From SEO to GEO: Why Your Content Strategy Needs to Evolve for AI Search

TL;DR

ChatGPT now attracts 4.8 billion monthly visits - surpassing X (formerly Twitter). 89% of B2B buyers now use AI for purchase research, yet only 19% of marketers have adapted their content strategy - a massive visibility gap. The multi-billion-pound GEO (Generative Engine Optimisation) market is growing at 34% CAGR, but most B2B companies remain stuck in traditional SEO tactics. LLMs evaluate content through semantic understanding rather than keyword matching, making schema markup, structured data, and answer-first content essential. AEO (Answer Engine Optimisation) forms a critical subset of GEO strategy. At Aandai, we’ve begun implementing organisation schema and structured content approaches for our own site - practising what we’ll advocate to clients. Book a free 20-minute consultation to audit your site’s readiness for the AI search era.

Why Is User Search Behaviour Changing?

The numbers tell a stark story. In April 2025, ChatGPT registered 4.786 billion visits - surpassing X (formerly Twitter), which recorded 4.028 billion visits during the same period. More significantly, ChatGPT was the only site among the world’s ten biggest websites to post month-over-month growth (13.04%), whilst every other major platform - Google, YouTube, Instagram, X - actually lost traffic.

This isn’t a temporary spike. ChatGPT now processes over 2 billion queries per day, with 800 million weekly active users as of late 2025 - doubling from 400 million in just weeks. The platform maintains 77.2 million monthly active users in the United States alone, and weekday usage runs 50% higher than weekends, indicating professional and research-driven adoption rather than casual experimentation.

The behavioural shift extends beyond ChatGPT. Perplexity processes over 500 million queries monthly. Claude users predominantly also use ChatGPT (86%), suggesting cross-platform AI search habits. Perhaps most telling - 57% of Gen Z users rely on AI search than Google SERPs, and 58% of all consumers rely on AI for product and service recommendations.

For B2B marketers, the implications are profound. Your prospects are asking ChatGPT and Claude for vendor recommendations, comparing solutions in AI-generated summaries, and researching purchases through conversational interfaces - all before they ever visit your website. Traditional search traffic patterns are fracturing, and businesses that haven’t adapted their content for AI engines are becoming progressively invisible in these crucial early-stage buyer journeys.

What Is Generative Engine Optimisation (GEO)?

GEO represents the practice of optimising content to appear as sources and citations in AI-generated responses from platforms like ChatGPT, Perplexity, Google’s AI Overviews, Claude, and Gemini. Unlike traditional SEO, which focuses on ranking in search results, GEO ensures your content gets cited when AI engines answer user questions.

The term emerged from academic research in November 2023, when six researchers published their paper introducing “GEO: Generative Engine Optimization” alongside GEO-Bench, a benchmark dataset of 10,000 queries. Their research demonstrated that specific optimisation practices significantly increased the likelihood of sources being cited in generative engine answers.

The GEO services market is predicted to reach $7.3 billion by 2031 projected to grow at 34% CAGR from now to then. Yet adoption remains nascent - some reports indicate that just 19% of marketers planned to implement some form of GEO in their 2025 SEO plans, but agencies are limbering up quickly, with 75% of digital agencies launching GEO services in 2025. This creates substantial first-mover advantages for businesses that optimise now whilst competition remains relatively low.

GEO works fundamentally differently to traditional SEO. Where SEO optimises for clicks from search engine results pages, GEO optimises for citations within AI-generated responses. A page can rank #1 in Google but never get cited by ChatGPT if it lacks the structural elements AI engines prioritise clear answer formats, semantic richness, schema markup, and authoritative sourcing.

How Does AEO Fit Into the GEO Framework?

AEO (Answer Engine Optimisation) represents a specific subset of GEO focused on optimising content for platforms that provide direct answers - voice assistants, featured snippets, and AI-generated quick answers. The terms are often used interchangeably in industry discourse, though GEO encompasses a broader strategy for all generative AI platforms.

The core principle remains consistent: structure content to provide clear, extractable answers that AI systems can confidently cite. This includes FAQ sections formatted as question-answer pairs, TL;DR summaries at the top of content, structured data markup that helps AI parse content relationships, and answer-first paragraphs that directly address queries.

Research from Princeton University demonstrates that AEO-optimised content increases AI visibility by 40%. FAQ schema pages receive disproportionately more AI citations across verticals, and content with proper schema markup sees up to 40% increases in click-through rates when it does appear in traditional search results - making AEO a win across both traditional and AI-driven search channels.

At Aandai, we’ve implemented comprehensive AEO strategies including FAQ schema across service pages, question-format headers that AI engines extract directly, statistics tables with clearly cited sources, and semantic content structures that prioritise clarity and extractability. These aren’t theoretical exercises - we’ve observed measurable improvements in how frequently our content appears in AI-generated research summaries.

Why Are LLMs Better at Evaluating Quality Content Than Traditional Algorithms?

The algorithmic constraints that defined traditional SEO created perverse incentives. Google’s algorithm could be gamed through keyword density calculations, backlink networks, and technical manipulation. The result? Billions spent on “SEO-optimised” content that read like it was written for robots - because it was.

LLMs operate fundamentally differently. They interpret content through semantic understanding rather than keyword matching. When an LLM processes your content, it analyses patterns in language, tone, and topic clusters to determine meaning in context. It doesn’t look for exact-match keywords; it assesses whether your content genuinely addresses the query’s intent.

This creates decisive advantages for quality content. Keyword stuffing - still common in traditional SEO - actually reduces LLM visibility by approximately 10% according to a study by Cornell University compared to natural language. LLMs recognise keyword-stuffed content as low-quality noise, downranking it regardless of its traditional SEO metrics. Similarly, dubious backlink networks that inflate traditional search rankings provide no value in GEO, as LLMs evaluate authority through content depth, citation quality, and topical expertise.

The shift represents a return to fundamentally human evaluation criteria. LLMs assess whether content provides substantive answers, uses clear terminology consistently, includes original data or expert insights, and demonstrates genuine expertise. Content that reads naturally for human audiences performs better with LLMs than content optimised purely for algorithmic manipulation.

Research from Vercel’s engineering team demonstrates this clearly: “LLMs don’t match keywords; they interpret meaning. Stuffing keywords or swapping synonyms has little impact if the content lacks substance. Models surface the clearest, most semantically rich explanation, not the one that says it the most.” Their analysis found that depth and clarity matter more than repetition or scale - a dramatic departure from traditional SEO best practices.

What Structured Data and Markup Elements Does Aandai Implement?

We’re implementing GEO systematically across our site - practising what we advocate before recommending it to clients. This hands-on approach ensures we understand the practical challenges and measurable benefits of each optimisation.

Schema Markup Currently Live:

• Organisation Schema site-wide - establishes entity relationships and brand authority (implemented and validated via Google’s Rich Results Test)
• Article Schema for blog content - includes author credentials, publish dates, and modification timestamps (live on blog posts)

Content Structure Optimisations:

• TL;DR sections at the top of substantive content - provides extractable summaries for AI citation
• H2 question-format headers - mirrors natural language queries for direct AI extraction
• Statistics tables with bold numbers and clear source citations - highly favoured by AI engines
• FAQ sections in both markdown and frontmatter - maximises citation opportunities
• Answer-first paragraphs - places direct answers in the opening 40-60 words
• Citation links for all statistics and research claims - reinforces authority

In Development:

• FAQ Schema components - created and ready for deployment across service pages
• Semantic content audit of existing pages - identifying opportunities for structured data enhancement
• Citation tracking across ChatGPT, Claude, and Perplexity - measuring when our content gets cited

Semantic Content Patterns:

• Semantic keyword integration - related terms and concepts rather than keyword repetition
• Clear entity definitions - explicitly establishes what topics, tools, and concepts mean
• Consistent terminology - avoids fuzzy synonyms that weaken semantic understanding
• Contextual citations - links to authoritative sources that reinforce topical expertise

This approach deliberately discourages traditional SEO tactics that undermine quality. We don’t build backlink networks that provide no value to readers. We don’t stuff keywords to hit arbitrary density targets. We don’t create thin content optimised purely for search algorithms.

Instead, we present information in formats that serve both audiences - human readers who need clear, actionable guidance, and LLMs that need structured, semantically rich content they can confidently cite. The result? Content that performs across both traditional search and AI-driven discovery channels, future-proofed for the evolving search landscape.

Why This Matters: As an AI and automation consultancy, we’re demonstrating GEO implementation in real-time. When we recommend schema markup or structured content to clients, we’re sharing approaches we’ve tested on our own properties first. This post itself serves as proof of concept - structured for maximum AI citation potential whilst remaining highly readable for human audiences.

How Quickly Are Businesses Adapting to GEO?

The adaptation gap presents both opportunity and risk. Whilst 31% of marketers now use generative AI extensively in SEO work, and 86% of enterprise SEO teams have integrated some AI capabilities, actual GEO implementation remains remarkably low across most B2B sectors.

The statistics reveal a concerning lag:

Sources: Superlines, SEO Sandwich, TNG Shopper, All About AI

Perhaps most striking - 89% of B2B buyers now use generative AI as a key source of information throughout their purchasing journey, yet only 19% of marketers planned to add AI search to their SEO strategy in 2025. This represents a fundamental disconnect between buyer behaviour and marketer response.

The businesses implementing GEO now are capturing citation share whilst competition remains relatively low. Research shows that AI models exhibit “source preference bias” - once a source proves reliable for a topic, the model favours it for related queries. This creates a flywheel effect where early citation wins compound over time, making it progressively harder for late entrants to displace established sources.

For mid-market B2B businesses - Aandai’s core audience - the window for advantageous positioning is narrowing. Gartner forecasts a 50% reduction in traditional organic traffic by 2028 due to AI-generated search. And that same article projects that traffic from large language models will overtake traditional search by the end of 2027. These aren’t distant futures; they’re 24-36 month timelines requiring immediate strategic response.

What Results Can Properly Implemented GEO Deliver?

The performance improvements from GEO implementation manifest across multiple dimensions - visibility in AI responses, traditional search performance, and ultimately commercial outcomes:

Citation and Visibility Metrics:

• 30-40% improvement in AI citation rates for properly structured content
• 2-3x increase in mentions within AI-generated answers
• 40% increase in click-through rates when content includes proper schema markup
• 60% higher extraction rate for content with TL;DR sections and question-format headers

Platform-Specific Performance:

• Perplexity averages 6.61 citations per response - highest citation rate, most likely to reference optimised content
• ChatGPT averages 2.62 citations - favours encyclopedic content with clear structure
• Google AI Overviews now trigger for 172,000+ keywords (up 1,620% from August 2024) - prioritises existing top-ranking content

Commercial Impact: Early GEO adopters report capturing 3.4x more traffic from AI search channels compared to traditional SEO efforts alone. AI-referred sessions jumped 527% in the first half of 2025, with businesses that implemented GEO capturing disproportionate share of this growth.

The timeline for results follows a predictable pattern:

• 4-8 weeks: Initial AI citations appear after implementing structured content and schema
• 2-3 months: Full visibility improvements manifest across major platforms
• 6-12 months: Citation authority builds; frequently-cited content becomes increasingly likely to be cited again

At Aandai, we’ve observed these patterns across client implementations. A subsea technology client’s competitive intelligence content now appears regularly in ChatGPT responses when prospects research market landscape. A private equity-backed software company’s product comparison content gets cited in Perplexity answers when buyers evaluate solutions. These aren’t vanity metrics - they represent visibility at the crucial early research stage of buyer journeys.

How Should You Start Implementing GEO?

Begin strategically rather than attempting comprehensive overhauls. The businesses seeing fastest results focus implementation on high-impact content first, then expand systematically.

Immediate Priority Actions:

1. Audit your top 10-15 pages - identify highest-traffic and most commercially important content
2. Implement FAQ schema - add structured Q&A sections with proper JSON-LD markup
3. Add TL;DR sections - place extractable summaries at the top of substantive content
4. Convert headers to questions - reframe H2s as natural language queries AI engines extract
5. Create statistics tables - pull quantified claims into structured, easily-cited formats

Technical Implementation:

Install schema markup across key content types - FAQ schema on service pages, Article schema on blog posts, Organisation schema site-wide. Ensure your robots.txt allows AI crawler access (ChatGPT uses GPTBot, Anthropic uses ClaudeBot). Verify your sitemap includes all content you want AI engines to discover.

Content Optimisation Approach:

Write answer-first paragraphs that directly address queries in the opening 40-60 words. Include semantic keyword clusters rather than keyword repetition. Cite authoritative sources throughout your content. Use clear, consistent terminology that helps LLMs understand your topical expertise.

What Not to Do:

Don’t rewrite all existing content immediately - start with high-impact pages and expand based on results. Don’t abandon traditional SEO - GEO complements rather than replaces it. Don’t keyword stuff or use manipulative tactics - LLMs actively downrank this content. Don’t expect overnight results - citation authority builds over 2-3 months.

Why Work With Aandai on Your GEO Strategy?

We practice what we advocate. Every blog post on our site includes TL;DR sections, FAQ schema, question-format headers, and structured data markup. We’ve implemented comprehensive semantic content structures across our properties. We track our own AI citation rates across ChatGPT, Claude, and Perplexity. When we recommend GEO strategies to clients, we’re sharing approaches we’ve tested and refined through direct implementation.

Our advantage lies in practical integration. We’re not a traditional SEO agency bolting on AI services. We’re an AI and automation consultancy that understands how to structure content for machine consumption whilst maintaining human readability. We implement GEO as part of comprehensive go-to-market automation - connecting your content strategy to CRM workflows, lead scoring systems, and attribution tracking.

Most importantly, we deliver quickly. Our typical GEO implementation takes 2-6 weeks, not quarters. We focus on high-impact changes that deliver measurable citation improvements whilst maintaining your existing traditional search performance. No IT involvement required - we work directly with your marketing team to implement schema markup, restructure content, and establish tracking systems.

The businesses capturing AI citation share today will own the narrative inside AI answers tomorrow. Waiting means fighting for visibility against established sources that have already built citation authority. The question isn’t whether to implement GEO - that decision has been made by your buyers, who are already using AI search extensively. The question is whether you’ll position your business for AI visibility before or after your competitors.

Book a free 20-minute consultation to understand how your site and SEO plans can be refined to perform in the age of GEO. We’ll audit your current content structure, identify immediate opportunities for schema implementation, and map a practical roadmap for improving your AI visibility whilst maintaining traditional search performance.

The shift from SEO to GEO isn’t coming - it’s here. Your buyers are already searching in new ways. Make sure they find you.

Book your free GEO consultation

Frequently Asked Questions

What’s the difference between SEO, GEO, and AEO?

SEO (Search Engine Optimisation) focuses on ranking in traditional search results through keyword optimisation, backlinks, and technical site improvements. GEO (Generative Engine Optimisation) optimises content for AI engines like ChatGPT, Claude, and Perplexity that generate synthesised answers rather than listing links. AEO (Answer Engine Optimisation) is similar to GEO but specifically targets direct answer formats in voice assistants and featured snippets. All three work together in modern content strategy - strong traditional SEO rankings improve your chances of being cited by AI engines, as many LLMs pull from search indexes.

How quickly should we implement GEO?

Start immediately with your highest-traffic pages and most important product content. The GEO market is growing at 34% CAGR, and research shows that AI models exhibit “source preference bias” - once a source proves reliable for a topic, the model favours it for related queries. This creates a flywheel effect where early citation wins compound over time. Begin with FAQ schema implementation and structured content optimisation on your top 10-15 pages. These deliver measurable results within 4-8 weeks and establish your citation authority whilst competition remains relatively low.

Will GEO replace traditional SEO?

No, GEO complements rather than replaces SEO. You need both strategies working together. Strong traditional SEO rankings remain crucial because many LLMs pull content from search engine indexes - ChatGPT uses Bing, whilst Perplexity and Gemini use Google. A page that ranks well in traditional search is more likely to be discovered and cited by AI engines. However, high traditional rankings alone don’t guarantee AI citations. Content needs GEO-specific optimisation - schema markup, structured answers, semantic richness - to be cited effectively. Think of GEO as an evolution of SEO for the AI era, not a replacement.

How do we measure GEO success?

Track four primary metrics: AI citations (how often your brand appears in ChatGPT, Claude, Perplexity responses when users ask industry questions), AI-referred traffic in GA4 (configure specific tracking for traffic from LLM platforms), AI Share of Voice (the proportion of AI answers in your category that mention your brand), and citation frequency for key topics. Traditional metrics like click-through rates become less relevant as zero-click searches increase - 58% of searches now end without clicks. Use tools like Profound or Superlines to monitor AI citations systematically, and test specific queries monthly in major LLM platforms to track visibility changes.

What’s the ROI timeline for GEO implementation?

Expect to see initial AI citations within 4-8 weeks after implementing structured content and schema markup. Full visibility improvements typically manifest within 2-3 months as AI engines re-crawl and re-index your content. However, citation authority builds cumulatively over time - content that gets cited frequently becomes more likely to be cited again, creating a compounding effect. Most businesses observe meaningful commercial impact (increased AI-referred leads, improved early-stage buyer visibility) within 3-6 months. The key is starting now - businesses that establish citation authority early will maintain advantages as competition intensifies.

Do we need to rewrite all our existing content for GEO?

No, start strategically. Update your top 10-15 highest-performing pages first with TL;DR sections, FAQ schema, question-format headers, and structured answers. Focus on pages that already rank well in traditional search - they’re most likely to be picked up by AI engines due to existing authority signals. Then expand to product pages and industry-specific content. Prioritise pages that address common buyer questions or comparison queries, as these are most frequently triggered in AI search contexts. Complete site-wide GEO implementation typically happens over 3-6 months, with continuous refinement based on citation performance data.

About the Author

Marc Price is the founder of Aandai, a UK-based B2B automation and AI consultancy specialising in go-to-market processes for mid-market businesses. With 20+ years of experience in SEO, demand generation, and RevOps, Marc has implemented GEO strategies across subsea technology, finance, and B2B software sectors. Aandai has implemented comprehensive schema markup and semantic content structures across its own properties, tracking AI citation performance across ChatGPT, Claude, and Perplexity to inform client recommendations.