Lately I’ve been asked one question a lot: what do I think the most impactful applications of AI will be in capital markets? My answer: I have no idea. No one knows. As the saying goes, “the map is mostly empty.” We’re just getting started. So instead, rather than figuring out the perfect use case and doubling down, I believe all our clients should focus on building optionality. How do you do that? Collect and organize all your data. That way you have the tools and assets to continue to experiment and iterate. 

Let’s take a step back: it feels like AI/Gen AI is in its primordial years. Everything is just getting started. To take the overused analogy, ‘it’s like the internet in the mid 90’s’. What I take from that is the tech industry is realizing how big the new technology is going to be but realizes a) adoption is just getting started and b) the best use cases are likely still undiscovered. 

If it’s the mid 90’s for Silicon Valley, then what does that mean for Wall Street? It means we’re in the very very early days. A few firms have launched a few early use cases, but they mostly have to do with summarization of content (and to a further degree, the summarization of research).

Personally, I look at the speed of innovation and change with the largest players in this industry, and I accept that a year from now the technology might already be unrecognizable from where it is today. That is how fast the industry is moving. So instead of sharing use cases, I’m going to share what I’m seeing firms do in terms of capturing data/information, and what use cases they might be setting up for. Hopefully this gets you thinking more broadly about how Gen AI is going to impact your day-to-day life, and how the industry is going to evolve. Thematically, I want to convey that the map is mostly empty – don’t worry about falling behind. Everyone is just getting started. Focus on getting yourself set up for success so that you can action the use cases once they become apparent/attainable. Here are some of the things you should set up:

• Content/Research feeds: historically lots of firms aggregated information for management purposes. Many of the largest funds had an email inbox such as “research@” or “deskcommentary@” or “corporateaccess@”. The purpose was to capture all the information in one place for anyone across the fund to access (and additionally, not have to manage the updating of interests and preferences, but that’s another story). Now we’re seeing buy side clients evolve this approach: they are looking for electronic feeds of content, preferably programmatic. They are looking for all the content as well: not only formal research, but events, sales notes, desk commentary, etc. The use case? There is definitely a focus on capturing all the content a fund produces to make it available to a LLM. This not only includes broker content (external), but also all internal content (more on that in a minute). 

• Inbox/Calendar capture: brokerages and funds are capturing everything in Outlook365/other email clients/calendars, and organizing/structuring that information. This gives them insights into who met with who, when, about what, who spoke to who, when, and about what. It also helps them understand resource availability (i.e. when is my calendar free over the next three weeks), and capture any missing content/research materials (because even if they aren’t sent to a shared inbox/feed, they are still sent to the investment professional). For what its worth, this is why broker dealers trying to ‘stop’ funds from indexing content for a LLM is like holding sand in your hand: there are too many gaps. It’s better to support the client with a feed (then you at least know utilization) vs. forcing them to find a workaround that is worse for both parties. 

• Email content and interaction data: this will come off as ‘talking my book’, but email interaction data looks like it will be one of the highest volume sources of ongoing data, which will be constantly ‘refreshed’ (an important feature of data for LLMs). More importantly, it will be organized and accessible. Any firm not capturing this data (whether working with us or another vendor) is missing out on one of the best data sets they have access to. 

• Chat data: I’m guessing we’re going to see a surge in firms using the ‘compliance’ feed from chat platforms like Bloomberg, but there seems to be an increasing focus on capturing and logging all the chats that individuals are having internally and externally. It essentially provides more ‘content’ that the model can use. 

• Event data: whether it’s a road show, IPO, conference, or any other kind of event, there is a massive opportunity to capture and index this data for future uses. 

When I look at that list, I see the foundational data sources that firms on both sides of the Street should be thinking about. Whatever your current AI product strategy is, you need to realize that we’re so early in the game, that most of what you’re building will probably be scrapped. We’re learning more, the models are evolving, and the use cases are evolving. 

The more important thing right now is to set yourself up for future success: to set up systems and processes to capture (on an ongoing basis) all the data you might one day need. To make sure you’re closing any gaps that might exist. Finally, you should make sure you’re setting up your clients to do the same: what data can you provide them in a more accessible or digestible way?

Fundamentally the goal right now is one thing: to create future optionality. The innovation will start coming fast, and when it does, the best prepared will win. 

As always, if you have any questions, don’t hesitate to reach out. 

Onward and upward,

Blair

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