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.
I’ve been fascinated by the ways social platforms and content formats can enhance AI visibility. Recently, I’ve discovered how platforms like YouTube and Reddit, along with long-form content, significantly influence AI citations.
The synergy between social media and AI search visibility cannot be overstated. I find it remarkable how the right content type can amplify AI’s reach and impact. Platforms such as YouTube and Reddit are at the forefront, leading the charge with extensive citations attributed to their diverse and dynamic content formats.
For two decades, I’ve witnessed the web operate on a simple transaction: create content to fulfill needs, secure a high search ranking, attract traffic, and then monetize through various channels like products, services, or ads.
However, zero-click answers and AI search are redefining this dynamic. The key question now is whether AI acknowledges you as a source and if that recognition translates into revenue.
In my quest to understand this shift, I conducted over 200 AI visibility audits spanning ten industries.
What I discovered was a pattern: most websites are easily scanned but rarely referenced. Surprisingly, those industries that depend most on organic traffic inadvertently make themselves the hardest to access.
How I Conducted the Audit
I executed 201 audits using a consistent rubric, generating an overall AI visibility score plus four detailed subscores:
Freshness.
Structure.
Authority and evidence.
Extractability.
Spanning ten industries:
Coupons.
Affiliate reviews.
Travel booking.
Local directories.
Personal finance comparison.
Health information.
Legal directories.
Online courses.
Job boards.
Recipes.
The dataset leaned heavily toward homepages, which are often more marketing-driven and less substantiated by concrete evidence.
I also monitored access issues, finding that 38 of the 201 audits (18.9%) returned errors, indicating AI systems were obstructed or couldn’t reliably retrieve content.
Eight more audits scored zero due to missing subscores, pointing to poor content extraction or problematic rendering styles that hinder accessibility.
When analyzing score distributions, I focused on successful audits (163 sites) to differentiate between “unreachable” and “low quality.” Each industry’s error rate acted as a signal of whether AI systems could consistently use a site as a source.
Where Industries Stand in AI Visibility
The table below displays industry performance based on the audits conducted:
Rank
Industry
Error rate
Median overall
Median authority
Median extractability
At risk
1
Travel booking and trip planning
33.3%
45.5
31.0
52.0
High
2
Job boards and career marketplaces
40.0%
64.0
44.0
74.0
High
3
Legal directories and lead gen
35.0%
63.0
44.0
74.0
High
4
Coupons and deals
20.0%
62.0
36.0
74.0
High
5
Local directories and lead gen
5.3%
64.0
38.0
74.0
Medium
6
Online courses and learning marketplaces
30.0%
67.5
46.5
80.0
Medium
7
Health info and symptom lookups
15.0%
69.0
52.0
80.0
Low
8
Personal finance comparison
5.0%
67.0
52.0
78.0
Low
9
Affiliate product reviews
0.0%
69.5
54.0
74.0
Low
10
Recipes and cooking content
5.0%
75.0
55.5
81.5
Low
What the Audits Actually Revealed
The findings illuminated that very few websites were consistently citation-friendly. Here are the critical insights:
Access Issues Are Bigger Than Most Teams Realize
A significant 18.9% of websites experienced access errors. In certain sectors, the issue intensified markedly: job boards (40%), legal directories (35%), travel booking (33%), and course marketplaces (30%).
Therefore, a substantial section of these markets is essentially inaccessible to AI by default.
Most Sites Are Caught in the Middle
Looking at the 163 successful audits:
Average overall score: 61.6
Median overall score: 66
70.6% fell into “Inconsistent visibility” (60 to 79)
Only 4.9% achieved “Strong foundation” (80 to 94)
0% reached “Exceptional” (95 plus)
Conclusion: Most brands aren’t constructed for predictable use and citation.
The Gap Lies in Proof, Not Formatting
Median sub-scores across the audits revealed:
Structure: 92
Extractability: 74
Authority and evidence: 48
Freshness: 45
While pages are easily parsed, fewer justify citation. Key issues included:
114 instances lacked a “last modified header,” demonstrating missing freshness.
Citations or outbound links were rare, appearing only 13 times.
Rather than fearing traffic loss, the larger risk is exclusion from AI’s consideration set.
Industries disappear for specific reasons, fitting three failure modes:
1. Access Failure: AI Can’t Reliably Reach Your Content
If AI agents can’t consistently access your material, they may bypass you, compensating with data from alternative sources.
What access failure entails:
Strict bot protections or WAF rules treating agents as hostile entities.
App-like rendering prevents critical information from loading with initial HTML.
Barriers like popups or scripts impede content access.
How this causes vanishing:
AI’s inability to extract makes citation impossible.
Other sources or AI-native solutions satisfy the user’s query instead.
2. Trust Failure: AI Can Read You, But Can’t Justify Citing You
Trust failure is subtle: your page is understandable, yet lacks authoritative proof for AI to source it.
This was a common trend. In simple terms, the content reads well, but lacks defensibility.
A telling observation compares page types:
Articles’ median authority score: 76
Homepages’ median authority score: 45
A crisp homepage isn’t proof of authority. Citable proof resides in articles, policy pages, and similar in-depth resources.
3. Utility Failure: Even If You’re Visible, the Click May Not Happen
Utility failure is frustrating. You’re visible, potentially cited, but if your value is purely informative, AI creates an answer and the user never visits.
Visibility dictates your role in discussions, but utility affects revenue realization.
An applicable perception:
If your page answers the question, AI can replace it.
Where your product or service completes a user’s need, AI still requires you.
Access issues leave you ignored, trust issues mean you’re bypassed, and utility failures get your content summarized.
Why Certain Industries Are Vulnerable
Examining access, trust, and utility together reveals why some industries appear particularly exposed.
Categories repeatedly showing high risk in my findings shared three characteristics:
Inconsistent access due to blocking and extraction issues.
Content easily condensed into a single-answer format.
Limited business progression after the user obtains an answer.
This is why travel booking, job boards, legal directories, and coupons emerged as the most exposed in my analysis.
The larger implication is that while your business might thrive, your website might inadvertently be structured for exclusion.
This transformation impacts some industries more than others. Websites sustained by high-volume searches face heightened zero-click risks. However, even in these realms, a singular focus on information is perilous.
The misstep lies in equating AI search changes with ranking shifts; it’s truly an economic shift. From the audits, I realized:
Many industries render themselves inaccessible, ensuring models circumvent them.
Even when models interpret a page, lacking proof often prevents mentioning it.
The danger is becoming invisible. Triumph doesn’t come from concealment; it comes from proving your worth and offering something indispensable post-answer.
Trust combined with utility forms the new moat. Anything else remains outdated strategy.
I’ve been diving deep into how AI is transforming the landscape of public relations. It’s amazing to witness how AI PR is reshaping the industry, and I’m excited to share some insights with you.
One of the fascinating aspects I’ve learned about is the importance of citations in AI-generated answers. They play a vital role in establishing credibility and authenticity, which is crucial in our digital age.
Another intriguing factor is how LLM visibility affects our processes. By understanding how AI models operate in the public domain, we can adapt and refine our strategies to enhance our PR efforts.
As PR teams, it’s essential for us to stay ahead of these changes and tweak our approaches accordingly. Embracing AI tools and strategies ensures we remain competitive and effective in our communication efforts.
When I first dove into the complexities of AI recommendations, the process seemed daunting. But understanding the AI engine pipeline and its 10 gates offers incredible opportunities to optimize brand visibility and gain a competitive edge.
AI engine pipelines, from discovery to the final winning moment, are intricate systems where small adjustments can yield significant results. By embracing the entire pipeline, from upstream disciplines to structural shifts, we can profoundly influence how AI recommends our content.
Every piece of digital content navigates through a 10-gate journey before becoming an AI recommendation. I refer to this progression as the AI engine pipeline, or DSCRI-ARGDW, encompassing these crucial stages:
Discovered: The bot becomes aware of your existence.
Selected: The bot opts to further investigate your content.
Crawled: The bot fetches your material.
Rendered: The bot comprehends the content it has gathered.
Indexed: Your content is committed to the algorithm’s memory.
Annotated: The algorithm classifies the meaning of your content.
Recruited: Your content is integrated for use by the algorithm.
Grounded: The system verifies your content’s credibility.
Displayed: The user is presented with your content.
Won: You’ve secured the prime spot in the AI decision-making process.
The journey through these gates determines the strength of your AI recommendation. After securing a ‘win,’ the eleventh gate, which focuses on how your brand serves post-decision, plays a crucial role in reinforcing or diminishing ongoing AI confidence.
It’s essential to create a seamless path that bots can easily navigate (DSCRI) and outperform your competitors during the stages of recruitment, grounding, and display (ARGDW).
As the AI engine progresses through each gate, it evaluates your content against checkpoints and standards. Skipping gates by using structured feeds or direct data pushes can give you a strategic advantage by circumventing traditional path constraints.
Ultimately, understanding and optimizing for each gate in the AI engine pipeline not only enhances your brand’s digital footprint but also helps secure long-term recommendations consistently. Join me as we unravel how to enhance our content throughout this AI landscape and ensure it stands out at every step.
As a B2B company, I’ve noticed a significant shift in how buyers conduct vendor research, especially with the growing use of AI-driven platforms like ChatGPT. This trend presents a unique opportunity for us to increase our visibility and be recommended during the buying process.
To capitalize on this, it’s essential to understand how AI search works and how we can optimize our presence to stand out. By leveraging AI visibility strategies, we can make sure our company appears at the top of vendor search results.
One of the key tactics I’ve explored is incorporating AI-powered SEO tools to fine-tune our website and content. This approach not only enhances our searchability but also aligns with the evolving digital landscape where AI is becoming a primary decision-making tool.
Moreover, staying informed about market trends and continuously adapting our strategies ensures that we remain competitive. Engaging with our audience through personalized content and targeted campaigns can build the brand authority needed to get recommended by AI systems.
In conclusion, as AI continues to reshape the purchasing journey, positioning ourselves strategically in AI searches is vital. By embracing these changes, we can effectively increase our B2B visibility and ensure we’re on the radar of potential buyers.
