I’m seeing travel planning move away from the traditional search bar and into AI answer engines like ChatGPT. For most of the past two decades, a traveler would type a destination-focused keyword into Google, open a dozen tabs, and stitch together a trip one page at a time.
Now, that same traveler can ask a question, keep the conversation going, and let the answer engine synthesize recommendations, compare options, or even help book the trip. The journey from curiosity to decision is becoming faster, more conversational, and far less dependent on traditional search results.
I believe this shift is rewriting how travelers discover brands. Visibility is no longer only about winning top-ranked blue links in Google. Increasingly, it depends on earning mentions, citations, and trust inside AI-generated answers.
For travel brands, that changes the competitive landscape. The companies that show up in AI search are the ones most likely to shape the itinerary, influence the booking decision, and ultimately win the trip.
For most of the past decade, I treated organic marketing as a visibility game. I wanted brands on Page 1, inside featured snippets, and in front of the people already searching.
That north star has moved.
When I spoke at SMX Advanced on June 5, the question I put to the room was not simply, “How do I get a brand found?” The harder question was, “How do I get that brand chosen?”
In 2026, those answers are no longer the same. The distance between being discovered and being selected is where I see many brands losing ground.
In AI search, my reputation shows up first
The old user journey was messy and multi-step. People explored, compared, checked reviews, read Reddit threads, visited comparison sites, and moved toward a decision over time. Now, a single AI prompt can compress much of that process into one synthesized answer.
AI search does not reward the brand that shouts the loudest in paid media or stuffs the most keywords into metadata. I see it rewarding the brand with the strongest reputation in the places that matter. Reddit discussions, review sites, comparison pages, expert commentary, forums, and editorial coverage are all being absorbed by large language models and blended into recommendations.
In other words, my brand is no longer defined only by what I say about it. It is shaped by how AI understands it, and AI is reading what everyone else has said, too.
Owned content on websites and social channels will always carry a promotional bias. AI systems look for outside validation to support, challenge, or clarify those claims.
That changes the work of organic marketing. I can no longer stop at visibility. I have to build a brand that is found, correctly understood, and ultimately chosen. Those are three separate challenges, and I need a strategy for each one.
Found: I need to appear where my audience actually looks
The first challenge is still discoverability, but the canvas is much wider than Google. People now discover brands through ChatGPT, Reddit, YouTube, TikTok, Google, Quora, LinkedIn, and word of mouth. I have to understand which of those entry points matter most to the specific audience I want to reach.
That starts with mapping the sources my audience genuinely trusts: the publications, platforms, communities, creators, analysts, newsletters, and peer groups that influence their decisions. The intersection of semantic relevance, domain authority, and audience affinity tells me which third-party properties are worth pursuing.
For one B2B audience, that might mean Wired, Tom’s Guide, or an active LinkedIn group where buyers discuss vendors in a specific vertical. For another, it might be r/smallbusiness or a Substack newsletter with 40,000 engaged subscribers.
Once I know where the audience spends time, I can create useful content, earn credible mentions, and participate in the conversations already shaping decisions. This is audience-first, performance-driven PR and organic strategy, not generic brand awareness.
AI search leans heavily on outside validation: this chart shows third-party communities, reviews, and earned media driving 93% of citations versus 7% from owned channels.
The data makes the case even stronger. Across the top commercial sectors analyzed, 93% of AI search citations came from third-party sources. If I only invest in content on my own domain, I risk being invisible to the systems now doing much of the brand discovery work.
Understood: I need consistent signals everywhere
Getting found matters, but it is not enough on its own. If machines are surfacing my brand, they also need to understand it accurately.
LLMs do more than crawl my website. They build a consensus picture from everything available online: reviews, Reddit discussions, press coverage, YouTube commentary, Trustpilot ratings, forum threads, and more. If those signals conflict with the story I am telling about myself, I have a real problem.
If I claim premium positioning while thousands of articles question whether the brand is truly luxury, heavy discounting is part of the public record, and review scores are poor, AI is unlikely to recommend that brand as a premium option. The model has read the broader story, not just the homepage copy.
That is why brand messaging consistency has become an SEO issue. Owned, earned, and paid content all need to reinforce the same core associations. Conflicting signals do not just confuse customers; they can weaken AI visibility.
Digital PR plays a critical role here because it helps shape the external narrative. Through strategic media placements, expert commentary, and search-informed coverage, I can influence what journalists write, what audiences remember, and what models learn.
I also have to think beyond one obvious keyword. The query fan-out, or the range of prompts a potential customer might use, requires positive and consistent answers across every touchpoint an LLM might evaluate.
Chosen: I need trust signals that influence the decision
The third challenge is the hardest and probably the most important. Trust has always been an SEO currency, but as clicks decline and zero-click search becomes more common, trust matters even more.
According to an Ahrefs study, brand appearance in AI Overviews is most strongly correlated with branded web mentions. In practical terms, that means the number of times a brand is positively named across authoritative third-party sources is becoming one of the most powerful signals organic marketers can influence.
That is also the core output of strong digital PR. Based on the last 4,000 pieces of U.S.- and U.K.-based coverage driven for clients, 91% of AI search citations included expert insight rather than branded content or product pages.
That tells me expert-backed, editorially independent coverage is critical. Internal experts are now one of the most valuable assets a brand has. Brands that invest in real thought leadership, original research, and data-backed studies are giving both people and AI systems stronger reasons to trust them.
The three content formats I see consistently supporting LLM inclusion are product roundups and listicles that place a brand inside trusted “best of” editorials, reliable data-backed research that journalists and LLMs can cite, and expert thought leadership that positions real people as credible voices in their category.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
What does not work is chasing inauthentic mentions through artificial link schemes, fake expert personas, or manufactured coverage. Google has already flagged these kinds of tactics in its GEO guidance, and models are getting better at distinguishing genuine authority from manipulated signals.
The reputational risk is also high. If I try to manufacture authority and get caught, I do not just lose visibility. I damage the trust I was trying to build.
This cannot be a one-time effort. Multiple studies, including research from Waseda University, have identified a correlation between AI brand visibility and content recency.
Brands that maintain a steady flow of credible, expert-backed third-party coverage do not just appear more often in AI responses. They appear with more confidence.
Frequency and freshness both matter. A one-off PR campaign is not enough. I need to treat credible external validation as an always-on strategic investment.
The framework I use in practice
When I think about brand discovery in 2026, I come back to three words: found, understood, and chosen.
Found: I map the audience’s real sources of influence and make sure the brand is credibly present across the fragmented ecosystem where discovery now happens.
Understood: I work to make sure everything said about the brand tells a consistent story, matches the desired positioning, and reinforces the associations that drive preference.
Chosen: I continuously build genuine trust signals through earned coverage, expert commentary, and third-party validation, so that when a person or machine compares the brand with a competitor, credible external evidence tips the decision in my favor.
The brands winning in organic search right now have not unlocked some secret technical trick. They have built reputations worth recommending, and they have made sure machines can understand those reputations clearly.
That is where I believe organic marketing has to go next. Instead of chasing the algorithm, I need to build something worth finding, worth understanding, and worth choosing.
I’m seeing traditional Google rankings deliver less predictable value than they once did. Ads, AI Overviews, and other search engine results page features are pushing organic links farther down the page, which means visibility no longer depends only on where a brand ranks in the classic blue-link results.
As search keeps shifting, I believe brands need to ask a more practical question: how do I make sure my brand is represented accurately inside AI-powered answers?
The more I understand how AI engines use brand information and when they cite it, the easier it becomes to build a real AI visibility strategy. This moves the conversation beyond whether an AI model “knows” a brand and toward how that brand can earn presence, trust, and discoverability in AI search.
The click economy is shrinking
I think most brands should start learning AI search and building an AI SEO strategy now. A full shift from organic search to AI search may still be years away, but the direction is clear enough that waiting creates risk.
Google is already leaning hard into AI search. In an April article from The Verge, CEO Sundar Pichai said that search had a strong quarter, with AI experiences driving usage, queries reaching an all-time high, and revenue growing 19%.
Users are changing their behavior too. A Pew Research study found that when people see an AI-powered summary in search results, they click a blue link only 8% of the time. When no AI summary appears, that click rate rises to 15%.
AI search traffic may still be smaller than organic traffic, but I would not dismiss it. According to Similarweb, AI traffic converted at 11.4%, compared with 5.3% for organic search traffic. That makes AI visibility worth tracking even before it becomes the dominant traffic source.
How I separate AI usage from AI citation
I think about brand presence in AI systems in two main ways: usage and citation.
Usage happens when an AI engine ingests information about a brand and draws on that information when answering a query. In some ways, this reminds me of how Google traditionally indexed pages before ranking and serving them in search results.
When an AI engine uses brand content, it may mention the brand without linking to it. Even an unlinked mention can matter because it can create discovery, influence perception, and prompt users to search for the brand directly.
Ahrefs data shows most Google AI Overview citations still come from high-ranking organic pages, with 76.10% in the top 10 and a smaller share outside the top 100.
Citation is different. A citation happens when an AI engine directly references a brand as a source of information. That reference might be a link to a web page, a social profile, or even a clickable phone link that lets someone contact the business.
Within OpenAI, usage and citation appear to depend on separate technical systems. As OpenAI’s documentation explains, OAI-SearchBot and GPTBot are deployed separately among four distinct user agents. Other AI systems have their own controls, but the same broader distinction still applies.
Why citations do not tell the whole story
I do not see citations as the full AI visibility picture. AI engines often answer questions directly without citing web sources, and this pattern is not entirely new. Before AI Overviews, Google was already moving in that direction with featured snippets.
Ahrefs found that ChatGPT retrieves almost the exact same number of cited and uncited URLs to generate an average response: about 16.57 cited URLs and 16.58 uncited URLs. But Reddit made up 67.8% of uncited URLs, which means comparing cited and uncited URLs is often really a comparison between search results and Reddit API output.
That matters because AI systems are not neutral in the uncited information they surface. Some platforms and websites are simply more influential than others. If I try to push a brand into AI answers without understanding where the model gets its information, I am working at a disadvantage.
How I would improve brand usage and citation
I would start by tracking the brand’s current AI visibility and monitoring progress over time. That means running a representative set of prompts through an AI visibility platform, reviewing the sources that get cited, and asking what those sources reveal about the model’s preferences.
There are already many AI citation tracking tools available, and established platforms like Semrush and Ahrefs have added AI tracking features as well. I would choose a tool based on the prompts, markets, and engines that matter most to the brand.
I would also scale tracking and research as much as budget allows. AI prompt tracking often depends on API calls, so it can cost more than traditional rank tracking. Still, the data is usually richer, even when the sample size is smaller.
As long as the prompt sample is broadly representative, most platforms can pull multiple responses and calculate an average. That gives me a more useful view of recurring patterns instead of relying on one-off answers.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
I would keep reading studies from AI platforms, SEO vendors, and data providers too. Those reports are valuable because they show which sources AI engines rely on and where brands may have the best chance to appear.
The key is continual monitoring. Over time, I can work to place a brand inside the sources AI engines already trust and use most heavily.
Why I still care about traditional rankings
Yes, I still think traditional search rankings matter, but not for the same reasons they used to. The relationship between organic position and business performance is less direct now, especially as SERP features and AI answers absorb more user attention.
At the same time, Ahrefs research suggests a relationship between AI citations and Google rankings, at least inside Google AI Overviews. A July 2025 study found that 76.1% of pages cited in AI Overviews ranked in Google’s top 10 organic results. If AI Overviews become a dominant AI search experience, traditional rankings will still influence visibility.
I also pay attention to content quality. Semrush found that AI engines rarely cite generic content that simply repeats what other sources already say. The content that earns citations usually contributes something distinct.
That fits closely with Google’s helpful content guidance, which rewards original information and useful perspective. In my view, content with trusted data, original insight, and a clear point of view can support both Google rankings and AI citations.
Because many classic SEO tactics can also support AI citations, I would not abandon traditional SEO. I would treat it as part of a broader visibility strategy that now includes AI usage, AI citations, and brand presence across trusted third-party sources.
Where I think AI visibility is heading
Both usage and citation need ongoing tracking and analysis. If I want AI engines to use a brand’s knowledge and content, I need to understand which sources each model relies on and help the brand appear in those places. If I want citations, I need the brand’s content to stay crawlable, rank well, and say something original.
Classic SEO still earns its place because the same work that improves organic visibility can often improve AI visibility too. But returns from traditional rankings are changing, and AI SEO may eventually become the primary discipline. For now, I would keep ranking, start tracking, and build for both usage and citation.
I do not measure AI search the same way I measure traditional search, because the user journey is no longer built around one query, one ranking page, and one click.
A prospect might ask ChatGPT for the best CRM for manufacturing companies, compare options in Google AI Mode, refine the requirements across several follow-up questions, and build a shortlist without ever visiting a website.
If my company appears in those conversations, I have influenced the buying process. The hard part is proving that influence with a measurement system I can trust.
Prompt-level visibility has become one of the fastest-growing areas of AI search optimization. It is also one of the easiest to misunderstand. I see plenty of promises about complete visibility into AI conversations, but the reality is far more complicated.
Here is how I think about what can be measured today, what cannot be measured reliably, and how I would build useful reporting despite the current limits.
A 5-step framework I use to track AI visibility
1. I accept that AI does not have traditional rankings
The first mistake I avoid is trying to recreate an old SEO ranking report. There is no universal position one inside ChatGPT.
The same prompt can produce different responses depending on conversation history, user location, personalization, follow-up questions, model version, web retrieval availability, and timing.
That means visibility is probabilistic rather than deterministic. Instead of asking, "Do we rank?" I ask, "How often are we included across the conversations that matter?"
That shift changes the entire measurement model.
2. I build a prompt library instead of only a keyword list
Keywords still matter, but I no longer treat them as enough on their own.
Instead of tracking only individual search terms, I build a library of prompts that reflects how real buyers research, compare, validate, and challenge their options.
I usually organize those prompts by intent. Discovery prompts ask for the best platforms in a category. Comparison prompts put vendors side by side. Evaluation prompts focus on specific use cases. Validation prompts ask whether a company is worth the cost. Objection prompts explore disadvantages. Alternative prompts ask what to use instead. Implementation prompts test how difficult a product may be to adopt.
Instead of monitoring 10 keywords, I may monitor 200 to 500 prompts across the full buying journey. That gives me a much more realistic view of AI visibility.
3. I measure prompt clusters, not isolated questions
One prompt rarely tells me enough to make a decision.
For example, "best CRM software" might not mention my company, while "best CRM for manufacturing companies" might. A more specific prompt, such as "CRM for manufacturers with field sales teams," could return a different set of recommendations altogether.
That is why I group similar prompts into clusters. A category cluster might include best project management software, best PM platform, and project management tools. An industry cluster might include best CRM for healthcare, manufacturing, and finance. A feature cluster might include CRM with AI automation, forecasting, or enterprise sales support.
The patterns across those clusters are more reliable than the result from any single prompt.
4. I combine synthetic prompts with real customer questions
This is where measurement becomes more difficult.
Most organizations do not know exactly what customers are typing into AI assistants, so I often start by generating synthetic prompts. That may include expanding keyword research into conversational questions, creating AI-generated prompt variations, and building comparison, objection, and follow-up prompts.
Synthetic prompts are useful because they are repeatable, but I do not treat them as perfect. Generated prompts often sound cleaner and more structured than real user behavior.
A real buyer might ask something much richer, such as: "We are a 250-person SaaS company with a small HR team. We already use Workday but need something better for payroll. Budget is not a huge issue. What would you recommend?"
That is much more useful than a short phrase like "best payroll software."
For the strongest measurement program, I use synthetic prompts for consistent benchmarking and then supplement them with real questions from sales calls, customer interviews, support conversations, community discussions, internal search logs, on-site search, and AI transcripts that customers voluntarily share.
I also assume the prompt library will need to change. Customer language evolves, and the measurement set has to evolve with it.
5. I measure multi-turn conversations
Most AI-assisted buying journeys do not happen in a single prompt. A buyer may start by asking for the best cybersecurity vendors, narrow the list to companies strong in healthcare, ask which ones integrate with CrowdStrike, and then compare pricing.
My company may not appear in the first answer, but it may become highly recommended by the third response.
If I only measure the opening prompt, I miss a large share of meaningful visibility.
That is why I want prompt tracking to evaluate full conversation paths, not just one-shot questions. Multi-turn testing often reveals patterns that single prompts hide.
The AI visibility metrics I care about most
Many traditional SEO metrics do not translate neatly to AI search. Rankings, clicks, and impressions still have value, but they no longer tell the whole story.
I focus on measurements that show whether a brand appears, how it is positioned, and how consistently it is recommended inside AI-generated responses.
Inclusion rate
If I could track only one AI visibility metric, I would start here.
Inclusion rate measures the percentage of tracked prompts where my brand appears in the AI response. If I monitor 500 prompts and my company appears in 185 of them, the inclusion rate is 37%.
That number is useful as a benchmark, but it becomes more valuable when I segment it by buying stage, product category, industry, geography, or AI model. Those slices often reveal opportunities that a single overall average would hide.
Position within the response
Being mentioned is not the same as being recommended.
Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.
I want to know whether my brand appears as the first recommendation, one of the first few options, a late mention, or merely an alternative. If the AI response includes a comparison table, I also want to know where my company appears there.
AI answers do not have traditional rankings, but prominence still matters. A top recommendation is more likely to shape a buyer’s perception than a passing mention several paragraphs later.
Brand framing
Visibility tells me whether my brand is included. Brand framing tells me how it is described.
There is a meaningful difference between an AI system describing a company as "widely considered an enterprise leader" and describing it as "best suited for smaller teams." Both may sound positive, but they position the brand very differently.
I look for recurring themes around strengths, weaknesses, differentiators, pricing, ideal customer profile, and competitive comparisons. Over time, those patterns can expose messaging gaps in my own content or show how the broader web is shaping AI’s understanding of the brand.
Sentiment and confidence
Sentiment is more than a simple positive-or-negative label. I also want to know how confidently the AI system presents my brand.
"Company A is generally considered the strongest option" carries a very different level of conviction than "Company A may be worth considering."
Neither statement is negative, but they do not create the same buyer impression. Tracking confidence, uncertainty, caution, skepticism, and strong endorsement gives me a more nuanced view of how AI systems present the company to prospective customers.
Competitive share of voice
My own visibility is only part of the picture. I also need to know how often competitors appear alongside me or instead of me.
If my inclusion rate stays at 40% month after month, that may look disappointing. But if every major competitor dropped by 20 percentage points after a model update, the story changes.
On the other hand, if one competitor jumps from 35% inclusion to 70% while everyone else stays flat, I would want to investigate what changed.
Competitive share of voice helps me separate category-wide movement from changes that are specific to my brand.
How I view the AI visibility tool landscape
The market for AI visibility platforms has grown quickly. Each product approaches the problem differently, but most are trying to answer the same core questions: does my brand appear, how often does it appear, which AI models include it, which competitors show up, and how is the brand described?
Many platforms now include prompt libraries, competitive benchmarking, citation tracking, answer monitoring, and trend reporting. These features can reduce the manual work required to test hundreds or thousands of prompts on a recurring basis.
Still, I have to be clear about what these tools are and are not measuring.
No tool has access to every AI conversation happening in the wild. Most rely on controlled prompt libraries, repeatable testing environments, or sampled interactions to create a representative view of visibility.
That is useful, but it is not the same as observing every real user interaction.
What I still cannot reliably track
This is the part I do not want to gloss over.
Even though AI measurement is improving quickly, some data is still not observable. I cannot comprehensively track every prompt where my brand appeared, every conversation that influenced a purchase, every recommendation made inside ChatGPT, every citation shown to every individual user, or exactly how personalization changed a response.
I also cannot see every multi-turn conversation across every AI platform or know how often someone acted on an AI recommendation without clicking a link.
The underlying AI platforms do not expose that level of data. If a vendor claims it can see every AI conversation involving my brand, I would ask exactly how that information is being collected.
The practical framework I would build
Rather than chasing perfect attribution, I focus on building a repeatable measurement system that I can track consistently over time.
For visibility, I would track inclusion rate, competitive share of voice, prompt coverage, and model coverage.
For response quality, I would track position within the response, brand framing, sentiment, and message consistency.
For technical signals, I would track citation frequency, content retrieval success, entity consistency, and freshness.
For business outcomes, I would look at AI referral traffic, assisted conversions, branded search lift, direct traffic trends, and pipeline influenced by AI discovery.
No single metric tells the full story. Together, these signals give me a more complete picture of how the brand is showing up and how it is being perceived across AI-assisted research.
The goal is not perfect measurement
Prompt-level visibility is not as mature as keyword tracking became over the past two decades.
Some signals are still emerging. Others remain inaccessible because AI platforms do not expose the underlying data. At the same time, user behavior is changing almost as quickly as the technology itself.
That does not mean measurement is impossible. It means the objective has changed.
Instead of trying to reconstruct every AI conversation, I focus on building a representative prompt library, tracking visibility consistently, benchmarking against competitors, and understanding how my brand is being framed.
Those trends are far more actionable than chasing a level of precision the current ecosystem cannot support.
The organizations making the most progress in AI search are not waiting for perfect attribution. They are establishing baselines, watching for meaningful movement, and adapting as both AI models and user behavior continue to evolve.
I see ChatGPT’s high-reasoning mode acting like a very different search surface for brand visibility. In a Semrush analysis with Kevin Indig, ChatGPT cited different domains than it did in minimal reasoning mode and ran nearly five times as many web searches before answering.
By the numbers, the shift is hard to ignore. Only 25.6% of cited domains overlapped between minimal and high reasoning for the same prompts. That means nearly three in four sources changed when ChatGPT moved from Instant-style answers to Thinking-style answers.
I also noticed that Thinking mode used more sources overall. Citation rates rose from 50% in minimal reasoning to 68% in high reasoning. When ChatGPT did cite sources, it used more of them too, increasing from 2.6 to 4.5 citations per response. Across the test set, high reasoning ran 1,130 web searches, compared with 245 for minimal reasoning.
Reddit lost ground in high-reasoning answers. Reddit’s citation share dropped from 15% to 7% when high reasoning was turned on. User-generated content and review sites also declined, falling from 14.3% to 6%.
At the same time, I saw more weight shift toward institutional and official sources. Government and academic sources rose from 1.9% to 8.8%, while official documentation and support pages grew from 12.4% to 17.5%.
Comparison prompts drove the most search activity. At the comparison stage, high reasoning averaged 24 sub-queries per prompt, compared with 5.5 for minimal reasoning. Average citations also peaked there, reaching 9.8 per high-reasoning response versus 5.8 for minimal reasoning.
For example, I would expect a CRM comparison to trigger separate searches for pricing, integrations, security, support pages, and documentation before ChatGPT forms its final answer.
Early citations also appeared to last longer. High reasoning was more likely to carry a brand from early research into later buying questions. In four of the 20 journeys tested, a brand cited at the problem stage still appeared at the selection stage. Minimal reasoning showed no full-journey persistence, meaning no brand cited at the Problem stage survived through to the Selection stage of the same journey.
I also found the domain reuse pattern important. High reasoning reused the same domains more often within a single answer, with the same domain appearing multiple times in 51 of 100 high-reasoning responses. Minimal reasoning did this in 26 of 100 responses.
Finance saw the biggest citation jump. The lift varied by category, but finance had the largest increase, with citation rates rising 28 percentage points in high reasoning. Health and lifestyle rose 24 points, while B2B SaaS gained 16 points.
Consumer tech barely moved, rising only 4 points. Even though high reasoning ran more sub-queries for consumer tech prompts than for any other category, it often landed on the same brands and sources as minimal reasoning.
Why I care about this: content can appear in fast ChatGPT answers but disappear when users ask more complex questions. Visibility depends on whether my pages, documentation, and third-party references can surface across the smaller searches ChatGPT runs before it answers.
About the data: Semrush and Indig tested 100 prompts across 20 buyer journeys in B2B SaaS, finance, consumer tech, and health and lifestyle. Each prompt ran once in minimal reasoning and once in high reasoning. The analysis tracked citation rate, cited sources, and fan-out queries.
When I look at Similarweb’s findings, the message is clear: users who saw a brand recommended by ChatGPT were much more likely to visit that brand’s website within a week.
What happened. I found the biggest takeaway in the behavior shift. On average, users were 2.5 times more likely to visit an AI-recommended brand than a direct competitor, based on Similarweb’s study of U.S. desktop activity across finance, travel, and beauty.
Similarweb tracked users who asked ChatGPT industry-relevant questions, received a specific brand recommendation, and then visited either that recommended brand’s website or a competitor’s site within seven days.
To keep the data focused, the study excluded users who had visited the brand’s site in the prior four weeks or had named the brand directly in their prompt.
Recommendations shifted traffic. I saw the same pattern appear across all three industries Similarweb analyzed, which makes this more than a one-category trend.
In finance, after an American Express recommendation, 7.2% of users visited American Express, compared with 3.1% who visited Capital One. After a Capital One recommendation, 14.2% visited Capital One, compared with 3.8% who visited American Express.
In travel, after a Skyscanner recommendation, 9.5% visited Skyscanner, compared with 7.6% who visited Kayak. After a Kayak recommendation, 12% visited Kayak, compared with 3.4% who visited Skyscanner.
In beauty, after a Sephora recommendation, 7.9% visited Sephora, compared with 3.3% who visited Ulta. After an Ulta recommendation, 7.6% visited Ulta, compared with 4.6% who visited Sephora.
AI demand showed up in search. What stands out to me is that most AI-influenced visits did not appear as AI referral traffic. ChatGPT may shape the user’s brand choice, but the later website visit often shows up in analytics as search traffic instead.
Similarweb found that 55.9% of AI-influenced visits came through search, compared with 40.4% of non-AI-influenced visits.
Direct traffic told a different story. It accounted for 19.9% of AI-influenced visits, compared with 38.8% of standard visits.
Recommended users stayed longer. I also think the engagement data matters. AI-influenced visitors viewed 12 pages and spent 11.8 minutes on site, on average, compared with 6.5 pages and 5.6 minutes for non-AI-influenced visitors.
That deeper engagement suggests these users may have already narrowed their options during the AI conversation before they ever reached the brand’s website, Similarweb said.
Why I care. AI visibility can drive meaningful visits even when referral reports miss the original source of influence. I need to understand whether ChatGPT is creating demand for my brand or sending that demand to a competitor.
About the data. Similarweb used its opted-in U.S. desktop web panel to track user journeys from July through December 2025. The report focused on finance, travel, and beauty brand pairs with competitive overlap.
In mid-October, I saw ChatGPT roll out a major response update that changed how brands show up in its answers.
What stood out to me was the shift in brand visibility. Mentions became harder to earn, and competition inside AI-generated responses appeared to get tougher across categories.
Using Answer Engine Insights, Profound analyzed millions of prompts across ChatGPT and other leading answer engines to better understand what changed, where visibility moved, and how brands were affected by the update.
I’m introducing the Profound Index as a new way to understand AI visibility. It is the first leaderboard built to rank brands by how often they appear in answers from leading AI models.
For me, this matters because visibility is shifting beyond traditional search results. As more people rely on AI-generated answers, I want a clearer way to see which brands are being mentioned, recommended, and surfaced across the AI platforms shaping discovery.
I’m looking at a major ChatGPT response update that rolled out in mid-October, and the shift is clear: brand visibility inside AI-generated answers has become more competitive.
With this update, ChatGPT changed how brands appear in its responses, which means fewer easy mentions and a tougher environment for companies trying to show up in answer engines.
Using Answer Engine Insights, Profound analyzed millions of prompts across ChatGPT and other leading answer engines to understand what changed, where visibility moved, and how different categories were affected.
For me, the key takeaway is that AI visibility now depends on stronger entity signals, clearer brand authority, and a deeper understanding of how answer engines decide which names deserve to appear.
Building a strong digital footprint is essential for helping AI understand my expertise, recognize my credibility, and recommend my brand to potential customers.
AI forms opinions about my brand from my online presence—my digital footprint. The challenge? AI often captures only pieces of my business: the website, content, reviews, and mentions. Unfortunately, much of the expertise and customer insight I offer doesn’t always make it into that footprint.
To address this, I’ve learned to surface that hidden knowledge, organize it into a single source of truth, and convert it into machine-readable signals. Here’s my strategy for collecting, organizing, and distributing this knowledge across the platforms AI uses to understand and recommend brands.
What You Feed the Machines: Understandability, Credibility, and Deliverability (UCD)
Everything I contribute to my digital footprint feeds into three key aspects for AI: understandability, credibility, and deliverability, which together form the whole funnel.
Does AI know who I am, what I do, and whom I serve? My about page, product pages, and structured data contribute to this understanding, but the operational details that highlight my business’s value are often overlooked.
Credibility: Building Trust with AI
Does AI trust I’m proficient in what I do? This is about N-E-E-A-T-T credibility—Notability, Experience, Expertise, Authoritativeness, Trustworthiness, and Transparency. It’s an extension based on Google’s E-E-A-T.
I am aware of the credibility signals I currently utilize: case studies, credentials, and testimonials. However, many businesses, including mine, often underestimate how much of this credibility is already woven into daily operations.
Deliverability: Reaching My Audience
Is my content available to the AI engine for delivering to my target audience? I recognize that my deliverability roots lie in topical content, marketing strategies, and authority pieces. Deliverability often hides within the content my business operations generate.
With AI viewing every brand in my category impartially, my task is to build a clearer and more trustworthy picture of who I am and what I represent. By showcasing my strengths more effectively than competitors and being transparent with AI, I position myself as the top recommendation for my target audience.
