Data vs relationships

Top performing Investor Relations teams focus on the “I” – the Investors – the type and style of the investor, their minimum investment size, etc., while also embracing the “R” – the relationship between the company and the investor, and the role that has in building trust in management, governance, and their delivery of corporate strategy.

Traditionally, IR professionals have relied on manual processes to gather, analyse, and disseminate information to institutional investors. The case for AI, with its ability to process huge amounts of data quickly and add significant value to this kind of data driven work is clear. However, when it comes to building relationships with Investors, which is built on trust and accurate information, its value is not so clear.

AI-Driven Data Analysis and Insights – The Investors

With vast amounts of financial and non-financial data available, AI algorithms can process and analyse this data at an unprecedented speed, uncovering meaningful insights and patterns that might be missed by human analysts.

For institutional high frequency traders and hedge funds, Natural Language Processing (NLP) algorithms are already used to predict market trends and share price behaviour. For several years now, these investors have analysed large volumes of textual data, such as earnings reports, press releases, and analyst transcripts, to generate sentiment analysis reports and identify key messages. Earnings announcements are often passed through their algorithms to analyse the tone of text used in announcements to predict short term movements in share price.  These investors are now seeking to adopt AI tools to undertake the same analysis on live earnings calls and webcasts.

For senior management and IR professionals, effective communication is the backbone of IR. The immediacy of this market behaviour means carefully preparing and testing earnings call scripts against the outcome of AI driven analytics will become increasingly important. Companies may look to third party support ahead of time to assess the tone and language used in an attempt to avoid unintended market reactions. This capability allows IR professionals to gauge investor sentiment, anticipate concerns, and tailor their communication strategies accordingly.

Efficiency vs accuracy – The Relationship

It is easy to imagine how AI could enhance investor engagement by personalising interactions, from an analysis of historical data and investor preferences, to recommend tailored content and communication strategies targeted to each investor. Such a personalised approach could transform engagement, strengthen relationships, and enhance the overall investor experience.

However, enhancing relationships with investors is built on simple principles which include trust, transparency, consistency, and accuracy.  If AI is to be truly revolutionary for Investor Relations, it must be able to enhance these principles.  However, while publicly accessible AI tools have shown themselves to offer potential, they pose great risks and have proven, on occasions, to be confidently wrong.

AI risk vs reward

Many professionals have already taken to ChatGPT to shortcut the drafting of text.  For IR, however, using such tools poses significant and unacceptable risks to information privacy and accuracy.

Earlier this year, ChatGPT confirmed a significant data breach, which allowed users to see the chat history of other active users. [www.cshub.com/data/news] In a highly regulated environment that demands no third party has access to market sensitive data ahead of the market announcement, using ChatGPT to draft announcements would not only risk being in breach of disclosure regulations, but may open the company to lawsuits for facilitating insider trading.

On the matter of accuracy, ChatGPT has a well-documented weakness of getting facts horribly wrong. In the world of law and financial markets, where facts, accuracy and words matter, this presents significant risks, as one US-based lawyer recently found out.  In April, Steven Schwartz, an attorney with Levidow, Levidow & Oberman and licensed in New York for over three decades, made a fateful mistake of allowing ChatGPT to draft an affidavit, which included six cases that “appear to be bogus judicial decisions with bogus quotes and bogus internal citations,” according to Judge Kevin Castel. [www.bbc.com/news]

AI and retail investors

For retail investors, AI-powered chatbots or virtual assistants are gaining interest in streamlining engagement, by providing instant responses to common shareholder queries, assist in scheduling meetings, and even conduct basic investor education. But just as is the case for written announcements, AI chatbots can suffer the same issues of privacy and accuracy.

Mark McCreary, the co-chair of the privacy and data security practice at law firm Fox Rothschild LLP, recently explained that ChatGPT and chatbots are like ‘the black box in an airplane’.

“The AI technology stores vast amounts of data and then uses that information to generate responses to questions and prompts. And anything in the chatbot’s memory becomes fair game for other users.

“For example, chatbots can record a single user’s notes on any topic and then summarize that information or search for more details. But if those notes include sensitive data — an organization’s intellectual property or sensitive customer information, for instance — it enters the chatbot library. The user no longer has control over the information.”

Conclusion

The emergence of AI in Investor Relations heralds a significant shift in the way businesses communicate with institutional investors. By leveraging AI-driven data analysis and insights, investment professionals may make more informed decisions and optimise their portfolio value. Intelligent investor communication, enabled by AI-powered analytics and chatbots, hints at better understanding of investor sentiment to facilitate efficient communication. Moreover, AI enhanced engagement could lead to more productive relationships and better investment outcomes.

While the integration of AI in IR brings numerous benefits, it also poses challenges. Ethical considerations, data privacy, and potential biases in AI algorithms must be addressed to ensure responsible and transparent practices.

Overall, the rise of AI in Investor Relations presents a tantalising new era of efficient and personalised communication with investors. As AI technologies continue to evolve, institutional investors will likely embrace these advancements to stay ahead in an increasingly competitive landscape. By harnessing the power of AI, companies too can strengthen their relationships with these investors, foster transparency, and drive long-term value creation.  

But until it gets closer to enhancing the “R” in Investor Relations, AI still has a long way to go.