Jamie Stanton

I build AI for investor relations — designed by someone who has run the workflows by hand.

NIRI 40 Under 40National Investor Relations Institute

I design the agents and workflows behind an AI platform for IR teams, and I've spent eleven years advising public company CFOs and IROs through earnings, targeting and shareholder engagement. The two halves are the same job. You cannot automate a process you have never had to run at 6am on the morning of a call.

The argument

Be the filter, be the synthesis

Most IR teams experimenting with AI right now are using it to summarize. Run the earnings script through a model, get three key takeaways back, move on.

It's fine. It's also close to worthless. Summarizing is the cheapest thing a model does, and your own earnings script is the document you least need summarized. You wrote it.

The work that actually pays is monitoring. An agent watching the tape and the register — a block crossing, size that doesn't fit the usual pattern, a peer announcement, a shift in who's reading your site — that tells you what happened and why it matters to you specifically. A competitor misses on a supply chain issue: so what? The so what is that you don't have that exposure, and here are two points for your next investor meeting. That's the difference between reporting what happened and being a real partner to your C-suite.

The gap isn't between teams with AI and teams without. It's between tools you have to remember to open and systems that interrupt you when it counts.

Read the whole piece

What I build

Turning IR workflows into things a model can actually run

I work on both ends of the platform: the agents themselves and the interfaces IR teams meet them through. The raw material is market, ownership, engagement and CRM data — four systems that rarely speak to each other, which is exactly why the useful signals hide in the gaps between them.

Market monitoring that runs without you Agents watching for blocks, unusual size, sector moves and peer news, then explaining what happened rather than just flagging that it did.
Building skills from scratch Taking a job an IR team does by hand every quarter and rebuilding it as something an agent runs continuously.
Anticipating the questions Research across peer earnings calls and CRM history to predict what an IRO, CFO and CEO will actually be asked — before the call, not after it.
Connecting systems that don't connect A meeting record in the CRM and a shift in the trading data are often the same story. Almost nobody sees them as one.
Deciding what not to automate Judgment, relationships and the call you make under pressure stay with the person. Knowing where that line sits is most of the work.

Background

Eleven years of doing this by hand first

11years in IR and capital markets
100+public companies advised
12analysts on the team I lead

I've worked with public companies from large-cap technology to small-cap biotech, across every sector, on earnings, investor days, targeting, roadshows and shareholder engagement. Before any of this was automated, it was me and a spreadsheet before the market opened. That's what makes the AI work credible rather than theoretical.

More about me

Writing and speaking

Where I've made the case in public

September 2026 Be the filter, be the synthesis Why summarizing is the least valuable thing an IR team can ask a model to do, and what to build instead.
Series Confessions of an IRO Host of an unscripted conversation with senior IR leaders about the moments that define the role rather than the best practices that describe it.
March 2026 The story behind the story: how IR teams prepare for volatile periods IR Impact briefing with the IR leads at Insmed and Rivian, on using AI as a force multiplier when markets move faster than the reporting cycle.

All writing and speaking

Contact

Get in touch

I'm always glad to hear from IR teams working out where AI fits, from people building in this space, and from anyone who thinks the argument above is wrong.