Did you know that 15% of all Google searches have never been searched before? Your AI SEO strategy must evolve rapidly. Google maintains about 90% of global search engine market share, yet the landscape keeps shifting. AI Overviews now appear in roughly 13% of search results, and the effects are significant. These AI snippets cause top-ranking sites to see their click-through rates drop by 34.5%. Several publishers have seen their desktop CTR fall from 13% to less than 5%. The silver lining shows that traffic from AI search doesn’t just grow – it converts better than other inbound traffic sources. However, AI-powered search pulls traffic away from traditional search results, particularly for informational queries. Users receive quick answers directly from AI tools rather than visiting websites. Your SEO approach needs a fresh perspective. This piece outlines five practical ways to adjust your strategy that will boost your visibility in both traditional and AI-powered search. Let’s take a closer look at how to secure your digital future.        

1. Shift to Long-Tail and Conversational Keywords

Basic keyword strategies don’t work anymore. Search is evolving, and natural, long-tail keywords are now crucial to stay visible in AI-powered search.

Why long-tail keywords matter for AI

Long-tail keywords are specific phrases with four or more words that match how people talk and think. Data shows queries with 8+ words that trigger AI Overviews have grown 7x since their launch in May 2024. These long-tail keywords now make up 70% of all search traffic. These keywords pack real value. A short keyword like “running shoes” gets about 120K monthly searches with tough competition. But “running shoes for flat feet” only has 4.0K monthly searches and faces much less competition. On top of that, long-tail keywords show clear user intent. Someone who looks up “how to optimize solar panel efficiency in cloudy climates” isn’t just browsing – they need specific answers. This focused intent explains why long-tail keywords are 2.5X more likely to convert than short-tail keywords. These detailed searches work perfectly with how AI search systems think. As more people talk to AI assistants in natural language, your content needs to match these conversation patterns.

How to find conversational queries

The quickest way to find good conversational keywords goes beyond regular keyword research:

1. People Also Ask boxes – These question groups show real phrases people type. Click any question to see more related searches.
2. Search trends and filters – Look at your current search data in Google Search Console. Vijay Chauhan says, “Sort GSC queries by ascending Impressions and check 1–10 impression queries. That’s where AI search engines leave their subtle trace”.
3. Stop filtering out low-volume terms – Many people ignore keywords with low search volume. But these terms often represent valuable conversational queries. One expert says, “Gone are the days where you automatically filter out keywords just because they don’t meet your arbitrary minimums”.
4. Reddit and community forums – These places show how people really talk about topics in your industry, offering plenty of natural phrases.

Tools like AnswerThePublic, BrightEdge Data Cube X, and Semrush’s Keyword Magic Tool help you find question-based keywords that match natural speech.

Using FAQs to capture natural language

FAQs excel at capturing conversational queries. They let you weave in question-based keywords naturally. FAQs mirror the way people search, especially through voice. Rather than writing about “referral requirements for physical therapy services,” ask it naturally: “Do I need a referral for physical therapy?” Your FAQs will work better when you:

• Put related questions under clear headings to help users and AI systems find information.
• Add FAQ schema markup so AI models can process your data better.
• Use exact phrases from “People Also Ask” boxes in your questions.

The content you create for FAQs helps both Google ranking and your website’s chatbots, improving visibility and user experience together. Natural, long-tail keywords help your content succeed in both traditional and AI-powered search.        

2. Structure Content for AI and Human Readability

Structure matters for both traditional SEO and AI visibility. Humans and AI systems prefer content they can process and understand easily.

Use clear headings and summaries

Headings act as vital signposts that direct readers through your content. Both users and search engines understand and traverse text better with proper headings. Research shows only 16% of visitors read content word-for-word – most people scan. Your readers will struggle without proper headings. Headings give AI systems valuable context about your content’s organization and topical focus.   The best results come from:

• Creating informative and descriptive headings instead of vague ones
• Following proper heading hierarchy (H1 → H2 → H3) without gaps
• Adding keywords naturally in headings to help AI understand relevance
• Placing headings above each group of paragraphs that form a thematic unit

Your structure should start with an H1 (like your page title), followed by H2s for main sections, and H3s for subsections. Take this example: if your H1 reads “AI SEO Strategy Guide,” your H2 might be “Technical Implementation” with H3s covering specific details. Each section should begin with a sentence that captures the main point. This helps humans and AI quickly learn the core idea.

Add jump links and tables of contents

Jump links (also called anchor links) take users directly to specific sections on the same page. These links improve user experience by eliminating endless scrolling, especially in long-form content. Research revealed a sharp increase in internal link clicks after implementing jump links. This suggests users value these navigation tools.   Your AI SEO strategy benefits because:

• Better user engagement positively affects search rankings
• AI systems can identify and extract key sections more easily
• Lower bounce rates show search engines your page’s value

A table of contents (ToC) at the start serves as a roadmap of main topics and enables quick navigation. The ToC works best above the fold to provide immediate context. Make your ToC clickable so readers can jump to relevant sections instead of scrolling through thousands of words. ToCs become more valuable for both human readers and AI systems as long-form content becomes standard for detailed AI and SEO strategies.

Break content into digestible sections

Content chunking – breaking information into smaller, manageable pieces – forms the foundation of human readability and AI processing. This method improves the accuracy and relevance of information that AI systems retrieve. Your content structure for generative AI SEO visibility should:

• Keep paragraphs short with simple text structure
• Maintain sections between headings at 250-350 words
• Set clear boundaries between chunks so each presents complete information
• Use typography to help readability – left-aligned text, proper spacing, and font size of at least 14 points

Chunking helps both AI and human users. It improves experiences in a variety of AI channels, including virtual assistants and knowledge bases. Content becomes clearer to computers and humans when chunks have clear organization and classification. Visual elements like images, videos, and diagrams make text more engaging. Studies show multimedia additions help readers understand content while adding value beyond the text. These structural elements optimize your content for both human readers and AI systems – a vital balance for any successful SEO content strategy with AI in 2025.        

3. Strengthen Technical SEO for AI Crawlers

Technical foundations can boost or limit your visibility in AI search systems. AI crawlers need more than keywords and content structure. They require technical optimizations to find, understand, and process your content properly.

Implement schema markup across content

Schema markup works like a translator between your content and AI systems. This structured data helps search engines grasp context beyond visible text. Websites that use schema markup jump four spots higher in search results on average. Here’s how to improve AI visibility:

• Use schema.org types like Article, FAQPage, Product, and HowTo
• Add JSON-LD format (JavaScript-based markup embedded in your page)
• Build connected schema markup that creates relationships between entities

Your organization’s knowledge graph takes shape through connected schema markup. Search is moving toward AI-first experiences. This makes semantic markup a vital tool to help models understand your content’s meaning and connections.

Ensure crawlability and fast load times

AI systems must crawl and index your content before they can cite it.   Page speed and Core Web Vitals play a vital role:

• Largest Contentful Paint (LCP) should be under 2.5 seconds
• Interaction To Next Paint (INP) should stay under 200 milliseconds
• Cumulative Layout Shift (CLS) needs to be under 0.1

Crawlers leave slow-loading pages before they finish processing them. Pages that rank on Google’s first page load in under 3 seconds on average.

Avoid JavaScript-heavy content

JavaScript creates big challenges for AI crawlers. Traditional search engines handle JavaScript well. Many AI crawlers can’t run it though. ChatGPT, Claude, and other AI systems download JavaScript files but can’t execute them. AI systems can’t see JavaScript-rendered content. Server-side rendering (SSR) solves this by showing content in the original HTML load. This makes your content available to all crawlers right away.

Think about llms.txt for AI-specific access

LLMS.txt serves as a rich source of information for AI systems. Unlike robots.txt that controls crawling, llms.txt points AI models to your best content. This plain text file lives in your root directory (example.com/llms.txt) and uses Markdown format to list your valuable URLs. OpenAI, Anthropic, and Perplexity already look for llms.txt when they crawl websites. Your llms.txt file should include your best evergreen content, authoritative pieces, and well-laid-out resources. Creating this file takes less than an hour and substantially improves your AI visibility.      

4. Build Authority with Data and Brand Mentions

Original data rules in the age of AI search. AI systems give priority to authoritative and credible information when they respond to user queries.

Use proprietary data and case studies

Your competitive edge comes from proprietary data that competitors cannot match. Take a look at this: 97% of companies find proprietary data valuable for differentiation. Original research instantly lifts your content’s authority and establishes your brand as an intellectual influence in your industry. Case studies show your ground expertise and application. They offer solid proof of your capabilities that helps potential customers imagine similar success with your solutions. Rilla, for example, used proprietary data studies to showcase how they extract insights from sales conversations – this strategy directly led to closing deals with major clients. Your data’s true power lies in the stories it tells. A narrative framework makes your findings more relatable and easier to remember. Note that strong visuals simplify complex information and better show relationships or trends.

Cite credible sources and link to originals

Clear attribution matters tremendously to AI systems. Rather than simply linking words like “this study,” state it clearly: “According to a 2023 study by McKinsey…”. AI models can better associate your content with authoritative sources through this explicit attribution. Both human audiences and AI systems trust content with transparent methodology. Original sources need direct links, proper citations, and verification through plagiarism checks.

Encourage brand mentions across the web

Brand mentions have become the new backlinks for AI visibility. Third-party mentions help AI models assess your brand’s trust and authority. These strategies work well:

• Reddit threads participation matters – Reddit leads as the top citation source for Perplexity (46.7%) and Google AI Overviews (21%)
• Enable internal subject matter experts to post on LinkedIn and contribute to industry publications
• Content repurposing across multiple formats (reports, blogs, videos, infographics) extends reach

Google’s E-E-A-T guidelines stress that experience and expertise matter more than ever for traditional SEO and AI visibility.          

5. Track AI-Specific SEO Metrics

Tracking the right metrics makes all the difference in your AI SEO success. AI platforms now generate 0.5% to 3% of total website traffic – a number that keeps growing.

Monitor LLM referral traffic

Live and interactive AI-generated search results need precise tracking.   Your traffic segmentation should help you understand:

• The percentage of total traffic from AI platforms
• Top traffic-sending AI platforms
• Content that draws AI-driven visitors
• Topics that attract quality AI referrals

Google Analytics 4 lets you track LLM referral traffic through a custom exploration report. Add a regex filter that captures traffic from ChatGPT, Perplexity, Gemini, and other AI platforms. The data works best as a line chart that shows trends over time. You could also create a custom GA4 channel group just for “AI Chatbots”.

Use tools like Scrunch AI or Semrush AI

Simple traffic metrics don’t tell the whole story. Specialized tools are a great way to get deeper insights into AI visibility. Scrunch AI leads the pack with versatile features, offering both SMB/agency and enterprise pricing plans that include concierge onboarding. Semrush’s AI Analytics component gives you a smooth start if you’re already on their platform. The right AI SEO tool should fit naturally into your workflow. Think over these elements before picking a tool:

• Data updates (live or daily data gives you better insights)
• Citation tracking (spots direct links and entity-based mentions)
• Context analysis (tells brand mentions from topic references)

Set up dashboards for AI visibility

Your AI visibility dashboard needs metrics that go beyond rankings and clicks. AI search focuses more on measuring brand exposure than positions. Essential metrics to watch:

• Brand mentions in AI responses
• Website citations through links
• Brand visibility from specific prompts
• Context quality of mentions
• Competitive share of voice
• Performance across AI tools

Pick metrics that match your business goals. Not every business needs all these metrics, and most tools don’t track everything.        

Conclusion

Adapting your SEO strategy for AI visibility is no longer optional – it’s crucial for digital survival. This piece outlines five practical approaches that can position your content in this new digital world. Long-tail, conversational keywords act as your gateway to AI visibility. Clear content structure helps both humans and machines understand your message better. Technical SEO basics substantially matter to AI crawlers. Schema markup, fast load times, and the emerging llms.txt standard affect how AI systems process your content. On top of that, original research and proprietary data make your brand stand out as the go-to source that AI platforms reference. The right metrics reveal what works in this new environment. Your success depends on looking beyond traditional SEO numbers. Focus needs to be on AI-specific indicators like LLM referral traffic and brand mentions. The move to AI-powered search brings both challenges and chances for smart marketers. Users still need quality information – they just find it differently now. These strategies will help you keep up with trends while your competitors stick to traditional SEO mindsets. Note that the goal hasn’t changed: create valuable content that answers questions and solves problems. The difference lies in how you package, structure, and distribute that value in an increasingly AI-driven digital world.