How to communicate when AI is the first reader

As artificial intelligence (‘AI’) increasingly sits between companies and investors, Investor Relations (IR) teams need to consider not only what they communicate, but how AI systems discover, interpret, summarise, and compare corporate information. 

In March 2026, Equitory published research exploring how AI is being adopted across the capital markets ecosystem. One of the more interesting findings was not how quickly AI is being adopted, but how it is being used. 

Despite the excitement surrounding generative AI, most buy-side and sell-side professionals are currently using AI for relatively practical purposes: summarising reports, analysing transcripts, reviewing company disclosures and improving efficiency. Human judgement remains critical and direct access to management teams is becoming more rather than less valuable. In our research, around a third of buy-side investors said they now place more reliance on direct meetings with management to sense-check what AI tells them underlining that, as adoption rises, the value of human engagement rises with it. 

Investors have always used tools to help process information. The difference today is that AI is ever more becoming part of the process through which investors discover, interpret and understand companies. In many cases, AI is reading company information before investors do. 

Previously, companies worried about whether investors had read the annual report. Going forward, they need to consider whether AI can read it. If AI cannot easily access and interpret company disclosures, it may rely on third-party sources instead. In an AI-enabled market, understanding how your information is consumed may become as important as the information itself. 

This creates an important question for IR teams. If AI is acting more and more as an intermediary between companies and investors, how should companies adapt the way they communicate? 

The first audience may no longer be human 

Historically, investor communication was designed for investors, analysts and journalists. Today, large language models such as ChatGPT, Gemini, Claude and Perplexity are increasingly being used to analyse, summarise and retrieve information.  

Investors are also using AI to compare companies, identify trends and conduct peer analysis at scale. As AI influences how companies are compared against competitors, the quality, consistency and accessibility of corporate disclosures become even more important. 

How an IR team understands and applies AI may itself become a competitive advantage. Companies that understand how AI tools discover, interpret and compare information may be better positioned to ensure that their investment case is accurately represented relative to peers. 

This does not mean investors are outsourcing investment decisions to AI. However, AI is shaping what information investors see first and how that information is presented. 

Simplicity versus nuance 

One of AI’s greatest strengths is its ability to distil large amounts of information into concise summaries and so, for investors covering hundreds of companies, this can be enormously valuable. However, there is a potential trade-off as AI systems are designed to identify patterns, extract key messages and simplify complexity. In doing so, they naturally favour clarity and brevity. 

Companies undergoing transformation, investing for long-term growth or operating complex business models often rely on context and nuance to explain their investment case. Beyond the risk of factual error or hallucination, which our research found remains investors’ biggest concern, a subtler risk is the loss of context through summarisation. A transformation story may become a restructuring story, or a long-term investment programme may become short-term margin pressure, or a diversified business may be reduced to a collection of unrelated assets. 

As more investors use similar tools and draw on similar sources, there is also the potential for market narratives to become more homogenised. Different investors may continue to reach different conclusions, but they may begin their analysis from the same AI-generated interpretation. 

Why structure suddenly matters 

Historically, companies focused on producing disclosures that were understandable to human readers and now, disclosures also need to be understandable to machines. 

This does not mean writing for algorithms at the expense of investors. Many of the characteristics that make information easier for AI to interpret are simply characteristics of good investor communication: clear language, consistent terminology, structured content and accessible information. 

This is where concepts such as Search Engine Optimisation (SEO), Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO) are beginning to enter the IR vocabulary. For IR teams, this means thinking not only about search visibility, but also about how AI-powered tools retrieve, interpret and present company information. 

SEO helps information get discovered. AEO helps AI systems extract information accurately. GEO helps ensure that information is interpreted and repeated correctly. Importantly, none of this is about gaming an algorithm or writing for keywords. It is about being discoverable and accurately understood, and the fundamentals that achieve this – clarity, consistency and structure – are the same ones that have always served human readers. 

Another important consideration is timeliness. AI systems typically prioritise the most recent and readily accessible information available to them. This places even greater importance on maintaining current disclosures, regularly updating investor websites and ensuring key information is published promptly and consistently. If not done, this can create confusion not only for investors, but also for the AI tools used to analyse companies. 

The annual report becomes more important, not less 

As investors use AI tools increasingly to retrieve and summarise information, those tools need trusted sources from which to draw information. The annual report remains the most authoritative source of information about a company now being read by AI systems as well as investors. 

The challenge is that in an AI-enabled world, companies need to consider not only what they disclose, but how those disclosures are structured, accessed and interpreted. 

Large language models rely on clear structure and consistent signals to identify and retrieve information accurately. Reporting periods, publication dates, executive names, performance measures and strategic priorities all help AI systems determine what information is relevant and trustworthy. When these signals are inconsistent or difficult to interpret, AI may look elsewhere for answers, and third-party sources may fill the gap. Put simply, if the company’s own disclosures are not the easiest source for AI to access, they may not be the source AI relies on most heavily. In effect, companies risk losing control of how their story is represented. 

This is where structured reporting formats, metadata, tagging and accessible digital reporting become very important. While iXBRL is already a regulatory requirement for UK listed companies, the benefits of structured data may increasingly extend beyond compliance as AI becomes more reliant on machine-readable information. 

Companies should also consider dedicated annual report microsites rather than relying solely on downloadable PDFs. Microsites can improve accessibility, navigation, searchability and machine readability, helping both investors and AI systems locate and interpret information more effectively. The same instinct underpins our three-click rule: if an investor – or an AI tool acting on their behalf – cannot find what they are looking for within three clicks, they move on. 

The same principle applies to other forms of investor communication. Webcasts, videos and capital markets day presentations can be highly effective engagement tools, but they are often less accessible to AI systems than written content. While this is evolving rapidly, timely transcripts remain one of the most effective ways of ensuring that important management commentary, strategic messages and explanations can be discovered, retrieved and accurately interpreted. 

The emergence of narrative risk 

For years, IR teams have focused on ensuring that the market understands the company’s strategy, performance and long-term investment case and that challenge has not changed. What is changing is how that understanding is formed. 

In the past, investors consumed information directly from company disclosures, management meetings and third-party research. Today, AI increasingly acts as a filter between companies and investors, influencing which information is surfaced, prioritised and summarised. This creates a new form of narrative risk so that, if company information is difficult to find, inconsistent across disclosures or lacks sufficient context, AI systems may rely on third-party commentary, media coverage, analyst reports and historical content that may carry greater weight in shaping how a company is presented. 

The more clearly and consistently a company communicates its strategy, priorities and value drivers, the greater the likelihood that those messages are reflected accurately in AI-generated summaries and investor briefings. 

What should listed companies be doing now? 

While AI may be changing how information is consumed, the response is not necessarily a new AI strategy. In many respects, it is an evolution of good IR practice. 

What companies should consider: 

Review the company’s digital footprint 

• Audit investor-facing content across websites, presentations, transcripts and filings. 
• Remove outdated information and ensure key materials remain current. 

Make disclosures easier to interpret 

• Use clear and concise language. 
• Maintain consistent terminology across reporting periods. 
• Ensure key messages are easy to identify and understand. 

Improve accessibility and machine readability 

• Review how annual reports and disclosures are structured and presented. 
• Consider the role of metadata, tagging, HTML reporting and annual report microsites. 

Understand how investors are using AI 

• Consider how AI tools may compare your company against peers. 
• Assess whether your key messages would survive AI interpretation. Stress-test your own disclosures through AI tools, a defensive IR practice, to see whether your key messages survive AI interpretation. 

Own the narrative 

• Ensure a coherent communications strategy across IR, finance, sustainability, legal, company secretariat, communications and technology teams. 
• Understand how AI is used internally to create content and externally to discover and analyse content. 
• Focus on consistency, credibility and transparency across all communications. 

Conclusion 

The rise of AI does not change the fundamentals of IR. Companies still need clear disclosure, consistent messaging, accessible management teams and a compelling equity story. What is changing is the way investors access and consume information. 

AI systems are increasingly becoming part of the information chain that connects companies and investors, helping users navigate, analyse and compare growing volumes of information. 

The companies that communicate most effectively will be those that recognise this shift now. AI rewards companies that communicate clearly, consistently and accessibly. In an AI-enabled market, clarity is no longer just a communication objective; it may become a competitive advantage.