When we talk about AI, we can usually classify it in three main types, depending on the tasks they are able to perform and the technology used:
- Artificial Narrow Intelligence (ANI): is programmed, trained, and focused to perform specific tasks also known as weak AI, however ANI is far from weak, it is mostly used in apps such as Siri, Alexa, self-driving cars, spam filters, etc.
- Artificial General Intelligence (AGI): also known as strong AI, is a type of artificial intelligence that mirrors human intelligence and behavior and applies it to solve any problem
- Artificial Super Intelligence (ASI): also known as the hypothetical AI because it doesn’t just mimic human intelligence and behavior, ASI machines become aware and surpass the capacity of human intelligence and capabilities.
Machine learning vs. deep learning
When talking about artificial intelligence, it is important to learn the difference between machine learning and deep learning. Machine learning algorithms are more dependent on human intervention to learn, whereas deep learning algorithms eliminate some of the manual human intervention required and can use larger data sets.
Artificial Intelligence in Investor Relations: considerations & implications
The use of AI in IR is still quite new. According to an article published by NIRI, the majority of IR professionals are still not consistent users of AI technology. However, as we mentioned before, AI is a rapidly growing technology and although many IR professionals are still not actively using it, public companies have been communicating to machines for years as the buy-side has been increasingly using algorithmic trading strategies.
According to the 2020 “CASTING THE NET: How Hedge Funds are Using Alternative Data” report, more than half of the hedge funds now use non-traditional data sets like on their day-to-day operation. These non-traditional sources include, for example, satellite imagery, sentiment extraction from news media and social media content, weather patterns, credit card receipts and shopping center traffic. There are more than 400 firms that offer this data nowadays compared to 20 about three decades ago. Although some IRO’s still see this technology with skepticism, 2020 came to show that it is crucial to adapt to new surroundings and technology. AI is reshaping the world of investor relations, in 2017 IBM estimated that 90% of the information available in the world had been generated in just the last two years. This percentage is expected to continue to increase in the coming years.
Investor relations professionals view AI as an opportunity and a threat. Here are some tips that may help companies who are starting to use AI: understand and adapt to how AI is used with the external audience, centralize external communications, determine how to use and benefit from artificial intelligence internally, and most importantly, always act in a responsible and ethical way maintaining integrity and credibility.
Artificial intelligence is still pretty new in investor relations, and its use and implications are yet to be discovered. Here are some examples on how AI is used in investor relations right now:
- Sentiment analysis in earning calls and investor days. FactSet and other tools use natural language processing (NLP) programs to make important judgments about companies’ perceived performance, and even triggering real-time buying and selling. We recommend being mindful of the words that you use and how they change every quarter and in every public appearance. You can read more about this topic on our Sentiment Analysis in IR & Mexico’s 3Q21 Earnings Calls blog post.
- Institutions like GBM are using AI to identify common topics among Mexican and global quarterly earnings calls to figure out trends and overall sentiment.
- Chatbots on IR websites can help answer investors and analysts’ frequently asked questions, freeing up IR staff for more relevant functions and giving them a cost effective 24/7 service.
- Automated journalism is a big part of AI in investor relations. Some news outlets like Bloomberg generate news articles with a computer programed algorithm. According to the NY Times, almost one third of Bloomberg’s news are generated using some kind of automation technology. These tools can quickly analyze a press release or earnings report and create a news story with the most relevant figures and facts.
- Some companies are using machine learning and NLP to create products based on their target audience’s most used words. This same strategy can be used by institutional investors to develop a better understanding of a company’s tone over time and the implications for corporate decision-making.
Artificial Intelligence has revolutionized many industries already and IR is not far behind. Public companies need to change the way they communicate to address AI and NLP in a better way. As always, in Miranda IR we would be more than happy to help you analyze how to communicate better for NLP and how AI can improve your IR strategy to take it to the next level.
Contacts at Miranda Partners