AI’s IR use goes a step further thanks to tailored tools, trusted data, and a little help from friends

It’s been about two years since ChatGPT launched and brought generative AI tools into almost everyone’s consciousness. Given this time frame, one would think that enthusiasm would gradually fade.

But the full house at our AI and Technology Forum in New York this week told a different story. Clearly, there is still a great need to understand the impact of AI on capital markets and how IR teams can use this technology to work more efficiently and effectively.

However, something has changed – you don’t want people just talking about AI’s potential to transform the IR role. You want to see this in action and try it for yourself. For this reason, we integrated practical workshops into the event where participants could experiment with various AI tools.

The first workshop, moderated by Gregg Lampf, VP of IR at Ciena, focused on using Google NotebookLM, a free virtual research assistant, to support your ESG efforts. Lampf explained how the product can be used to compare competitors’ sustainability reports, generate ideas for your own output and prepare for investor questions.

He also showed how it can create a proposed roadmap for dealing with new regulations, such as the SEC’s climate disclosure rules, and even create a podcast briefing – presented as a chat between two people – to talk about while commuting Keep the topic up to date. Other workshops covered areas such as sentiment analysis, targeting, the earning process and competitive intelligence.

From these sessions, you can see how IR teams’ implementation of AI has evolved significantly compared to, for example, a year ago. People who do this themselves use tailored tools that meet a specific need and often provide a data audit trail so you can have more confidence in the results.

IR technology companies are now integrating AI capabilities into more and more of their workflow tools – which is handy for those of you who don’t have the time or inclination to learn prompt engineering yourself.

For those taking the DIY approach, some key advice was heard throughout the day: use data you trust and a tool that shows you which sources have been accessed; Bring in subject matter experts to review the output – you need to double-check what your virtual assistant suggests. Finally, AI isn’t always the answer – for some tasks, the older, simpler way may still be the most effective.

What is your favorite AI tool to support your IR work? Let us know at (email protected) or via LinkedIn.