Winning Visibility in the Age of AI Search

Search is shifting from a model focused almost entirely on rankings to one where visibility is also shaped by whether a brand is cited within AI-generated answers. As AI Overviews, conversational assistants and large language model–powered search features become more prevalent, they increasingly influence how users discover information and which organisations they encounter first. In this environment, it is entirely possible to rank well in traditional organic results yet still lose attention and traffic if AI-generated responses consistently reference alternative sources.

This evolution does not replace SEO; rather, it expands its scope. Organisations now need to think beyond how they rank and consider how both people and machines interpret, evaluate and select their content. The central question moves away from rankings alone and towards how brands are found, understood and trusted by AI systems as well as human audiences.

Historically, SEO success has been measured through positions and organic traffic. AI-driven search introduces a new dimension: whether content is selected and cited within generated answers. When an AI Overview provides what appears to be a complete response, users may not continue scrolling. If competitors are referenced in that answer instead of your organisation, visibility is lost even when your pages appear prominently on the same results page. As a result, SEO teams must now track not only rankings, but also whether their content is being surfaced and referenced within AI-generated outputs for priority topics.

Organic search remains a critical acquisition channel and continues to drive significant demand. At the same time, AI-led discovery is growing and increasingly shapes how people research products, services and problems. Effective strategies therefore require dual optimisation. On one side, traditional SEO supports rankings, click-through, demand capture and measurable outcomes such as leads and conversions. On the other, optimisation for AI discovery, often referred to as Answer Engine Optimisation or Generative Engine Optimisation, improves the likelihood of being cited and recommended by large language models. This strengthens brand authority early in the user journey and supports downstream consideration and conversion. Focusing on only one of these areas creates an imbalance, either limiting exposure within AI results or missing the scale and reliability of established search behaviour.

How content is structured plays a significant role in whether it is reused and cited by AI systems. Large language models process information differently from human readers and favour content that is explicit, well organised and easy to extract. Signals such as expert bylines, clear publication or update dates, concise answer summaries near the top of a page and the use of structured elements like tables, comparisons and data points all increase the likelihood of content being referenced. Pages should be designed as clear, data-rich answers rather than unstructured long-form narratives, with a defined purpose, concise conclusions and supporting evidence that is easy for both users and machines to interpret.

Authority also takes on a broader meaning in an AI-driven context. While backlinks remain important, large language models assess credibility through a wider lens. This includes how consistently a brand appears in reputable publications, the sentiment and context of mentions across the web, and signals that demonstrate expertise, reliability and relevance to user needs. Building authority therefore extends beyond link acquisition into PR, review management, thought leadership, partnerships and social presence. The objective is to create a recognisable and trusted brand that appears credible across multiple independent sources.

Measuring success in this environment requires more than traditional analytics alone. AI tools are already influencing how people discover brands, but their impact is not fully visible in standard reporting. Additional indicators are needed, such as whether a brand is cited within Google’s AI Overviews, whether large language models like ChatGPT, Gemini, Claude or Perplexity mention the organisation when answering relevant questions, and how accurately these systems describe its services and expertise. These signals help reveal early visibility and trust within AI-driven journeys, often before changes appear in rankings or traffic data.

User behaviour is also evolving. Adoption of AI assistants for everyday search tasks varies by intent and sector, with many users still preferring conventional search interfaces and trust in AI-generated answers continuing to develop. Usage tends to be higher for broad research and technology-related queries, and lower for high-stakes or local intent searches where proximity, reviews and real-world context are critical. However, as AI becomes more deeply embedded into search and browsing experiences, usage is likely to increase. Brands that act early gain valuable time to improve their presence within models, refine structured content and accumulate citations, creating a compounding advantage as behaviour shifts further towards AI-mediated discovery.

Looking ahead, websites themselves are becoming both brand environments and data assets. Large language models are expected to rely increasingly on structured feeds, APIs and machine-readable sources to access accurate and up-to-date information. Websites will continue to serve human users as trust and validation layers, while also acting as reliable sources of structured information for machines. Technical investment therefore needs to support both roles through robust structured data, clear information architecture, consistent naming and taxonomies, and, where appropriate, APIs or feeds that expose current information in a machine-friendly format.

The modern search landscape now combines traditional rankings with AI-generated answers and recommendations. To remain visible, organisations must maintain strong SEO foundations, structure content and authority signals so AI systems can confidently cite them, expand measurement to include AI visibility and brand understanding, and treat their websites as both human-facing trust platforms and structured data assets. This integrated approach enables brands to perform effectively today while preparing for a future in which AI plays an increasingly central role in how information is discovered, evaluated and trusted.