Reimagining Investor Engagement Through Agentic AI

In each episode , we'll explore the cutting-edge trends , tools and strategies driving today's financial landscapes and paving the way for the future . With the finance industry at a pivotal point , influenced by groundbreaking innovations , it's more crucial than ever to understand how these technological advancements interact with market dynamics .

Today , we're exploring one of the most transformative shifts in capital markets the rise of agentic AI , autonomous , goal-driven AI systems that are changing

how financial decisions are made . From predictive analytics to real-time investment insights , ai agents are ushering in a new era of efficiency and intelligence . Joining us is Chris Cummings , chief Strategy Officer at InvestorFlow , a fast-growing platform helping investment professionals replace legacy infrastructure with intelligent automation .

Chris brings a wealth of experience in product and go-to-market strategy , having advised numerous B2B and SaaS technology startups . He holds senior roles in leading tech firms like Cleversafe and NetApp .

At InvestorFlow , he's helping to drive strategic growth as the company pioneers ways AI and agentic technology can reshape investor communications , fund administration and data-driven decision-making . Chris , welcome to the podcast .

And that's really what drives this value of agentic , because now you can get to the bottom of an issue but not have it be just a simple input output .

So imagine trying to deal with the level to provide the level of service they want to provide , but do it at that kind of scale . You know , agenti really has the capability of helping these investor services professionals , engage these folks , get them everything they need , because they are the number one source for their next fund .

So , instead of bringing in their team and mining through , looking for conversations here and there where they might have found an institution that says , hey , I'm interested in this type of fund , I'm interested in digital infrastructure fund , or I'm interested in this type of return with this type of risk rate , well , instead they can use us as a way of mining

through not only the records that they may have in a system , but meeting notes , emails , and come up with a targeted investor list based upon pre-existing interest that they have , and they can do this with the click of a button .

Now , we're not saying that this is your 100% list go forth and conquer , but you are shaving massive amounts of time off of this process and you're giving a much higher probability of which institutions are going to be interested .

So this one use case gives you a flavor for just how powerful this can be for these high-powered firms and these high-powered professionals .

These are the people that are really pushing the envelope on the work life boundary , let's say , because that's what it takes to get ahead in this space .

But if I can have AI be a force multiplier for me , find more opportunities , find on-target conversations and then decrease the time that it takes to get to an answer in and around a particular whether it's a fundraise or whether it's a particular investment that wants to be made , that they want to make If they can drive that and get advantage out of AI , it

really is where they are now .

But you know it's a time-honored tradition that nobody likes updating their customer relationship management systems , and this is where a lot of this information is stored . This is where you know . I spoke with Jim today and we talked about the following things .

Jim was interested in the following things Nobody likes to do that and , as a consequence , if you can just make this whole data capture problem just simpler so that it's not a tax , it's not a burden on people , the more data you have , if you can look at emails , meeting notes , records , anything that may be stored in a meeting itself , if you can tap all of

that , you can get a 360 on an opportunity way faster . So bringing all of that together is critical . And in the private markets , as you know , there's the raising of the funds , there's doing the initial deal and oftentimes there is a secondary deal which might be looking for a co-investor , because now I've got to find somebody .

This is just too big for my profile or my risk profile . I want to have more people in on the deal . Or maybe it's in the debt area . They want to find a way to syndicate that debt . If you've got what's going on on the fundraising side integrated with what's going on in the deal side , you short circuit this conversation . You make it faster .

So data is critical . But I think you know we spent a long time thinking about the need for data cleansing . I think you know AI is the thing that helps us just aggregate and extract and synthesize .

You have , you know , an Office 365 ecosystem . You know , how are you getting all of these to play right ? Because you're almost getting different sandboxes along the way . That's right .

That's not exactly the key to adoption , and and the reason why I talk about adoption is not just that you and I say are comfortable using this technology , but adoption is key to actually getting the data and therefore starting that cycle of more data , better insight and better outcomes .

So what we're trying to do is really take the core workflows that we serve in these firms the fundraising workflow , the capital deployment workflow , whatever type of transaction it may be and the investor services workflow and have those integrated together and on a common data platform , which again propagates the data , but stitch AI into these different applications , as

opposed to AI as sort of a bolt-on add-on . After the fact . That just makes it . It's going to make it a lot simpler for people to use . That's our at least that's our premise .

team redefining that experience ? That's a huge element here , I mean , if you think about it . Let's go back to that example of thousands of LPs . Well , those thousands of LPs in a particular fund they do not want to be treated like a number . And the firms that rely on them as part of their funds they don't want to treat them like a number either .

So engagement is all about making them . You know , we talk about enabling these investor services teams to deliver the white glove treatment that these institutions expect . That means their data , their preferences , their profile and make it engaging , not just a document repository .

I still have some scars , I think , from SharePoint in the early 2000s , so that is not the point . They want to profile and make it truly engaging . They want to profile what's unique about their firm and their firm strategies , not just provide a bunch of documentation .

So so when you're in a conversation , and then automatically provisioning them with a diligence room to get access to that information so they can understand if this fund , the fund profile , is right for them , that's that's key , and we've seen some results , some early results , where just shaving the cycle time off of that process means you as a firm can just be

moving a lot more fast , a lot faster than , say , your competitors , because it's not , as you know , it's no longer unique . What's your particular fund ? I mean , everyone right now wants to go after a digital infrastructure fund .

Okay , we all read the same news and see the same returns , so it's got to be built on relationship strength and efficiency , so security is always top of mind for anyone at this point .

The final mile , if you will that has been the feedback that we universally have gotten from our early adopters is , you know , we're not trying to take , say , the call center approach where you know the idea is we could have a virtual agent take you from zero to to , you know , all the way through the end of the process .

Really , really , it's about how do we just enable these folks to just be better and deliver a much higher service level to these high touch clients because they want to . These firms want to retain them and keep operating with that kind of high touch .

What could be served digitally just to make it lightning fast for them to get an answer to a particular very discreet thing , versus , you know , a much more nuanced sort of inquiry that really does require an investor services professional to have their their you know their eyeballs on top of that , on top of that response .

So I think that's the next , one of the next steps and that's one of the of that , uh , on top of that response . So I think that's the next , one of the next steps and that's one of the things that , uh , you know , our , our dev team is is at work on .

Yesterday , openai decided to buy Johnny Ives' company , which is a device making company , and you know who knows how this is going to be brought together in the future . But I definitely think we're going to see this integrated into various you know all different experiences , so watching . You know , how is this integrated into email ?

That's something that we already do but how is this integrated into these different systems of record and different systems of engagement ? This is going to be a key trend to watch .