Join me on April 1 for the inaugural SMX Now event, where iPullRank will unveil their presentation, ‘AI Search Picks Winners: Here’s the GEO Strategy Behind It’.
Visibility today means more than just ranking well; it’s about ensuring your content is found, evaluated, and chosen by AI-driven search platforms. On April 1 at 1 p.m. ET, I’m excited to launch our new monthly SMX Now webinar series featuring insights from iPullRank’s experts Zach Chahalis, Patrick Schofield, and Garrett Sussman.
During the session, you’ll be introduced to iPullRank’s innovative Relevance Engineering (r19g) framework, which applies Generative Engine Optimization (GEO) using a comprehensive omnichannel content strategy. Engaging with this will deepen your understanding of how AI search leverages query fan-outs to discover and elect content sources, and how best to structure your content for optimal retrieval, visibility, and citation.
It’s crucial to note that success with GEO is not a one-size-fits-all solution. It demands continuous testing, tailored strategies, and a robust three-tier measurement model that covers discovery, selection, and citation impact.
Reserve your spot now and explore how you can elevate your brand’s visibility in an AI-powered world.
I’m proud to partner with Search Engine Land as a media sponsor for the upcoming SEO Week by iPullRank.
Most content out there tends to be too generic, making it less effective in AI search. I’ve discovered that using customer personas allows me to pinpoint real problems and step into the search space much earlier.
Whenever buyers pose a question, my goal is to deliver a clear answer. That’s essentially the “They Ask, You Answer” (TAYA) framework, which thrives even in AI-driven discovery.
Though it sounds straightforward, I’ve seen many teams struggle to anchor their approach. This typically results in generic questions that lead to generic content.
This is problematic since AI is transforming search behavior, shifting from simple queries to in-depth, context-rich questions. The difference lies in the questions we choose to answer, and that’s where customer personas shine.
The Problem with Generic Questions
Chances are, both I and my competitors have tackled these generic questions already or could do so quite easily.
The trap of generic questions occurs when marketing teams, including mine at times, begin brainstorming content ideas with broad topics like:
What is CRM software?
What is marketing automation?
What is warehouse management?
While reasonable, these questions are not what real buyers ask. Real buyers ask questions based on their specific situations, such as:
“What CRM should a 10-person sales team use?”
“Why are leads slipping through the cracks in our marketing?”
“Why is our warehouse picking speed so slow?”
This distinction is subtle but crucial. The second set of questions integrates a person and a problem, transforming the quality of the content I produce.
Why This Matters More in AI-Driven Discovery
With AI, buyers are asking detailed, context-rich questions, such as:
“I run a 15-person marketing team, and we’re struggling to track leads properly. What should we do?”
The AI provides explanations, outlines solutions, and suggests vendors, essentially giving the buyer a consultation. My content’s job is to explain why a specific persona faces a specific issue, framing how it should be perceived.
This positions me into the conversation earlier, increasing the likelihood of staying top of mind as the user’s understanding evolves.
Imagine this scenario, using myself as the subject:
Marcus.
50 years old.
Meeting old friends in Birmingham, UK.
Looking for things to do for the day.
I might start with a broad question:
“I’m looking for some things to do with friends in Birmingham on the weekend. I’m 50, and I have some old friends visiting for a day. We’ll enjoy some beers, but need activities too.”
The answers might include bars, food, and activity bars. An F1 gaming arcade could be suggested, sparking my interest since I enjoy games but not cars, which prompts my follow-up question:
“Ah, we all like games. What gaming arcades could you recommend?”
The responses might highlight a pinball arcade in Digbeth.
“Pinball Factory in Digbeth sounds fun. What else is there to do around there, food- and drinks-wise?”
This kind of dialogue allows me to refine my day’s plan perfectly for my friends.
Being part of the conversation from the start helps shape the dialogue and boosts the chance of being included in the final decision.
Personas Make TAYA Far More Precise
With personas, I think like my customers, identifying the questions they might ask long before they reach my offerings.
When I define a customer segment, I delve into that persona, understanding their problems and goals to think like them, which helps in crafting content that answers their early-stage questions.
Instead of creating content for a vague audience, I focus on real people, addressing specific needs like, “The best day out in Birmingham for a group of 50-year-old gamers.”
This small shift often leads to valuable content, positioning me within meaningful conversations rather than competing on crowded commercial queries.
A Simple Way to Uncover Better Questions
No need for a complex persona framework. Often, a simple three-question exercise reveals the problems buyers seek to solve.
For each persona, I ask:
What are they responsible for? Examples include sales targets, marketing leads, or warehouse operations.
What problems complicate that responsibility? Issues like missed targets or inefficient operations might arise.
What might they search for when facing these problems?
Now, the questions I generate differ greatly from generic ones:
Instead of saying: “What is CRM software?”
I see questions like:
“Why are leads slipping through the cracks in our CRM?”
“What CRM should a small sales team use?”
“Why is our warehouse picking speed so slow?”
These questions reflect real situations, providing the most substantial content opportunities.
‘They Ask, You Answer’ Works Better with Personas
TAYA covers five key areas: cost, problems, comparisons, reviews, and best-of. These topics offer structure, but approached generically, they mirror what everyone else is doing.
Generic questions like:
“How much does CRM software cost?”
“What problems do warehouse systems have?”
“HubSpot vs. Salesforce”
“Best CRM systems”
“Salesforce review”
Can be transformed into more targeted questions:
“What does CRM cost for a 10-person sales team?”
“Why do my warehouse managers struggle with picking accuracy?”
“HubSpot vs. Salesforce for a small B2B marketing team”
“Best CRM for growing sales teams”
“Is Salesforce suitable for a mid-size sales organization?”
Although the topic remains the same, the approach is tailored to the buyer’s reality. This makes the content more useful and aligns with AI interactions.
Targeted questions might include:
“We’re a small marketing team struggling to track leads properly. What CRM should we use?”
If my content already answers these persona-centered questions, it increases the chance of my explanations becoming part of their conversation.
In short, personas enhance TAYA by transitioning from broad topics to specific questions associated with real problems, improving the content and aligning better with buyers’ needs.
Start with the Problem, Not the Product
A common misstep in content marketing is leading with the product. Buyers, however, start with a problem.
By using personas, I anchor content in the buyer’s perspective rather than my own, ensuring the focus is on the customer.
This change can mean the difference between influence and mere existence of my content.
Where You Enter the Conversation Matters
“They Ask, You Answer” is an effective framework when the questions I address are of high quality.
Personas help in turning vague topics into precise problems, resulting in content that resonates with buyers and AI systems while earning their trust.
As I delve into the evolving landscape of web traffic, I find Yahoo CEO Jim Lanzone’s insights on AI-powered search engines, particularly Google’s AI Mode, incredibly fascinating. He believes this technological evolution poses a significant threat to the web’s traditional traffic model.
Jim highlights a major concern: “I think that the LLMs are one big reason they’re under threat, with AI Mode in Google being the biggest challenge.” This makes me ponder the impact on publishers who rely heavily on these traffic flows.
I resonate with Jim’s view that publishers truly deserve this traffic. He articulates a fundamental truth: “Those publishers deserve [traffic], and we’re not going to have the content to consume to give great answers if publishers aren’t healthy.” This reflects the delicate balance required in the digital content ecosystem.
Why I care. Many websites, mine included, are noticing a dip in traffic coming from answer engines such as Google and OpenAI. It feels like a looming concern that could worsen. Yahoo’s dedication to maintaining the “search sends traffic” model is reassuring, as Jim passionately explains: “We have very purposefully highlighted and linked very explicitly and bent over backwards to try to send more traffic downstream to the people who created the content.”
Yahoo’s unique AI approach. Listening to Jim on the Decoder podcast, I learn that Yahoo is carving its own path with AI. Unlike the more conversational chatbot models, Yahoo isn’t pursuing to be an AI assistant: “Ours looks a lot more like traditional search and it is more paragraph-driven. It’s not a chatbot that’s trying to act like it’s a person and be your friend.” I see this as a move towards emphasizing informative search experiences.
Moreover, “We’re not a large language model. We’re not going to be the place you come to code. We’ve really launched Scout as an answer engine.” This strategy, I believe, could provide a clearer, more reliable information source online.
What’s next: Embracing personalization. In observing Yahoo’s strategy, I’m excited to see their efforts to evolve. They’re embedding AI across platforms: “You are very shortly going to see us get into very personalized results. You’re going to see us get into very agentic actions that you can take.” This indicates a future where user-specific solutions take precedence.
For instance, Jim notes, “There’s a button in Yahoo Finance that does analysis of a given stock on the fly… It is in Yahoo Mail to help summarize and process emails.” Such tools could transform how I interact with content on various platforms.
Yahoo vs. Google: A non-competition. Interestingly, Yahoo isn’t trying to directly outplay Google. Instead, as Jim points out, the focus is on existing users and enhancing their experience: “Nobody chooses, you will not be surprised, Yahoo over Google or somewhere else to search. The way that we get our search volume is because we have 250 million US users and 700 million global users in the Yahoo network at any given time. There’s a search box there. And infrequently, they use it.” It’s more about nurturing the loyalties of existing users.
A word of caution. The conversation also shines a light on the potential pitfalls of heavily relying on AI platforms. Jim references past experiences with Google: “You are tempting fate by opening up a way for consumers to access your product within a large language model.” This analogy resonates with me deeply, remembering the cautionary tales in tech history.
Yet, he warns: “The big bad wolf will come to your door and say everything’s cool.” It’s a timely reminder of the ever-competitive and unpredictable nature of tech alliances.
I realized that the traditional webpage is no longer the center of digital visibility. We’ve been relying on URLs and keywords, a structure made for a journey that AI now bypasses entirely.
In this era where search is everywhere, the entity—a precise, machine-readable concept of a product, organization, or individual—has become the core unit of power.
Brands that dominate now in the AI landscape are those creating strong entity authority. The key to surviving the shift to generative discovery is not merely about the page anymore. It’s about developing entity linkages to build the foundation of AI visibility.
We need to acknowledge a profound transformation in how the web is indexed. We’ve moved beyond just retrieving information to a new three-stage evolutionary process.
Phase 1 (Strings): We focused on optimizing keyword strings in traditional SEO. The goal was to align queries with text on a page.
Phase 2 (Things): With modern search, we understand entities. Knowledge graphs now recognize brands, founders, and products as distinct entities.
Phase 3 (Entities): AI systems use structured entity ecosystems today. The aim is to become a verified authority within this interconnected network of entities and capabilities.
In this current phase, search engines evolve into reasoning engines, analyzing content and your brand’s ecosystem role.
The evolution is powered by economic necessity: the comprehension budget. AI systems are resource-intensive, processing content and calculating interpretations.
Whenever an engine clarifies a brand or assumes a relationship, it exhausts valuable resources. Unstructured or inconsistent data increases this computational load.
To optimize performance, I use a comprehension subsidy, employing Schema.org to make data more accessible to machines, reducing the inference needs for AI systems.
Shifting from traditional SEO to generative engine optimization (GEO), I focus on relevance engineering, structuring content to be part of AI-generated answers.
GEO is about making your brand’s information easily interpretable, verifiable, and useful in AI-generated responses across platforms like ChatGPT and Google’s AI Overviews.
Most enterprise sites have some structured data, but for AI, basic and fragmented schema is insufficient. It creates separate data islands and complicates the AI’s effort to form connections.
The correct approach is implementing a content knowledge graph, mapping entities hierarchically and ensuring they’re machine-readable through Schema.org and JSON-LD.
To be globally recognized, properties such as @id for consistency and sameAs for linking to reputable sources help in entity disambiguation, boosting credibility.
To maintain a strong AI relationship, move beyond simple tagging to entity governance—establishing verifiable sources of truth for AI platforms at scale.
As the AI experience evolves toward active agents managing user actions, I focus on schema actions that make my entity callable and ready to support AI-driven interactions.
If my entity isn’t clearly defined, AI may overlook it, turning to competitors prepared with actionable data pathways for users and AI systems.
Schema drift is a risk: inconsistencies between human-visible content and machine-readable formats can lead to lower confidence scores, reducing citations.
Monitoring and continually updating schema with real-time signals ensure I remain present and operationally capable in the agentic web ecosystem.
The new key performance indicators in AI environments go beyond traffic metrics, emphasizing model share and citation value, ensuring AI reflects my brand accurately.
Maintaining AI trust requires precise alignment of schema with declared business specifics, preventing entity drift and supporting positive AI interactions.
Embracing entity-first strategies allows me to build credibility and presence in AI searches, where content knowledge graphs enhance my brand’s visibility.
Ultimately, it’s not just about being on the page — it’s about the confidence AI places in my entity, ensuring it remains a powerful tool for discovery.
Key Takeaways:
From strings to things to systems: Transition from keyword targeting to entity authority, focusing on overall concept dominance.
Efficiency is currency: Streamlined, structured data helps AI access your information more efficiently, enhancing citation potential.
Citations are the new clicks: Achieving top visibility now involves influencing AI recommendations rather than just page visits.
Governance is revenue protection: Avoid schema drift to maintain AI confidence and brand presence.
Callability = survival: Ensure your brand’s entities are ready for AI agent interactions with actionable schema.
As I delve into the world of AI-driven search, it’s clear that advice around AI is becoming way too simple. What really sets you apart are knowledge graphs, expert entities, and how you influence trusted datasets.
Recently, I came across a Harvard Business Review article that resonates with the shifts we’re noticing in SEO. AI Overviews and Google’s AI-enhanced search features are not only creating what’s known as a zero-click environment but they’re also redefining user journeys and behaviors.
User journeys that were once multi-touch are now compressed into a single, synthesized answer. The metaphor of the “Search” monolith crumbling visually captures this transformation.
In this dramatic shift, brands like mine lose many traditional touchpoints, requiring a change in marketing strategy. HBR brilliantly highlights how algorithms are reshaping first impressions. However, while pointing in the right direction, the article’s tactical advice feels too generic and superficial.
Much of the advice sounds strategic yet lacks deep operational insight. This gap is crucial for sustainable visibility and long-term success.
The challenge is deeper than what appears as simple advice to navigate at an executive level. Real structural change is essential to adapt to the evolving search landscape.
The Problem with Flock Tactics
The HBR article brings forward schema, authorship signals, and branded concepts but these suggestions risk becoming “flock tactics.” They spread because they’re easy to grasp, yet they lose their edge once widely adopted.
Schema
Schema is highly debated in LLM and AI optimization. Although Microsoft Bing uses schema for its LLMs, Google’s models have a more complex relationship with third-party LLMs.
Incorporating schema in AI and SEO activities is useful, but presenting it as a fundamental tactic neglects its diminishing returns when everyone implements it.
Another oversight is the importance of external knowledge systems such as Wikidata. LLMs often rely on these authorities more than on any single website.
There’s a significant gap in understanding how models process structured versus unstructured data signals.
E-E-A-T — Shallow Authorship Signals
Using real experts’ credentials aligns with E-E-A-T but often becomes superficial, focusing on bios and headshots without actually strengthening expertise.
There’s a profound difference between mere display of bios and nurturing an expert entity recognized in academia or industry.
Only genuine expertise creates the signals that AI models trust.
Vanity Concepts
Creating branded concepts like “The Acme Index” sounds appealing but is difficult to successfully execute. External adoption is key for them to gain traction.
These concepts must be embraced by reputable sources, which is a hurdle many brands fail to overcome.
The Structural Blind Spots
Beyond tactics, there are deeper structural issues in perceiving AI solely as an external shift rather than an opportunity to innovate internally.
Internalizing AI Infrastructure
The potential to integrate AI deeply into operations, through AI assistants or domain-specific agents, is often overlooked.
In controlled environments, fundamentals like site architecture and data structures remain crucial for success, even if they need to be reimagined for AI.
When I think about how ChatGPT retrieves information, I find it fascinating that most sources it pulls in don’t make it to the final answers. According to a report by AirOps, a whopping 85% of the sources identified by ChatGPT never appear in its final response.
Why this matters to me. If I’m aiming to have my content mentioned in AI-generated answers, it’s clear that simply being discovered by the AI isn’t sufficient. Most pages that get retrieved ultimately don’t get the exposure I’m hoping for.
Key insight. It’s interesting to note that just because a page ranks and is retrieved doesn’t mean it gets cited. My content has to align closely with the prompt or the context it supports to be chosen.
Per the report: the focus shifts to how well I can optimize my content for selection in the AI synthesis process, beyond just showing up in the search results.
By the numbers:
82,108 citations appeared in final responses, but only 15% of the retrieved pages were mentioned. That means 85% of the pages that surfaced during research didn’t make it into the answers.
Citation rates also varied based on query type:
18.3% for product discovery queries, 16.9% for how-to queries, and 11.3% for validation searches.
Fan-out queries. I noticed that when ChatGPT generates an answer, it often triggers additional internal searches, resulting in a “second citation surface.” This stood out in the dataset findings:
89.6% of prompts prompted two or more follow-up searches. Fan-out searches expanded 15,000 prompts into 43,233 queries. Interestingly, 32.9% of the cited pages were results from these fan-outs and not the original prompt.
95% of fan-out queries had zero traditional search volume.
Google ranking correlation. I’ve learned that high rankings in Google significantly improve chances of citation:
55.8% of cited pages ranked within Google’s top 20. Pages in Position 1 were cited 3.5 times more often than those outside the top 20.
About the data. AirOps examined 548,534 pages from 15,000 prompts to understand how ChatGPT expands queries and selects which citations to include.
AI search expands the long tail into a multitude of prompt variations. Let me guide you through how fan-out queries, grounding, and task completion are reshaping SEO.
When I speak naturally, my language flows. It’s often messy, incomplete, and not always coherent. In contrast, the Google search bar made me condense my needs into short-tail or long-tail queries.
To navigate this, I would stack queries along a journey, refining them from A to B by stripping out personal nuances to suit what I thought the search engine could grasp. SEO experts built strategies around this, organizing queries by search volume and intent.
That’s evolving now. With Google promoting Gemini and companies like Samsung highlight AI features as key selling points, the landscape is shifting. I’m encouraged to be more expressive and detailed with my searches.
Moving from Keyword Research to Prompt Research
We need to transition from keyword research to prompt research. Traditionally, keyword research involved quantifying demand and optimizing at a phrase level. The new AI-driven search environment calls for understanding demand as generative concepts, preserving needs across numerous prompt formats.
This shift doesn’t render keyword research obsolete, but changes its scope. I’m learning to model user journeys, considering decision stages and user uncertainty, rather than just relying on search volume.
What I get from this isn’t merely a keyword map, but a task map reflecting real audience constraints. This signifies a shift from short and long-tail keywords to an infinite tail of prompt research.
The infinite tail is more than just an expansion of the long tail. It’s about personalization at each request. Users, like me, are layering contexts and preferences, creating unique prompt combinations.
As Ai systems evaluate these prompts, they predict responses probabilistically, shifting away from exact-match keywords. Now, it’s not just about ranking for specific phrases but ensuring my content solves the user’s problems.
In this journey, finding what users truly seek is as crucial as completing a task. With divergent user paths, flexibility replaces rigid step-by-step processes.
Query fan-out is crucial in AI search. It breaks complex prompts into subquestions, enabling a deeper evaluation framework.
Content now needs to satisfy clusters of queries instead of single matches. Covering multiple dimensions of a task creates resilience in this network-centric world.
Grounding queries ensure AI answers are validated against the broader web, checking consistency and reputability across sources. For my content to be part of AI responses, it must seamlessly fit this network.
This evolution redefines authority in how corroborated content appears over technically manipulated content. It emphasizes structure, data consistency, and external validation, significantly easing an AI system’s decision-making process by reducing uncertainty.
Organic search remains integral. It still dictates discovery and influences crawlability. However, AI now layers on top, impacting which brands feature in conversational responses. It’s a blend where organic visibility and AI selection coexist.
In this hybrid mode, the infinite tail favors genuine audience understanding, where my content should be designed to satisfy users’ situations instead of merely matching keywords.
This isn’t just a process renamed from keyword research to prompt research. It’s about understanding search motivations, decision-making, uncertainties, and evidential needs, fostering the infinite tail by prioritizing task completion over string matching.
“Content is king” has long been the mantra in the world of SEO. I’ve always leaned into content creation, though I know some focus on backlinks or technical SEO.
While I still believe content is crucial for search visibility, I’ve realized that it’s now essential to amplify its reach through effective distribution strategies.
With AI search evolving, asking, “What should I write next?” might not cut it. The game-changing question is, “Where should I distribute this content next?”
AI tools are further fragmenting search
Content distribution hasn’t always been our focus as SEOs. It was often a task for social media managers, PR specialists, and community managers.
But with AI search revolutionizing the landscape, distribution has become integral to achieving SEO success.
Here’s why:
AI tools draw from broader sources.
They operate under shifting logic.
The visibility strategies for AI differ from traditional methods.
If that sounds abstract, let me break down the evidence behind these changes.
Different tools have different sourcing logic
As search tools diversify, a one-size-fits-all strategy is no longer viable. AI tools cite different sources, often with less overlap with traditional SERPs.
Users are more adaptable, shifting from tools like ChatGPT to Gemini quickly, challenging us to rethink our strategy.
Instead of focusing solely on one tool, I need a distribution strategy that considers a variety of AI systems.
AI search uses different logic from traditional search
AI models generally have low overlap with Google searches. This variance highlights the need for a diverse strategy to ensure visibility across platforms.
AI searches tap into a wider array of resources, sometimes prioritizing lesser-known sites, complicating the path to dominance.
The sourcing logic is changeable
This shifting logic, marked by phenomena like citation drift, further complicates our reach. Over time, AI tools significantly alter their source domains, up to 90% in just six months.
The fragmentation of search demands a comprehensive distribution strategy. But how can we really make it work for us?
The key is not just in predicting where our content might appear but in expanding our reach across a variety of channels.
Our approach must adapt, embracing multi-channel distribution to reveal our brand in AI’s broad digital landscape. It involves targeting diverse platforms and collaborating with others, as third-party sources often overshadow personal domains.
1. Get good at collaborating
Winning in this fragmented environment requires teamwork. By integrating efforts across PR, social media, and community management, I leverage skills beyond traditional SEO.
I have to trust others with my projects and accept the shared accountability necessary for broader visibility.
2. Broaden your skillset
Understanding fields like digital PR and thought leadership is now part of my expanded role. I still focus on SEO, but I’m prepared to pivot where necessary.
While I may not master every skill, enhancing my knowledge of these interconnected fields enhances distribution capabilities.
3. Shift your mindset from ranking to presence
Google ranks remain important, but it’s equally crucial to populate as many platforms as possible. My goal is to plant hooks in the digital ecosystem that draw AI searches to my content.
I focus on presence rather than mere ranking, creating broader visibility to capture AI-driven searches.
4. Redesign your workflow
Integrating distribution into my workflow involves clear strategies from the outset. By planning post-launch phases and periodic content refreshes, I ensure a consistent distribution cycle.
Clear responsibilities and reusable elements prevent my distribution strategy from becoming an afterthought.
5. Start with these easy-to-implement best practices
Immediate actions help streamline this transformation, such as partnering with fellow businesses and adapting content for third-party sites like Quora or LinkedIn.
Keeping tabs on AI’s preferred sources and redistributing older content expands my reach and mitigates citation drift’s impact.
By prioritizing these initiatives, I boost my visibility in a world where distribution stands equal to creation.
Rethinking SEO processes for fragmented AI search
The landscape has shifted, urging me to adapt my SEO approach. As AI tools proliferate, navigating this fragmented terrain requires new methodologies.
SEO now demands more collaboration, intersecting with other teams like never before. The challenges are significant, but manageable strides toward cross-team coordination will set the foundation for future success.
Starting small allows me to slowly leverage these changes into formidable strategies, one step at a time.
In the past three months, I’ve noticed LinkedIn emerging as a key authority in AI-driven discovery. It’s fascinating to see how rapidly it’s progressed, skyrocketing from outside the top 20 to claim the top spot as the most-cited source for professional queries on AI platforms like ChatGPT.
This shift occurred between November 2025 and February 2026, a time of remarkable growth for LinkedIn. For me, these stats underline the platform’s potential for both companies and individuals eager to enhance their influence in the AI sector.
I recently came across an intriguing study that shows AI tools are now responsible for generating 45 billion monthly sessions globally. This accounts for an impressive 56% of all search engine activity, according to Graphite.io CEO Ethan Smith.
The analysis combines web and mobile app usage across leading AI platforms and suggests that AI activity matches 56% of global search use and 34% in the U.S.
The surge is particularly evident in mobile applications like ChatGPT, Gemini, Perplexity, Grok, and Claude.
Why it matters: AI is broadening the horizons of discovery, rather than limiting the demand for search. Since 2023, combined usage across search engines and AI assistants has increased by 26% globally. It’s clear that having visibility in both LLMs and traditional rankings is crucial.
Key insights: The report dives into the performance of the top five LLM products—ChatGPT, Gemini, Perplexity, Grok, and Claude—and compares them to the biggest search engines. Here are some standout insights:
AI platforms generate 45 billion monthly sessions worldwide.
Within the U.S., AI accounts for roughly 5.4 billion monthly sessions.
An astounding 83% of global AI usage takes place within mobile apps (75% in the U.S.).
ChatGPT is leading the charge, representing 89% of AI sessions globally.
When looking at search-like prompts, AI usage constitutes 28% of the global search and 17% within the U.S.
The report leaves out prompts in the “doing” or “expressing” categories. According to OpenAI, around 52% of prompts focus on seeking information, akin to traditional search queries.
Reading between the lines: Most forecasts comparing AI and search focus only on website traffic, often just Google.com and ChatGPT site visits. This approach overlooks much of AI’s impact.
The research suggests these comparisons undervalue AI activity by a factor of 4-5 times because a significant chunk occurs on mobile apps.
The analysis takes into account various LLMs and search engines, rather than only comparing Google and ChatGPT.
What to keep an eye on: Google remains a dominant force in discovery, but the report estimates its share of search-related activity has declined from 89% in 2023 to 71% by the fourth quarter of 2025.
While global AI usage seems stabilized since July 2025, the U.S. usage is still on a rapid climb—up about 300% year over year by December 2025.
