. That’s a +47.7 percentage points growth from the 20.9% overlap when AIO was first introduced.
E-commerce has very little overlap with the organic search results: 22.9% overlap (only +0.6 percentage points change).
I’m going to speculate here and say that Healthcare, Education, and Insurance search results may have a strong overlap because the pool of authoritative sites that users expect to see may be smaller. This may mean that websites in these verticals may have to work hard to be the kind of site that users expect to see. A broad and simplified explanation is that FastSearch does not use traditional organic search ranking factors. It’s ranking the kinds of web pages that match user expectations, meet certain quality standards, and are semantically relevant to the query.
What Is Going On With E-Commerce?
E-commerce is the one area where overlap between organic and AIO remained relatively steady with very little change. BrightEdge notes that AIO coverage actually decreased by 7.6%. AIO may be a good fit for research but is not a good format for users who are ready to make a purchase.
Final Takeaways
Although BrightEdge recommends focusing on traditional SEO for sites in verticals that have over 60% of overlap with organic search, it’s a good idea for all sites, regardless of vertical, to focus on traditional SEO and also to focus on precision, matching user expectations for each query, and pay attention to what users are saying so as to be able to react swiftly to changing trends.
BrightEdge offers the following advice:
“Step 1: Identify Your Overlap Profile Measure what percentage of your AI Overview citations also rank organically and benchmark against the 54% average to understand where you stand.
Step 2: Match Strategy to Intent. High overlap (>60%) means focus on SEO; low overlap (<30%>
Step 3: Monitor the Convergence Track your overlap percentage monthly as it has grown +22% industry-wide in 16 months, watching for shifts like September 2024’s +5.4% jump.”
Scot Wingo observed consumer shopping journeys while running ChannelAdvisor, the marketplace management firm he started in 2001. He says consumers approach the process in three stages: researching the market, finding suitable products, and buying the right item.
The acronym — ReFiBuy — is the name of his latest company. It’s a generative engine optimization platform for retailers and brands.
By any measure, Scot is an ecommerce pioneer. We first interviewed him in 2006, when he introduced us to marketplace selling.
Last week, I asked him about ReFiBuy. The entire audio of our conversation is embedded below. The transcript is edited for length and clarity.
Kerry Murdock: Tell us about your ecommerce journey.
Scot Wingo: It began in 1999 when I launched Auction Rover, an auction search engine. We sold it to GoTo.com, which became Overture, the company that invented paid search. The auction search engine wasn’t great after the dot-com bubble burst. But we had built the selling tools, which became ChannelAdvisor, which I launched in 2001.
Murdock: What is ReFiBuy, your new venture?
Wingo: The idea started with my experience at ChannelAdvisor. The company went public in 2013, and I was still CEO and founder. By 2015, running a public company had become a drag.
I resigned from the CEO role but stayed on the board. ChannelAdvisor was ultimately taken private by a private equity firm, which merged it with Commerce Hub. It’s now called Rithum.
In 2015 I launched an on-demand car care company called Spiffy. Then, in August of 2024, I decided to start what is now ReFiBuy. I wanted to do something in the AI world. I have a technical degree, and as a technologist, I thought AI would create much disruption, which creates opportunity.
So I was poking around, learning more about it. And then, in December 2024, Anthropic, the makers of Claude, published a paper on “agentic” AI that can perform tasks. Prior to that, large language models were read-only. The agentic component meant they could do things.
And that reminded me of a problem we had at ChannelAdvisor. Our clients were retailers and brands with large product catalogs. The issue for us was the absence of an industry standard for electronically storing and sharing the product info, such as specs, colors, dimensions, and weight.
Clients would send us a file of their product catalog in a disorganized mess. Yet we had a hundred marketplaces that wanted to receive beautiful, clean catalog files. So our job became catalog cleaners, to convert clients’ inventory files into a format acceptable to those external channels. Again, there was no industry standard.
We came up with algorithms for cleaning the catalog that worked only half the time. The other half required humans. Eventually, when we had 300 people in Bulgaria working on it, serving our 3,000 customers and 15 billion annual transactions.
That memory was my light bulb moment for agentic AI. Could we solve the product catalog problem for LLMs? We started working on it late last year.
Simultaneously, Perplexity introduced what we now call agentic commerce, or agentic shopping, where you can not only research products but also buy them.
That’s the inspiration for our name. ReFiBuy is “research, find, buy.” It’s the shopper’s journey.
We launched our Commerce Intelligence Engine last week. It ensures that the LLMs — Perplexity, Claude, ChatGPT, and others — have accurate, current, and comprehensive product catalog data from our clients, which are retailers and brands.
Murdock: How do you do that — organize the data and then ensure the LLMs digest it?
Wingo: We start with the product catalog. We take a traditional Google Shopping feed or even data from a merchant’s ecommerce site. We analyze it through the lens of an LLM, which helps us identify missing or incorrect components. We then recommend changes, fixes, and additions. LLMs want every piece of content that ties products to the context of prompts. That includes Schema.org markup, Reddit discussions, prompt history — much more than product data alone.
That’s our evaluation phase. Then we help our clients whitelist the right bots to crawl their sites. Most retailers and brands block all bots except for Google. Certainly there are good reasons to do that, as many bots are malicious or from competitors.
So we help merchants know which LLM bots to allow.
Murdock: How do you know that an LLM receives and stores your optimized data?
Wingo: We monitor product cards, the visual representations by LLMs of recommended products. We run thousands of prompts daily across all the LLM engines to ensure our clients’ products appear in those cards and that the data is accurate.
Our AI agents evaluate the cards and classify them into buckets. If our client owns the product card, our job is done. We have achieved Nirvana for that SKU. If our client’s item appears in a card of another merchant, there are 20 to 30 things that have likely gone wrong. Our AI agents detect it. Sometimes it’s as simple as a missing slash or an extra space in the file.
The agent also detects missing SKUs — when our clients’ goods don’t appear in the cards at all. That’s usually caused by an infrastructure problem with the crawler, or something is broken on the merchant’s site.
We keep cranking the process until we’ve optimized our clients’ entire catalog.
Murdock: What is the cost of ReFiBuy?
Wingo: It depends on the number of SKUs. We start at roughly $2,000 per month — $20,000 to $25,000 per year.
