How AI is reshaping brand visibility

Visibility used to be a function of reach. Today, it’s increasingly a function of interpretation. 

For years, brand visibility followed a relatively consistent pattern. Larger budgets enabled broader content distribution, greater media coverage and stronger search presence. Established organisations were able to reinforce their narratives over time. 

But as AI tools such as ChatGPT, Claude and Gemini reshape how stakeholders discover and consume information, visibility is no longer determined by reach alone. It now depends on whether content can be clearly interpreted, verified and selected by AI systems. For communications leaders, this marks a shift not just in channels – but in control.

Traditional search engines emphasise ranking, where higher placement increases the likelihood of visibility. AI systems operate differently. They generate responses by selecting and synthesising information from multiple sources, favouring content that is clear, attributable and easy to interpret. This reflects how these systems process information – prioritising inputs that can be quickly validated and reused with confidence. 

For communications teams, this changes how performance is evaluated. Volume and distribution still matter. But clarity, attribution and timeliness are becoming more consistent signals of inclusion. Content can perform well by traditional metrics and still not appear in AI generated answers, creating a growing gap between visibility and influence. 

To better understand this shift, we conducted a study with Profound, an AI analytics platform that tracks how content is cited across large language models (LLMs). In our research, we analysed more than 200,000 press releases and 200 million AI citations. 

Our analysis reviewed 30 days of AI citation data, examining how frequently content was included in AI-generated responses and what characteristics were shared among highly cited materials. 

A consistent pattern emerged. Content that appeared more frequently in AI answers tended to be clearly structured, included attributable information and was current and easy to interpret. These patterns held across organisations of varying sizes and sectors. 

One example clearly illustrates this point. An earnings press release from American Battery Technology Company  generated more than 1,600 AI citations within this period, exceeding both a nationally recognised retail brand and a comparable peer in the same timeframe. 

These findings suggest that traditional advantages tied to scale are being evaluated differently. Larger organisations continue to benefit from established narratives, brand recognition and a greater volume of published content. These factors still contribute to overall visibility. 

At the same time, AI systems introduce an additional layer of evaluation focused on how usable individual pieces of content are. Rather than relying solely on scale-related signals, these systems assess whether information can be clearly interpreted, verified and incorporated into responses. 

This creates a meaningful shift. Organisations without the same historical footprint can still appear prominently in AI-generated answers when their content meets these criteria. 

For communications leaders, this has clear implications. Visibility is no longer just about amplification – it’s about ensuring an organisation’s story can be accurately understood and reused in AI-driven contexts. 

For public relations and investor relations teams, these patterns point to four factors associated with higher AI visibility. 

Often referred to as the SOAR Content Framework, these principles can be applied across a wide range of stakeholder communications, from press releases and earnings materials to executive commentary and ESG reporting. At its core, the framework emphasises four qualities. Content should be structured so that key information is easy to identify, using clear headlines and a logical flow. It should also be original, incorporating distinct insights, first-party data or attributable statements that add genuine value.  

Authority is equally important, with sources clearly identified and verifiable to reinforce credibility. Finally, content should demonstrate recency, reflecting current information through visible dates, timely updates and relevant context. 

For many teams, these elements are already part of existing work. The distinction lies in how consistently and deliberately they are applied. 

Established organisations will continue to evolve their approaches, combining structured content practices with existing strategies across media, investor and stakeholder engagement. 

At the same time, AI systems are reshaping how information is surfaced and reused, creating new pathways to visibility that depend on clarity and credibility at the content level. 

For communications leaders, this signals a broader shift. Beyond reach and coverage, visibility increasingly depends on how effectively information can be understood, attributed and reused across AI-driven environments. In this context, reputation is no longer defined solely by what is published – but by what is selected, understood and carried forward.