This is the reality you will face in 2026. According to KEO Marketing:
- 73% of B2B websites experienced significant traffic losses between 2024 and 2025, with an average decline of 34% year-over-year.
- The impact is not evenly distributed. If your content is primarily informational, you’ve probably been hit harder: In some industries, organic traffic has fallen 15% to 64% since the launch of AI Overviews.
- News publishers in particular are in the spotlight, with Google referrals down 33% globally in the 12 months ending November 2025.
These are not normal fluctuations. They reflect a structural shift in the way people find information online, disrupting the foundation’s business models based on website traffic.
What’s driving the shift in organic discovery?
Organic clicks are declining for two overlapping reasons. You need to understand both, because each requires a different answer:
- Google has spent years developing zero-click behavior through featured snippets and knowledge panels. These SERP features answer questions directly on the results page, so you don’t have to click through to get an answer. Ten years ago, about 25% of searches ended without a click. Today it is more than 65%. AI summaries – which now appear in ~16% of desktop searches and ~41% of mobile searches – have dramatically accelerated this trend.
- A growing share of users completely bypasses the traditional search function. Nearly 52% of American adults now regularly use AI tools 28% of working Americans use AI at work. When someone asks ChatGPT or any other LLM a question, they usually get an answer without visiting a website. Your content can provide that answer, but you’ll get no traffic and no attribution.
What metrics should I consider when measuring AEO?
Traditional content marketing KPIs (impressions, clicks, CTR, sessions, bounce rate, and page views) no longer show you how discoverable your brand is. They measure behavior on your site, not how you perform in AI responses that now intercept a large portion of your upstream traffic.
Five metrics are most important for AI visibility:
- Citations in AI responses measures how often your own content is directly cited when an LLM answers a question. A citation indicates three things: your content is relevant, it is structured so that LLMs can parse and retrieve it efficiently, and your domain has enough authority to be trusted.
- Brand stated are different from quotations. LLMs often list brands without citing their property content, relying on review sites, forums, third-party articles, and competitor content. An unattributed listing means that the wider internet is talking about you, but your content is not the source. That distinction will help you decide where to invest.
- Share of the vote compares your citation and mention frequency against competitors through a defined set of category-relevant prompts.
- Brand sentiment tracks whether AI responses rate you favorably, neutrally, or negatively.
- AI-influenced traffic measures how much of your traffic comes from LLM referrals. Early data shows that this traffic converts at three to five times the rate of other sources, making it worth tracking even at low volume.
Various tools now allow you to track these metrics at scale without manually querying LLMs. They are worth exploring.
But even a simple benchmark – having large LLMs ask your target questions and tracking where and how you appear – is better than not measuring at all.
How should I optimize my content for AEO?
Gaining visibility in AI search doesn’t require an entirely new content playbook. But it requires pulling back on practices that no longer work and doubling down on principles that matter more than ever.
EEAT remains the basis
Experience, expertise, authority, and trustworthiness were dominant signals in Google SEO before AI Overviews, and they remain dominant in AEO. LLMs prioritize sources that demonstrate real expertise and are trusted by other authoritative sources.
If you earn citations from credible sites, publish content written by subject matter experts, and cover topics with depth and specificity, you will consistently outperform content that doesn’t, regardless of how well it is optimized for other factors.
Structure and clarity have become non-negotiable
LLMs retrieve content by identifying passages that directly answer questions. Organizing the content around clear questions and direct answers, using structured bulleted summaries, and avoiding bulky paragraphs will help you find them more easily than burying the answers in narrative prose.
This means making your information architecture readable by both human readers and LLM retrieval systems. Adding a Q&A section to existing content (or restructuring posts around clear Q&A pairs) is one of the most effective updates you can make right now.
Human-written, human-led content has a measurable benefit
After Google’s last core update, mass-produced AI content saw an 87% drop in rankings and citation frequency, and keyword-optimized content dropped 63%. LLMs are getting better at detecting AI writing patterns and prioritizing that content.
The pressure you felt in 2025 to produce volume with AI created a quality problem that is now visible in performance data. The strongest strategy is quality over quantity. If you use AI, use it for drafting and editing, not for generating final content. Add a review step to highlight common phrasing or a synthetic tone, via AI detection tools or human editors.
Recency matters for AI citation
Answering machines look at publication and update dates when choosing sources. A well-structured, authoritative piece from 2022 may be overlooked in favor of an updated 2025 version.
Check your high-traffic pages and hero items for outdated content and refresh them with current data and examples. It’s a quick win that many teams miss.
Spicy language is not quoted
If your content reads as promotional – leading with product claims and brand-focused language – response engines will often deprioritize it in favor of more objective sources.
That doesn’t mean you can’t mention your product or brand. It means writing about it as a neutral third party would: acknowledge compromises, provide context, and let the facts settle the matter. Lists and comparison articles work particularly well here.
AI systems respond to structured, objective comparisons, even when one option is clearly favored.
What content performs well in AEO, outside of my own channels?
There’s one clear pattern in how LLMs decide which brands to list: they look for consensus across multiple sources, not just your content. If you only appear on your own blog, you’ll lose to a brand with fewer properties but stronger third-party coverage.
That makes your external content ecosystem a strategic priority. Reviews on G2, Capterra, Google and similar platforms are often used in AI training. User-generated content on Reddit and other forums is heavily indexed. Third-party articles, tutorials, YouTube videos, and newsletter mentions all build the consensus across multiple sources, getting you quoted in AI responses.
Content partnerships deserve focused attention. When you sponsor articles or newsletter placements with relevant publications, you do two things: drive referral traffic outside of search results and earn trusted external citations that increase AI visibility. Newsletter readership is growing as audiences seek curated, human-written content. YouTube citations are particularly strong and increasing, and ChatGPT shows a documented preference for citing authoritative creators.
The goal is not to make mentions. It’s about telling a consistent story about your brand through credible third-party sources so that LLMs encounter that story repeatedly. Consistency across partners, review platforms, and third-party content increases your AI share.
How do I build landing pages that convert traffic better?
With organic traffic down 30% or more, the visitors coming to your site are more valuable and targeted than in years past. That makes conversion optimization on key landing pages more important.
The principle is simple: one offer, one message, minimal copy.
Each landing page should contain one call to action and one argument. If you have multiple conversion goals, create multiple landing pages, not one page that tries to do everything.
Your header should convey the entire value proposition. Supporting points should be short. A visitor must understand the offer and act without scrolling.
This differs from the content of blogs and thought leadership, which must be detailed, well-researched and structured for LLM to be retrieved. The two serve different purposes and require different standards. Conversion-oriented landing pages are not the place for nuance or verbose prose.
The takeaway
The traffic drop is not a temporary setback that will correct itself. Users get answers from AI instead of clicking through to websites, and that behavior will increase. A content strategy based solely on click ranking is no longer enough.
What takes its place is a dual mandate: optimizing to get cited by response engines and building the external brand presence that gives LLMs reason to consistently list you. These goals align with what you should have been doing all along: publishing clear, authoritative, well-structured content based on real expertise.
The brands that will win in AI-driven discovery are the ones that get the fundamentals right: build real credibility, earn trusted third-party mentions, and write for readers rather than algorithms.
That was always the right approach. AI search has simply made it mandatory.
Written by Tim Burke and Lauren Yanez
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