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rtificial intelligence (AI) was on the minds of NIRI members who attended the NIRI 2025 Annual Conference in Boston in June. The conference fea-


tured sessions that explored AI from different angles, including use cases that demonstrated how AI can benefit investor relations professionals in practical ways. Summaries of two sessions are featured here.


Predictive Power of AI in Capital Markets In “Te Predictive Power of AI in Capital Markets – Transforming IR Trough Data-Driven Insights,” moderator Fatma Sardina, Senior Financial Analyst at Copart, Inc., led a group of panelists through an insightful discussion about the use of AI in sentiment analysis and earnings preparation. Te panelists were


“If we’re going to be in a world that is driven by data, we’re going to have to respond with data.” Tim Quast, ModernIR


Tim Quast, President of ModernIR; Moriah Shilton, NIRI Fellow; and Tim Lind, Head of Data Products at Depository Trust Clearing Corporation (DTCC). Sardina explained that the session would ex-


plore capital market structure, behavioral analysis of investors, and how AI machine learning can help synthesize vast amounts of information to generate valuable data-driven insights. Quast cited a Wall Street Journal article about


Marshall Wace, a large hedge fund, that feeds all the quantitative data it can find—the equivalent of 400 billion emails per day—into an algorithmic structure that drives its trading returns. “If we’re going to be in a world that is driven by data, we’re going to have to respond with data,” he


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declared. “Most of the money now is running on models. About 10% of the volume in the equity market on any given day is coming from stock picking, which means 90% of it is coming from something else. Te IR profession needs to understand this and go beyond just telling the story. If we have a story strategy, we need a product strategy too, because funds such as Marshall Wace are consuming products. They’re looking for alpha in the products comprising the equity asset class.” In building AI machine learning models to gener-


ate this data, Sardina recommended picking relevant variables and scraping the internet to choose only relevant information, which she says can help avoid AI-generated “hallucinations.” Lind provided valuable insight into the massive


AI-driven shift in data analytics resulting in tradi- tional Wall Street liquidity providers giving way—at least in the equity markets—to proprietary trading companies such as Susquehanna, Jane Street, Citadel, and Hudson River. “Tey’re competing with Citadel and Virtu,” Quast


added. “Tese firms got into the wholesale business buying retail order flow. For example, if you’re a customer of Schwab, they’re selling your order flow to Citadel or Hudson River to execute the trade. It works; it’s why we can trade for free.” Lind explained, “If the market’s going quantita-


tive, your understanding of what drives pricing dy- namics must also be quantitative. At DTCC, we are the digital voice of corporate America, meaning we must translate many of the prospectuses and legal offering documents that you present to the deposi- tory, because we communicate six or seven layers of intermediation and ultimately to your investors. We’re all trying to translate what you meant by holding a particular event or in the terms and conditions of a new bond or stock issue. “The more standardized we become as an in-


dustry, as issuers, the better you make the plight of your investor. Every corporate action that you issue literally bounces a million times within the investor community between custodians, agents, platforms that serve them, the Securities and Exchange Commis- sion, lenders, index funds, administrators, and others. “We’re trying to extend our data collection and


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