As someone deeply invested in improving my SEO processes, I’ve discovered an innovative way to transform my workflows using AI-powered tools that adapt to my unique methods.
By leveraging platforms like ChatGPT and Google’s Gemini, I can get standard on-page SEO reviews. However, these initial responses often feel generic and devoid of specific context related to my business needs.
This generic nature of AI is both its limitation and its potential opportunity. While out-of-the-box AI provides broad solutions, it lacks the personalization that comes from my own business insights.
Fortunately, tools like GPTs, Gems, and Claude Projects allow me to embed my SEO process into custom assistants, making the complex seem straightforward without needing complex coding skills.
I’ve also learned that large language models predict responses from a vast array of internet data, often resulting in average opinions rather than tailored advice for my business specifics.
In SEO, these broad opinions typically revolve around general content improvements and link building, which might not address the unique challenges I face.
What I needed was a tool that factored in my business’s unique landscape, including customer needs and competitive environment. That’s where the personalization of AI tools comes into play.
Contextualizing inputs to AI tools transforms them into powerful assistants that enhance my specific workflow, making it less about generic data and more about strategic insights.
The process of creating a customized AI tool is more about narrating my workflows rather than needing a deep technical background. Tools like GPTs and Gems have become essential as I package my expertise into reusable, intelligent assistants.
Among the various AI platforms, I find GPTs, Gems, and Claude Projects especially user-friendly for most of my SEO tasks. These platforms are intuitive, allowing even non-developers like me to transform repetitive tasks into automated, efficient processes.
However, generic SEO tools, despite their widespread use, don’t pay attention to my company’s unique strategic priorities, unlike the AI applications I’ve tailored to fit my specific needs.
Moreover, crafting personalized AI apps not only aids in SEO but also transforms how I manage and execute marketing strategies, encompassing tasks like keyword research and content strategy more effectively.
My takeaway is that the true value lies not in AI itself but in the expertise I embed into it. My hard-earned industry skills are the real product, and AI simply empowers me to scale my efforts more efficiently.
It’s been enlightening to see how enhancing my AI tools with my knowledge improves productivity, ultimately strengthening my business impact. This process of encoding my SEO knowledge into AI-propelled systems is groundbreaking and transformative.
I’m excited to share how Adobe’s latest tool is changing the game for businesses eager to boost their brand visibility in AI-driven searches.
With the backing of 300 million AI prompts and the comprehensive data of Semrush, this platform is adept at tracking mentions, gauging share of voice, and identifying content gaps across prominent AI platforms.
Adobe introduced a pioneering solution for brands aiming to bolster their visibility and trustworthiness across AI interfaces. As part of the Adobe CX Enterprise, this tool offers an agentic AI system to streamline customer lifecycle management, covering everything from initial acquisition to fostering long-term loyalty.
AI traffic is skyrocketing. The way LLMs are utilized for product and service research represents a major pivot for both marketers and consumers. Recently, Adobe revealed data underlining this massive surge in AI traffic to U.S. retail sites—up by an impressive 1,324% from October 2024 to May 2026. The travel industry saw an even greater increase of 2,215% in the same timeframe.
As Vice President of strategy and product, Loni Stark, remarked to MarTech, “We used to get back the same thing—a SERP page with links. Now results seem random, but aren’t when scaled, and companies lack tools for this.”
Understanding brand visibility in AI search. Adobe Brand Visibility marks Adobe’s first venture into generative engine optimization (GEO), following its acquisition of Semrush. By integrating Adobe LLM Optimizer with Semrush’s AI Optimization tool, it provides unmatched insights.
Drawing from a staggering database of 300 million real-world AI search prompts, Adobe Brand Visibility helps teams pinpoint which prompts lead to brand exposure or loss.
Additionally, utilizing Adobe’s first-party data from owned channels, marketers gain a holistic view of how their brands appear on platforms like ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics encompass mention frequency, reach, competitive share of voice, and content gaps, allowing AI agents to offer prioritized recommendations that teams can rapidly implement and evaluate results.
Competitive intelligence unleashed. Adobe Brand Visibility offers tools for competitive brand analysis, comparison, and trend tracking, enabling marketers to effectively benchmark against competitors.
Featuring advanced SEO intelligence driven by Semrush’s extensive data of 28.5 billion keywords and 43 trillion backlinks, this platform underscores the continued importance of SEO fundamentals for AI search visibility. It shows the potential for existing search authority to yield AI citations and identifies opportunities for content investments across channels.
While there’s still much to learn about leveraging LLMs for brand visibility, Stark is confident in Adobe’s leadership position in this emerging space.
As Stark stated, “Adobe had proprietary data while Semrush offered data and trends. Though we may not have all answers, we possess unrivaled data.”
Recently, I’ve been delving into an intriguing study by Lily Ray, which reveals some unexpected findings about Google’s AI Overviews. Apparently, these Overviews frequently reference brands’ own listicles but tend to recommend their competitors.
The study highlighted that Google AI Overviews cited these self-promotional listicles in a whopping 69% of B2B software-related queries. Yet, they favored rival brands in their recommendations. This got me thinking about the strategies brands employ to influence AI search outcomes.
Detailed Findings. I discovered that the analysis was quite comprehensive. Ray reviewed 100 B2B queries spanning categories like “best [category] software.” She gathered data across three specific periods: April 15, May 15, and June 8.
The study found that out of 80 queries that triggered an AI Overview, self-serving listicles were referenced 323 times, yet in 224 instances, Google didn’t actually recommend those brands. This mismatch intrigued me.
Analysis of Recommendations. While examining specific cases, it became evident that Google sometimes cited a brand’s listicle but opted to recommend more renowned competitors instead. For instance, in the search for “best LMS for selling courses,” Oasis LMS was mentioned, yet Kajabi and others were pushed forward as the preferred options.
This pattern wasn’t just isolated to LMS software; it appeared in multiple domains like help desk tools, task management, and more. It made me ponder over the dominance of stronger brands in recommendations.
Observing Organic Declines. An interesting trend noted was a drop in organic visibility for websites heavily leaning on self-promotional listicles. I noticed beginnings of these declines back in January and observed further drops post-Google’s May 2026 core update.
Interestingly, these sites also seemed to have expanded into AI-generated content and other “best” pages prominently featuring their own brands.
Rise of Third-party Citations. Ray’s analysis also showed an upsurge in Google comprising third-party content for “best” queries. Platforms like Reddit, Forbes, and YouTube gained traction in citations.
Understanding Impact. I believe it’s crucial to realize that merely having your content cited doesn’t equate to a recommendation. This situation offers competitors the chance to snag attention and, ultimately, valuable visibility.
Keeping Up with Changes. Previously, Search Engine Land shared insights on how some SaaS and B2B businesses witnessed visibility losses after banking on self-ranked “best” lists. The risks are significant when company-driven content doesn’t transparently disclose material relationships as mandated by the FTC’s Consumer Review Rule.
About Ray’s Data. To reach her conclusions, Ray employed Ahrefs Brand Radar to examine numerous AI Overview responses. Her analysis spanned 100 B2B software queries, focusing on citations versus actual recommendations.
AI search is reshaping the marketing landscape faster than anything I’ve seen before.
During my time at Zero Click NY, I witnessed how significantly AI search has evolved over the last six months and identified emerging features that might define its future.
Among all the discussions, these seven trends were the most compelling.
From the emergence of marketing engineers, to the way Claude and ChatGPT differ in results, and Claude’s rapid ascent in the business world over the past year, here are the key insights I gathered.
1. Every AI relies on different content
According to Profound data, only 8% of citations are shared between ChatGPT and Claude. This means 92% of the sources that ChatGPT refers to would not be recognized by Claude for the same inquiry. Essentially, a brand may have high visibility in one AI and not exist in another.
Moreover, each AI favors different types of content.
ChatGPT frequently indexes community content: Reddit, Quora, and forums make up around 16% of its citations.
In contrast, Claude cites listicles 36% and opinion content 13.2% of the time, compared to ChatGPT’s ~20% and 7.2%, respectively.
The disparity also applies to traditional search. A significant 64% of websites Claude cites appear in Google’s top 50 for equivalent queries, whereas it’s only 37% with ChatGPT.
Takeaway: It’s vital to inform stakeholders that AI visibility differs between LLMs, and strategic prioritization is necessary to reach your audience.
Track your visibility by engine because effective strategies in one platform may not translate to another. UGC helps drive ChatGPT while listicles boost presence on Claude.
2. Claude is quietly winning B2B — so sequence your optimization by audience
Claude may appear insubstantial in AI traffic-share charts, but it’s a different story when it comes to enterprise usage.
Web traffic doesn’t tell the whole tale. Anthropic derives about 85% of its revenue from enterprise and API usage, not visible in consumer data.
A critical chart from Ramp’s AI Index reveals the true penetration of Anthropic in the business sector. A year ago, only a small number of businesses used Anthropic. Now, it’s at 34.4%, surpassing OpenAI at 32.3%.
This insight led me to reconsider: if more business users are engaging with Claude and consumers are on ChatGPT, shouldn’t our optimization priorities reflect audience preferences?
Should B2B entities focus on Claude first, while B2C aim for ChatGPT visibility?
Currently, few distinguish between ChatGPT, Gemini, or Claude usage. This distinction is bound to grow.
3. ChatGPT ads are here, and this is what we’re seeing
The game has changed: competitors are securing visibility through ChatGPT ads. These ads are now live and available for self-serve directly within the chat interface.
Recent weeks also saw the debut of GPT 5.5, citation chips morphing into clickable links (leading to a 60% spike in referral traffic overnight), and Google integrating AI Mode into its main search functionality.
This wasn’t incidental. The hyperlinks are crucial for an ads business. Analyzing over 100,000 ad placements highlighted three essential revelations.
ChatGPT Ads match on topic
Ads align with topic similarity, not intent. Only 14% of real user prompts express commercial intent, yet 20% show ads, even if the prompt involves a math problem.
Embedding analysis indicates that ad titles and descriptions significantly influence which conversations you appear in, transforming them into tactical targeting tools.
Paying for ads
We have entered a “pay-to-play” era. Approximately one-fifth of ad placements appear when a direct competitor is mentioned, but only 8% of organic references belong to the associated brand.
Competitors are twice as likely to advertise around your brand’s organic mentions than you are.
For instance, Startup CRM Adia is targeting prompts mentioning Salesforce, with Salesforce responding by showing paid ads 40% of the time, defending their position even when organically mentioned.
Ad inventory is scarce and expensive
Currently, ChatGPT presents about one ad per conversation, with the median exchange spanning three turns. Only 30% of eligible users ever see ads, and CPMs/CPCs are about four times Meta’s rates.
Expect future changes: additional ad slots per reply, ads woven deeper into conversations, and engineered suggestions to prolong interactions, ultimately increasing inventory.
The insight: Understanding both organic AEO and paid defense strategies is essential. Monitoring your brand’s organic citations without tracking who advertises against them offers a partial view.
4. Claude is the most directly optimizable AI right now
Claude sources web content directly from Brave searches, not merely influenced by it, as discussed in the presentation I attended.
In recent testing by Profound, 79.2% of Claude’s citations were directly aligned with Brave’s top 10 search results for equivalent queries.
Reshuffling is minimal; no other AI model trusts its search provider so extensively.
This transparency makes Claude the most straightforward AI to optimize for: a visible index, checkable rankings, and, as we’ll explore next, predictable retrieval.
If I’ve convinced you of the importance of Claude for B2B, here’s your approach: identify where you stand on Brave for key prompts and use that as your roadmap for Claude visibility.
This level of transparency won’t last forever. Take advantage now while it’s possible.
5. Claude only performs web searches a third of the time
There’s a significant caveat: ChatGPT initiates web searches for nearly 95% of prompts, but Claude does so only a third of the time, likely due to cost ($5 per thousand searches via Brave’s API).
You can optimize Claude effectively only when it conducts a search.
The encouraging part is its predictable search habits. Prompts framed around recent events (“best X in 2026”) initiate searches about 81% of the time.
Ranking-related prompts lead to 67% search initiation, location-specific prompts 55%, and comparisons 51%.
Prompts concerning definitions and procedures rarely trigger searches, making them poor targets for Claude optimization.
The lesson: Before investing to enhance Claude visibility for a prompt category, determine if Claude actually conducts searches for it.
Focus on recent events, rankings, locations, and comparisons for effective Claude optimization using Brave rankings as a guide.
Other areas rely on internal memory beyond our reach.
6. Query fan-out: A raffle on one platform, near-deterministic on another
Two speakers offered perspectives on query fan-out, presenting a contrast worth exploring.
Query fan-out entails background synthetic queries to collect content prior to providing an AI-generated response.
Mike King of iPullRank viewed it as a raffle: The task is to gain more tickets through a wider content range across owned, earned, and shared channels, and the right content formats make all the difference.
Even if you rank for a fanned-out query, the wrong format renders you ineligible.
According to his research, content-to-query cosine similarity and information gain strongly correlate with success in AI search.
Conversely, Josh Blyskal from Profound notes that Claude’s fan-outs are highly predictable; the same prompt results in consistent fan-out strings 65% of the time. Interestingly, 94% of Claude’s fan-outs are current-year stamped, unlike ChatGPT’s 17%
Where ChatGPT’s fan-outs constantly evolve, Claude’s remain relatively stable. Thus, both perspectives may hold true for different engines.
With stable fan-outs like in Claude, content creation can directly focus on them. The year-stamping trend suggests using the current year in titles.
For volatile fan-outs as in ChatGPT, King’s approach applies: maximize exposure through format variety.
One mechanism demands two strategies, tailored by engine, potentially requiring prioritization between them.
7. The marketing engineer is here, and agents are the new workforce
The role of a “marketing engineer” might sound like a buzzword, but the hiring trends prove otherwise.
Google’s recently hired its first marketing engineer, Figma has an opening at a $295,000 salary, and both RBC and Autodesk have placed hires.
It’s a rapidly growing search term, and Google’s AI marketing lead dubbed it “the hire for 2026.”
What makes someone ideal for this role? Is the priority given to an engineer learning marketing or vice-versa?
The emerging profile emphasizes marketing experiences such as someone with channel expertise who builds and runs AI systems, reports to the marketing head, and supports the team by removing obstacles. They are marketers advancing the state-of-the-art.
The underlying concept is that marketing functions decompose into pipelines: data extraction, transformations, and loading into useful formats. Agents can now automate these pipelines.
Monitoring competitor pricing and auto-generating sales content.
Scheduling and assessing AEO presence and landing page efficiency.
Analyzing sales call objections and drafting relevant content solutions.
What previously were backlogged tasks now become brief agent-building exercises. Creativity replaces headcount as the limiting factor.
If marketing engineering isn’t a role in your team yet, it’s likely only a matter of time before it is.
The job now: Figuring out how this all works
There remains no definitive roadmap for AI search. When a guidebook emerges, the key step will be prioritizing one LLM contingent upon who you wish to reach.
In many instances, that “who” will now be agents, simultaneously assisting us in our endeavors and highlighting the rising need for professionals adept at engineering such systems.
I’ve noticed a fascinating trend recently: AI referrals to U.S. travel sites have surged significantly in May. According to Adobe, travelers coming from AI sources tend to spend more time on these sites and are less likely to leave immediately compared to those from traditional referral sources.
By the numbers: This remarkable growth is backed by data showing a 194% increase in AI-driven traffic year-over-year for May 2026. Since Adobe started monitoring AI traffic in October 2024, there’s been an astounding 2,215% rise.
AI-assisted travel planning has moved beyond initial stages. Now, it’s common for travelers to utilize large language models for comparing destinations, examining hotel features, creating itineraries, discovering promotions, and making bookings.
AI visitors showed stronger engagement: Although AI-referred visitors currently convert 28% less than non-AI visitors, the gap is closing. Adobe reports that the difference has narrowed by nearly 70% since October 2024.
Engagement metrics reveal that AI-referred travelers are 21% more engaged than their non-AI counterparts, spending 70% more time per visit and having a 41% lower bounce rate.
Adobe suggests that such patterns indicate more deliberate and high-intent behavior, even though AI-referred traffic still lags slightly in conversion rates.
Travel pages and AI readability: Adobe has also been assessing the readability of travel websites by AI systems. They developed an AI Content Visibility Checker to evaluate how much page content AI can process.
Within the travel sector, hotels and car rentals are ahead. Hotel homepages scored 63% readability, while car rental homepages reached 59%. Individual product pages performed even better, with hotels at 73% and car rentals at 71%.
Nonetheless, Adobe reports that over a third of content on leading travel pages is still unreadable by AI systems.
Where travel sites scored best: Hotels seem to excel in several page categories, including destination guides, activity pages, search results, customer service, and promotions.
Car rentals excelled on FAQ pages, while cruises led in blogs and news content. Conversely, airlines lagged behind other major travel sectors across all page types analyzed by Adobe.
This trend illustrates how well-structured, information-rich pages allow AI systems to better interpret content, thanks to detailed property descriptions, amenities, and core offerings.
Retail’s conversion advantage: AI-driven traffic to U.S. retail sites also set a new record in May, surging 138% year-over-year and an impressive 1,324% since October 2024.
Unlike in the travel sector, AI-referred retail visitors had a 54% higher conversion rate than non-AI traffic, overturning last year’s trend where AI conversion rates were nearly half.
Cosmetics and electronics shine in retail readability due to detailed content like ingredient lists, tutorials, product specs, and how-to guides, while grocery and furniture lagged.
Why we care: Adobe’s insights suggest AI referrals are increasingly valuable commercially, particularly in retail. However, many sites miss the mark by having significant content inaccessible to AI systems. If key content is hidden, poorly structured, or blocked, you could lose visibility before users reach your site.
About the data: Adobe’s research draws on over 8 million visits to U.S. travel sites, over 1 trillion visits to U.S. retail sites, and more than 100 million SKUs. Additionally, they surveyed more than 5,000 U.S. consumers in March regarding their use of AI in shopping and travel planning.
For a long time, “ultimate guides” were my go-to for SEO dominance. They were carefully crafted to meet Google’s algorithm standards for high-value content.
Incorporating the “skyscraper technique” further solidified the idea that length equates to depth.
Yet, as the web evolved, so did search intent. Users’ desire for quick answers and AI’s rise diminished the importance of lengthy content. Google’s system now frowns upon content that offers zero informational gain.
So, what are my next steps?
Extractability is the new content challenge, affecting every stage from briefing to publication.
AI platforms like Gemini limit approximately 380 words for query grounding, making it crucial for me to adapt.
The extraction data reveals:
Pages under 5,000 characters: 66% AI extraction rate.
Pages over 20,000 characters: 12% AI extraction rate.
The once high-traffic “ultimate guides” now stand in the way of effective AI visibility.
What steps into this void is a new, challenging form of content—where every sentence must pull its own weight by clearly stating entities, relationships, conditions, or citable claims.
The “padlock principle” is now my guide, turning search from keyword chasing to addressing specific problems for specific people. My content became more like solutions than broad categories.
For instance, a car insurance page now targets new drivers under 25, declined by standard insurers, turning from general to particular needs.
Breaking from tradition, each content piece now aims to solve a defined user problem. With AI’s impact on SEO, I’ve embraced strategic shifts to make my content more credible and logically structured.
Here are the three strategic rewrites I apply for effective problem-first positioning:
Replace categorical identity with problem identity
Before: “We are an insurance provider.”
After: “We solve the underwriting problem for first-time drivers under 25 who are declined by standard insurers.”
Rewrite titles as outcomes, not labels
Before: “Car Insurance | BrandName”
After: “Car insurance for new drivers under 25 declined by most providers”
Lean into constraints rather than suppressing them
Recognizing target limitations adds credibility to my service offerings, contrasting the generalized advice typically available for free.
The content landscape has radically shifted from information archives to pieces serving individual, extraction-friendly sentences. My approach leverages structured, meaning-rich content that AI systems can confidently source.
Building an LLM-friendly foundation involves familiarizing myself with semantic triples, because AI judges content with a retrieval efficiency that applies across various format types.
So, whether I’m crafting a blog or a product description, explicit headings signal relevance, boosting my content’s retrieval likelihood by 17.54%.
Adopting the citation-bait formula, I begin each paragraph with a direct declarative opening, followed by trimmed-down contextualization and structured evidence—ensuring the content is both extractable and engaging.
In pursuing content harmony between machine readability and human interest, I capitalize on the AI inverted pyramid approach. By positioning narrative transitions after structured answers, I balance AI efficiency with engaging storytelling.
Every part of my content creation—from heading formulation to section structuring—serves a dual purpose: making content AI-retrievable while nurturing human trust and engagement. I constantly refine this synergy, ensuring each piece of content wholly aligns with emerging AI standards.
Ultimately, I strive for a content strategy that doesn’t yet exist, one that will meet evolving needs by balancing the semantic precision AI demands with the rich narratives only human creativity can offer.
I realized that many web pages effectively address initial search queries, but often fall short when it comes to guiding the user toward their final decision. This is where the concept of next-question intent becomes crucial. It’s a tool that not only aids users but also aligns with AI systems for enhanced content utility and visibility.
In the world of GEO, much of the discussion revolves around how AI systems discover, extract, and suggest content. While these aspects are essential, I’ve learned that what truly determines visibility is the substantive content these systems find once they’ve reached my pages.
Next-question intent isn’t just about answering the initial query. It’s about whether my page provides enough depth for the user to take their next step, be it selecting a product or making a decision.
Often, a user’s first search is just a starting point. Key decisions hinge on follow-up questions and considerations that must be addressed.
By crafting content that anticipates these subsequent inquiries, I equip AI systems with rich materials to synthesize, compare, and recommend.
From Results to Narratives: Traditional Search vs. AI Search
Traditional search was once about offering a suite of links for users to peruse and decipher. Now, AI search focuses on delivering synthesized responses, pulling information from multiple sources.
This shift emphasizes the need for my content to provide comprehensive information that can help build AI-generated answers. Next-question intent is vital here.
While search intent asks what the user wants to do, next-question intent goes further. It asks what the user will need to know next to trust, compare, or decide.
In this AI-driven environment, content must support a complete answer pathway, far beyond the initial query.
The initial search often serves as just the beginning, an entry point. True decision-making occurs through follow-ups and specific concerns that arise thereafter.
Take the query “best CRM software for small business” as an example. It opens the door, but the true selection journey starts with follow-up questions.
Which platform is easiest for a two-person team?
Which integrates best with QuickBooks?
Which one works for a business without a formal sales department?
Which one is best for a local service company rather than a software startup?
Which one won’t frustrate owners or interns with tech complexity?
These aren’t ancillary. They define the decision-making path.
Otherwise well-structured content may falter if it fails to engage at this level, leaving AI systems with less context to assemble an answer, thereby reducing visibility.
Next-Question Intent is Not Just a Writing Exercise
As I’ve delved into content creation, it’s clear that next-question intent goes beyond simply writing better content—it ensures my pages support the next steps in a user’s decision-making process.
Practically speaking, it means crafting answer-ready content that addresses initial user needs, foresees additional decision layers, and provides concrete, verifiable information.
Visibility in AI search isn’t just about where I rank. It’s about citations and whether my brand becomes a trusted source in context-rich settings.
To achieve this, my content must offer enough substance for systems to understand what my brand does, whom it serves, when it’s useful, why it’s trustworthy, and how it fares against alternatives.
Where Good Content Goes Thin
While I often find that brands have content that’s accurate and keyword-optimized, it still might not suffice in the AI search environment.
AI systems require clarity and context to determine what I offer, who benefits from it, when it’s applicable, and why claims are valid.
This depth is where many pages fall short.
A service claim like “customized marketing strategies” begs the question: customized how?
A product claim like “safe for families” prompts: safe for which family members?
A software claim like “built for small businesses” asks: which type of business?
General claims offer little for people and even less for AI systems to utilize. Specific, structured, evidence-backed content serves a far better purpose.
AI Overviews and Google AI Mode are increasingly shaping the discussions within the SEO community. In this evolving landscape, search is transitioning from a mere information retrieval tool to a powerful recommendation engine.
As a travel brand, this shifts the dynamics of online discovery. It’s no longer just about making your website understandable to search engines; it’s about ensuring AI systems recognize when to recommend your business.
How AI is Revolutionizing Travel Planning
Interacting with large language models (LLMs) has become a routine for many of us. We use them to structure conversations by project, creating folders for our upcoming trips and building on previous chats to refine our preferences and travel profiles.
This is a major shift from the conventional searching methods. Traditionally, we would start our travel plans with Google searches for terms like:
“Hotels in Porto”
“Things to do in Rome”
“Best restaurants in Barcelona”
Today, the process is much more conversational. Instead of a series of disjointed searches, I might open a new folder labeled “Summer 2026” in ChatGPT and begin with a broad question, gradually sculpting it into a complete itinerary.
“Where should I stay in Porto for a quiet weekend within walking distance of the historic center?”
“Which area of Rome is best for families with young children?”
These discussions naturally expand to include restaurant recommendations, tourist attractions, accommodation options, transportation tips, and more detailed daily plans.
When I ask my AI assistant these questions, I’m not looking for a list of websites. What I truly want is an insightful recommendation.
Impact of AI Overviews on Travel Search
AI Overviews gather data from multiple points to deliver highly curated recommendations instead of just a list of links. For this reason, trust, consistency, and context have become vital factors for online visibility.
A traveler might decide to book my hotel based on an AI-generated suggestion without even visiting the website. Instead, their next steps could include a branded search or a visit to a review platform where they might finalize their booking through an OTA.
To win over AI model recommendations, I need to precisely define my brand. It’s crucial for AI to be certain of who I am, what I offer, whom I serve, and the contexts in which my brand is relevant.
Selecting a primary category and maintaining a clear brand position are imperative. Investing in digital PR and securing mentions beyond my own website can help too. Being featured in travel articles on relevant topics can significantly boost visibility.
Moreover, ensuring that my business information is consistent, accurate, and easy to find across my website, Google Business Profile, TripAdvisor, OTA listings, and social media is essential.
Understanding the Role of Zero Click Visibility
The methods for evaluating search performance are evolving. While traditional SEO metrics will remain relevant, it’s important for travel marketers like myself to broaden how visibility is measured.
One critical error is viewing fewer clicks as a decrease in visibility.
A traveler might learn about my property through an AI response and then decide to search for it later or visit a review profile on a platform like TripAdvisor.
That’s why seeing growth in branded searches is a promising sign of AI visibility. Monitoring AI mentions, citations, and assisted conversions is also worthwhile.
Assisted conversions highlight the channels and touchpoints that lead to bookings, even if they aren’t the final source of conversion. I can track these in Google Analytics 4 by navigating to Advertising > Attribution > Conversion Paths and Attribution Reports.
Leveraging TripAdvisor and OTA Listings
Platforms like TripAdvisor have grown beyond being review sites, and OTAs offer more than just booking services.
When someone requests AI recommendations, the system doesn’t rely on a single data point but synthesizes information from multiple avenues.
My website forms a part of this ecosystem.
AI builds confidence in its guidance by cross-referencing data across different platforms. What others say about my brand through reviews, travel guides, media references, OTA listings, or local mentions is increasingly significant. It’s large-scale reputation management.
This additional context helps AI identify when my property is relevant to specific traveler needs, like:
Family-friendly environments.
Ideal for business travelers.
Located in walk-friendly areas.
Renowned for exquisite dining.
Suitable for luxury or budget travel.
Distinguishing My Travel Brand
For example, if I manage a family-friendly hotel, it’s important to highlight features like family suites, kids’ activities, and family-oriented reviews. Alternatively, a romantic destination should emphasize aspects like cozy atmospheres, spa facilities, and exclusive packages.
Similarly, a hotel catering to business travelers should spotlight meeting rooms, workspaces, high-speed internet, and its proximity to business hubs. On the other hand, a restaurant known for its culinary excellence should consistently be mentioned in reviews, receive media attention, and third-party accolades focusing on its food quality, head chef, or dining experience.
While some businesses naturally fit various categories, having a clear primary positioning helps generative search engines easily identify when my brand is appropriate for a recommendation.
This principle holds for travel destinations too. AI-driven engines depend on signals from reviews, travel guides, local listings, and related content when suggesting where tourists should stay, visit, or explore.
Strengthening Entity Signals Across Platforms
As AI systems place more focus on entities instead of individual web pages, I must create a robust and consistent digital presence.
1. Clarifying Attributes with Structured Data
Structured data aids search engines and AI in interpreting key business details. For travel entities like mine, this includes lodging types, amenities, locations, and more.
Emphasize the attributes that truly set my property apart. This can span from family-friendly amenities to wellness-centered experiences, renowned dining options, pet-friendliness, or proximity to major landmarks.
The clearer and more structured my information is, the better the chances AI-powered experiences will spotlight my business in relevant recommendations.
2. Resolving Entity Ambiguities
It’s crucial to review third-party portrayals of my brand. Inconsistencies can diminish the trust AI systems have in my brand information, as AI pulls data from various sources.
Think of a hotel with differing phone numbers, outdated details, varying categories, or conflicting amenity information across platforms—these inconsistencies confuse AI systems.
Ensuring my business data is consistent across my website, Google Business Profile, TripAdvisor listings, and OTA profiles will reduce ambiguity and strengthen AI’s confidence.
3. Prioritizing Operational Information
Start by evaluating existing customer reviews.
What did they enjoy most during their visit?
What made their stay memorable?
What areas need improvement?
Such feedback provides insight into what genuinely differentiates my brand. Details about amenities, accessibility features, business hours, parking, and pet policies help AI address specific travel-related queries with confidence.
Google Business Profile is another vital source for operational data. The categories, attributes, amenities, and working hours mentioned on the profile enhance AI’s ability to answer travel queries accurately and helpfully.
To provide further context, I can also use Google Business Profile to publish posts that link back to my site’s content. Consistently posting on Google Business Profile can boost engagement, increase profile visits, and encourage customer interaction, ensuring my listing remains updated with fresh content about my offerings.
Cultivating AI-Trusted Signals
Generative search levels the playing field more than traditional search. AI favors recommending businesses, not just their websites. Visibility isn’t solely determined by what transpires on my site; it encompasses the comprehensive digital footprint that my brand projects.
For travel brands, this means I must think broader than just rankings and clicks. Reviews, OTA listings, travel guides, media mentions, and business profiles all contribute to how AI recognizes and recommends my brand.
It’s time to get creative, try new approaches, and collaborate with complementary businesses. Most crucially, it’s time to build the trust signals that AI systems rely on.
I’ve discovered something intriguing about Claude’s reliance on Brave Search rankings. Based on insights shared by Jonathan Clark during a Profound session on Zero Click, it seems that Claude frequently taps into Brave’s search results, particularly when dealing with recency, ranking, or comparison prompts.
Clark, who is the managing partner at Moving Traffic Media, emphasized a key point from the session: Claude doesn’t rearrange search results but instead incorporates Brave’s top 10 search results directly into its answers.
Claude’s web searches are selective. In fact, I learned that Claude uses web search in only 36.6% of cases compared to about 90% for ChatGPT, as per Clark’s observation.
Claude is triggered to search most often by prompts that signal current trends, rankings, location, or comparisons. For example, queries like “best XYZ” caused a search 81% of the time. Ranking focus prompts had a search rate of 67%.
Location prompts initiated searches 55% of the time, while comparison prompts such as “X vs. Y” led to searches 51% of the time.
Brave rankings are crucial. Another interesting point is that Claude’s answers only matched ChatGPT’s citations in 8% of cases for the same queries, according to Clark.
Claude’s results showed a 64% overlap with Google rankings. This indicates that Google-focused SEO strategies might be more effective for Claude than efforts targeted at boosting visibility in ChatGPT.
The analysis also highlights the significance of tracking Brave search rankings. Clark mentioned that Claude relies on Brave, and achieving good rankings in Brave provides us with measurable insights.
Memory in prompts. I found it interesting that prompts like “how does,” “what is,” and “steps to” are less likely to prompt Claude to conduct a web search. Without searching, Claude cannot cite online sources.
According to Clark, Claude searches most often for prompts with keywords like “best,” “top,” or comparative phrases.
The pattern of years in queries. Clark noted that there are consistent patterns that might simplify testing with Claude:
One noticeable trait is Claude’s query fan-outs, which consistently produced the same results 65% of the time across users.
These fan-outs frequently involve years, suggesting that titles featuring the current year might be advantageous in Claude-initiated searches, especially for queries driven by ranking and recency.
Why this matters to us. It appears that Claude’s visibility hinges more on the rankings within the search results it utilizes. Clark suggests Claude might be one of the most amendable AI answer engines due to its consistent search patterns closely tied to measurable rankings.
For the past two years, I’ve been deeply engaged in optimizing my content for AI visibility. This journey has focused on expressing clearly what my brand represents, crafting more compelling About pages, implementing precise schema, and offering straightforward answers to user queries.
These strategies are crucial during an LLM’s brand processing phase—where clarity and relevance are key. Yet, my study with João da Silva on Friction AI’s platform exposed a critical factor that wasn’t previously quantified.
Even when brands were well-recognized within their categories, this didn’t always translate into being recommended in related queries. This intriguing gap between recognition and recommendation has been termed the ‘framing gap.’
We tested 12 activewear brands like Gymshark, Reebok, and Nike across AI platforms, running over 14,000 API tests. We wanted to see if Knowledge Graph (KG) strength correlated with being recommended outside their direct category.
Interestingly, high-KG brands didn’t always dominate recommendations. Some mid-KG brands displayed a more noticeable gap between recognition and recommendation.
We also examined co-mention data, revealing fascinating insights into brand associations. For example, lululemon frequently co-appeared with Alo Yoga and Nike in athleisure-themed content, forming a recognized cluster.
Nike, despite sharing the ‘Footwear company’ description with New Balance and Reebok, featured prominently in recommendation prompts—thanks to its consistent association with category leaders.
This emphasizes the power of context and co-mentions in shaping brand visibility. It’s clear that external third-party content carries more weight in recommendations than single-brand narratives.
To enhance my SEO strategies, I focus on appearing in the ‘right company.’ Understanding where my brand is mentioned alongside competitors is crucial. This approach is more than just appearing in lists—it’s about strategic positioning.
This study is just the beginning. While it highlights trends in the UK athleisure sector, expanding our focus to other categories and regions will likely yield even more insights. The real question lies in whether my brand is part of the right conversation in my industry.
