Something significant happened to Australian website traffic in 2025, and many business owners didn’t immediately understand why. Traffic from Google dropped — sometimes by 20%, sometimes by 60% — despite rankings holding steady. The businesses in Australia weren’t doing anything wrong. Their pages hadn’t fallen in position. But something had appeared above their results that was intercepting the clicks before users ever reached the organic listings: Google AI Overviews. This article explains what AI Overviews are, why they’re affecting Australian businesses so significantly, and how to turn a threat into a competitive advantage. What Are Google AI Overviews? Google AI Overviews (AIOs) are AI-generated summaries that appear at the very top of Google search results pages — above all organic rankings, above ads, and above featured snippets. They use Google’s Gemini model to synthesise information from multiple sources and deliver a direct answer to the user’s query. A user who searches “how to register a company in Australia” or “best accountant for small business Melbourne” increasingly sees an AI-generated paragraph answer at the top of the page. That answer cites a few sources (sometimes with expandable links), and often satisfies the user’s immediate need without requiring any click at all. Google began rolling out AIOs in the United States in mid-2024 and has since expanded aggressively across English-language markets. By mid-2025, AI Overviews were appearing in approximately 39% of Australian search queries — a rate that exceeded the global average and has continued to rise into 2026. The Traffic Impact on Australian Businesses The data is unambiguous: The irony is brutal: businesses that invested heavily in SEO to reach position 1 or 2 have found those positions generate less traffic than they did two years ago, because an AI answer now sits above them and intercepts the click. Which Australian Businesses Are Most Affected? Not all query types are equally impacted. AI Overviews appear most frequently for: Informational queries — “how to set up a trust in Australia,” “what is a SMSF,” “symptoms of [condition].” These are almost universally now captured by AIOs. Definitional queries — “what is stamp duty in Victoria,” “definition of redundancy payment Australia.” Direct factual answers appear at the top of the page. List-based queries — “best restaurants in Melbourne CBD,” “top accountants in Sydney.” AIOs increasingly generate their own ranked lists rather than linking to external ranking sites. Local service queries — “plumber near me,” “accountant in Brisbane.” These often combine an AI Overview with a map pack, reducing the available real estate for organic results further. The categories most severely affected in Australia: Why Traditional SEO Alone Can’t Solve This The instinct of many businesses facing traffic decline is to double down on traditional SEO — more content, better keywords, stronger backlinks. This is not wrong, but it addresses the wrong problem. Traffic is declining not because rankings dropped but because rankings matter less. The floor of the SERP has dropped. Position 1 now earns fewer clicks than position 3 used to, because AI Overviews have inserted a new layer above the organic results. You cannot rank above an AI Overview. You can only get into one. This is the strategic shift that GEO represents: stop competing for the real estate below AI answers and start competing for inclusion in the AI answer itself. How to Get into Google AI Overviews Google’s AI Overviews draw on Gemini, which in turn draws on Google’s index, its Knowledge Graph, and structured signals from across the web. Getting cited in an AI Overview is partly a function of traditional SEO authority, but it requires additional optimisation that most Australian businesses haven’t implemented. 1. Write for Extraction, Not Just Engagement AI Overviews prefer content that can be extracted and synthesised quickly. This means: 2. Implement FAQ Schema FAQ schema markup signals to Google that your page contains question-and-answer formatted content — exactly what AI Overviews are designed to surface. Adding properly coded FAQ schema to your key pages significantly increases their eligibility for AIO inclusion. This applies particularly to service pages, product pages, and guide content. 3. Establish E-E-A-T Signals Google’s AI Overviews strongly favour sources that demonstrate Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T). For Australian businesses, this means: 4. Target the Queries That Generate AIOs Not all queries trigger AI Overviews. Use Google Search Console and direct observation to identify which queries in your category are generating AIOs, and prioritise those for AIO-optimised content. Queries that are informational, long-tail, and question-format are the most likely candidates. 5. Build Local Authority Signals For local service businesses in Melbourne, Sydney, Brisbane, and other Australian cities, AI Overviews that respond to local queries draw on Google Business Profile data, local review signals, and locally-referenced content. Ensure your GBP is fully optimised, reviews are current, and your website content references your local service area explicitly. Beyond Google: The Broader AI Search Landscape Fixating on Google AI Overviews alone is understandable — Google remains Australia’s dominant search engine with around 90% market share. But it misses the broader shift occurring in parallel. ChatGPT now handles an estimated 10 million daily queries in Australia. Perplexity is growing rapidly as a research tool for high-value professional queries. Claude and Gemini are being used for business research and decision support. These platforms are not Google, and they’re not governed by Google’s ranking logic. They’re governed by their own training data, retrieval architectures, and authority signals. A business that optimises for Google AIOs using purely traditional SEO logic may improve its Gemini visibility but remain invisible on ChatGPT and Perplexity. A complete AI search strategy addresses all four engines — which is precisely what Generative Engine Optimization (GEO) is designed to do. Turning the AI Overview Threat into an Opportunity The businesses that emerge strongest from the AI Overview era are not the ones that fight hardest to protect their organic click share. They’re the ones that recognise the shift and position themselves to be inside the AI answer — not below it. This requires

Every discipline that depends on measurement eventually develops its canonical metric. In traditional SEO, that metric is keyword ranking — position 1 to 10 on Google’s results page. It’s imperfect, but it’s legible and actionable. In generative search, there is no results page. There is no position 1. There is only the answer — and either your brand is in it, or it isn’t. This is the measurement problem that Share of Model solves. It’s the foundational KPI of Generative Engine Optimization, and for Australian businesses investing in AI search visibility, it’s the number that matters most in 2026. What Is Share of Model? Share of Model (SoM) measures how frequently your brand is cited, mentioned, or recommended when AI engines — ChatGPT, Google Gemini, Perplexity, Claude — respond to queries relevant to your industry, service category, or geographic market. It is expressed as a proportion: of all the AI-generated responses to queries in your category, what percentage include a mention or recommendation of your brand? Example: A Melbourne-based financial planning firm runs 50 relevant prompts across four AI engines — questions like “who are the best financial advisers in Melbourne?”, “which financial planning firm should I use for SMSF advice in Victoria?”, “recommend a financial planner for small business owners in Melbourne.” If the firm appears in 18 of those 50 responses, its Share of Model in that query set is 36%. That 36% is a baseline. The goal of GEO is to increase it — and to ensure that when the brand does appear, it appears favourably. Why Share of Model Matters More Than Keyword Rankings in 2026 Keyword rankings measure your position in a list. Share of Model measures your presence in a recommendation. These are not equivalent. Consider two scenarios: Scenario A: Your law firm ranks #3 on Google for “commercial lawyer Melbourne.” Organic CTR at position 3 averages around 5-8%. Of users who search that phrase, roughly 6 in 100 click your result. Scenario B: When a user asks ChatGPT “which commercial law firm should I use in Melbourne?”, your firm is recommended in the AI’s answer. The user reads one recommendation, not a list. Conversion rates from AI-referred traffic are consistently higher than from organic search because the user arrives with a pre-formed recommendation rather than a choice to make. As 65% of Australian searches end without a click and AI Overviews intercept an increasing share of high-intent queries, the value of a keyword ranking diminishes. The value of being the brand an AI recommends increases. Share of Model captures the metric that actually reflects competitive position in AI search. The Three Dimensions of Share of Model A complete Share of Model analysis tracks three dimensions, not just citation frequency. 1. Citation Rate How often does your brand appear in AI responses to relevant queries? This is the headline number — the most direct measure of AI search visibility. It’s calculated by running a representative set of prompts across target AI engines and recording what percentage of responses include your brand. Citation rate varies significantly by: 2. Sentiment Score When your brand appears, how does the AI characterise it? An AI that says “You might consider [Brand X], though some users have noted inconsistent response times” is not an endorsement. Share of Model analysis must account for the quality of the mention, not just its presence. Sentiment scoring involves analysing the language used when your brand appears: whether it leads the recommendation or is mentioned as an afterthought, whether it’s associated with positive attributes, and whether negative qualifiers appear. 3. Competitive Share What is your citation rate relative to your direct competitors? Share of Model becomes most useful as a relative measure. If your firm has a 36% citation rate but your closest competitor has 65%, the gap tells you more about your strategic position than the absolute number alone. Tracking competitive share over time reveals whether your GEO investments are gaining ground, holding position, or losing ground to competitors who are also investing in AI visibility. How to Measure Share of Model: A Practical Framework Step 1: Define Your Query Set Build a representative set of 30-100 prompts that mirror how your potential clients actually query AI systems. Include: Avoid overly branded queries. The goal is to measure organic AI recommendation behaviour, not responses to prompts that mention your brand by name. Step 2: Select Target Engines At minimum, measure across: Each engine has different retrieval logic and will produce different Share of Model results for the same brand. Tracking all four gives you a full picture of your AI search landscape. Step 3: Run Prompts Systematically Run each prompt through each engine and record: Consistency matters. Use the same prompts over time to track movement rather than changing the query set between measurement cycles. Step 4: Calculate and Baseline Calculate your citation rate per engine and overall. Document which query types produce citations and which don’t — the gaps reveal where your GEO strategy should focus. Establish this as a baseline before any GEO interventions begin. Without a baseline, you cannot measure the impact of your strategy. Step 5: Track Monthly Share of Model is a lagging indicator for some AI engines (ChatGPT and Claude update their retrieval layers on longer cycles) and a leading indicator for others (Perplexity indexes continuously). Monthly tracking provides enough granularity to detect movement without the noise of daily variance. What Drives Share of Model? The Key Levers Based on our analysis of Australian brands across multiple industries, the factors most strongly correlated with high Share of Model are: Topical authority — Brands that comprehensively cover their subject domain across multiple pieces of structured content consistently outperform single-page optimisers. Third-party mentions — Appearances in industry publications, Australian business media, professional directories, and educational resources (particularly .edu.au domains) significantly increase the probability of AI citation. Content structure — Pages with clear Q&A formatting, defined entities (organisation names, locations, services), and direct factual statements are extracted more frequently

For two decades, Search Engine Optimization was the backbone of digital visibility. Rank well on Google, get found, get clients. The logic was simple and the rulebook, while always evolving, was at least legible. In 2026, a second rulebook has emerged — and most Australian businesses haven’t opened it yet. Generative Engine Optimization (GEO) and traditional SEO are not the same discipline. They share some tools and some underlying principles, but they answer fundamentally different questions. Understanding the distinction — and knowing when to apply each — is now a baseline requirement for any Australian business that depends on being found online. The Core Difference: Ranked vs. Cited The simplest way to frame the difference: SEO gets you ranked. GEO gets you cited. When a user searches Google using traditional SEO, they see a list of results. They choose which link to click. Your brand is one option among many, and the user exercises judgment. When a user asks ChatGPT, Perplexity, or Gemini the same question, they receive a synthesised answer. The AI has already decided which brand to trust and reference. Your brand is either in that answer or it isn’t. There is no second place. This distinction has enormous commercial implications, particularly for high-consideration queries — the kind where people ask “who should I use?” or “what’s the best option for X?” Those questions used to land on comparison pages, directory listings, and review sites. Today, they increasingly land in a conversation with an AI that gives a single, confident recommendation. Side-by-Side Comparison Dimension Traditional SEO Generative Engine Optimization (GEO) Target system Search engine algorithms (Google, Bing) Large language models (ChatGPT, Gemini, Perplexity, Claude) Output Ranked list of links Synthesised answer with cited sources User behaviour Clicks a link Reads and acts on the AI’s recommendation Key signals Backlinks, on-page keywords, technical health Fact density, semantic authority, citation-worthiness, training data presence Measurement Keyword rankings, organic traffic, CTR Share of Model, citation rate, sentiment in AI responses Time to results 3-12 months (varies by competitiveness) 3-8 weeks (Perplexity) to 3-6 months (ChatGPT/Claude) Content format Keyword-optimised pages Structured, extractable, fact-dense content Local signals NAP consistency, Google Business Profile, local links NAP consistency + geo-contextual content + local authority seeding Competitive dynamic Rank against competitors in a list Be the brand an AI recommends over competitors What SEO Gets Right That GEO Builds On GEO is not a replacement for SEO — it’s an extension of it. Many foundational SEO practices remain essential inputs to GEO performance: Technical health — Fast load speeds, clean site architecture, proper canonicalisation, and mobile optimisation all contribute to how well AI engines can crawl and index your content. Backlink authority — Inbound links from authoritative Australian domains (industry associations, established publications, .gov.au and .edu.au sources) remain strong trust signals that LLMs draw on. On-page structure — Proper heading hierarchies, meta descriptions, and clear content organisation benefit both traditional search algorithms and AI retrieval systems. Local SEO signals — Google Business Profile optimisation, consistent Name/Address/Phone (NAP) data, and local citations matter for both Google Maps visibility and AI-generated local recommendations. A business that has neglected SEO fundamentals will find GEO harder to execute, not easier. The relationship between the two is additive. Where GEO Requires a Different Approach Beyond the SEO foundation, GEO demands strategies that have no real equivalent in traditional search optimisation. Semantic Authority Over Keyword Density Traditional SEO rewards pages that match user keywords. GEO rewards brands that comprehensively own a topic in the AI’s conceptual model. This means building a body of content that covers your domain exhaustively — answering the range of questions a potential client might ask an AI, not just placing keywords on service pages. An accounting firm in Melbourne doesn’t just need a page for “tax accountant Melbourne.” It needs content that establishes it as the authoritative voice on Melbourne business taxation, SMSF strategies for Victorian SMEs, BAS lodgement processes, and the full range of questions its clients ask AI systems. Citation-Worthy Content Structure LLMs extract information differently from how humans read. Content that converts well in GEO is structured for extraction: direct answers immediately after questions, short paragraphs containing one clear claim, data points with sources, and consistent use of structured formats like Q&A, tables, and numbered lists. A well-written 2,000-word blog post in traditional SEO terms can be nearly invisible to an LLM if it’s written in flowing prose without extractable facts. Rewriting the same content with GEO structure in mind dramatically increases citation probability. Training Data and Authority Seeding ChatGPT and Claude draw significantly on training data — the body of text the models were trained on. Brands that were well-represented in authoritative sources before the training cutoff have a structural advantage. For newer brands or those underrepresented in AI training data, authority seeding — strategic placement in publications, forums, and sources that LLMs treat as authoritative — is essential to building presence in the model’s latent space. Share of Model as the Primary KPI In SEO, you track keyword rankings. In GEO, the equivalent metric is Share of Model: how frequently your brand appears when an AI is asked about your category, and how positively it characterises your brand relative to competitors. This requires querying multiple AI engines with the range of prompts your potential customers might use, then analysing both citation frequency and sentiment. It’s a more complex measurement task than rank tracking, but it’s the only accurate picture of AI search visibility. The Risk of Optimising for Only One Australian businesses that focus exclusively on traditional SEO are increasingly exposed. With 65% of searches ending without a click and AI Overviews appearing in 39% of Australian queries, the organic traffic that once flowed from top rankings is being redirected — to the AI answer that appears above the results page. But businesses that abandon SEO entirely for GEO face a different risk. Google’s traditional index remains a major source of the authority signals that AI engines rely on. A brand with

The way Australians search for information has fundamentally changed. When someone types “best accountant in Sydney” or “top digital marketing agency Melbourne” into their phone today, they’re increasingly receiving a direct AI-generated answer — not a list of blue links to scroll through. That answer comes from ChatGPT, Google Gemini, Perplexity, or Claude. And the business it recommends? That’s the result of Generative Engine Optimization. This guide explains what GEO is, why it matters for Australian businesses in 2026, and what the foundations of a sound GEO strategy look like. What Is Generative Engine Optimization? Generative Engine Optimization (GEO) is the discipline of structuring your brand’s digital presence — its content, technical architecture, and authority signals — so that large language models (LLMs) retrieve, trust, and cite your business in their AI-generated answers. Traditional SEO gets you ranked on Google’s results page. GEO gets you cited inside the answer itself. When a user asks ChatGPT “which Melbourne law firm specialises in commercial leasing?”, the model doesn’t run a keyword search. It draws on its training data, its retrieval layer, and the authority signals baked into the web to construct an answer. GEO is the practice of ensuring your brand is part of that answer. Why GEO Matters for Australian Businesses in 2026 The numbers tell a clear story: What this means in practice: a business that ranks #1 on Google but has no GEO strategy is losing ground every week. Meanwhile, a competitor with lower traditional rankings but strong AI citation signals is being recommended by ChatGPT to thousands of Australian users daily. How Is GEO Different from SEO? SEO and GEO share the same goal — visibility — but operate on entirely different logic. SEO optimises for retrieval systems that rank pages by relevance and authority signals (backlinks, on-page signals, technical health). The output is a list of links. The user decides which link to click. GEO optimises for generative systems that synthesise information into a single, authoritative answer. The output is a recommendation. The AI has already decided who to trust. The key variables in GEO include: Which AI Engines Should Australian Businesses Optimise For? The four engines that matter most for Australian market visibility: ChatGPT (OpenAI) — The most widely used generative AI globally. Relies heavily on training data and semantic authority. Takes 3-6 months for new content strategies to influence outputs. Best targeted through deep content authority and third-party brand mentions. Google Gemini — Integrated into Google Search. Benefits significantly from traditional SEO signals combined with AI-readable content structure. Fastest to respond to new content due to Google’s crawling infrastructure. Perplexity AI — A citations-first engine with live web indexing. More like a real-time search engine than a static LLM. Responds faster to SEO technical health, recent content, and clear sourcing. Claude (Anthropic) — Strong on reasoning and nuanced queries. Values well-structured, evidence-backed content with clear authorship signals. Each engine has a distinct retrieval logic. A comprehensive GEO strategy accounts for all four rather than treating them as interchangeable. The Foundations of a GEO Strategy for Australian Businesses 1. Establish Topical Authority AI models don’t cite generalists. They cite the brand that most comprehensively covers a topic. If you’re an accounting firm in Brisbane, you need to own the full conceptual space of accounting services in Queensland — not just have a homepage that mentions “tax returns.” This means publishing structured, data-backed content that covers your domain exhaustively, answering the questions your clients actually ask AI systems. 2. Structure Content for Extraction LLMs favour content that is easy to parse and quote. Practically, this means: 3. Build Authority Across the Web AI models use the broader web as a signal of brand authority. Guest articles in industry publications, mentions in Australian business media, citations from professional associations, and consistent NAP (Name, Address, Phone) signals all contribute to the trust layer that GEO depends on. 4. Implement Technical GEO Infrastructure Schema markup (particularly FAQ, Article, LocalBusiness, and HowTo schemas), structured internal linking, fast load speeds, and clean canonical architecture all improve the probability of AI retrieval. 5. Measure Share of Model Unlike traditional SEO where rank tracking is straightforward, GEO requires a different measurement framework. Share of Model measures how often your brand is cited when AI answers queries in your category — and how positively it is characterised. This is the core KPI of any GEO engagement. Who Should Invest in GEO? GEO is most immediately impactful for: If your business depends on being found online, GEO is no longer optional. How Latent Analytics Approaches GEO At Latent Analytics, we begin every engagement with a Latent Space Audit — a rigorous analysis of how AI models currently perceive and represent your brand, what your Share of Model is relative to competitors, and where the structural gaps in your GEO architecture lie. From there, we build custom GEO strategies using technical scripts, semantic architecture restructuring, and authority seeding — then monitor algorithmic outcomes continuously. Ready to see where your brand stands in AI search? Start with a Latent Audit (A$299) → Frequently Asked Questions How long does GEO take to show results? It varies by platform. Perplexity can respond to technical improvements in 3-8 weeks due to live indexing. ChatGPT and Claude move on longer cycles (3-6 months) tied to training and retrieval updates. Google Gemini sits somewhere in between given Google’s crawling infrastructure. Is GEO replacing SEO? No — at least not yet. The most effective strategies in 2026 treat SEO and GEO as complementary. Strong traditional SEO signals (technical health, backlinks, authority) are actually inputs to GEO. The difference is the additional layer of content structure and semantic authority that GEO requires. What’s the difference between GEO and AEO (Answer Engine Optimization)? The terms are often used interchangeably. AEO tends to focus specifically on appearing in featured snippets and direct answers. GEO is broader — it encompasses the full strategy of being cited, recommended, and trusted by generative AI systems. Does GEO work for