How to Actually Use AI in Wealth Management

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Go

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video1742168322: Hello everyone, and

welcome to Zephyr's Adjusted for Risk

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podcast from the shores of Lake Tahoe.

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I'm Ryan Nauman, the market

strategist here at Zephyr.

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Over the past couple of years,

the wealth management space

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has been grappling with AI.

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First, it was understanding what AI is.

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Now, it is all about how to implement it

to help enhance the advisor experience.

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Well, I have on an industry expert

who's going to share his thoughts

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on the impact AI is having on wealth

management and the opportunities that

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AI presents to financial advisors.

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But first, today's episode is sponsored

by the award-winning Zephyr, which

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helps investment professionals

make more informed investment

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decisions on behalf of their clients.

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All right, enough from me.

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I have already talked enough.

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Let's go ahead and move on to the

star of the show I'd like to give a

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very warm welcome to Mohan Gurpakiam.

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Mohan is the Chief Information

Officer at Steward Partners.

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Mohan, thank you so much for

coming on the show again.

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You were on the show, we were

live at the Edge Conference the

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last time you were on the show.

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We had a fantastic conversation, so

I'm really looking forward to this one.

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A lot has changed in a year, so

there's a lot to, uh, discuss.

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So thank you.

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It's an honor to have you back on.

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Can you please tell us a little bit more

about yourself and Steward Partners?

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Absolutely.

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So Ryan, first of all, thank you

for having me back on this channel.

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I greatly enjoyed our conversation

last year when we met in Boca.

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Uh, what a terrible place.

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But here we are again to talk

about a very timely topic.

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Let me start off with Steward Partners.

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Steward Partners is an employee-owned

full service independent partnership.

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We cater to family, institutional,

multi-generational investors.

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Our partners specialize in comprehensive

wealth planning, investment strategy,

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and professional asset management.

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Uh, we serve in a, a large clientele.

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We pride ourselves on delivering,

you know, personalized service

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with commitment to excellence.

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We operate in more than 30 states and

roughly around 90 different offices.

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So that's who Steward Partners is.

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Um, a- about me, I've been…

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My name is Mohan Gurpakiam.

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I've been in the industry

for close to 30 years.

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My experience spans all the way

from direct to consumer, you know,

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uh, wealth management at scale.

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I worked at E-Trade for a long time.

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I also understand the independent

broker-dealer business where I

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was the, uh, chief technology

officer at Cetera Financial.

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And then I ran both management at

AssetMark, and I've been with, uh,

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Steward Partners for the last three and

a half years as our chief information

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officer, and it's a fantastic place.

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I cannot say enough

about Steward Partners.

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But that's me.

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Uh, you know, I'm aging myself,

but, uh, three, um, close to

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three decades in the industry.

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I've seen several reincarnations of

this industry and, um, and we're seeing

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one in, in, uh, real life right now

as we, as we are watching through it.

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Yeah.

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Mohan, that's fantastic, and I have

had the privilege of speaking with

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a few other folks at, from Stewart

Partners, from, uh, Jim Gold and Jeff.

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Great team.

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A lot of great people at Stewart Partners.

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It's always a pleasure speaking with them.

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I've always enjoyed it.

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And you are exactly right on

the AI side, technology side.

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It's amazing how the space has evolved so

quickly and just over the past few years

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and just, uh, I think it, it's moving so

quickly, and we talked about it last week.

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Like, just there, there's still so much

uncertainty, and I think a lot of it has

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to do with just how quickly technology

is evolving and changing the space.

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It adds uncertainty and, and just adds

more plate to the financial advisor, and

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we're gonna get on into that shortly.

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But, you know, the people in the wealth

management space have now had a couple

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of years to digest what AI really

means for the wealth management space.

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Do you feel there's been a transition

here on how wealth management firms

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and more specifically financial

advisors view and think about

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AI from last year to this year?

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Have we shifted in our mindset?

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I, I would, I would say yes.

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There has been a clear and, uh, I would

say a measurable shift from, I would

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say, over the last 18, 24 months, right?

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I think 24, two- 2024 and 2025, there

was more of, I would say, curiosity

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c- you know, along with skepticism.

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I would say we were more, more in the

experimentation phase, or people were,

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you know, uh, using isolated use cases.

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Oh, should I use it for meeting summaries?

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Uh, there was also a lot of hesitation

from, uh, compliance, right?

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Because the regulations usually

catch up with advances in technology,

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especially when technology is

advancing at a much faster pace.

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And there was minimal

integration into core workflows.

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Uh, fast forward 18 months, 24

months, when I speak to a lot of

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our industry peers, um, I think they

have gone from this curiosity phase.

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You know, they have moved away from

the question of, is AI useful, to

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more like, how can I scale it safely?

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Right.

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It is about an acceptance, it's

about operationalization, and

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more importantly, it's a strategic

priority for a lot of organizations.

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There is a mindset gap.

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People have moved away from pilots

to production, uh, to a strategic,

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um, I would say, v-view of AI, and

the governance is also catching up.

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So at all fronts, the answer is

yes, clear and measurable shift from

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last year to this year, I would say.

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Yeah, I completely agree, and just a

mindset and people, I think, are realizing

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they understand what is, and just now

it's like, okay, it can really help us,

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whether it's just a AI agent chatbot

to now making more efficient workflow.

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And, and the firms out there, technology

for AI firms are just helping, you know,

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give financial advisors more options

on implementation and how to do it.

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So how-- speaking of that and the

people that you talk to, your, um,

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colleagues, whether it's at Steward

or maybe just, um, other people in the

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industry, how are firms implementing

AI to enhance that advisor experience?

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Is it just mostly on the, you know,

chatbot support, or are they getting

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more on the operational side?

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I would say that would have been the

case, like, uh, like 18 months ago.

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But I, if, if I walk into a room today,

and, uh, I would say that advisors

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are actually looking at AI as part

of their daily operating system.

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And, uh, the, the best example

I would give is, you know,

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using something like, say, um,

Microsoft Word and Microsoft Excel.

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I think AI is going to be

something similar to that.

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It's core part of your, core

part of your operating system.

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And what I have heard from a lot of our

peers and our partners within Steward,

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uh, we use our-- uh, I would say not we.

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The advisors are using AI as part of

their operating system, and it varies

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all the way from meeting prep, meeting

automation, things like tasking.

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This is a, something-- This is a function

I would say the advisors spend a lot

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of time on, almost on a daily basis.

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There are also, I would say, you know,

two or three other big use cases.

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Uh, opportunity identification, whether

it's, you know, uh, new life events,

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additional products, tax strategies.

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So that is a, another place where

AI is being used extensively.

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Uh, communication and content.

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Uh, this is actually become a lot more

commonplace, extensive use of AI for

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drafting emails or adjusting the tone.

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And we have also seen automation

of, uh, non-value work.

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Uh, I want to move my file that comes

in via email into a file repository.

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That's a good example.

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So these are like use cases we are already

seeing being used quite extensively in a,

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across this, in a wealth management space,

irrespective of the size of the practice.

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Mm-hmm.

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No, those are great examples, and I just-

Feel, Mohan, that we're just on the tip

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of the iceberg right now on different ways

AI is gonna be used in advisory practices.

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And you brought up a very good point, too,

about if-- We all know wealth management

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is a very highly regulated industry.

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Like, wha-how is, you know, regulatory

comp-uh, compliance gonna handle AI, too?

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Especially when we start doing

more with AI and investment

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management and recommendations.

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Like, that I feel as if we're still

haven't even really reached yet.

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Is that correct?

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That is correct, yeah.

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Yeah.

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Do you think some firms are missing

the point or missing the most important

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aspect of AI when trying to implement it?

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Maybe they're just focusing too much on

communication part or, you know, reading

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maybe, uh, the output of a deliverable

and putting it into summary notes.

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Or do you think their advisories

and firms are missing the point?

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Or, and really, what is the

most important aspect of AI?

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I, I think it is a very--

it's a fantastic question.

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First of all, it's, uh, you know,

the, the industry is a very h-human

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touch, high touch industry, right?

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And I think this is one of the

most underrated and under-discussed

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dynamics right now, right?

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I think the m-most important question

you gotta ask is, what is the

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advisor's role in a AI world, right?

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AI is not going to replace advisor, right?

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I think that is the

biggest misunderstanding.

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You have to still put the, uh, the

human in the loop, human in the middle

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kind of a, you know, architecture,

business architecture when you

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start off with implementing or

thinking of implementing AI, right?

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That I would say is something that

most of the firms actually do not think

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of putting the human first when you

develop a AI-based operating system.

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So that's number one in my mind.

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The second thing that people do

is ignore the data foundation.

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Problem.

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I, I always try to use a simple analogy.

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Using a AI, uh, to build an

operating system is like building

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the twentieth floor in a building.

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You can't build the

twentieth floor straight up.

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You gotta build nineteen floors

underneath to basically get the

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twentieth floor up and running, right?

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So ignore the data foundation problem.

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I think that's a big issue, you

know, whether it's fragmentation,

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especially the disconnected tools.

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And there is a lot of unstructured data.

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You know, take CRM, for example.

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It's highly unstructured data, right?

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And the third thing that-- This

is actually a very common mistake.

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When I talk to people and they say

like, "Hey, do you have a AI strategy?"

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A lot of times they talk

about bolt-on AI, right?

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That is not really a AI strategy, right?

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Um, I would say the industry has to

move away from using bolt-on AI to,

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how can I transform my core workflows?

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Introducing a tool is

be-- it becomes a novelty.

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Is it a real productivity multiplier?

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That is a question that,

you know, firms have to ask.

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What is going to become a productivity

multiplier when I introduce AI, right?

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That's a good question to ask, but

also the more important one is,

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put a human in the middle, right?

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Ask, ask the question, how is AI

going to supplement the human in

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the middle?

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Yeah, Mohan, that is such a good point.

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And going back to, you know,

your, uh, comments regarding

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that, you're exactly right.

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AI is not gonna replace the advisor.

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It's gonna, like you said, supplement

the advisor and help the advisor, I feel.

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But-- And I also love that you brought

up, you know, we often talk in this

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industry, we always wanna get to the

20th floor or wherever really quickly.

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We often forget about the planning

and how important planning is in that

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foundation of building something.

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You need to build the foundation of

a house first before you can, you

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know, start building the kitchen.

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So w- are there other obstacles?

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You mentioned data, how important data is.

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Are there other obstacles to implementing

AI you're coming across when firms wanna

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implement AI, but they're, you know…

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Or they have, but they missed a

step, such as building a foundation?

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I think first of all, you

know, data is the place.

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You know, people always

miss that step, right?

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Especially when you walk into complex

practices, you have to aggregate

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data across so many sources.

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Not just custodian, but also across

your direct business, insurance,

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annuities, you name it, right?

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And being able to get all this data,

have, uh, proper classification,

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taxonomy, that's important, and usually

takes a long, long time and quite a

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bit of engineering effort in that.

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But that's not the only

impediment that I could think of.

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The other one that I would say is

the risk and regulatory uncertainty.

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Like, regulations usually, you know, are

catching up to reality, especially when

228

00:14:57,343 --> 00:14:59,573

the technology is advancing so fast.

229

00:14:59,933 --> 00:15:05,783

The, um, the average time for

a, a LLM model to be, I would

230

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say, uh, replaced with a newer

version is four months, right?

231

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So there is, uh, you know, think about

like, you know, when, um, you know, Int-

232

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Intel CEO said, "Oh, the processing power

is going to increase every 18 months."

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We are talking about four months.

234

00:15:22,143 --> 00:15:26,623

It's, it's a very highly compressed,

you know, changing environment.

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00:15:26,713 --> 00:15:32,313

So risk and regulatory uncertainty,

if you don't have clarity, most of

236

00:15:32,313 --> 00:15:37,723

the firms are going to resort to the

most conservative approach, which

237

00:15:37,793 --> 00:15:42,113

allows for, uh, not a whole lot of

experimentation, so to say, right?

238

00:15:42,303 --> 00:15:45,453

So that's, I think, also

a big, uh, obstacle.

239

00:15:46,173 --> 00:15:50,993

The other one that is- We, we don't

really think about it that deeply.

240

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It's about advisor trust and, more

importantly, behavioral resistance, the

241

00:15:56,563 --> 00:15:59,673

change management aspect of a practice.

242

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Most of the advisors, their offices,

they have built-- they have perfected an

243

00:16:05,013 --> 00:16:09,663

operating system that meets their needs

over the last fifteen, twenty years.

244

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That change management

is going to be difficult.

245

00:16:13,213 --> 00:16:14,353

It has to be measured.

246

00:16:14,553 --> 00:16:16,093

It, it requires coaching.

247

00:16:16,843 --> 00:16:20,973

Uh, so that is actually another

impediment that I have seen.

248

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Uh, the other one is something that

we have seen especially with larger

249

00:16:26,073 --> 00:16:30,853

established firms which actually

have legacy architecture, right?

250

00:16:30,903 --> 00:16:36,023

And this is an interesting one because

we see medium-sized firms that are in the

251

00:16:36,023 --> 00:16:40,673

sweet spot between, like, they have newer

architecture, they can move faster, move

252

00:16:40,723 --> 00:16:42,613

things, push things a lot faster, right?

253

00:16:42,873 --> 00:16:48,633

So the legacy architecture and the

integration debt is an impediment, right?

254

00:16:48,873 --> 00:16:53,733

Especially when we are talking

about, um, implementing an, you

255

00:16:53,733 --> 00:16:59,293

know, a, an organization-wide core

operating system that's AI native.

256

00:16:59,933 --> 00:17:01,313

That becomes a bigger challenge.

257

00:17:01,733 --> 00:17:05,433

And last but not the least,

talent and change management gap.

258

00:17:05,983 --> 00:17:08,022

Uh, technology guys, we struggle.

259

00:17:08,093 --> 00:17:12,803

I cannot go to the, you know, market right

now to find, you know, prompt engineers.

260

00:17:13,063 --> 00:17:13,752

There are very few.

261

00:17:13,762 --> 00:17:15,213

Forget about prompt engineers.

262

00:17:15,242 --> 00:17:21,353

Try getting a, a, uh, training program,

right, for your, for your organization.

263

00:17:21,363 --> 00:17:26,163

There is very little, you

know, uh, talent actually.

264

00:17:26,553 --> 00:17:30,733

The talent usually takes a lot

of time to catch up, right?

265

00:17:30,803 --> 00:17:34,603

And I think that is another area

where we are all struggling.

266

00:17:34,803 --> 00:17:39,783

As technology leaders, we struggle

to find the right, um, talent to

267

00:17:39,783 --> 00:17:41,853

basically meet our immediate needs.

268

00:17:42,243 --> 00:17:46,603

I think two years from now, it'll be

a different situation, but right now,

269

00:17:46,703 --> 00:17:51,733

we are in a place where the demand

far outstrips supply at this point.

270

00:17:53,059 --> 00:17:55,359

Yeah, Mohan, those are great points.

271

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And going back, I love

that you brought up trust.

272

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As we know, this industry

really is built on trust.

273

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If you have the trust of your

clients as a financial advisor,

274

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you can be very successful.

275

00:18:07,099 --> 00:18:12,419

I-- when I use AI for research,

I'll be honest with you, I

276

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still don't trust some of it.

277

00:18:14,029 --> 00:18:19,169

I will end up, I end up spending more

time double-checking the numbers, the

278

00:18:19,169 --> 00:18:24,409

returns, the analytics that AI produces,

you know, gives me from a prompt.

279

00:18:24,709 --> 00:18:27,949

I'll go research and then it's like,

"I should have just done this on my

280

00:18:27,959 --> 00:18:30,619

own," because it took me more time.

281

00:18:30,739 --> 00:18:34,709

Uh, and ninety-nine point

nine percent of the time, the

282

00:18:34,749 --> 00:18:37,489

AI-generated response was correct.

283

00:18:37,929 --> 00:18:38,569

But there it is.

284

00:18:38,569 --> 00:18:39,639

It's all about trust.

285

00:18:39,679 --> 00:18:46,069

I, I'm still trying to trust it

when I create, um, you know, prompt

286

00:18:46,069 --> 00:18:47,669

it for some research and so on.

287

00:18:47,689 --> 00:18:53,429

And with that being said too, and going

back to your education, I do use AI

288

00:18:53,629 --> 00:19:01,229

quite a bit, but it's am I asking it the

right prompt or am I being clear enough?

289

00:19:01,239 --> 00:19:02,099

'Cause I don't know.

290

00:19:02,099 --> 00:19:03,169

I, I'll do…

291

00:19:03,549 --> 00:19:06,559

And I always ask please and

thank you and all that stuff

292

00:19:06,559 --> 00:19:07,669

like I'm talking to somebody.

293

00:19:07,889 --> 00:19:12,609

I probably don't need to, but we

do it, and it, it's those things

294

00:19:12,609 --> 00:19:15,959

that, yes, we need more training

because I'm sure my prompts could

295

00:19:15,959 --> 00:19:17,779

get better, and then the trust.

296

00:19:17,779 --> 00:19:23,315

I'm so glad you brought those up

But, um, you know, where can AI

297

00:19:23,385 --> 00:19:26,565

have the biggest impact within

a wealth management practice?

298

00:19:26,565 --> 00:19:31,495

You know, as our viewers know, you know,

I focus more on the investment management.

299

00:19:31,535 --> 00:19:37,255

I would love it if we had an AI tool

that, uh, said, "Create an optimized

300

00:19:37,295 --> 00:19:42,585

portfolio that gives me the largest

sharp ratio across these asset classes.

301

00:19:43,015 --> 00:19:46,415

Include all these asset classes,

produces a efficient frontier."

302

00:19:46,505 --> 00:19:46,865

Boom.

303

00:19:47,475 --> 00:19:48,695

That's how I think.

304

00:19:49,025 --> 00:19:53,475

But are there other, you know, areas

within the wealth management practice

305

00:19:53,475 --> 00:19:55,305

that can have the biggest impact?

306

00:19:55,515 --> 00:19:58,525

You know, we talked about chat,

um, you know, the chatbot.

307

00:19:58,525 --> 00:20:01,605

Is there anything else there

that you're thinking of?

308

00:20:02,255 --> 00:20:02,865

Absolutely.

309

00:20:02,865 --> 00:20:06,015

So first of all, I want to start

off with the, the one that you

310

00:20:06,015 --> 00:20:10,525

brought up, investment, um, you

know, research, investment prep.

311

00:20:11,725 --> 00:20:15,395

Unfortunately, that's not an

area where, you know, there

312

00:20:15,395 --> 00:20:17,195

is not widespread adoption.

313

00:20:17,585 --> 00:20:22,445

Even among advisors who use AI

extensively, only 7% of them use

314

00:20:22,445 --> 00:20:26,985

it for the, um, investment research

and portfolio construction.

315

00:20:27,425 --> 00:20:32,185

Part of it is actually ha- I think it

has to do with the, the trust factor, and

316

00:20:32,235 --> 00:20:34,495

it, it, it's going to take time, right?

317

00:20:34,865 --> 00:20:39,965

But coming back to biggest impact

in terms of, uh, within and with

318

00:20:40,105 --> 00:20:45,105

management practice, I would say the

first one we should start off with

319

00:20:45,215 --> 00:20:47,975

is advisor productivity and capacity.

320

00:20:48,015 --> 00:20:49,275

There are a couple of reasons.

321

00:20:49,275 --> 00:20:54,805

Number one, um, that's time,

you know, I would say, that's

322

00:20:54,825 --> 00:20:57,015

spent almost on a daily basis.

323

00:20:57,345 --> 00:21:00,935

The most amount of time spent

by an advisor is around, you

324

00:21:00,935 --> 00:21:04,495

know, things like meeting prep,

meeting the clients, automated

325

00:21:04,565 --> 00:21:08,075

com-compliance, call to actions, right?

326

00:21:08,135 --> 00:21:12,505

Um, and it-- this is also

about, you know, the capacity.

327

00:21:12,505 --> 00:21:17,235

You know, there's a well-established

study by McKinsey that says, "Oh, in

328

00:21:17,235 --> 00:21:21,915

the next ten years, we are gonna have

100,000 advisors shortage," which is true.

329

00:21:21,955 --> 00:21:26,105

We don't see a lot of, uh,

fresh, I would say, supply of

330

00:21:26,105 --> 00:21:27,475

labor coming into the industry.

331

00:21:27,485 --> 00:21:32,735

So I think AI is probably going to

address a good chunk of, I would

332

00:21:32,735 --> 00:21:34,455

say, the shortfall that you expect.

333

00:21:34,855 --> 00:21:38,995

I don't think it'll be uncommon

to see, you know, a billion or $2

334

00:21:39,845 --> 00:21:44,685

billion advisor practices in the

future because AI could automate a

335

00:21:44,695 --> 00:21:46,815

lot of the low-value work, right?

336

00:21:47,275 --> 00:21:50,115

Um, there are a couple of

other areas, I would say.

337

00:21:50,205 --> 00:21:53,885

Uh, client engagement,

uh, proactive advice.

338

00:21:53,905 --> 00:21:59,045

Um, one of the things that when I talk

to advisors, they always talk about

339

00:21:59,535 --> 00:22:01,115

what they call their morning routine.

340

00:22:01,855 --> 00:22:06,615

They're like, "Oh, I come in, walk into

the office, get my cup of joe, and I do

341

00:22:06,615 --> 00:22:12,165

the same thing, which is I go try and

pull 10 reports to figure out what are

342

00:22:12,165 --> 00:22:15,645

the five things I need to focus on, and

that's going to take me two hours," right?

343

00:22:16,925 --> 00:22:20,575

So it's interesting, the advisors

basically spend the first two hours

344

00:22:20,595 --> 00:22:25,235

trying to figure out what they're gonna

work on for the next two hours, right?

345

00:22:25,635 --> 00:22:29,535

So one of the things the advisors

have said is like, "We want

346

00:22:29,585 --> 00:22:35,505

event-triggered," I would say,

"notification or advice from the system."

347

00:22:35,735 --> 00:22:35,985

Right.

348

00:22:36,455 --> 00:22:40,905

Um, they expect when they walk in,

there is a screen that says, "Hey,

349

00:22:41,395 --> 00:22:43,695

ten accounts received cash yesterday.

350

00:22:43,695 --> 00:22:44,705

Do something about it.

351

00:22:45,125 --> 00:22:47,635

Uh, five accounts submitted

their account paperwork.

352

00:22:47,635 --> 00:22:49,305

Three of them are not in good order."

353

00:22:49,775 --> 00:22:50,075

Right?

354

00:22:50,145 --> 00:22:53,735

Or like, oh, there is like, you

know, cash that came into IRA account

355

00:22:54,015 --> 00:22:54,965

which requires a rollover, right?

356

00:22:56,095 --> 00:23:00,385

And I think that is an example of

like, you know, where advisors want

357

00:23:00,455 --> 00:23:05,085

event-triggered advice or, you know,

they want a notification saying,

358

00:23:05,095 --> 00:23:09,945

"By the way, that client is hitting

the age limit for RMD," right?

359

00:23:10,225 --> 00:23:11,955

So this is a good example.

360

00:23:12,285 --> 00:23:16,795

If I wa- you know, en- envision how

the, you know, advisor screen is going

361

00:23:16,795 --> 00:23:18,325

to look like in the next few years.

362

00:23:18,325 --> 00:23:22,305

You're gonna walk in, there's gonna be

like ten agents, and these ten agents

363

00:23:22,305 --> 00:23:25,435

are basically gonna say, "Hey, these

are the things you need to do", which

364

00:23:25,435 --> 00:23:31,275

is probably going to automate a good

eighty percent, ninety percent of all of

365

00:23:31,275 --> 00:23:33,315

your notifications and actions, right?

366

00:23:33,815 --> 00:23:37,655

The other area that is not, you

know, uh, discussed very widely

367

00:23:37,655 --> 00:23:40,075

is around workflow automation.

368

00:23:40,195 --> 00:23:45,005

One of the bigger, I would say, complaints

if you ask the advisors is a friction

369

00:23:45,015 --> 00:23:51,285

between the advisor's practice and

the operational back offices, right?

370

00:23:51,395 --> 00:23:56,965

Reducing the operational burden,

re-reducing the cost to serve is mutually

371

00:23:56,965 --> 00:24:03,775

beneficial for both the advisors and

firms like us because it reduces the

372

00:24:03,775 --> 00:24:08,495

friction or eliminates the friction

and improves productivity vastly.

373

00:24:08,505 --> 00:24:12,255

So these are, I would say, three

areas where you can have a really

374

00:24:12,255 --> 00:24:17,315

big impact for the advisor's

practice on a day-to-day, uh, basis.

375

00:24:18,605 --> 00:24:19,725

Yeah, I agree.

376

00:24:19,755 --> 00:24:26,095

I can't wait for when I come in

because you-- It, it hits home when

377

00:24:26,095 --> 00:24:30,545

you said it takes basically two

hours of me to plan what I'm gonna

378

00:24:30,555 --> 00:24:32,255

be doing for the next two hours.

379

00:24:32,255 --> 00:24:33,415

That's exactly what I do.

380

00:24:33,415 --> 00:24:36,375

I wake up, sit here, and it's

like, "Okay, what am I gonna do?"

381

00:24:36,595 --> 00:24:36,985

You know?

382

00:24:36,985 --> 00:24:41,095

It's like two hours later, it's

like, "Oh, I finally figured it out."

383

00:24:41,525 --> 00:24:45,905

Um, just there's only so

much time in the day, Mohan.

384

00:24:46,375 --> 00:24:51,445

So if we can leverage AI to make it

more efficient, it helps everyone.

385

00:24:53,063 --> 00:24:57,623

So, you know, can you-- all our

listeners out there, the financial

386

00:24:57,633 --> 00:25:03,083

advisors who are still uncertain about

AI and the implementation, like we

387

00:25:03,093 --> 00:25:05,063

said at the be- it's moving so quickly.

388

00:25:05,073 --> 00:25:07,793

Y-your head is spinning, analysis.

389

00:25:07,803 --> 00:25:09,803

Y-you, you don't know where to start.

390

00:25:10,093 --> 00:25:12,293

You know you need to do

it, you just don't know…

391

00:25:12,393 --> 00:25:16,323

Can you provide some tips to financial

advisors who are still uncertain?

392

00:25:16,323 --> 00:25:19,023

They want to, just don't

know where to get started.

393

00:25:19,773 --> 00:25:24,863

Um, a good example I would

use is think of an AI-based

394

00:25:24,863 --> 00:25:26,643

operating system as a large pizza.

395

00:25:27,133 --> 00:25:29,593

You're gonna eat one

slice at a time, right?

396

00:25:30,613 --> 00:25:32,333

You have to always start small.

397

00:25:32,563 --> 00:25:40,893

If you try to think of like, oh, how

would AI change my practice en-entirely?

398

00:25:41,473 --> 00:25:45,133

Uh, well, it's going to take you like

eight, 12 months to figure that out.

399

00:25:45,173 --> 00:25:46,833

The industry would have moved on, right?

400

00:25:47,383 --> 00:25:52,997

Um, the best advice I would tell

is, you know, start small Identify

401

00:25:52,997 --> 00:25:55,857

a good return on investment.

402

00:25:56,217 --> 00:26:01,317

Ask the very simple question,

where am I losing time every day?

403

00:26:02,017 --> 00:26:02,267

Right?

404

00:26:02,587 --> 00:26:04,857

Rather than trying to bolt on something.

405

00:26:04,867 --> 00:26:10,347

You know, and that changes or that

differs from practice to practice, right?

406

00:26:10,357 --> 00:26:16,107

If you are, um, probably in a practice

that has fewer number of large

407

00:26:16,127 --> 00:26:21,417

clients, your needs might be very

different from a practice that have

408

00:26:21,887 --> 00:26:24,067

larger number of small clients, right?

409

00:26:24,097 --> 00:26:28,647

It's, it's where you spend time

depends on what your practice is.

410

00:26:28,677 --> 00:26:32,317

When they-- when we say you talk to one

advisor, you talk to one advisor, right?

411

00:26:32,317 --> 00:26:33,407

Every advisor is unique.

412

00:26:33,817 --> 00:26:35,637

So I think that's the

best advice I would say.

413

00:26:35,647 --> 00:26:39,417

You know, start small, identify

a good return on investment.

414

00:26:39,477 --> 00:26:41,747

You know, solve the problem, take the win.

415

00:26:42,337 --> 00:26:46,727

You know, start thinking about how

would AI change my next few hours of

416

00:26:46,767 --> 00:26:51,217

my time, versus start thinking about,

oh, how is it going to change the

417

00:26:51,227 --> 00:26:53,367

entire wealth management practice.

418

00:26:53,737 --> 00:26:57,657

You cannot control what you cannot

control, but you can control the ones that

419

00:26:57,667 --> 00:27:05,297

are in your immediate sphere of, I would

say, control, which is, hey, what can I…

420

00:27:05,647 --> 00:27:07,787

You know, where can I save time today?

421

00:27:08,057 --> 00:27:11,947

Like, what are the activities I can

now, you know, uh, automate, right?

422

00:27:11,947 --> 00:27:18,247

That's the, that's the way I would

think about, um, implementing AI.

423

00:27:18,387 --> 00:27:22,647

That is such a great point, because I

think it can be overwhelming to some.

424

00:27:23,167 --> 00:27:26,607

And like you said, don't try and chew

off more than that piece of pizza.

425

00:27:26,677 --> 00:27:28,567

Just take one slice at a time.

426

00:27:28,807 --> 00:27:34,577

No reason to, you know, try and

revamp your whole practice right away.

427

00:27:34,627 --> 00:27:37,257

And I love that advice there, Mohan.

428

00:27:37,257 --> 00:27:37,907

That's really good.

429

00:27:37,917 --> 00:27:40,957

Just focus on one piece at a time.

430

00:27:41,277 --> 00:27:43,507

Where are you spending more

time than what you should?

431

00:27:43,507 --> 00:27:45,587

Can you make that process more efficient?

432

00:27:45,937 --> 00:27:47,747

Great, great thoughts there.

433

00:27:47,977 --> 00:27:51,867

Do you think there is some advisor

fatigue when it comes to technology?

434

00:27:51,867 --> 00:27:55,687

We've been talking about technology

for years, how it's evolved,

435

00:27:55,707 --> 00:27:58,867

all the different tech stacks,

how important a tech stack is.

436

00:27:59,097 --> 00:28:00,907

Do you think there's

some fatigue going on?

437

00:28:01,427 --> 00:28:02,487

Hundred percent, right.

438

00:28:02,507 --> 00:28:03,247

I think it's a…

439

00:28:03,617 --> 00:28:07,627

Advisor technology fatigue

is very real right now.

440

00:28:08,147 --> 00:28:14,729

I would go to Kitces', um, you know,

fintech roadmap Like 10 years ago, I

441

00:28:14,729 --> 00:28:17,119

could have printed it on an A4 size paper.

442

00:28:17,179 --> 00:28:22,159

Now, if I want to print the, the,

in a fintech, uh, ecosystem, I have

443

00:28:22,159 --> 00:28:26,269

to go to Kinko's and print it out

on a, you know, huge plotter, right?

444

00:28:26,269 --> 00:28:30,209

There has been an explosion in

terms of the fintech products.

445

00:28:30,229 --> 00:28:34,389

A lot of those were, you know,

came in along with the evolution

446

00:28:34,449 --> 00:28:39,009

of cloud-based services, which

is the dominant model right now.

447

00:28:39,069 --> 00:28:42,889

It lowered the entry barrier

significantly, which is fantastic.

448

00:28:43,539 --> 00:28:45,719

Lot of great ideas have come to fruition.

449

00:28:46,309 --> 00:28:50,979

But the system, the ecosystem

is just exploding, right?

450

00:28:51,009 --> 00:28:57,419

I think, um, we call it the tool

overload or the stacks sprawl, right?

451

00:28:57,449 --> 00:29:02,049

It just, you know, uh, like five

years ago, or even like 10 years

452

00:29:02,049 --> 00:29:06,149

ago, I could fit my tech stack

on, like, a single page of paper

453

00:29:06,149 --> 00:29:07,389

and say, "This is what I support."

454

00:29:07,849 --> 00:29:10,359

Now, it's like multiple

sheets of paper, right?

455

00:29:10,629 --> 00:29:13,549

So I think that is a real problem, right?

456

00:29:13,969 --> 00:29:18,609

And I think, well, the other thing

that is also something you have

457

00:29:18,609 --> 00:29:23,799

to consider is a constant change,

sometimes with questionable payoff.

458

00:29:24,359 --> 00:29:30,723

You know, what- We have to factor in,

uh, heavily into this conversation

459

00:29:30,723 --> 00:29:36,313

is the learning curve for the

advisors, their practices, right?

460

00:29:36,723 --> 00:29:41,563

The lowest level of every

organization needs to be familiar

461

00:29:41,563 --> 00:29:43,383

with any platform that we bring in.

462

00:29:43,823 --> 00:29:46,683

The disruption it causes

to existing practices.

463

00:29:47,063 --> 00:29:49,793

And a lot of times it's

a limited benefits.

464

00:29:49,803 --> 00:29:54,443

When, when the systems and the

tools have been exploring, the

465

00:29:54,443 --> 00:29:59,793

marginal increase in benefits

becomes smaller and smaller, right?

466

00:29:59,793 --> 00:30:04,053

And it raises a question in the

minds of the advisor, right?

467

00:30:04,343 --> 00:30:10,383

Is it worth that change when the

marginal benefit is limited, right?

468

00:30:10,883 --> 00:30:14,283

And sometimes we have

to bring in innovation.

469

00:30:14,503 --> 00:30:19,233

However, it has to be a balance

between, you know, understanding,

470

00:30:19,243 --> 00:30:23,353

like, what is beneficial to my

organization, and is it worth the

471

00:30:23,383 --> 00:30:27,433

disruption it's going to cause, and am

I committed to making it successful?

472

00:30:28,063 --> 00:30:32,783

I think those are the questions that-

Mm … leaders like us grab and say,

473

00:30:32,783 --> 00:30:35,673

"Hey, how do I solve that problem?"

474

00:30:35,693 --> 00:30:40,533

But the advisor fatigue, technology

fatigue is very real right now.

475

00:30:41,633 --> 00:30:42,943

Yeah, I think so too.

476

00:30:43,183 --> 00:30:47,953

It's hard now to have a conversation

that d- in this space that doesn't evolve

477

00:30:47,953 --> 00:30:53,323

around technology or AI doesn't come up

at some point during that conversation.

478

00:30:54,083 --> 00:30:57,423

Lastly, Mohan, great conversation.

479

00:30:57,433 --> 00:31:01,003

Real quick, you know, in this space

because of technology, there's a lot

480

00:31:01,003 --> 00:31:04,413

of elements within wealth management

that are becoming commoditized,

481

00:31:04,413 --> 00:31:08,973

whether it's investment management,

all the different ETFs out there.

482

00:31:09,193 --> 00:31:13,363

You can get, go online and get

a lot of analytics for free.

483

00:31:13,683 --> 00:31:14,883

It's being commoditized.

484

00:31:14,943 --> 00:31:19,683

Is there something that can give firms

a strategic advantage moving forward,

485

00:31:19,983 --> 00:31:23,823

kind of separate themselves apart

that maybe they're not thinking of

486

00:31:23,823 --> 00:31:26,253

in, in a space that's so competitive?

487

00:31:27,053 --> 00:31:28,163

So three things, right?

488

00:31:28,193 --> 00:31:31,243

I would say the first, the

biggest difference maker is the

489

00:31:31,253 --> 00:31:34,443

unified data and context, right?

490

00:31:34,473 --> 00:31:39,163

Without that, AI is going to

be way too generic, right?

491

00:31:39,213 --> 00:31:44,763

So I think getting in that layer is

going to be the biggest difference maker.

492

00:31:45,879 --> 00:31:49,499

Couple of other areas I would

say, uh, productivity economics is

493

00:31:49,509 --> 00:31:55,299

something that I would say is the

biggest, um, uh, strategic advantage.

494

00:31:55,539 --> 00:31:58,579

Productivity economics, it's, you

have to have a relentless focus.

495

00:31:59,059 --> 00:32:02,559

Uh, how much time is spent on productive

work is a question you gotta ask,

496

00:32:03,029 --> 00:32:07,199

versus what can you do to, you know,

move away from the non-productive work.

497

00:32:07,639 --> 00:32:11,799

And the third thing I would

say is end-to-end automation.

498

00:32:12,679 --> 00:32:17,089

Rather than introducing a specific AI

tool that doesn't integrate well into

499

00:32:17,089 --> 00:32:23,619

the, you know, the entire ecosystem,

the end-to-end automation is going

500

00:32:23,619 --> 00:32:25,959

to be a big, uh, strategic advantage.

501

00:32:26,239 --> 00:32:29,329

Firms that do these three really

well are going to reap the

502

00:32:29,329 --> 00:32:32,069

benefits very quickly, right?

503

00:32:32,109 --> 00:32:38,009

So those are, I would say, three things

that firms can use to ga- gain a strategic

504

00:32:38,399 --> 00:32:41,049

advantage- Mm-hmm … in the near future.

505

00:32:41,779 --> 00:32:45,949

Mohan, that's a great way of wrapping

up this conversation, bringing it

506

00:32:45,949 --> 00:32:48,469

all together, and kind of goes back

to the beginning, you know, starting

507

00:32:48,469 --> 00:32:53,389

with data, how important data is,

and that's, you know, where you

508

00:32:53,389 --> 00:32:56,149

can add most value Right there.

509

00:32:57,849 --> 00:32:59,919

Mohan, thank you so much

for coming on the show.

510

00:32:59,979 --> 00:33:01,439

Such a great conversation.

511

00:33:01,439 --> 00:33:04,609

I was- been looking forward to

this conversation sometime since

512

00:33:04,609 --> 00:33:07,219

our previous one a year ago.

513

00:33:07,979 --> 00:33:11,239

Um, thank you so much for

sharing such great insight.

514

00:33:11,239 --> 00:33:12,829

It was an honor to have you on.

515

00:33:13,089 --> 00:33:17,089

Where can our audience get more

information about Steward Partners?

516

00:33:17,589 --> 00:33:22,719

So obviously, you, you can go to

our, uh, website stewardpartners.com.

517

00:33:22,809 --> 00:33:25,839

We also have our LinkedIn channel.

518

00:33:26,009 --> 00:33:32,309

We have our, uh, channels on, um, on,

uh, you know, Facebook, I would say.

519

00:33:32,339 --> 00:33:36,339

But, uh, we are also very active

in the, in the press space.

520

00:33:36,339 --> 00:33:39,539

We are participating in a lot of

industry conferences, but if you

521

00:33:39,539 --> 00:33:44,689

want to learn a lot about, uh, our

practice, our advisors, go to our, our,

522

00:33:44,689 --> 00:33:48,839

uh, website www.stewardpartners.com

523

00:33:49,259 --> 00:33:52,899

and, you know, we'd love to have

a conversation with you guys.

524

00:33:53,309 --> 00:33:58,309

I would like to stop and, you know,

give, like, my final pitch about,

525

00:33:58,549 --> 00:34:01,999

like, um, AI along with advisors.

526

00:34:02,609 --> 00:34:09,839

Advisors are not going to win by, you

know, uh, competing with AI, right?

527

00:34:10,188 --> 00:34:17,768

Co-opt, like, I would say, uh,

focus on your strengths as advisors.

528

00:34:18,399 --> 00:34:24,018

Uh, that's the best way to win in a

AI first world in wealth management.

529

00:34:25,839 --> 00:34:28,389

Yeah, you're exactly correct, Mohan.

530

00:34:28,389 --> 00:34:31,418

Just-- And it goes back to

where do you get started?

531

00:34:31,449 --> 00:34:35,649

Focus on the things that take the most

time, that maybe you struggle with.

532

00:34:35,759 --> 00:34:40,679

Have AI help you there, but it's a

complement, and it can help your practice

533

00:34:40,728 --> 00:34:46,129

immensely having that complement to

what your strengths are, um, out there.

534

00:34:46,129 --> 00:34:48,779

So great advice, Mohan.

535

00:34:48,829 --> 00:34:49,998

Way to wrap it up.

536

00:34:50,478 --> 00:34:54,089

And thank you so much for listening

to this episode of Zephyr's

537

00:34:54,168 --> 00:34:56,018

Adjusted for Risk podcast.

538

00:34:56,059 --> 00:34:59,019

You can watch all of our other

episodes on the Zephyr YouTube

539

00:34:59,049 --> 00:35:03,859

channel, Spotify, and wherever else

you watch your, uh, favorite podcasts.

540

00:35:04,299 --> 00:35:07,429

Be-- please be sure to like and

subscribe to those channels and

541

00:35:07,439 --> 00:35:08,549

give us a follow on LinkedIn.

542

00:35:08,549 --> 00:35:11,709

Thank you very much, and have

a great rest of your week.

543

00:35:12,389 --> 00:35:13,189

Thank you, Ryan.

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