Digital marketing leaders and CMOs have been talking about how SEO is changing for over a year, but what exactly shifts when we move from traditional search engine optimisation (SEO) to AI-driven or generative search optimisation (GEO, AEO, or similar)? This article explains the core differences between traditional SEO and AI-driven search optimisation, how each works, and what it means for marketing strategy in 2025, and beyond.
Read time: 6 minutes
What is traditional SEO?
In short? Traditional SEO refers to optimising websites so that they rank well in search engines like Google and Bing by meeting their algorithmic criteria (keywords, links, technical performance, user signals).
Specific characteristics
- The classic SEO workflow includes keyword research, on-page optimisation (title tags, headings, content), link building, and technical SEO audits (site speed, mobile friendliness, crawlability)
- Success is measured via metrics like organic traffic, search rankings, click-through rate (CTR), bounce rate, and conversion rates
- Traditional SEO assumes users click through a list of results (the “ten blue links” model) to reach a web page
- SEO is a mature, well understood discipline with many expert tools and standards already established.
What is AI-driven or generative search optimisation?
In short? Also called Generative Engine Optimisation (GEO) or Answer Engine Optimisation (AEO), AI-driven search optimisation aims to make content visible within AI-generated answers or summaries (e.g. in ChatGPT, Google’s generative overviews, or Perplexity, among others) rather than just in ranked search result lists.
Specific characteristics
- Instead of relying solely on ranking, the goal is to have your content cited, summarised, or surfaced as part of a broader AI answer
- AI systems synthesise information from multiple sources and then generate responses; optimisation here aims to make your content machine-readable and trusted in that synthesis
- Key signals include structured content, factual clarity, entity recognition, citations, and schema markup that helps AI systems parse your content.
How does user behaviour differ in AI vs traditional search?
In short? AI search queries tend to be more conversational and multi-step. The results are often delivered directly without requiring a click.
Behavioural differences
Traditional search: short keyword queries, user reads several result snippets, and picks a link to click through.
AI search: multi-turn or follow-up queries (e.g. “Why did X happen?” → “What was consequence?”) and the AI often provides a full answer in the interface.
- Importantly users are increasingly expecting to get the answers they need inside the AI interface rather than navigating to multiple web pages.
- As a result, some traffic could shift away from click-through to direct answer formats.
What are the key technical and signal differences?
| Feature | Traditional SEO | AI-Driven / Generative SEO |
| Target unit | Page level | Passage / paragraph / fact level |
| Authority signal | Backlinks, domain strength | Citations, mentions, external validation |
| Content structure | Narrative content, keyword density | Highly structured, fact-first, entity emphasis |
| Metadata & markup | Meta tags, sitemaps, robots.txt | Schema, llms.txt, entity graphs, AI-friendly markup |
| Query alignment | Keyword matching | Semantic and intent alignment |
| Metric focus | Keyword rankings, traffic, bounce rate | Citation rate, AI visibility share, impressions in AI responses |
Why is the distinction becoming more important now?
In short? As AI is becoming a primary search interface, relying solely on traditional SEO brings the risk of lost visibility if your content is not surfaced in AI responses.
Trends and risks
- Declining click traffic
Gartner projects that traditional search volumes may drop by about 25 per cent as users shift to AI tools - Zero-click dominance
In AI-driven search results, many queries can be answered directly, and the user may leave without clicking through to a website. Bain and Company found 80% of consumers rely on these “zero-click” results at least 40% of the time - AI models favour earned authority
A large-scale research study has shown that generative engines tend to bias toward third-party, authoritative sources over brand-owned content unless the latter is strongly validated - Rapid updates and model evolution
AI models evolve quickly so your content may need frequent adaptation (e.g. changes to schema, entity clarity) to remain visible in new AI versions.
Where do traditional SEO and AI-driven optimisation overlap or complement?
In short? Don’t worry, many of your core principles remain valid (quality content, user intent, technical performance), but they need to be adapted and extended to tackle AI’s synthesising logic.
Areas of overlap
- User intent still matters — whether in ranking or AI response.
- Content quality and authority are essential in both paradigms
- Technical best practice (fast loading, mobile readiness, crawlability) continues to underpin both
- Keyword research and semantic approach: traditional keyword insight remains useful but must be overlaid with semantic and topical models for AI.
And so … SEO and generative optimisation should not be seen as separate silos but as integrated layers in a modern strategy.
What strategic changes do marketing leaders need to consider?
In short? Update processes, metrics, and resource allocation to prioritise AI visibility in parallel with traditional SEO.
You should make these key strategic moves now
- Conduct an AI search visibility audit on your current offer
Assess how often your brand or content is cited or referenced in AI generated answers (e.g. via tools such as Otterly.ai designed specifically for AI search visibility) - Improve your content structuring and entity definition
Use a clear schema approach, clarify entity relationships (people, products, concepts), and break your content into extractable facts - Seek and embed external validation
Aim for mentions and citations by reputable external sources, across new digital territories, to boost your AI authority - Adopt new metrics
Track citation rate, AI answer share, and impressions in AI interfaces in addition to your traditional KPIs - Organise your resources for adaptability
Structure your content teams and engage partners to monitor AI model changes and rapidly iterate content formats or markup to align with new focus - Blend human and AI tools
Use AI to analyse and suggest but maintain a human voice and an eye on review to preserve nuance, creativity, and brand identity - Experiment in low-risk domains first
Choose topics or formats with lower stakes to trial generative optimisation before scaling across strategic focus points and core business silos.
What challenges and caveats should leaders be aware of?
- Trust and accuracy concerns: Not all audiences fully trust AI-driven answers. A 2025 Gartner survey found 53 per cent of consumers lacked confidence in AI-informed search results
- Model opacity: Many generative systems are black boxes. It can be very hard to predict how or why content is included in their responses. Trial and error (and learning from mistakes) is your friend
- Resource demands: Continuous iteration, structured markup, and content auditing will require investment. Make sure you are using the experts
- Over-reliance risk: Fully automating content for AI output can really degrade authenticity, tone, or brand differentiation. Humans are still the best writers for humans.
As AI becomes a dominant interface for search, the divide between traditional SEO and AI-driven optimisation is growing clearer. Traditional SEO still provides foundational value in user experience, technical soundness, and content quality. But a brand wanting to win in the AI search era must bring in new practices that make their content visible to language models, citable in synthesis, and formatted for extraction. The future absolutely lies in hybrid strategies, where SEO and GEO/AEO work together.
Traditional SEO focuses on optimising web pages to rank in search engine result pages via keywords, links, and technical signals. AI-driven (generative) search optimisation aims to have content cited or surfaced within AI-generated answers by focusing on structure, entity clarity, and citation authority.
Top three takeaways
- AI search changes the rules: it values citable content more than mere ranking.
- Your content must be machine-readable, factually structured, and recognised via citations.
- A hybrid approach, retaining traditional SEO while layering generative optimisation, is essential for future visibility.
About the author
What we do
Frontier15 helps brands achieve visibility in the era of AI search, creating content strategies that make your expertise discoverable and trusted by both people and machines. Get in touch and ask us how we can map a GEO-informed content strategy that helps future-proof your brand.
Further reading
- How can I measure my brand’s visibility in AI search?
- Can I test how my site appears in ChatGPT, Claude, Co-Pilot, Perplexity or other LLMs?
- What Is Google’s Search Generative Experience (SGE) — and How Does It Change SEO?
- When will generative AI search overtake traditional organic search?
- What is the difference between traditional SEO and AI-driven search optimisation?
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