Dipping Your Toe or Taking the Plunge: How IROs Can Get Started with AI
Rapid economic changes and innovative technological advancements have seen the investor relations world pivot quickly to stay in touch. With digital communication channels becoming an integral part of investor relations, Artificial Intelligence tools are becoming popular, critiqued and leveraged by investor relations officers and finance industry experts. Exploring the potential of AI in IR opens a new world of opportunities, from marketing and data analytics to creative communications workflow and anything in between. The growing importance of AI in ‘modern’ investor relations calls for a deeper understanding of its use cases and its power when applied strategically.
To get a grasp of AI's connection to finance and investing, let's begin by exploring. Many IROs are using AI to help reach current and potential investors, streamline workflow and expand the IR lens. However, there are concerns and differing opinions about AI’s ethics and how it works. People are still figuring out how AI can improve investor relations practice.
As the investment community enters the fourth quarter of the year, equipped with new and exciting tools and a lot more learning, many foresee investors relying more on AI-powered tools. These tools will help investors make investment decisions and gain insights about the companies in their portfolios. In predictions about the top trends for Investor Relations in 2023, irlabs highlighted that AI would be a big factor – and that has proven to be correct. Investors are quickly adopting AI to analyze earnings calls, and companies are using this tool to understand what investors are feeling. AI has firmly established itself and swiftly become a new expense item in IR budgets.
The merits of exploring AI tools and features
In a world where IROs are tasked with wearing multiple hats, from analyst and writer to spokesperson, graphic designer, social media expert, capital raiser and even ‘therapist’ for both the C-suite and the investment community, AI has become an important tool to simplify the increasingly diverse roles.
AI tools can act like an extension of the IR team. Understanding how to approach various AI tools, recognizing their usefulness, and grasping their potential applications, can greatly enhance an IRO’s ability to work efficiently and add value to the company. For issuers that are just beginning to embrace AI, the intricacies and potential risks can seem overwhelming. While some IROs have integrated AI into their daily routines, others are still cautiously testing the waters.
Equally important is the IRO's grasp of the AI tools that investors are employing. By understanding the tools investors use, IROs can better tailor their communications and engagement strategies. This insight allows IROs to align their approaches with investors' preferences, enhancing interactions and the level of understanding between companies and their stakeholders.
For data analysis, try TrendSpider
TrendSpider is helpful for developing a technical investment strategy. The technical analysis software is useful for people who want to automate their trading strategies. TrendSpider can help you easily build, test, and optimize a custom trading algorithm. It allows you to instantly test any strategy on any market, explore the behaviour of price before and after entry, and track key backtesting KPIs and metrics automatically.
For IROs, being aware of these data tools that investors are leveraging is important for understanding how your company is performing in the market.
For market insights, try Black Box Stocks and MarketReader
Black Box Stocks is a reliable software to use for getting alerts before the stock market opens. It is one of the most user-friendly software options for trading, providing a simple platform that allows you to enter a stock symbol and run a screener based on your predefined criteria.
MarketReader, a market analytics company, provides a valuable resource for investor relations professionals. It enables them to comprehend the real-time dynamics driving market movements. Access to such insights is very beneficial, especially when reporting to the Board and C-suite. It allows for informed decision-making and strategic planning by IR teams, improving the effectiveness of communication and engagement strategies.
For communications and digital content, leverage ChatGPT
Many IROs have tried and tested ChatGPT by now. However, it’s essential to exercise caution when dealing with potentially sensitive or material non-public information (MNPI) while using this AI-driven language processing tool. Accuracy is paramount, and this tool should be viewed as a helpful assistant rather than a human replacement. ChatGPT can serve as an extra pair of hands, offering fresh perspectives and it can aid in brainstorming sessions. It’s an excellent entry point for those new to AI due to its user-friendly nature. When used correctly, ChatGPT can generate digital communication prompts and suggestions that jumpstart your creative ideas. Nevertheless, it’s important to remember that human oversight is indispensable, particularly when dealing with confidential or sensitive information. Always verify and confirm data accuracy before relying on AI-generated content, especially when it pertains to MNPI. ChatGPT is a powerful tool, but human discretion and diligence remain irreplaceable.
For supporting the C-suite and understanding sentiment, consider Quantified
Quantified Communications is an AI tool that uses machine learning algorithms to measure, benchmark and improve communication skills. Quantified can evaluate earnings calls, investor presentations and even a phone call – whether with an analyst, current investor, potential investor or other stakeholders – to gauge intangible qualities like trustworthiness, likeability, confidence and the elusive ‘X-factor’. In other words, you can run an investor presentation through Quantified Communications for an assessment of behavioural factors such as content, voice and visual delivery to find out how the audience perceived the message. Even better, you can leverage Quantified Communications to rehearse a presentation to predict audience perception and refine the impact.
For translation, consider Alexa Translations AI
Alexa Translations can be utilized to translate news releases, annual reports, financial documents and more. We live in a globalized world and IR is borderless. As companies look abroad to access capital, it’s imperative to communicate with an international clientele, incorporating language translation into IR programs.
Gary Kalaci, CEO of Alexa Translations AI, calls it a helpful tool in the fast-paced environment of AI that can generate a high-quality product almost immediately but still needs the refinement of professionals – especially in an environment where inaccurate information can directly affect stock value.
Embracing hybrid thinking – an interaction between humans and artificial intelligence – can open up a wealth of opportunities to streamline processes and amplify the effectiveness of IR professionals and their investor relations programs.
Practical Applications of AI for IROs
For companies that are just starting to use AI, there is a learning curve, but the rewards can be impactful. AI technologies lead to an ecosystem where virtual robots, voice assistants, and robotic process automation (RPA) can collaborate with humans to streamline communications and operations. For this to work successfully, knowledge of the proper use of AI is essential, including acting responsibly and ethically.
Some of the qualms expressed about AI come from its ability to be misused. Proper input is key to the right output. When asking something of an AI tool, it is imperative to be clear and concise. Avoid open-ended questions and ask for a precise response. When inputting information, use specific keywords to give as much context as possible.
Interpreting and utilizing results obtained from AI analysis is a skill, requiring critical thinking and a detailed eye for the humanness that AI lacks. Depending on what tool you are using and the results it delivers, you might need to do some fact-checking, further research, or reimagine your initial ask to receive a more specific outcome.
While AI offers the potential to transform investor relations, it's not without pitfalls. One concern is the risk that overreliance on AI-generated insights can lead to oversimplification of complex financial dynamics. For instance, relying solely on AI predictions for stock performance could neglect important qualitative factors that influence investor decisions. Another pitfall is the potential for bias in AI algorithms, which might inadvertently amplify existing market biases or result in unfair treatment of certain investors. Moreover, AI tools can sometimes struggle with interpreting context and emotions accurately, impacting the nuanced aspects of investor communications. These risks highlight the importance of maintaining a balanced approach in which human expertise complements AI's capabilities. In IR, ensuring accurate data sourcing and adhering to the facts is paramount.
Navigating the Pitfalls of AI Integration
We’ve all heard the buzz about how smart robots are stealing jobs and causing plagiarism nightmares. That scenario is not exactly the case, but the fear isn’t unfounded. Fortunately for IROs, the investment community values human interaction.
Concerns about data privacy and compliance have also been at the forefront of conversations about AI’s pitfalls. Without thorough human oversight, personal data like names, addresses, and contact details can fall through the cracks of these systems, inadvertently being processed by generative AI algorithms, resulting in unintended exposure and the mishandling of sensitive information. Determining which data sets must be tokenized and protected is a good place to start.
Here are some ways to make sure that you’re not falling into unconscious bias and can ensure accurate analysis and ethical use of AI:
Review training data
Most good AI tools come with an explanation of how they work and what information has been used to train the artificial intelligence. An understanding of this data will allow you to decide whether or not you trust the system and if it aligns with your intentions for the use of the tool. Understanding what the tool is using to generate its results can help mitigate potential unseen biases.
Look for feedback
User feedback can tell you what the experience has been like for others in the industry and might prompt or deter you from using particular AI tools. Feedback provides valuable perspective.
Always ensure a personal touch
AI can’t ever be the first and last thing to touch something that moves the needle on your IR program. Any type of AI tool should be overseen by a team member who understands its value and has the best interests of your company in mind.
It’s Time to Take the Plunge
In today’s ever-evolving investor relations landscape, IROs need to be proactive about AI. The adoption of new technologies has long been a key differentiator between businesses that succeed and those that fall behind.
There can be many benefits of AI for investor relations, and you don’t have to overwhelm yourself by onboarding all the tools at once. Start by learning, learn by doing, and do a little more than just dipping your toe in the water.
According to Ray Kurzweil, one of the best-known AI thinkers, this creative process begins with Hybrid Thinking, an interplay between human and cyber intelligence that he sees as the next big leap in thinking. Hybrid thinking reflects the importance of the interplay between people and tech. Human oversight is required to keep things on track, and AI integration can empower your overall processes. If you want to be on the cutting edge of IR practice, it’s time to get your feet wet!
Decoding Acronyms: Your AI Glossary
AI Ethics: The ethical considerations and guidelines related to the development and deployment of artificial intelligence technologies, addressing issues such as bias, transparency, accountability and potential societal impacts.
Algorithm: A step-by-step procedure or set of rules for solving a problem or accomplishing a specific task, often used in machine learning to process data and make predictions.
Artificial Intelligence (AI): The study and development of computer systems that can replicate intelligent human behaviour by simulating cognitive processes such as learning, reasoning, problem-solving and decision-making.
Augmented Reality (AR): Technology that overlays digital information, such as images or data, onto the real-world environment, enhancing users’ perceptions and interactions with their surroundings.
Autonomous Systems: Systems, often powered by AI, that can perform tasks or make decisions without human intervention. Examples include self-driving cars, drones and industrial robots.
Big Data: Extremely large and complex datasets that require specialized tools and techniques for storage, processing, and analysis, often used to identify patterns and trends that may not be apparent with smaller datasets.
Chatbot: A computer program designed to simulate human conversation, typically through text or voice interactions. Chatbots use NLP techniques to understand and respond to user queries.
Cognitive Computing: A field that aims to create computer systems capable of mimicking human cognitive abilities, including understanding natural language, learning from experience and making contextually informed decisions.
Computer Vision: A field within AI that focuses on enabling computers to interpret and understand visual information from the world, such as images and videos. Computer vision is applied in tasks like image classification, object detection and facial recognition.
Data Mining: The process of discovering patterns, trends, and useful information from large datasets using various techniques, often to support decision-making and predictive analysis.
Deep Learning: An advanced form of machine learning that employs deep neural networks with multiple layers to analyze and interpret data, particularly useful for tasks like image recognition, speech recognition and natural language understanding.
Expert Systems: AI systems designed to replicate the decision-making processes of human experts in specific domains, often utilizing rules and knowledge bases.
Internet of Things (IoT): A network of interconnected physical devices (such as sensors, appliances, and vehicles) embedded with software, sensors and network connectivity to exchange data and perform actions.
Machine Learning: A subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to learn from and improve over time based on large volumes of data, without being explicitly programmed for specific tasks.
Natural Language Processing (NLP): A branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. NLP is used for tasks like language translation, sentiment analysis and chatbot interactions.
Neural Networks: A type of machine learning model inspired by the structure and functioning of the human brain, comprising interconnected nodes (neurons) that process and transmit information. Deep learning, a subfield of machine learning, often uses deep neural networks for complex tasks.
Reinforcement Learning: A machine learning approach in which an algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties. It's commonly used in training autonomous agents, like self-driving cars or game-playing bots.
Robotic Process Automation (RPA): The use of software robots to automate repetitive and rule-based tasks in business processes, increasing efficiency and reducing manual labour.
Sentiment Analysis: A type of NLP that involves determining the emotional tone or sentiment expressed in textual content, commonly used to gauge public opinion on topics or products.
Virtual Reality (VR): A computer-generated environment that simulates a realistic experience, allowing users to interact with and immerse themselves in a digital world.
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