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For more than two decades, the rules of online discovery were simple: optimise for Google, target a keyword, climb the SERPs, repeat. That playbook built entire industries.
But today, the search landscape is mutating faster than ever. Google’s AI Overviews and the rise of LLM-powered assistants like ChatGPT, Perplexity, and Gemini are rewriting the rules of visibility, relevance and conversion.
At BrainZ Digital, we’re helping clients move from Search Engine Optimisation (SEO) to Answer Engine Optimisation (AEO) – a new discipline focused on training large language models to understand, trust and surface your brand in AI-generated answers.
Read here about the differences between SEO and AEO
This shift isn’t theoretical; it’s already happening. And it demands a completely new mindset.
From Keywords to Context: The Death of the SERP Game
In traditional SEO, winning meant ranking for high-volume keywords. You built a content cluster, tuned the meta tags and prayed for backlinks.
That model is fading. Today’s AI Overviews and conversational searches no longer depend on exact keyword matches. They rely on context, authority, and the ability to explain rather than just mention.
“For traditional SEO, persona-driven content is becoming more important than chasing search volume. Focused, intent-driven content is now being surfaced. It’s less traffic, but higher likelihood of educating a lead.” Gary Power
That line captures the new reality perfectly. Visibility is no longer about how often you repeat a phrase – it’s about how deeply you teach.
Brands that once dominated the SERPs are finding their articles summarised – or worse, replaced – by AI Overviews that cite someone else.
The solution isn’t panic. It’s precision.
Phase 1: Teaching the Machines – Foundational LLM Content
If SEO once meant optimising for Googlebot, AEO means educating the model.
Large language models learn from structured, factual, and brand-specific content that answers real questions in a consistent, contextual way.
That’s why we’re working with clients to create foundational LLM pages – clear, factual explainers about products, features and use cases.
“These are Phase 1 pages. The goal is to teach LLMs about product features and use cases, then give it enough data so we can influence comparisons. Right now, comparisons are almost always shown during mid- or bottom-of-funnel research.”
Think of it as content for the model, not just for the reader.
When an AI model compares your brand against a competitor, it relies on the information it has ingested. If your product features, pricing logic or case studies are missing from the model’s knowledge base, you’ve already lost the conversation – even before the user opens their browser.
Growth Hacking the AI Era: Short-Term Opportunities
While Google, OpenAI and Anthropic continue refining how they display citations, we’re in what Gary calls a growth-hacking window – a rare moment when agile brands can shape how they appear in AI Overviews.
In this stage, experimentation is everything:
- Rewrite high-performing ERP or SaaS pages in a style that resonates with AI Overviews – concise, factual, paragraph-driven and citation-ready.
- Embed AI summaries at the top of blog templates to make long-form content instantly digestible.
- Test interactive “Summarise with AI” buttons that open articles inside ChatGPT or Perplexity with a pre-loaded prompt – effectively nudging the model to associate your content with the topic.
Gary points to one of our own examples: AI Mentions & Backlinks, where we added a “Summarise with AI” option beneath the header image.
It’s a simple idea that blends user value and machine influence: every time a user summarises your page through a model, you reinforce brand association within the LLM’s memory.
It’s both UX enhancement and training signal – an elegant loop between human and machine discovery.
Re-architecting for Intent: The Rise of Hyper-Targeted Pages
Another major mindset shift: AI systems favour specificity over breadth.
In the keyword era, creating multiple product pages for each industry or persona risked cannibalisation. Today, hyper-specific subpages thrive.
“Product pages can be broken down into subpages focused on specific industries and ICPs,” Gary notes. “This hasn’t worked in traditional Google search before, but LLMs surface hyper-targeted pages.”
These pages don’t need to read like long SEO essays. Instead, they behave like exit-ready landing pages – clear, conversion-oriented and intent-matched.
When a buyer asks ChatGPT, ‘What’s the best ERP for construction firms?’ the model doesn’t pull a generic overview. It seeks niche-specific, example-rich content.
If your brand has a page optimised exactly for that intent, structured clearly and cited elsewhere, it’s more likely to appear within the AI Overview or comparison response.
That’s AEO in action: precision over volume, relevance over reach.
Rethinking FAQs: Teaching Through Real Questions
Few elements are as underestimated as FAQs.
In the age of LLMs, FAQs aren’t for Google’s Featured Snippets – they’re a direct teaching mechanism.
“Make them focused purely on your brand and product,” Gary advises. “Avoid the traditional top-of-funnel queries. Use them to educate LLMs, get FAQs from sales so we’re covering what people are actually asking. They’re likely asking LLMs these questions too.”
That’s the key insight. The questions your sales team hears every week are the same ones users ask AI assistants.
Every time your website clearly answers those questions – in structured markup, with clean language – you give the model another reliable reason to surface you.
It’s not SEO fluff. It’s product education at scale.
The Conversion Question: Measuring the Invisible
One of the early challenges in this new landscape is attribution. Traditional analytics focus on clicks and conversions, but informational content rarely converts directly.
That’s why GA4 setup and CTA tracking are essential to understanding how users engage beyond direct conversions.
AI Overviews may not send floods of traffic, but the visitors they do send are more qualified, more educated, and closer to decision.
That means fewer visitors – but more conversations that matter.
We’re seeing this across clients: when content aligns with AI intent, lead quality improves even if sessions drop. That’s the trade-off of modern SEO – and it’s worth it.
Long-Term Vision: Preparing for the Agent-First Web
The future is already peeking over the horizon.
As LLMs evolve into autonomous AI agents, search will shift again – from asking questions to delegating tasks. Users won’t type; they’ll instruct.
“Long term we need to be ready for a shift in user behaviour inside LLMs,” Gary writes. “Visitors from LLMs will have already passed the awareness stage, read, asked, compared and found their ideal features before even talking to sales.”
In this scenario, the marketing funnel collapses.
Your next prospect might arrive via an AI agent that’s already read your case studies, compared pricing, and decided your product is a fit – all before human contact.
To prepare, brands must start optimising machine-facing assets:
- Support documentation that’s structured, consistent and accessible.
- Developer docs enriched with schema and entity relationships.
- Case studies rewritten as structured proof – data, metrics, outcomes – that LLMs can easily ingest and cite.
- Interactive demos and APIs that AI agents can query directly to retrieve real-time information.
That’s the next frontier of AEO: building brand APIs for machines, not just landing pages for humans.
From SEO to GEO: A New Discipline Emerges
At BrainZ Digital, we call this broader shift GEO – Generative Engine Optimisation.
It’s the discipline of designing your entire digital ecosystem – content, structure, schema, and strategy – for a world where generative engines mediate discovery.
It merges SEO, AEO and CRO into a single framework focused on visibility, verification and validation across both human and machine audiences.
The brands that master this won’t just adapt to the next algorithm update – they’ll train the algorithms themselves.
The Takeaway: Optimise for Minds, Not Machines
The irony of all this talk about AI is that it brings us back to something deeply human.
Good content, the kind LLMs and users both trust—is empathetic, clear, and useful. It answers real questions without manipulation.
That’s the future of discovery: not tricking the system, but teaching it.
SEO is not dead; it’s evolving into education. And those who learn to teach – consistently, transparently, across every format and language – will be the ones whose voices AI carries forward.
Welcome to the era of Answer Engine Optimisation.
