Reimagining Investor Engagement Through Agentic AI

Imagine an AI that doesn’t just respond, it thinks, acts, and drives results. That’s the promise of agentic AI, and it’s already shaking up the world of investor communications.

In this episode of Trading Tomorrow - Navigating Trends in Capital Markets, host Jim Jockle chats with Chris Cummings, Chief Strategy Officer at InvestorFlow, about how this next wave of automation is changing the game for private market firms. From raising funds to managing thousands of LPs, firms are turning to AI agents to deliver speed, precision, and personalization at scale.

Episode Transcript

Jim (00:06):
Welcome to Trading Tomorrow Navigating Trends in Capital
Markets the podcast where wedeep dive into technologies
reshaping the world of capitalmarkets.
I'm your host, jim Jockle, aveteran of the finance industry
with a passion for thecomplexities of financial
technologies and market trends.
In each episode, we'll explorethe cutting-edge trends, tools
and strategies driving today'sfinancial landscapes and paving

(00:29):
the way for the future.
With the finance industry at apivotal point, influenced by
groundbreaking innovations, it'smore crucial than ever to
understand how thesetechnological advancements
interact with market dynamics.
Today, we're exploring one ofthe most transformative shifts

(00:55):
in capital markets the rise ofagentic AI, autonomous,
goal-driven AI systems that arechanging how financial decisions
are made.
From predictive analytics toreal-time investment insights,
ai agents are ushering in a newera of efficiency and
intelligence.
Joining us is Chris Cummings,chief Strategy Officer at
InvestorFlow, a fast-growingplatform helping investment

(01:18):
professionals replace legacyinfrastructure with intelligent
automation.
Chris brings a wealth ofexperience in product and
go-to-market strategy, havingadvised numerous B2B and SaaS
technology startups.
He holds senior roles inleading tech firms like
Cleversafe and NetApp.
At InvestorFlow, he's helpingto drive strategic growth as the
company pioneers ways AI andagentic technology can reshape

(01:41):
investor communications, fundadministration and data-driven
decision-making.
Chris, welcome to the podcast.

Chris (01:47):
Thanks for having me, jim .
Really appreciate it, yeah, solet's just dive in.

Jim (01:51):
So agentic AI is being called the next frontier of
enterprise technology.
How do you define agentic AIand what makes it so disruptive
in the capital market space?

Chris (02:00):
So I think we saw the first round of AI, which was
sort of a prompt and answer, andnow we're able to deal with a
whole series of prompt andanswers that run in a stream and
you can deal with theif-then-else scenarios.
And that's really what drivesthis value of agentic, because

(02:24):
now you can get to the bottom ofan issue but not have it be
just a simple input output.

Jim (02:31):
And you've been focused on digital investor experiences
since the early 2000s.
How is agentic AI pushing thatevolution even further?

Chris (02:39):
Yeah, it's having a real impact.
Having a real impact If youthink about investor experiences
and in our case, forInvestorFlo, we're really trying
to serve both the mid-sized tolarge firms that are growing
their count of institutionalinvestors by literally the
hundreds.
One of our largest has 5,000LPs in a single fund.

(03:05):
So imagine trying to deal withthe level to provide the level
of service they want to provide,but do it at that kind of scale
.
You know, agenti really has thecapability of helping these
investor services professionals,engage these folks, get them
everything they need, becausethey are the number one source

(03:27):
for their next fund.

Jim (03:29):
So now at InvestorFlow, you're reducing processing time
by up to 60% by using AI.
Perhaps you can walk us throughsome specific examples of how
Sure.

Chris (03:41):
So let's start from the beginning.
The first thing that the firmneeds to do is say we're raising
a new fund, what do we go do?
And you think about that processfor the head of IR, who tends
to be the first for driving thatnext fund.
So, instead of bringing intheir team and mining through,

(04:08):
looking for conversations hereand there where they might have
found an institution that says,hey, I'm interested in this type
of fund, I'm interested indigital infrastructure fund, or
I'm interested in this type ofreturn with this type of risk
rate, well, instead they can useus as a way of mining through
not only the records that theymay have in a system, but

(04:31):
meeting notes, emails, and comeup with a targeted investor list
based upon pre-existinginterest that they have, and
they can do this with the clickof a button.
Now, we're not saying that thisis your 100% list go forth and
conquer, but you are shavingmassive amounts of time off of

(04:52):
this process and you're giving amuch higher probability of
which institutions are going tobe interested.
So this one use case gives youa flavor for just how powerful
this can be for thesehigh-powered firms and these
high-powered professionals.

Jim (05:08):
So some tech leaders like Oracle or predicting a future,
AI agents are going to surpasshuman uses in many financial
systems.
I mean, do you see thathappening in private markets?
And if yes, you know when isthis coming?

Chris (05:23):
I certainly think that it's possible in the future.
I would say, based upon ourconversations with our client
base right now, this is reallyabout making their people better
, and you're talking aboutextremely educated, extremely
smart and extremely drivenpeople.

(05:44):
These are the people that arereally pushing the envelope on
the work life boundary, let'ssay, because that's what it
takes to get ahead in this space.
But if I can have AI be a forcemultiplier for me, find more
opportunities, find on-targetconversations and then decrease

(06:04):
the time that it takes to get toan answer in and around a
particular whether it's afundraise or whether it's a
particular investment that wantsto be made, that they want to
make If they can drive that andget advantage out of AI, it
really is where they are now.

Jim (06:21):
But one of the biggest challenges is data.
Yeah, and so what are thefoundational data requirements
for a firm that really wants toimplement AI agents effectively?

Chris (06:30):
So this is a great question.
It turns out that it seemsreally like it's the more data
the better.
And you know one of the thingsthat we know and you brought up
Oracle, interestingly enough,which was, you know, an older
player, say, in the CRM marketat one point in time.
But you know it's atime-honored tradition that

(06:53):
nobody likes updating theircustomer relationship management
systems, and this is where alot of this information is
stored.
This is where you know.
I spoke with Jim today and wetalked about the following
things.
Jim was interested in thefollowing things Nobody likes to
do that and, as a consequence,if you can just make this whole

(07:15):
data capture problem justsimpler so that it's not a tax,
it's not a burden on people, themore data you have, if you can
look at emails, meeting notes,records, anything that may be
stored in a meeting itself, ifyou can tap all of that, you can
get a 360 on an opportunity wayfaster.

(07:38):
So bringing all of that togetheris critical.
And in the private markets, asyou know, there's the raising of
the funds, there's doing theinitial deal and oftentimes
there is a secondary deal whichmight be looking for a
co-investor, because now I'vegot to find somebody.
This is just too big for myprofile or my risk profile.

(08:00):
I want to have more people inon the deal.
Or maybe it's in the debt area.
They want to find a way tosyndicate that debt.
If you've got what's going onon the fundraising side
integrated with what's going onin the deal side, you short
circuit this conversation.
You make it faster.
So data is critical.
But I think you know we spent along time thinking about the

(08:23):
need for data cleansing.
I think you know AI is thething that helps us just
aggregate and extract andsynthesize.

Jim (08:30):
Well, one of the bigger challenges in that is, as all
these tools are coming to market.
They don't necessarily all playtogether, though.
Right, we utilize, where youknow we have our CRM, or we have
, you know, tools that arelistening into phone calls and
providing transcripts andinsights from the sales

(08:51):
organization.
You have, you know, an Office365 ecosystem.
You know, how are you gettingall of these to play right?
Because you're almost gettingdifferent sandboxes along the
way.
That's right.

Chris (09:04):
So one of the big things here is, as you say, how can you
tap this?
But how you make it easy forthe user, and and so this is
where proper, you know, toolpropagation we've seen in so
many other instances.
That's not exactly the key toadoption, and and the reason why
I talk about adoption is notjust that you and I say are

(09:27):
comfortable using thistechnology, but adoption is key
to actually getting the data andtherefore starting that cycle
of more data, better insight andbetter outcomes.
So what we're trying to do isreally take the core workflows
that we serve in these firms thefundraising workflow, the

(09:48):
capital deployment workflow,whatever type of transaction it
may be and the investor servicesworkflow and have those
integrated together and on acommon data platform, which
again propagates the data, butstitch AI into these different
applications, as opposed to AIas sort of a bolt-on add-on.

(10:11):
After the fact.
That just makes it.
It's going to make it a lotsimpler for people to use.
That's our at least that's ourpremise.

Jim (10:18):
You know, looking back historically, investor portals
used to be just documentrepositories.
You know, today there'ssophisticated engagement tools.

Chris (10:31):
How is your team redefining that experience?
That's a huge element here, Imean, if you think about it.
Let's go back to that exampleof thousands of LPs.
Well, those thousands of LPs ina particular fund they do not
want to be treated like a number.
And the firms that rely on themas part of their funds they
don't want to treat them like anumber either.
So engagement is all aboutmaking them.

(10:54):
You know, we talk aboutenabling these investor services
teams to deliver the whiteglove treatment that these
institutions expect.
That means their data, theirpreferences, their profile and
make it engaging, not just adocument repository.
I still have some scars, Ithink, from SharePoint in the

(11:18):
early 2000s, so that is not thepoint.
They want to profile and makeit truly engaging.
They want to profile what'sunique about their firm and
their firm strategies, not justprovide a bunch of documentation
.

Jim (11:31):
I'm not going to comment on SharePoint.
Let's talk fundraising for asecond.
With tools like interactivePPMs and virtual diligence, how
are AI and automation reshapingthe capital raising lifecycle
understand more?

Chris (11:44):
clearly who do you have strong relationships with and
who in your firm has thoserelationships, so that you can

(12:08):
have your best people talk tothe most appropriate contact at
these various institutions.
That's what leads to a muchbetter sales cycle.
It really is a sales processfor them and making that
seamless for them.
So so when you're in aconversation, and then
automatically provisioning themwith a diligence room to get

(12:30):
access to that information sothey can understand if this fund
, the fund profile, is right forthem, that's that's key, and
we've seen some results, someearly results, where just
shaving the cycle time off ofthat process means you as a firm
can just be moving a lot morefast, a lot faster than, say,
your competitors, because it'snot, as you know, it's no longer

(12:54):
unique.
What's your particular fund?
I mean, everyone right nowwants to go after a digital
infrastructure fund.
Okay, we all read the same newsand see the same returns, so
it's got to be built onrelationship strength and
efficiency, so security isalways top of mind for anyone at

(13:14):
this point.

Jim (13:15):
What are the best practices that GPs should follow when
introducing AI into sensitiveworkflows like capital calls or
wire instructions?

Chris (13:25):
So we spend a lot of time thinking about the following,
which is helping them get to amuch faster answer but still
have that human control.
The final mile, if you willthat has been the feedback that
we universally have gotten fromour early adopters is, you know,

(13:49):
we're not trying to take, say,the call center approach where
you know the idea is we couldhave a virtual agent take you
from zero to to, you know, allthe way through the end of the
process.
Really, really, it's about howdo we just enable these folks to
just be better and deliver amuch higher service level to

(14:10):
these high touch clients becausethey want to.
These firms want to retain themand keep operating with that
kind of high touch.

Jim (14:18):
And as finance becomes more autonomous, how can firms
balance AI driven efficiencieswith the personalized
experiences that LPs are stillexpecting?

Chris (14:27):
Yeah, I see this playing out where there could be
multiple classes, and one of thethings that we're working on is
how do we classify what theirincoming requests are?
What could be served digitallyjust to make it lightning fast
for them to get an answer to aparticular very discreet thing,

(14:50):
versus, you know, a much morenuanced sort of inquiry that
really does require an investorservices professional to have
their their you know theireyeballs on top of that, on top
of that response.
So I think that's the next, oneof the next steps and that's
one of the of that, uh, on topof that response.
So I think that's the next, oneof the next steps and that's
one of the things that, uh, youknow, our, our dev team is is at
work on.

Jim (15:11):
So, chris, unfortunately we've made it to the final
question in the podcast.
We call it the trend drop.
It's like a desert Islandquestion.
So if you could only watch ortrack one trend in AI, uh, at
this point in time, what wouldthat be?

Chris (15:25):
Boy, this is a tough one, given how fast this is moving.
You know, one of the thingsthat we saw very recently is how
is this going to be integratedinto your own personal devices,
right?
So you know I'll go real timeon you.
Yesterday, openai decided tobuy Johnny Ives' company, which

(15:46):
is a device making company, andyou know who knows how this is
going to be brought together inthe future.
But I definitely think we'regoing to see this integrated
into various you know alldifferent experiences, so
watching.
You know, how is thisintegrated into email?
That's something that wealready do but how is this

(16:08):
integrated into these differentsystems of record and different
systems of engagement?
This is going to be a key trendto watch.

Jim (16:18):
Chips in the head are coming.
Yeah Well, Chris, I want tothank you so much for your time
and your insight.
Really appreciate it.

Chris (16:26):
Thank you, jim, really appreciate you.

Jim (16:35):
Thanks so much for listening to today's episode and
if you're enjoying TradingTomorrow, navigating Trends and
Capital Markets, be sure to like, subscribe and share, and we'll
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