How Investors Are Using AI Before They Meet You
AI is already changing how analysts and investors review investor materials, and research companies. Many are using tools like ChatGPT and Gemini to analyze disclosures, frame questions, and identify gaps or inconsistencies in a company’s investor story before they even speak with IR teams.
In a recent Enquire podcast episode, Ross Hawley, Head of IR at ZIGUP (a UK-based public company in the mobility solutions space), spoke about how AI is influencing everyday IR work. Below are three takeaways from that discussion, adapted with our own perspective to make them practical for public company IROs.
1. Expect investors to use AI to prepare more targeted questions
Ross shared an example from a recent conference, where over dinner a buy-side investor spoke about how he now prepares for company meetings.
This investor uses ChatGPT to generate questions across the 30 companies he’s tracking. But he’s not just prompting it with generic queries. He’s built custom personas within ChatGPT based on the specific metrics his fund prioritizes. He’s essentially teaching the AI what matters to his investment thesis.
The result? Every question the AI generates is automatically filtered through that lens. By the time he walks into a meeting, his questions are sharper, more focused, and grounded in publicly available data that’s already been analyzed against his firm’s criteria.
Our take for IROs: Investors are increasingly showing up with a pre-filtered view of your company. One shaped by AI tools trained on their priorities, not yours. Before earnings calls or key investor meetings, pressure-test your materials the same way. Ask an AI tool to scan public information about your company, and even run your press releases, presentations, and filings through it. Ask: What questions would surface for an investor focused on unit economics, free cash flow, or balance sheet strength?
This puts you in a stronger position to anticipate where the conversation is likely to go and walk in with responses that are prepared, not reactive.
2. Make your content easy for AI to read, or it will look elsewhere
As investors increasingly use AI tools to research companies, these tools favor content that is easy to extract and interpret. Image-heavy PDFs, poorly structured tables, and videos without transcripts are harder for AI to process and are more likely to be skipped or loosely summarized.
Our take for IROs:
– PDFs: If earnings releases, prepared remarks, or investor decks are published as PDFs, ensure they are text-based (not images), searchable, and clearly structured with headings and tables that can be parsed.
– Investment thesis: Is your investment thesis easy to find and clearly articulated on your IR website, not buried across multiple PDFs or outdated pages?
– Tables and data: Are tables labeled, consistently structured, and readable when copied or extracted?
– Transcripts: Do earnings calls and management videos include transcripts that tools can scan?
When AI tools can’t extract clear answers from your disclosures, they rely more heavily on third-party sources like media coverage, analyst notes, and online commentary. That means investors may encounter someone else’s interpretation of your story before yours.
For reference, check out ZIGUP’s IR page. I think Ross and his team have done a great job putting the investment thesis front and center in a way that’s easy for LLMs to read. And btw, Ross also publishes an investor newsletter that’s worth checking out.
3. Use AI to pressure-test your thinking, not to write your story
Ross describes using AI as a thinking aid, not a writing tool. He uses it to explore ideas, test assumptions, and surface questions that might come up. What he avoids is letting AI draft earnings language or speak on behalf of the company.
He summed it up memorably: AI can save him ‘three showers and two dog walks’ worth of thinking, but it’s not going to write the CEO statement for him.
AI can help clarify logic, surface gaps, and tighten language. But it can’t decide what matters most, what trade-offs to make, or how leadership should sound. Those calls still sit squarely with management and the IR team.
Our take for IROs: Use AI to challenge and refine your work, not to outsource judgment. Ask where your message feels unclear, what assumptions a reader might draw from your disclosures, or where the narrative lacks support. Let AI help with editing and clarity, but keep ownership of substance, positioning, and final decisions. Be explicit with your team about where AI adds value and where it’s off-limits.
📌 To hear the full conversation, you can listen to the Enquire podcast episode featuring Ross Hawley.
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