Winning Visibility in the Age of AI Search

Search is evolving from a model based purely on rankings to one where citations in AI-generated answers play a significant role. As AI Overviews, chat-style assistants, and LLM-powered search features become more common, they influence how users discover information and which brands they see first. In this environment, it is possible to rank well in traditional results but still lose visibility and traffic if AI-generated responses cite other sources. 

This development does not replace SEO; it broadens it. Organisations now need to consider how both users and machines interpret, evaluate, and reference their content. The focus shifts from “How do we rank?” to “How do humans and AI systems find, understand, and select us as a source?” 

1. From Rankings to Citations 

Traditional SEO measures success largely through rankings and organic traffic. AI-driven results introduce an additional layer: whether content is selected and cited inside generated answers. When an AI Overview answers a query, users may not scroll further if the response appears complete. If that response cites competitors rather than your brand, you lose visibility even if your pages rank on the same results page. 

This means SEO teams need to monitor not only where they rank, but also whether they are being surfaced and referenced within AI-generated outputs for their key topics. 

2. Dual Optimisation: SEO and AI Discovery 

Organic search still drives substantial demand and remains a core acquisition channel. At the same time, AI-led discovery is growing and influences how people research products, services, and problems. Effective strategies now require dual optimisation: 

• SEO activities that support rankings, click-through, demand capture, and measurable outcomes like lead generation and conversion growth. 

• AI-focused optimisation (often described as Answer Engine Optimisation, AEO, or Generative Engine Optimisation, GEO) that improves the chances of being cited and recommended by LLMs, strengthening brand authority and trust early in the journey and increasing downstream consideration and conversions. 

Neglecting either side creates a gap: relying only on SEO limits exposure within AI results, while focusing only on AI misses the established scale and reliability of traditional search. 

3. Structuring Content to Be Citable 

Large language models (LLMs) process content differently from human readers. They respond well to information that is clearly structured, explicit, and easy to extract. Certain features increase the likelihood that content is reused and cited: 

• Expert bylines that show subject-matter authority. 

• Clear timestamps that indicate recency and relevance. 

• Direct answer summaries that address key questions near the top of the page. 

• Structured elements, such as tables, lists, data points, and comparisons. 

To support this, content should be designed as data-rich answers rather than unstructured long-form text. Each page should clearly state its purpose, present concise conclusions, and organise supporting evidence in a way that is easy for both users and models to interpret. 

4. Authority Beyond Links 

Backlinks remain important, but authority in an AI context extends beyond link profiles. To be recommended confidently, LLMs assess a broader picture of brand credibility. This can include: 

• How consistently a brand appears in reputable publications and sector-relevant sources. 

• The sentiment and context of mentions across the web. 

• Signals that indicate expertise, reliability, and alignment with user needs. 

As a result, authority-building spans PR, review management, thought leadership, partnerships, and social presence, not just link acquisition. The aim is to build a recognisable, trusted brand that appears credible from multiple independent perspectives. 

5. Measuring AI Visibility 

AI tools are increasingly influencing how people discover brands, but this isn’t fully reflected in traditional performance reporting. To understand whether content is being surfaced and trusted by AI systems, we need to look at additional signals beyond traffic alone. 

• AI Overview visibility: Whether the brand is cited or referenced within Google’s AI Overviews for relevant queries. 

• LLM mentions: Whether Large Language Models such as ChatGPT, Gemini, Claude, or Perplexity surface the brand when answering related questions. 

• Accuracy of AI responses: How clearly and consistently these systems describe the organisation, its services, and its expertise. 

These indicators help show early visibility and relevance in AI-driven journeys, before impact is visible in rankings or analytics. 

6. Behaviour Change and First-Mover Advantage 

Current adoption of AI assistants for everyday search tasks is uneven and varies by intent and sector. Many users still prefer conventional search interfaces, and trust in AI-generated answers continues to develop. AI use is typically higher for broad research and technology-led queries, but lower for high-stakes or local intent searches such as finding a nearby hairdresser, tradesperson, or cleaner where proximity, reviews, and real-world context matter. However, as these tools become more embedded in search and browsing experiences, usage is likely to increase. 

Brands that act early gain more time to improve their presence in models, refine structured content, and accumulate citations. This creates a compounding advantage as behaviour shifts further towards AI-mediated discovery, especially in categories where AI adoption accelerates first. 

7. Websites as Brand and Data Assets 

In the longer term, LLMs are expected to rely more heavily on structured feeds, APIs, and machine-readable sources to obtain accurate, current information. This changes the role of the website: 

• It remains a key environment for human users, acting as a trust and validation layer. 

• It also becomes a structured data source for machines, providing clear, reliable information about products, services, policies, and expertise. 

Technical investment should therefore support both roles. This includes robust structured data, clear information architecture, consistent naming and taxonomies, and where appropriate, APIs and feeds that expose up-to-date information in a machine-friendly format. 

Summary 

The landscape of search and discovery now combines traditional rankings with AI-generated answers and recommendations. To remain visible, organisations need to: 

• Maintain strong SEO foundations for rankings and demand capture. 

• Optimise content structure and authority signals so that AI systems can confidently cite them. 

• Expand measurement to include AI visibility and brand understanding within models. 

• Treat their website as both a human-facing trust layer and a structured data asset. 

This combined approach positions brands to serve users effectively today while preparing for a future where AI plays a larger role in how information is found, evaluated, and trusted.