Making the argument for regular AI ‘perception studies’
For decades, investor relations focused on one overarching objective: shaping market perception. Companies invested in perception studies to understand how investors, analysts and the financial media viewed their strategy, competitive positioning, and long-term value creation.
Today, however, a new audience is interpreting corporate narratives before many human investors ever do and that audience is AI.
As investors increasingly rely on ChatGPT, Claude, Gemini, Perplexity and other AI assistants to research companies, compare peers and summarize investment cases, Often, AI systems are becoming the first ‘reader’ of investor relations content. The narrative they generate can influence perception long before an investor visits the IR website, reads an annual report or joins an earnings call.
This shift calls for a new discipline in investor relations: AI perception studies.
A new layer between companies and investors
Traditional market perception studies ask questions such as: Do investors understand our strategy? Is management credible? Why do we trade at a premium or discount? Are our competitive advantages well understood?
These studies remain invaluable, but they measure human perception after information has already been interpreted by AI.
AI systems are becoming the first ‘reader’ of investor relations content
Generative AI introduces a new intermediary. Instead of asking only, What do investors think?, IR teams should also ask: What does AI think investors should think? The distinction matters because AI is increasingly shaping the information investors consume before they form a view at all.
If AI cannot explain your equity story…
Generative AI rarely invents narratives. Instead, it synthesizes publicly available information and identifies recurring themes across annual reports, earnings releases, investor presentations, websites, press releases and executive interviews.
When messaging is clear and consistent, AI typically produces an accurate summary of the company’s investment case. When disclosures are fragmented, inconsistent or overly complex, AI reflects that ambiguity back to users.
The implication is straightforward: if AI cannot clearly explain your equity story, there is a good chance investors cannot either. The problem is not AI but often communication.
From SEO to AEO and GEO
Search engine optimization taught companies to think about how algorithms, not just people, interpret their content. The same logic now applies to investor communications.
Answer Engine Optimization (AEO) focuses on structuring IR content so AI assistants can accurately answer investor questions – clear business descriptions, explicit articulation of competitive advantages and concise explanations of growth drivers that AI can surface rather than approximate.
If AI cannot clearly explain your equity story, there is a good chance investors cannot either
Generative Engine Optimization (GEO) extends this further: ensuring the company’s narrative is consistent across every public source that AI models draw from – annual reports, capital markets presentations, sustainability disclosures, CEO interviews and press releases. Where AEO shapes individual assets, GEO governs the full information footprint.
Unlike traditional SEO, success is not measured by website traffic. It is measured by whether AI consistently tells the right investment story – to every analyst, investor or researcher who asks.
Introducing AI perception studies
Just as companies periodically conduct market perception studies, they should begin running AI perception studies – systematic evaluations of whether AI systems consistently understand and communicate the company’s investment case.
Questions might include:
- Why should investors own this stock?
- What differentiates the company from competitors?
- What are its structural growth drivers?
- What are the key risks?
- Why does it trade at its current valuation?
- Who are its closest peers?
If five leading AI platforms produce five different answers, the company’s messaging may lack clarity. Common signals include unclear peer positioning; a growth story framed as ‘management targets’ instead of structural drivers; or a capital-allocation strategy reduced to a single line despite being central to the investment case. These are not AI errors – they are disclosure gaps made visible.
A new early warning system
The greatest value of AI perception studies is their ability to surface communication weaknesses before they become valuation issues.
An unclear capital allocation strategy, an overlooked competitive advantage or an inconsistent transformation narrative may not surface immediately through investor feedback. AI testing exposes these gaps instantly, revealing how the company’s story is being interpreted across the entire public domain – not just what investors say in a perception interview, but what any AI-assisted researcher encounters on day one of their diligence.
Rather than replacing traditional perception studies, AI testing complements them: more scalable, more repeatable and active rather than retrospective.
The next evolution of investor relations
The companies that move earliest on this will have a structural advantage: tighter narratives, fewer mischaracterizations in the analyst community and an IR program built for the way capital is actually allocated today.
As AI becomes the first interpreter of corporate narratives, the question every IR team should be asking is not whether AI matters to their investor audience. It is whether their equity story is built to be understood by both.
Stefano De Caterina is a senior investor relations manager with cross-industry experience spanning Europe, the US, the UAE and Saudi Arabia. He is also a regular contributor to IR Impact and a faculty member of the Euronext IR Academy.
