I structure content for AI search by making every page clear, credible, and easy for answer engines to understand. That means I do not rely on keywords alone. I combine strong SEO fundamentals with topical authority, earned media, and answer-first formatting so AI systems can recognize what my brand knows, where it is trusted, and why it should be surfaced in relevant responses.
When I think about AI visibility, I focus on discovery from the start. I want my content to answer real questions directly, connect related topics naturally, and support each claim with signals that build confidence. This approach helps improve how my brand appears across AI search experiences, traditional search results, and emerging discovery platforms.
For me, the goal is simple: create content that is useful for people and understandable for machines. By organizing information around intent, authority, and clarity, I make it easier for AI tools to cite, summarize, and recommend my brand when users are looking for trusted answers.
A year ago, I saw 82% of consumers say AI-powered search was more helpful than traditional search. By 2026, that number had fallen to 54%, a 28-point drop in sentiment in just 12 months.
That does not mean people are abandoning AI search. In fact, 70% of consumers say they are using AI tools for search more than they did last year. The tension is clear: adoption is rising, but trust is slipping.
That is the core issue I believe search marketers need to solve in 2026. It is no longer enough to appear in AI answers. I need my brand, and the brands I work with, to be visible, accurate, credible, and trusted when AI systems surface information.
To understand the shift, Fractl partnered with Search Engine Land to expand our 2025 research. We surveyed 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are changing in the AI search era. Disclosure: I am the co-founder of Fractl.
Here is what I believe the data means for 2026 search strategy.
Consumers are using AI more, but trusting it less
AI search adoption is no longer the main story. Seventy percent of consumers report increased use of AI tools for search over the past year, while only 3% say their use has decreased. The bigger question is whether people trust what those tools return.
One surprising finding is that baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically embrace AI while older users lag behind. What I see instead is a more complicated market where trust has to be earned across every generation.
In 2025, only 3% of consumers said AI was less helpful than traditional search. By 2026, that skeptic group had grown to 17%, nearly six times larger than the year before. Even among the 54% who still find AI helpful, enthusiasm is softer: 37% say it is only somewhat more helpful, while 17% say it is much more helpful.
I think hallucinations and low-quality AI content are changing how people evaluate the entire channel. Consumers may use AI because it is convenient, but convenience does not automatically create confidence.
AI content volume has become a brand trust risk
In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%. For me, that makes AI content scale a reputational issue, not just an operational decision.
If I publish AI-assisted content at scale without disclosure, strong editorial standards, or obvious quality signals, I am asking my audience to trust a process they are increasingly skeptical of. That is a risk more brands need to take seriously.
Gen Z is especially strict. Fifty-four percent of Gen Z consumers say heavy AI use in a brand’s marketing would decrease their trust, compared with 32% of baby boomers and 33% of Gen X. Women are also more likely than men to penalize brands for heavy AI use, 44% vs. 34%.
That matters because Gen Z is often the audience most likely to engage deeply, share content, shape online conversations, and influence long-term organic visibility. If that audience matters to a brand, AI-generated filler is not a harmless shortcut.
Disclosure is now a consumer expectation
Across every major content format, more than 80% of consumers want AI-generated content labeled. Video leads at 91%, followed by images at 90%, audio at 87%, and written content at 84%. More than half of respondents strongly agree with labeling in every category.
I do not read that as a mild preference. I read it as a near-universal expectation. The brands that treat AI disclosure as optional are creating a gap between how they operate and what their audiences want.
Consumers still believe AI will shape the future of search. Sixty-four percent agree that AI will replace traditional search engines within five years, nearly unchanged from 66% in 2025. The channel is not going away. But being present in AI results and being trusted in AI results are now two different challenges.
Google still leads on trust, especially for buying decisions
When consumers are making purchase decisions, 39% turn to Google first. Reddit follows at 15%, AI tools at 14%, and review sites and friends or family each at 11%. The trust people have built with Google has not automatically transferred to AI tools.
Platform preference also changes by query type. Google dominates five of six major search categories. It is the first stop for local businesses, product research, travel planning, and health questions. YouTube overtakes Google for how-to content, while ChatGPT is now the second-most-used destination for health questions and ranks strongly for product research, travel planning, and how-to content.
That tells me there is no single AI search platform to optimize for. I need to map content strategy to actual user behavior: where people search, what they are trying to decide, and which platforms influence confidence at each stage.
Before making a purchase decision, the average consumer checks 2.4 platforms. Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2. This behavior is consistent enough that I now think of search optimization as a multi-platform visibility strategy, not a rankings-only discipline.
A brand that appears in Google results but nowhere else can lose to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has strong third-party review content. Visibility now has to travel with the buyer.
AI is changing marketing operations quickly
AI now touches 53% of marketing work on average, up from 38% in 2025. In practical terms, the equivalent of one full workday per week has shifted to AI-assisted workflows in just 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it is involved in three-quarters or more.
For SEO and content teams, this means competitors are moving faster. But speed alone is becoming commoditized. Accuracy, original insight, expert judgment, and brand credibility are much harder to copy.
Marketers are also feeling pressure to adopt AI. Fifty-five percent of marketing roles report a 7-out-of-10 level of pressure to use it. SEO and analytics teams feel that pressure most, while PR is not far behind. As AI makes generic content easier to produce, the advantage shifts toward what AI cannot automate well: judgment, relationships, trust, and reputation.
The quality tradeoff is real. Only 26% of marketers say AI made their work both faster and better. Nearly half say it made their work faster but more generic, and 7% report an outright quality decline.
That is where I see a major competitive opening. If other teams are scaling generic AI content while I invest in original data, expert quotes, third-party validation, and earned brand mentions, I am building assets that are more visible, credible, and retrievable across search engines, social platforms, and LLMs.
AI governance is still too weak
About three in four organizations conduct human editorial review before publishing AI-generated content. Sixty-two percent check for brand voice, 54% check facts, and 42% conduct legal or compliance review. Only 27% evaluate content for bias.
That means nearly half of AI-generated content may enter the market without fact-checking, legal review, or plagiarism checks. Too many teams are still relying on surface-level review: Does it sound right? Is the tone appropriate? Are there typos?
In a year when consumers are already prepared to distrust generic AI content, I see governance as one of the cheapest gaps to close and one of the most expensive to ignore.
The disclosure gap is just as serious. Heavy, generic AI use is now a brand-trust liability, yet only 20% of organizations always disclose AI use to their audiences. Compare that with the 84% average consumer demand for labeling written content, and the disconnect is obvious.
The takeaway is not to abandon AI. It is to stop treating governance as optional. Every AI workflow needs accuracy checks, transparency standards, bias review, and human accountability before content reaches an audience.
AI hallucinations are already a brand problem
A year ago, about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved to 24%. At the same time, 27% of brands have already been misrepresented in AI-generated responses, and 14% say an AI inaccuracy has affected a customer relationship, sale, or PR situation.
More brands have been misrepresented by AI than have a formal monitoring process. That should concern every search and communications team.
If AI is summarizing my category, comparing my product, or explaining my brand incorrectly, that is not only an SEO issue. It is a reputation risk, a revenue risk, and a PR issue waiting to escalate.
When AI misrepresents a brand, I believe fixing the source matters more than arguing with the output. That can mean reaching out to publishers for updates, correcting owned profiles, improving brand pages, and publishing clear correction content tied to the entity.
Organic traffic is under pressure, not in freefall
Half of the marketers surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI. That is meaningful, but it is not the whole story.
The larger shift is not simply from Google to ChatGPT. It is from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across platforms, communities, assistants, and review environments.
The same marketers reporting organic losses are often finding visibility elsewhere. Fifty-seven percent report growth from social platforms such as TikTok, Reddit, and YouTube. Forty percent see growth from AI assistants such as ChatGPT, Gemini, and Perplexity. Thirty-one percent see growth in direct or branded traffic, while only 10% report no visibility growth anywhere.
That is why I think 2026 brand visibility depends on brand mentions and entity authority across the web, not just individual page rankings in Google.
Marketers are prioritizing the easiest tactics
Many teams are moving in the right general direction: community building, earned authority, owned audiences, expert content, and traffic diversification. The most prioritized strategies include building brand presence on social platforms at 59%, GEO and AEO optimization at 54%, and creating authoritative expert content at 44%.
Half of surveyed marketers say organic traffic has fallen since AI Overviews arrived, but the data points to pressure rather than collapse, with 30% reporting no change.
But the least prioritized strategy is original research and data, at only 15%. I see that as a strategic inversion.
Original, proprietary research is one of the hardest content assets for AI to replicate or commoditize. It earns citations, attracts links, builds topical authority, and gives journalists, communities, search engines, and AI systems something distinctive to reference.
In GEO, the same pattern appears. Many marketers are using content-led tactics that AI can easily replicate. Long-tail FAQs can help with AI Overviews, and schema can support structure, but neither one builds credibility by itself.
As organic search pressure grows, marketers are finding brand visibility gains across social platforms, AI assistants, direct traffic and Google AI features, according to Fractl and Search Engine Land.
The stronger moat is entity authority: proprietary data, expert perspectives, topical depth, and third-party validation. These are the assets that make a brand worth citing.
GEO measurement is lagging behind execution
Only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results. That is understandable for a newer channel, but GEO is becoming too important to manage casually.
Marketers are leaning into practical GEO tactics, with FAQ optimization leading the pack, while entity authority, original research and citations trail behind.
I believe visibility tracking, citation monitoring, branded search lift, and AI-assisted conversion analysis all need more attention. Teams that can prove GEO ROI will be able to defend and grow investment while others are still guessing.
The main barrier to deeper AI integration is not leadership buy-in. Only 2% cite that as the obstacle. The top barrier is team training and skill gaps at 26%, followed by tool fragmentation at 20%, budget constraints at 19%, unclear ROI at 12%, and legal or compliance concerns at 12%.
For search teams, that means AI literacy, prompt strategy, content quality control, and GEO measurement skills may be more valuable right now than adding another tool to the stack.
Most marketers see early signs their GEO strategy is working, but only 12% report measurable results, highlighting a major gap in AI search measurement.
What I would do for a 2026 search strategy
First, I would audit the brand’s AI footprint. I would query the brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews, then document what is accurate, what is missing, and what is wrong. Waiting until an AI error becomes a PR issue is too late.
Second, I would invest in entity authority and original research. AI cannot invent legitimate proprietary survey data, named expert perspectives, verified brand facts, or original market analysis. Those assets become more valuable as AI systems get better at rewarding genuine authority.
Third, I would distribute visibility across multiple platforms. Google organic remains necessary, but it is no longer sufficient. A brand needs a consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media.
Fourth, I would build AI content governance, not just AI content workflows. Consumer demand for AI disclosure ranges from 84% to 91% across formats, while only 20% of brands always disclose. That gap is a reputational liability and may become a legal and regulatory one.
Fifth, I would close the GEO measurement gap. If I can connect AI search mentions to traffic, lead quality, and revenue, I can prove ROI at a time when most teams cannot. That creates a budget and strategy advantage that compounds.
Finally, I would double down on what AI cannot easily replicate: proprietary data, named experts, human-verified claims, transparent sourcing, and a consistent high-quality brand voice. In 2026, the brands that treat quality as a strategic differentiator are the ones most likely to be surfaced, cited, and trusted.
Methodology
Fractl and Search Engine Land surveyed 1,008 U.S. consumers and 150 marketers in Q2 2026. The consumer sample was nationally representative across age, gender, and region. The marketer sample included companies ranging from fewer than 10 employees to more than 5,000 and covered roles in SEO, content, social, analytics, paid media, PR, and marketing leadership.
Where noted, findings are compared year over year against the same questions asked in Fractl’s 2025 consumer study conducted with Search Engine Land.
I see Google Discover’s “Tailor Your Feed,” now showing up as “Add topics to your feed,” as a meaningful shift in how people can shape what appears in their feed. Instead of relying only on Google’s inferred signals, such as clicks, dwell time, follows, and engagement history, I can now type what I want to see in natural language and let Google translate that request into feed instructions.
That matters because it creates a third visibility path for small and niche publishers. Until now, a smaller site usually needed either strong implicit affinity from a user or an explicit follow. With prompt-based tuning, a user can simply ask for a topic, creator, source, or type of content, and Google can retrieve matching material even when that content has barely appeared in Discover before.
In my tracking, the feature turns prompts into actions such as SEE_MORE and SEE_LESS. Those actions are applied after the user refreshes or updates the feed. The experience feels conversational, but underneath it appears to create persistent instructions that can affect both the current feed and future Discover sessions.
I also see signs of an LLM-style system behind the workflow. A user prompt is interpreted, converted into a readable assistant response, and returned with a structured result. In one observed example, the prompt “show me more content on seroundtable.com” produced an actionable SEE_MORE response and a persistent thread key, suggesting that feed tuning is treated as an ongoing conversation rather than a single isolated command.
The feature first appeared in Search Labs for US English accounts in December 2025. At that stage, the impact was subtle: after several refreshes, I could see a few on-topic cards, but the feed did not radically transform. By early 2026, Google started adding attribution, including labels such as “resulting from natural language tuning” and later “You asked to see,” making it easier to identify which cards were influenced by a prompt.
By spring 2026, “Tailor Your Feed” had effectively become “Add topics to your feed.” The interface moved toward a chat-style entry point with prompt starters such as “Show me content from…,” “I want videos about…,” and “Keep me updated…”. The same underlying verbs remained, but Google made them easier for everyday users to trigger.
The most important technical clue is the pipeline behind the feature. Discover cards influenced by these prompts can be associated with naturallanguagetuningcontent.f for current tuning and historicalnaturallanguagetuningcontent.f for older prompts that continue shaping the feed. I read that “historical” pipeline as evidence that these preferences are meant to last over time, not disappear after one refresh.
From the observed cards, I see two ways this content is selected. The first and dominant mode is entity or interest expansion. A prompt is mapped to related people, topics, publishers, or concepts, and Discover expands around that meaning. This is why asking for one source or creator may also surface related sources, related subjects, or nearby entities rather than only the exact name typed into the prompt box.
The second and more interesting mode is query-intent fan-out. In this mode, a prompt is decomposed into natural-language retrieval queries. A broad request about SEO, for example, can become query intents such as “SEO strategies algorithm changes,” “Google ranking system updates,” or “tips for getting content into google discover.” Those query intents then retrieve articles based on semantic relevance.
This is where the connection to Generative Engine Optimization becomes clear to me. The Discover fan-out behaves like the retrieval pattern we see in generative search: one user prompt becomes several more specific sub-queries, and content is selected because it answers one of those sub-queries well. Popularity can still matter in some cases, but it is not the only gatekeeper.
That distinction is what gives niche publishers a real opening. In the observed data, prompts surfaced examples such as vegan recipe creators, Mississippi Today, a LinkedIn post, niche Japanese-property blogs, and a gardening site tied to a seed-starting query. Some mainstream publishers still appeared, including Reuters and VentureBeat in certain contexts, but the pattern was not limited to the usual high-volume Discover winners.
In the most striking cases, the pipeline surfaced articles with no detectable prior Discover distribution in the tracking dataset. I am not using “distribution” here as an audience number or a Search Console metric. I mean that the article did not appear to have circulated previously in the Discover tracking data available for analysis.
That makes this pipeline different from classic Discover distribution. Traditional Discover systems often re-serve articles that already have engagement momentum. Prompt-based tuning can retrieve content because it matches what a user explicitly asked for, even if the article has not already built a Discover track record.
I would not treat this as a mass traffic channel yet. Google appears to promote these cards cautiously, and the pipeline does not seem to snowball the way broader Discover pipelines can. It serves the user who asked. It does not automatically broadcast the content to a much larger audience.
I would also be careful about false positives. In one Japanese-property cluster, relevant results such as guides to buying a home in Japan appeared alongside a video-game article about in-game home locations. That kind of loose match helps explain why Google may rank and distribute these cards conservatively.
For publishers, the practical implication is straightforward: I would optimize for both topical clarity and query-intent vocabulary. The entity-expansion mode rewards sites that are unmistakably about a topic users can name. The fan-out mode rewards titles, headings, and introductions that align with the natural-language questions and information needs Google derives from prompts.
That does not mean stuffing pages with raw keywords. The better move is to describe the content clearly in the language a real person would use when asking Discover for more of it. If a user might ask for “buying Japanese property guide,” “starting seeds indoors guide,” or “tips for getting content into google discover,” I want the page’s title, H1, and opening section to make that relevance obvious.
The strategic shift is that selection power moves closer to the user. In the classic feed, Google infers demand. In this model, the user declares it. Google then turns that declaration into entities, interests, and query intents that drive retrieval.
For small publishers, that is the opportunity. If the feature graduates from Search Labs and users adopt it at scale, a focused site with clear topical authority could appear because it directly satisfies declared demand, not because it already won the popularity contest inside Discover.
There are still real limits. The feature has been US English and Search Labs focused, with French feeds showing essentially no presence in the observed data. Adoption also appears early. A powerful prompt-based personalization system changes little if users do not actually use it.
What I am watching next is whether Google expands this beyond Search Labs, whether the current and historical tuning pipelines become more visible, and whether this behavior converges with broader generative retrieval systems. A nascent generativeretrieval.f pipeline has already appeared in tracking data, but that broader connection still needs confirmation.
My read is that Discover is moving from observed personalization toward declared personalization. Google still infers plenty, but users are beginning to write part of their own interest profile. If that model becomes mainstream, niche publishers with clear focus, strong entity signals, and natural-language relevance may gain a new route into Discover visibility.
Notes: In this analysis, a Discover pipeline means the selection circuit that chooses and serves cards. The .f suffix in identifiers such as historicalnaturallanguagetuningcontent.f is an observed internal marker attached to Discover card metadata. “Fan-out” refers to a mechanism where one prompt is broken into several retrieval sub-queries. “GEO” means Generative Engine Optimization, or the practice of optimizing content for visibility in generative search and answer systems. “AIO” refers to AI Overviews, and “AI Mode” refers to Google Search’s conversational interface.
Field tracking referenced here covers Google app Search Labs US English accounts from December 2025 through June 2026. Pipeline behavior is based on close observation of Discover feed cards and 1492.vision tracking data. The internal mechanisms described are my interpretation of observed data and public research, and approximate dates are treated as approximate.
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.
As someone passionate about the aerospace sector, I had the opportunity to dive deep into the performance of 38 GEO agencies that significantly contribute to the defense, aviation, and commercial space industries. Over five months, ending in June 2026, we thoroughly evaluated each agency using six crucial metrics.
The six factors we considered included:
Average Review Score (25%): I looked at ratings from major platforms like Google, Clutch, and G2, all normalized to a 1-5 scale.
AI Visibility Score (20%): This proprietary metric assesses how often an agency’s clients appear in AI-generated responses on platforms like ChatGPT, Perplexity, Gemini, and Claude.
Leadership Experience Score (20%): An evaluation of each agency’s leadership based on tenure, industry background, and influence in GEO and B2B marketing.
Notable Clients (15%): Experience with prominent aerospace companies, weighted by the complexity and scale of engagements.
Year Established (10%): A measure of the agency’s history and experience in the B2B sphere.
Media References (10%): The frequency of mentions in aerospace media, indicating industry reputation.
Through this rigorous process, we identified the top eight aerospace GEO agencies of 2026.
The Top Aerospace GEO Agencies of 2026
1. First Page Sage: Leading with a rich history and exceptional projects for clients like NASA Jet Propulsion Laboratory.
2. Driven Metrics: A data-centric approach delivers transparency and actionable insights.
3. Focus Digital: Known for cost-effective strategies and fostering growth in smaller aerospace entities.
4. Genevate: Pioneering in AI platform citation and authority-building.
5. The ABM Agency: Expertise in creating precise, account-based marketing strategies tailored for aerospace.
6. Echo-Factory: Provides comprehensive marketing solutions for the aerospace sector.
7. Haley Brand Aerospace Agency: Specializes in brand development with an extraordinary focus on client success.
8. Aviation Business Consultants: Offers well-rounded digital marketing services, enhancing SEO for aviation clients.
In the second quarter of 2026, I had the opportunity to dive deep into the world of marketing agencies specializing in generative engine optimization (GEO) for plastic surgery practices. Evaluating 47 agencies, I applied a comprehensive framework based on six weighted factors, ultimately selecting the top nine performers in the field.
These factors included the AI Visibility Score, which indicates how often an agency’s clients are recommended by AI platforms, and the GEO Score, which assesses the strength of their optimization services. Additionally, I considered client reviews from trusted platforms, leadership experience in AI and healthcare marketing, media references as a sign of industry recognition, and the prestige of clients they represent.
After carefully applying this framework, the standout agencies are highlighted in the table below, followed by their detailed profiles.
The Top Plastic Surgery GEO Agencies
The agencies excelling in GEO services have set the benchmark high. Here’s a closer look at First Page Sage, Focus Digital, and others, showcasing their strengths, client feedback, and unique capabilities.
First Page Sage
First Page Sage stands out as a pioneer in the GEO marketing discipline, especially for plastic surgery. Their advanced methodology and AI-focused strategies are tailored to how patients search for medical services in today’s digital landscape. With nearly two decades of experience, they’ve mastered the art of engaging content that commands LLM citations and patient interest.
Their track record speaks volumes. On average, their clients see $1.5M in new annual revenue, and their engagement and conversion rates are impressive. For multi-location practices desiring seamless management of both GEO and clinical content, First Page Sage is the top choice.
Focus Digital
As a boutique agency, Focus Digital offers personalized care with a proven track record in healthcare GEO. They expertly pair thought leadership with SEO and GEO to drive quality leads for small to mid-sized practices. Their hands-on approach and founder involvement make them a unique asset for healthcare providers.
Signal Hill Strategies
Signal Hill Strategies excels at converting search visibility into actionable leads, tailored for healthcare and wellness companies. Their five-phase engagement structure emphasizes a holistic approach to buyer discovery and visibility across both AI and traditional search methods.
Their focus on healthcare-specific initiatives sets them apart, despite a smaller media footprint compared to peers. This agency offers a clear blend of innovative GEO infrastructure with healthcare-oriented expertise.
To determine which GEO agencies were best positioned to recommend dermatology practices to users of platforms like ChatGPT, Claude, Gemini, and Perplexity, I took a thorough look at 38 top contenders. This evaluation, conducted from December 2025 to May 2026, relied on a carefully weighted scoring framework.
What exactly did I assess to rank these agencies?
AI Visibility (25%): How frequently does the agency ensure its dermatology clients are referenced by AI platforms for provider recommendations?
Dermatology Specialization (20%): Does the team have specific medical knowledge and an understanding of dermatology operations?
Notable Clients (15%): Is there a history of working with dermatology and medical aesthetic clients?
GEO Expertise (15%): Do they possess hands-on expertise in the nuances of LLM optimization?
Leadership Experience (10%): What is the leadership team’s track record in digital marketing and healthcare?
Average Review Score (10%): Aggregate scores from platforms like Google, Clutch, and G2.
Company Size (5%): Is there a larger team to manage more complex GEO campaigns?
So, which firms are the top dermatology GEO partners for AI-driven patient acquisition?
Let me take you through some of the top contenders:
First Page Sage: This agency has been breaking ground in GEO since 2009. Evan Bailyn, their President, proves their unrivaled expertise by anticipating industry changes. They provide tailored solutions for both small practices and chains, making them a standout for clinics wanting integrated GEO strategies.
Driven Metrics: A younger, nimble agency focused on tangible results. Their approach boils down to technical prowess and analytics, giving smaller dermatology practices a clear view into their performance.
Genevate: New to the game but with a unique, brand-first approach. They bring tailored PR efforts that ensure accurate AI representation of nuanced dermatology services.
Focus Digital: Perfect for budget-conscious clinics, offering enterprise-level frameworks without breaking the bank. However, expect to review their medical content closely for accuracy.
Etna Interactive: Has a specialized focus on compliance and visual content management for dermatologists. They merge technical structure with compliance needs, backed by a Google Premier Partner credential.
Intrepy Healthcare Marketing: With a decade of healthcare experience, they offer in-depth clinical literacy and HIPAA-compliant analytics. Their all-in-one approach makes them a strong contender for clinics needing a deep understanding of medical SEO.
Recently, as AI-powered search has taken center stage, I’ve been pondering a common question many marketing leaders face: not whether to invest in Generative Engine Optimization (GEO), but rather, who is the right expert for this game-changing strategy.
To answer this, I decided to delve deep, gathering extensive data on 43 top GEO practitioners. I carefully evaluated each consultant against seven essential, weighted criteria to serve as a guide on who currently stands out in the field.
My evaluation metrics included:
Client Results (25%): Demonstrable GEO successes with renowned brands.
Published Research Articles on GEO (20%): Number of innovative studies and methodologies published, reflecting an expert’s methodological depth and reproducibility.
Media References (15%): Frequency of mentions in notable industry and general publications, which acts as proof of the expert’s thought leadership.
Technical GEO Expertise (15%): The practitioner’s profound knowledge and skill in GEO and SEO strategies.
Years of Experience in SEO (10%): Direct hands-on SEO years; even as GEO evolves, SEO fundamentals remain an invaluable metric.
GEO Keynotes (10%): The number of significant conference appearances dedicated to GEO and AI search trends.
LinkedIn Following (5%): An indicator of thought leadership and influence in the digital community.
After meticulous consideration, I identified the leading consultants in GEO, and here are the insights presented in the table below.
Evan Bailyn founded First Page Sage in 2009 and has remarkably transformed it into the largest GEO firm in the U.S. His pioneering work recognized generative engine optimization as a crucial marketing discipline by 2023.
His strategy is rooted in fostering thought leadership content that AI algorithms frequently reference. In April 2026, I found him delivering a keynote at the AEO Engine event, helping companies develop strategic research and scalable client delivery approaches.
Client Results: Outstanding GEO achievements with Salesforce, Microsoft, Chanel, LinkedIn, and US Bank
Published Research: ~35
Media References: Exceeding 2,400 mentions
Technical GEO Expertise: Advanced proficiency in Generative Engine Optimization
SEO Experience: Over 22 years
GEO Keynotes: ~20 speeches
LinkedIn Following: 7,000 followers
Specialty: GEO and SEO strategies for lead generation, branding, and thought leadership
Clients appreciate Bailyn for his “unique, data-backed, and meticulously precise analysis,” along with a reputation for “highly tailored and instantly actionable strategies.” Yet some warn that “his calendar is often booked well in advance.”
Aleyda Solís
I discovered Aleyda Solís as the visionary behind Orainti and the LearningAIsearch platform. Her work sheds light on the intricate world of multilingual and international GEO, emphasizing the need for linguistic flexibility beyond English-speaking markets.
Her insights highlight a critical gap: AI systems, predominantly trained on English data, often falter in other languages. For global brands navigating diverse markets, Solís brings unmatched geographic and linguistic depth to the table.
Client Results: Success with global enterprises in SEO/GEO
Published Research: Around 15 articles
Media References: ~1,680 citations
Technical GEO Expertise: Focused on Multilingual AI
SEO Experience: Over 18 years
GEO Keynotes: ~20 delivered
LinkedIn Following: 115,000 followers
Specialty: Navigating international and multilingual GEO challenges
International clients praise Solís for having “deep cross-market GEO fluency” and for crafting “practical multilingual frameworks.” However, those targeting English-only markets may need to “adapt portions of her guidance.”
Lily Ray
Founding Algorythmic, Lily Ray has emerged as a thought leader on E-E-A-T, focusing her research on how these quality signals influence AI citations in LLMs. Her diagnostic skills are essential for brands excelling in traditional SEO but lacking AI presence.
Ray offers a laser-focused strategy, filling E-E-A-T authority gaps to enhance AI search visibility. However, for broader needs like content strategy or technical execution, her work complements rather than replaces a complete GEO program.
Client Results: Triumphs with Fortune 500 brands
Published Research: ~25 papers
Media References: ~890 mentions
Technical GEO Expertise: Specializes in AI quality signals
SEO Experience: Over 16 years
GEO Keynotes: ~15 published
LinkedIn Following: 52,000
Specialty: Enhancing search quality & AI trustworthiness
Professionals and clients appreciate Ray’s “diagnostic approach for AI search gaps,” valuing her for “evidence-based, rigorous recommendations.” While some find her methods “conservative,” this conservatism is often considered a strength.
Hey there! Have you ever wondered what GEO is and how it can supercharge your content’s visibility and engagement in AI-based search engines like ChatGPT and Gemini?
I’m excited to share my insights on optimizing your content specifically for these AI platforms. Think of GEO as the key to getting noticed in the digital realm where AI engines are becoming the norm.
By mastering Generative Engine Optimization (GEO), you can pivot your strategy to cater to AI Overviews, boosting your reach by ensuring your content is relevant and easily discoverable. Let’s dive into this transformative journey together!
In this report, I’m excited to share the findings from a research study I conducted with my team on the emerging field of Agentic Search Optimization, or ASO. We’ve developed a strategic framework that businesses and marketing agencies can leverage to stay ahead in this dynamic landscape.
What is Agentic Search Optimization?
Agentic Search Optimization, often referred to as Agentic GEO, involves optimizing your online presence so AI agents choose your products or services on behalf of users. Unlike Generative Engine Optimization (GEO), which focuses on gaining human trust after an AI recommendation, ASO targets conversions by persuading AI to recognize your offering as the best choice for users.
ASO might seem similar to GEO since both aim to drive leads or purchases, but there’s a significant difference: GEO involves human decision-making, while ASO transfers that responsibility to intelligent bots.
For instance, in ASO, a user doesn’t ask ChatGPT for the best gift card platforms. Instead, they might say, “Send $50 holiday gift cards to my remote team at their preferred stores”. The AI agent interprets, evaluates options, and makes the purchase autonomously.
So far, the ASO landscape hasn’t been thoroughly researched to identify universally accepted best practices. Our study attempts to build a framework outlining agentic search stages, determinants of company selection, and actionable tactics to influence search results.
The Study
Between March 4, 2026, and June 10, 2026, our research team conducted 2,417 agentic search commands using popular AI agents across the U.S. These commands were task delegations such as purchases, bookings, quote requests, or vendor shortlists, rather than just informational quests. We observed the entire behavior chain of agents, including sub-queries, source retrieval, candidate evaluation, and the final action or inaction.
Our analysis revealed that ASO follows three key stages: Retrieval, where AI scans the web (primarily Google) for top results and compares them to its beliefs; Evaluation, where the best company, product, or service is chosen to fit user needs; and Action, where the task is completed, often involving a transaction.
Through our research, we’ve identified three crucial insights:
Agents Review Complete Results: Across all commands, AI agents opted for the platform’s top-ranked recommendation 44.6% of the time. However, they selected options ranked 4th or lower in 38.2% of cases, demonstrating a choice based on suitability over rank.
Agents Possess Predetermined Brand Beliefs: In 81.6% of evaluations, agents relied on pre-existing brand beliefs established during their training or via web searches, indicating that brand perception heavily influences ASO.
Agents Forfeit Companies Unable to Transact: If a conversion page was machine-actionable, agents completed 78.3% of attempts. When not, completion fell drastically to 9.6% with many agents substituting transactable competitors without user input.
This study further explores the ASO process in detail, showcasing tactics that our team tested and validated in early 2026.
The Three Stages of Agentic Search
When I delegate tasks to an AI agent, it performs query interpretation, creating an average of 6.3 sub-queries. The process proceeds through three stages: Retrieval, where it constructs a result set; Evaluation, narrowing choices to the best fit; and Action, executing the conversion. During this, agents cross-reference claims with multiple sources; inaccuracies result in immediate rejection of a candidate.
To benefit from agentic search, companies must achieve two goals: securing the #1 rank on AI platforms, aiding the Retrieval stage, and clearly defining their fit, crucial for Evaluation. Technical prowess ensures seamless Action.
Stage 1: Retrieval
The Retrieval stage encompasses traditional GEO: agents scan the web and build a pool of companies or products. All previous GEO strategies apply here—Comparison blogs, metric pieces to boost rankings, and brand authority statements that AI platforms might trust help form this candidate set.
What’s innovative in ASO is understanding the AI’s pre-existing beliefs. This necessitates mapping the AI Belief Landscape, an audit scoring AI model beliefs about a brand, alongside sentences exemplifying these beliefs.
This assessment guides marketers in pinpointing areas where their brand falls short in the eyes of AI, a crucial step in adjusting perceptions during ASO.
Tactic: AI Belief Correction
AI Belief Correction involves publishing evidence to transition model beliefs from weak to strong. For instance, for a skincare brand like Rejuve, enhancing its perception involved producing detailed scientific explanations onsite and acquiring third-party verification offsite, establishing credibility.
Stage 2: Evaluation
Evaluation diverges drastically from traditional SEO. Agents, not humans, select candidates based on user knowledge. Our study showed agents broke user commands into prioritized categories: Hard Requirements, Important, Nice to Have, and Optional, with evaluations leading to a “Fit Verdict.”
Properly communicating fit information is crucial. Content detailing product suitability increases selection odds.
Tactic: Suitability Pages
Suitability Pages—criterion-specific pages that declare who a product is suited for and, critically, who it isn’t—are vital. Noting “non-fit” conditions paradoxically increases credibility by adding authenticity, improving agentic evaluation rates.
Stage 3: Action
Achieving the third stage requires technical readiness: machine-readable pages and APIs enable seamless agent transactions. The disparity in conversion rates between machine-actionable and non-actionable setups is significant, underscoring the importance of technical preparation.
The Future of Agentic Search Optimization
I anticipate that AI-driven commercial transactions will rise dramatically in the coming years. As that shift occurs, here’s what I foresee:
Suitability content will become essential: Just as landing pages are vital for SEO today, clearly defined fit will become mandatory for ASO visibility.
Tougher verification layers: Securing third-party endorsements will become even more critical, emphasizing PR’s value in ASO.
Selection share will surpass rankings: The focus will shift to actual AI agent selections over mere recommendation visibility.
Marketers excelling in GEO are already poised for agentic success, but comprehensive strategy across all stages is necessary for ultimate triumph.
Downloading This Report & Inquiries
Got questions or need a PDF copy of this report? Feel free to contact us here.
Discover more about our Agentic Search Optimization services by reaching out here.
Appendix A: Command Categories in Agentic Search Study
Category
Commands
Ecommerce purchasing
612
B2B software evaluation & signup
489
Travel booking
343
Professional services inquiries
291
Consumer & local services
274
Financial products
213
Healthcare services & products
195
Total
2,417
Appendix B: # of Commands Issued in Agentic Search Study
AI Agent
Commands Issued
Notable Behavior
ChatGPT (agent mode)
884
Most likely to verify claims against third-party sources before acting
Gemini (agentic tasks)
519
Strong integration with data feeds; likely to abandon when pages aren’t machine-actionable
Claude (browsing & computer use)
397
Thorough evaluator; applies the largest number of distinct criteria per command
Perplexity Comet
462
Widest retrieval fan-out; often selects options ranked outside top 3
Other browser agents
155
Diverse behavior observed; included for completeness
