We are all asking ourselves: ‘How are we leveraging AI to get better and smarter?‘ For investor relations teams, the potential is certainly there. But before getting too excited about AI capabilities, IR professionals should give serious consideration to the fact that everything starts with data quality
AI is an immensely powerful tool to categorise, summarise, and analyse information. It helps reduce time spent on administrative work and provides new ways of identifying connections and dependencies in vast amounts of data.
However, if the data quality isn’t there, the results will reflect that. As my old finance professor at university said about financial models: ‘Shit in, shit out.’ The same applies to AI tools. For investor relations departments, this means placing increased importance on having best-in-class share register data, complete peer data, and detailed metadata on all entries in your investor CRM. That’s where to start if you want to fully benefit from AI.
At Modular Finance, data quality is at the core of our offering. Our in-house team has a decade of experience conducting all share register analysis ourselves, using a wide range of data sources to ensure accuracy and being up-to-date. We do this work on a client-by-client basis, but also cover the full peer landscape through our in-house sourcing and analysis. Having access to this proprietary data gives us the ability to train our own closed models — enabling truly actionable insights, along with strong data security. This puts us in a unique position within the IR software space.
Once your data quality processes are in place, it’s a good time to start taking advantage of the available AI features. At Modular Finance, these include:
- AI summaries of CRM notes
- AI auto-logging, including owner mapping to the CRM from any email provider
- AI owner match and reversed targeting
- AI import of roadshow itineraries
- AI investor targeting
- AI summaries of transcripts
To summarise, investor relations work today involves many time-consuming and repetitive tasks, such as logging meetings, conducting investor targeting, preparing for roadshows, and more. Many of these tasks can benefit from AI — but to truly take advantage of it, everything starts with data quality.
