GEO & AEO Insights · May 2026 · 8 min read
Your customers are already using ChatGPT, Perplexity, and Google AI Mode to research products, compare agencies, and make purchasing decisions — and they’re doing it in multiple languages. The question is no longer whether AI search matters for your brand. The question is whether your brand shows up when it does.
This guide breaks down a practical 4-step framework for building an LLM-first content strategy — one that earns genuine AI visibility, not just traditional search rankings. Whether you’re a global brand entering new markets or an agency helping clients expand cross-border, these principles apply directly to how you create, distribute, and measure content in 2026.
The Shift Is Real — And the Numbers Prove It
AI-powered search is not a future trend. It is already embedded in how your customers discover brands, evaluate options, and decide who to trust. According to data from Adobe Analytics, 77% of U.S. ChatGPT users say they use it like a search engine. Meanwhile, 1 in 4 users report that ChatGPT gives better product suggestions than Google — a signal that AI is not just supplementing search, it is actively replacing parts of it for a growing segment of buyers.
Perhaps most importantly for forward-thinking marketers: 65% of generative AI users are Gen Z or Millennials — the exact demographic driving purchase decisions in consumer and B2B markets globally.[Salesforce, 2025]
But here is where many marketers get the picture wrong. When we think about “AI search,” most of us picture ChatGPT, Perplexity, and Gemini as separate standalone tools. In reality, the largest share of AI-influenced search is happening inside Google AI Overviews — the AI-generated summaries that now appear at the top of Google results pages. ChatGPT, Gemini, and other LLMs are growing fast, but Google’s AI integration is already at massive scale.
AI Tool Visits vs Traditional Search — USA 2023 to 2025
% of desktop devices with at least 1 visit/month · Source: SparkToro / Datos (Semrush company), 2025
The trajectory is clear. AI tool usage has grown from under 10% to nearly 40% of desktop users in just over two years — and projections suggest AI search visitors will surpass traditional organic search visitors by 2028.[Semrush, 2025] More importantly, the average visitor arriving from an LLM is estimated to be worth 4.4 times more than a traditional organic search visitor in terms of engagement and conversion behaviour.
This is not a channel to optimise later. It is a channel to build now.
What AI Visibility Actually Means (And Why It’s Different From SEO)
Before building a strategy, it helps to define the goal clearly. AI visibility is made up of two distinct components: brand mentions (your brand being referenced regardless of whether your site is the source) and citations (your own content being directly linked or cited as the source). A strong AI visibility strategy works on both simultaneously.
There are three distinct channels through which AI search operates today:
- AI in web search — Google AI Overviews and Google AI Mode. These sit inside the world’s most-used search engine and already influence billions of queries daily.
- Standalone LLMs — ChatGPT, Gemini, Claude, Perplexity, Deepseek, Grok. Users interact with these directly, often with detailed, conversational prompts rather than keywords.
- AI-native search engines — SearchGPT and Perplexity in search mode. These blend LLM reasoning with real-time web indexing.
When generating answers, these systems do not just crawl your website. They draw from review platforms for product comparisons, Reddit and Quora threads for real user recommendations, news sites for credibility signals, support documentation for feature-level detail, and your company’s own content pages. Your .com is one input among many — and often not the most trusted one.
According to a Semrush study of 150,000 citations conducted in June 2025, the single most-cited domain across ChatGPT, Perplexity, Google AI Mode, and AI Overviews is reddit.com at 40.11% — nearly double Wikipedia at 26.33%. YouTube and Google.com both sit around 23%.
Top Domains Cited by LLMs
Citation frequency across ChatGPT, Perplexity, AI Mode & AI Overviews · Source: Semrush, 150K citations, June 2025
The implication is significant: 76% of marketers and businesses now say it is “essential” to appear in ChatGPT and other AI answers in 2025 — and AI visibility has overtaken Google SERP rankings as the number one content marketing priority heading into 2026.[Semrush State of Content Marketing, 2025]
The 4-Step LLM-First Content Framework
Step 1 — Look at the Full Picture of Your Digital Footprint
Most brands are operating with a dangerously narrow view of their own digital presence. If your content strategy focuses almost entirely on your website, you are covering roughly 40% of the digital surface area that AI systems actually reference when forming opinions about your brand. Brands that expand into YouTube, Reddit, high-authority review platforms, LinkedIn thought leadership, and industry publications move into the 40–80% range. Getting to true AI dominance — above 81% share of voice — requires thinking about behavioural residue: the organic trail of citations, mentions, and community discussions that builds over time.
Audit where your brand currently appears across the full ecosystem. Where are you cited? Where are you not cited but your competitors are? This gap analysis becomes your content roadmap.
Step 2 — Identify the Specific Sources AI Trusts in Your Space
Not all platforms carry equal weight for every industry or every language. The Semrush citation data gives us a global picture, but the sources AI trusts vary significantly by query type, topic, and — critically — by language.
There are also important complexities to monitor. Each AI model has a different training data cutoff — ChatGPT-5 uses data up to October 2024, while Gemini 2.5 Pro cuts off at January 2025. Some queries trigger real-time search; others draw purely from the model’s trained knowledge. This means your content needs to be present in trusted sources consistently over time, not just around a launch moment.
Map the specific platforms, forums, and publications that your target audience uses — and that AI systems reference — within your niche. Then build a presence there deliberately.
Step 3 — Influence the Narrative Across Those Sources
Once you know where AI looks, the next step is shaping what it finds. This involves three core tactics:
Build presence on the right review platforms. AI systems weight review sites heavily when comparing products and services. Not all review sites are treated equally — identify which platforms rank highest in AI responses within your category, and build a genuine, detailed presence there. G2, Clutch, Trustpilot, and Capterra each dominate different verticals.
Participate in community discussions. Reddit and Quora consistently appear among the top sources cited by both ChatGPT and Google AI Mode. This is not a signal to spam forums — it is a signal to contribute genuinely useful, expert-level content in the communities where your audience already gathers. Helpful answers to real questions build the kind of organic citation trail that AI systems reward.
Engineer user-generated content and social proof. Customer reviews, case study quotes, employee advocacy on LinkedIn, and affiliate content all create a distributed signal that AI systems interpret as credibility. The brands winning in AI search are not just publishing — they are activating networks of authentic voices speaking about them across multiple platforms simultaneously.
A useful diagnostic prompt: ask ChatGPT or Gemini “Why is [your competitor] better than [your brand]?” The answer tells you exactly what narrative gaps you need to fill.
Step 4 — Test, Measure, and Iterate Faster Than You Would With SEO
One of the most important differences between traditional SEO and LLM-first content strategy is the speed of the feedback loop. Where SEO improvements might take 3–6 months to show measurable ranking changes, AI visibility can shift within weeks. Semrush’s own case study showed measurable Share of Voice improvements across ChatGPT, Google AI Mode, and Google AI Overviews within a single month of focused activity.
Track your AI Share of Voice regularly. Monitor how your brand is described — and whether that description is accurate and favourable — across the major AI platforms. When visibility drops or a competitor gains ground, treat it as a signal to audit your source presence and content freshness. Do not wait for a perfect attribution model before acting. The brands building AI visibility now will hold compounding advantages as the channel grows.
Why Language — Not Location — Is the Most Critical Variable for Global Brands
This is the insight that most global marketing guides overlook entirely: when it comes to AI search outputs, the language you prompt in has a larger impact on the results you receive than the country or region you are searching from. Two users in the same city asking the same question — one in English, one in Chinese — will receive fundamentally different answers, sourced from entirely different ecosystems of websites, platforms, and community forums.
Consider the query “smart home” in English versus “智能家居” in Chinese. The English query surfaces Wikipedia, Reddit, The Verge, Wired, and TechRadar as primary citation sources. The Chinese query returns a completely different set: Sohu, Baidu, Tuya, and Chinese-language technology forums. A brand that has optimised only for English-language AI visibility is essentially invisible to Chinese-speaking users asking AI for recommendations — even if those users are based in Singapore, Malaysia, Australia, or North America.
“Smart Home” vs “智能家居” — AI Citation Sources by Language
Different languages return entirely different trusted source ecosystems · Source: Semrush GEO Summit, 2025
This has direct strategic implications for any brand with a cross-border growth agenda:
- Bilingual content is not optional — it is a separate AI visibility strategy. Your English blog and your Chinese blog need distinct source-building efforts, distinct community presences, and distinct review platform strategies. A translated version of your English content is not enough; it needs to be positioned within the trusted source ecosystem of that language.
- Chinese-speaking markets require Chinese-platform presence. If you are targeting Chinese-speaking audiences — whether in mainland China, Hong Kong, Taiwan, Singapore, or the Chinese diaspora globally — your brand needs genuine presence on Baidu, Sohu, Zhihu, WeChat, and Chinese-language review platforms for AI systems to cite you as a credible source in that language.
- Monitor AI outputs in every language you operate in. A brand that appears prominently in English AI responses but is absent from Chinese-language AI responses has a significant visibility gap — one that competitors with multilingual content strategies will exploit.
- Prompt intent differs by language and culture. Chinese-speaking users often phrase AI queries differently, seek different types of proof points, and trust different platform types. Your content strategy needs to account for these differences, not just translate around them.
For agencies and brands operating across Asian markets and Western markets simultaneously, this language-layer complexity is both a challenge and a significant competitive opportunity. Most global brands are still optimising AI visibility in English only — leaving the Chinese-language AI ecosystem largely uncontested.
Projected Visitor Value: LLM vs Traditional Organic Search (2025–2029)
Average LLM visitor is estimated to be worth 4.4x a traditional organic search visitor · Source: Semrush, 2025
The value trajectory makes the case for urgency. As AI-referred visitors grow as a share of total traffic, and as each of those visitors converts at a higher rate than traditional search visitors, the compounding effect on revenue is substantial. Brands that establish AI visibility in 2026 — across multiple languages and markets — will benefit from that position for years. Brands that wait will face an increasingly crowded and expensive landscape to enter.
Where to Start: A Practical Checklist for 2026
If you are mapping this framework to your own brand or client portfolio, here is a condensed starting checklist:
| Action | Priority | What to Look For |
|---|---|---|
| Audit your current AI Share of Voice | 🔴 Immediate | Search your brand + category in ChatGPT, Gemini, Perplexity — in every language you operate in |
| Map which sources AI cites in your niche | 🔴 Immediate | Identify the top 5–10 platforms appearing as sources for your key topics |
| Build or strengthen review platform presence | 🟡 Month 1 | Focus on platforms already appearing in AI citations for your category |
| Begin community participation (Reddit, Quora, Zhihu) | 🟡 Month 1 | Answer real questions; do not pitch — contribute expertise |
| Audit bilingual content gaps | 🟡 Month 1–2 | Run key prompts in English AND Chinese; compare citation sources |
| Build Chinese-language platform presence | 🟢 Month 2–3 | Zhihu, Baidu Baijiahao, WeChat Official Account, Xiaohongshu if relevant |
| Activate customer and employee advocacy | 🟢 Month 2–3 | LinkedIn posts, case studies, forum contributions from real voices |
| Set up monthly AI visibility tracking | 🟢 Ongoing | Track Share of Voice changes across platforms and languages monthly |
The Shift Has Already Happened. The Opportunity Is Still Open.
The brands that will dominate AI search results in 2027 and beyond are the ones building that presence today — not waiting for a clear ROI model, not waiting for the channel to mature, and not assuming their existing SEO foundation is enough.
For brands expanding into global markets — especially across language boundaries — the opportunity is even more pronounced. The Chinese-language AI ecosystem is largely uncharted territory for most Western-origin brands. The English-language AI ecosystem is increasingly crowded. The brands with the agility to operate in both will hold a structural advantage that is very difficult to replicate quickly.
If you are thinking about where to start — whether that is understanding your current AI visibility, building a content strategy that works across languages, or figuring out how GEO and AEO fits alongside your existing paid and organic efforts — we are happy to think through it with you.
You might also find these related reads useful as you build out your strategy:
- How SEO and AI visibility work together in 2026 — the foundation still matters, but the goal has shifted
- Cross-border marketing strategy — how to build a market entry plan that accounts for AI search from day one
- Paid advertising in the age of AI search — where paid still plays a critical role alongside organic and AI visibility
No hard sell. If you are working through these questions and want a second perspective, our contact page is the easiest place to start a conversation.