As I look back on 2025, it’s astonishing to see the AI search traffic growth leap by an impressive 180% year-over-year. I’m diving into the data to better understand how this impacts our visibility strategies. We’ll explore insights on ChatGPT, Gemini, Perplexity, and Claude usage trends in this review.
With AI technologies rapidly advancing, I’ve noticed how they continue to reshape how we think about search and brand visibility. The increased use of AI-powered tools signifies a pivotal shift in the way we approach digital marketing strategies.
In 2025, ChatGPT saw a remarkable surge in use, closely followed by interest in platforms like Gemini and Claude. This data is crucial as we plan for future visibility tactics, ensuring that our brand remains competitive in an ever-evolving digital landscape.
How does this data affect your brand’s approach? I believe understanding and leveraging these trends will be key to optimizing AI-driven search capabilities and visibility while crafting more personalized and effective content strategies.
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As I navigate the rapidly evolving world of digital marketing, I’ve discovered that partnering with a social media agency that offers Answer Engine Optimization (AEO) services is a game changer. These agencies have the unique ability to transform social content into enhanced AI visibility, build citations, and drive significant growth for brands like mine.
If you’re looking to boost your brand’s online presence, understanding the value of AEO services is crucial. I’ve personally seen how they enhance AI recognition, leading to better citations and more impactful growth metrics.
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.
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When I think about the leading agencies that help brands achieve stellar AI visibility, a few standout names come to mind. These agencies are experts in enhancing LLM citations and ensuring that brands remain discoverable across cutting-edge platforms like ChatGPT, Gemini, and Perplexity.
It’s fascinating to see how these agencies navigate the complexities of AI optimization to ensure their clients not only capture the audience’s attention but also maintain a strong presence in the digital realm. Their expertise is invaluable for brands looking to thrive in an ever-evolving technological landscape.
By leveraging their skills in areas such as AEO, AI SEO, and other digital strategies, these agencies provide comprehensive solutions to enhance online visibility and brand strength. Their innovative approaches keep brands at the forefront of AI advancements, making them essential partners in digital success.
As I immerse myself in the ever-evolving landscape of artificial intelligence, I can’t help but notice how the ongoing battles over data access are reshaping AI’s capabilities. The influence of these data wars is felt across the board, altering how AI answers are structured and presented.
What’s particularly fascinating is observing the crucial deals, restrictions, and lawsuits that have emerged, which are consistently driving AI into a fragmented state of visibility. These shifts are not just legal battles; they define the framework within which AI must operate in the coming years.
The platform dynamics are constantly changing, and it’s compelling to see how these transformations dictate the future of AI. As someone deeply invested in this field, I find tracking these developments essential for understanding where AI is headed from 2023 to 2026.
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.
With Google referrals declining and LLM usage on the rise, I’ve discovered that successful discoverability now hinges on metrics, structure, and authority—not just rankings.
If your organic traffic is decreasing while impressions rise, AI might be citing your content without generating clicks. If both metrics are down, it’s likely your content is being overlooked. Either way, the conventional search behavior that shaped your marketing strategy has transformed, and merely waiting for traffic to rebound is not a viable strategy.
The year 2026 presents a new reality. According to KEO Marketing, 73% of B2B websites faced significant traffic declines between 2024 and 2025, averaging a 34% year-over-year drop.
These drops aren’t uniform. Websites with predominantly informational content have been more adversely affected, experiencing declines between 15% and 64% since AI Overviews emerged.
News publishers, in particular, have been vulnerable, with Google referrals decreasing globally by 33% in the year leading up to November 2025.
These aren’t typical fluctuations; they signify a fundamental shift in how information is discovered online, posing a threat to business models reliant on site traffic.
Organic clicks are diminishing due to two intersecting reasons, each necessitating a different approach:
Google has fostered zero-click behavior through features like featured snippets and knowledge panels. These provide answers directly on the search results page, often eliminating the need to click on search results. While 25% of searches concluded without clicks ten years ago, today it’s over 65%. This trend has rapidly accelerated with AI Overviews, now found in about 16% of desktop searches and 41% of mobile searches.
On top of that, a growing number of users are bypassing traditional searches entirely. Nearly 52% of U.S. adults now frequently use AI tools, and approximately 28% of employed Americans incorporate AI at work. When they seek answers from ChatGPT or other LLMs, they often get responses without visiting any websites. While your content might contribute to that answer, it doesn’t translate to traffic or attribution.
Traditional metrics such as impressions, clicks, and page views no longer accurately reflect discoverability. They measure site behavior without informing how your brand performs in AI-mediated interactions, impacting upstream traffic.
Here are the five key metrics for AI visibility:
Citations in AI responses indicate how often your content is directly referenced when an LLM responds to a query. A citation suggests your content is valuable, well-structured for AI parsing, and authoritative.
Brand mentions differ from citations. LLMs may mention your brand without citing your content, often pulling data from review sites, forums, and third-party articles. A mention absent a citation implies your brand is recognized but not sourced from your content, guiding where to focus investments.
Share of voice measures your frequency of citations and mentions relative to competitors within specific categories.
Brand sentiment evaluates whether AI-generated responses portray your brand positively, neutrally, or negatively.
AI-influenced traffic gauges the proportion of traffic generated from LLM referrals. Initial data indicates this traffic has a conversion rate 3-5 times higher than other sources, making it valuable to track even if minor in volume.
Modern tools can track these metrics at scale, eliminating the necessity for manual LLM prompts. However, even conducting basic benchmarks by querying major LLMs with your target questions and tracking mentions is advantageous over not measuring at all.
Achieving visibility in AI-driven search doesn’t involve rewriting your content strategy but instead requires shedding ineffective practices and pivoting towards lasting principles.
Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) continue to form the foundation of content credibility. LLMs give precedence to sources that demonstrate real expertise and are trusted by authoritative figures.
By earning citations from reputable sites, producing content authored by subject matter experts, and delving into topics thoroughly, you can outshine content that fails to meet these criteria, regardless of optimization efforts for other factors.
Structure and clarity are essential because LLMs extract content by pinpointing passages that effectively answer questions. Structuring content around clear questions and answers, utilizing bullet point summaries, and avoiding dense paragraphs enhance retrievability over embedding answers in narrative prose.
Your information architecture should be comprehensible to both human readers and LLM systems. Introducing a Q&A section or reorganizing posts around clear question-and-answer pairs provides significant improvements.
Human-written, human-led content has a distinct advantage. After Google’s recent core update, AI-generated content saw an 87% drop in rankings and citation frequency, with keyword-optimized content seeing a 63% fall. LLMs are becoming adept at detecting AI-created content and rank it lower.
The 2025 demand for AI-produced content has highlighted a quality issue now evident in performance data. Prioritizing quality over quantity is essential. Use AI for drafting and editing, but not for generating final content. Implement a review process to catch generic phrasing or a synthetic tone, either through AI-detection tools or human editors.
Recency is crucial for AI citations. AI systems consider both the publication and update dates when selecting sources. A high-quality piece from 2022 can be dismissed for a newer version from 2025.
Audit your high-traffic pages and key assets for outdated data, refreshing them with recent examples and data. It’s a quick yet often overlooked strategy.
Promotional language will not get cited. If your writing appears too commercial—emphasizing product claims and brand-forward language—answer engines may deprioritize it over more neutral sources.
This doesn’t mean you should avoid mentioning your product; rather, write about it like an impartial party by acknowledging trade-offs, providing context, and letting facts speak for themselves. Listicles and comparison articles excel here.
LLMs respond best to organized, objective comparisons—even when one option is clearly preferred.
If my presence is limited to my own blog, I’m at a disadvantage against a brand with less expressive assets but more robust third-party coverage.
That is why cultivating an external content ecosystem is critical. Reviews on sites like G2, Capterra, and Google are frequently used in AI curation. User-generated content on forums like Reddit is heavily indexed. Third-party articles, tutorial videos, and newsletter mentions build the multi-source consensus essential for AI citations.
Content partnerships also deserve focused effort. Sponsoring articles or placing newsletters in relevant publications not only drives referral traffic but also earns trusted, external citations that elevate AI visibility. With a growing readership, newsletters — offering curated, human-authored content — are vital, with YouTube citations becoming increasingly influential. ChatGPT favors authoritative video creators for citations.
The goal isn’t to merely generate mentions but to consistently express your brand’s narrative through credible external sources so LLMs consistently recognize that narrative. Consistency across partners, review platforms, and third-party content strengthens your AI share of voice.
With organic traffic plummeting by 30% or more, the visitors arriving at your site are more deliberate and valuable than before, making conversion optimization on landing pages crucial.
Focus on simplicity: one offer, one message, minimal text.
Each landing page should focus on a single call to action and a singular argument. If there are multiple conversion goals, develop separate landing pages rather than a single page attempting everything.
Ensure the header conveys the full value proposition succinctly, with supporting points kept brief. Visitors should instantly grasp the offer and know how to act without needing to scroll.
This approach contrasts with blog and thought leadership content, which should be detailed, well-sourced, and designed for LLM retrieval. Each serves different objectives and requires varied standards. Conversion-centric landing pages are not the place for nuance or elaborate prose.
The decline in traffic isn’t a temporary issue that will resolve itself. Users increasingly get answers directly from AI, bypassing websites, and this trend will only intensify. A strategy focused solely on ranking for clicks is now insufficient.
The new strategy involves a dual focus: optimizing for citations by AI answer engines and cultivating an external brand presence that offers LLMs compelling reasons to consistently mention you. These objectives align with longstanding best practices: crafting clear, authoritative content grounded in expertise.
AI-driven discovery favors brands excelling in the fundamentals: building real credibility, securing trusted external mentions, and writing for audiences rather than algorithms.
This approach was always the best, and now AI search makes it essential.
As someone who’s deeply involved in the world of e-commerce, I know how crucial it is to understand whether your Shopify store’s pages are being referenced by LLMs (Language Learning Models). Up until now, that insight has remained elusive for those of us using Shopify. But everything is about to change.
Partnering with Nostra, Profound is bringing comprehensive Agent Analytics capabilities to Shopify brands for the very first time. This groundbreaking opportunity means that we can finally gain an overview of how our web presence is echoed in the digital realm, opening the doors to advanced marketing and strategy opportunities.
