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8 Ways Artificial Intelligence Could Benefit Investor Relations Staffs

Data Science and AI

By Tom Abrams, CFA  |  August 9, 2023

Generative AI, Large Language Models, and Machine Learning could offer investor relations professionals valuable support in the months and years ahead. Their relationships with investors, financial analysts, and reporters will always be paramount, and there is real potential for AI to help free up time to strengthen those relationships and advance strategic priorities.

AI and related technologies can identify patterns and trends that human analysts might missincluding factors that could affect company stock price. Considering model computing power (scale) and the amount of data available (breadth), the power of AI and its potential uses grows.

Let’s take a look at how AI co-pilots could provide support across eight key IR activities.

Draft IR communications

A subset of AI, generative AI models can produce first drafts of new textual content informed by a range of existing company information. This could save IR departments time in developing:

  • Proxy statements with a range of inputs

  • Quarterly earnings reports and presentations to analysts

  • Company backgrounders for new shareholders

  • Anticipated questions (and suggested answers) based on scripts for earnings calls

Discern external sentiment

Generative AI can indicate positive and negative sentiment toward the company across news reports, social media, investment fund shareholder letters, and other publicly available documents. This enables IR to tailor communications to the interests of their target investors. It could also help staff anticipate investor concerns and prepare management to respond.

FactSet utilizes this technology to provide clients with AI-generated themes and sentiment in our Document Search function. FactSet also offers a generative-AI-powered API that can answer questions of any unstructured text, including e-mails, website content, news, and financial documents.

Answer investor inquiries 24x7

AI-powered conversational support chatbots can answer investors’ questions and provide updates on company fundamentals. Because of the potential for inaccurate responses called hallucinations, consider using a controlled source of information and have a staff member validate answers and correct errors before making the chatbot available to investors. (Learn more about reducing hallucinations from generative AI.)

Identify high net worth and private investors

Using generative AI tools could also help investor relations departments more easily track shareholders’ activities through filings and fund documents.

  • Simplify the review of filings for contextually significant text additions and removals

  • Provide quick comparison of filings tables from different time periods

Identify potential for shareholder activist campaigns

It is possible to create Machine Learning models to predict potential future events, like we’ve done with FactSet Predictive Signals. These AI models intelligently surface insights, such as whether a company is likely to be the target of an activism campaign, is predicted to issue a follow-on, or may experience a credit-rating change, as just a few examples.

Using AI in government and public affairs

Given that IR staff are sometimes involved with government and public affairs, Gen AI can help summarize legislative documents and hearings and assist with media copy. This enables IR and government affairs to quickly note changes in policy biases and analyze testimonial language, committee commentary, and questions from public hearings.

In addition, generative AI could help IR staffs confirm their firm’s external communications—typically developed across different internal teams—are consistent to investors, the media, and in regulatory situations. Being consistent can also help external AI models avoid making incorrect conclusions.

Building relationships amid the continued shift to passive asset management

The shift toward quantitative and passive asset management was underway before AI, generative AI and Large Language Models caught public attention, and the shift is expected to continue. In addition, robo-advisors could continue to gain traction as they use AI and Machine Learning algorithms to analyze market data, assess risk tolerance, and create diversified portfolios for investors. As AI capabilities evolve, robo-advisors could become more sophisticated. 

Investor interests and fund types may proliferate further because of AI. For example, generative AI could incorporate risks and returns from a wider variety of potential assets a fund could holdalternative investments such as real estate, infrastructure, private equity, venture capital, commodities, and gold. Such a change would diminish asset flows toward a company’s equity.

Active funds will persist even as they use AI to augment the human decision-making abilities of portfolio managers. A continuation of active management will underscore the importance of relationship building and regular contact with active investors as key activities of the investor relations role.

Preparing for higher volumes and the heightened need for fact checking

Data sources are increasing to include, for example, satellite imagery, sentiment extraction from news media and social media content, tone and word choice in public presentations, weather patterns, flood risks, credit card receipts, retail store traffic, farm data, transportation, emissions, state and local property filings, and public-hearing transcripts. And the list goes on.

Financial professionals could further harness quantitative and AI tools to benefit from expanded datasets. This can help investors gain more investible insights and produce more sophisticated company analyses.

As that happens, it’s important for IR staffs to be aware of conclusions from external AI models. Internal use of AI could help with that. AI monitoring of social media, trading blogs, and investor publications, for example, can help IR teams remain informed about what external opinion makers are saying. Generative AI summaries of the large amounts of data will help IR professionals stay informed without becoming overwhelmed.

In summary

The AI landscape is evolving quicky and could feel a bit “wild west” at times given unfettered growth in application development, the rise of ethical considerations, and the potential for regulatory oversight.

As new investment management datasets spread rapidly and investors accelerate their activities, IR staffs could benefit from the capabilities that AI and related technologies offer. Awareness of AI’s use outside their organizations will also be increasingly important.


This blog post is for informational purposes only. The information contained in this blog post is not legal, tax, or investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.

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Tom Abrams, CFA

Associate Director, Deep Sector Content

Mr. Tom Abrams is the Associate Director for deep sector content at FactSet. In this role, he is responsible for integrating additional energy data onto the FactSet workstation, including drilling, production, cost, regulatory, and price information. Prior, he spent over 30 years working at sell- and buy-side firms, most recently as the sell-side midstream analyst at Morgan Stanley. He also held positions at Columbia Management, Dreyfus, Credit Suisse First Boston, Oppenheimer, and Lord Abbett. Mr. Abrams earned an MBA from the Cornell Graduate School of Business and holds a BA in economics from Hamilton College. He is a CFA charterholder and holds certificates in ESG investing, sustainable investments, and real estate analysis. 


The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.