Scaling AI in Asset & Wealth Management | Beyond The Noise Podcast

Hello everyone and welcome to Beyond the Noise, the M.M podcast where we cut through the hype and dive deep into the real world applications and impact that

artificial intelligence can have in asset and wealth management. I'm Andra

Salah and today I'm going to be your host. In today's conversation, we are

host. In today's conversation, we are talking about scaling AI, not just experimenting with it, but truly embedding it within the investment

process of asset and wealth managers.

Joining me today are what I think are four incredible voices leading this transformation. First of all, of course,

transformation. First of all, of course, in-house guests are Tomaso Mi, CEO and co-founder of M.M, and Jim Hayes, uh,

the an adviser for M.M. and former CEO of Wells Fargo Advisors. Now, our

special outside guests are from EY both and first of all here with me alongside is Joanni Carnato who is EMA FSO business consulting and Ursul Pieri

global center for wealth management leader at EY. Thank you all so much for being here today. It's great to be here.

Good to be here. Lovely setup by the way.

So we have a little bit of you know a hybrid setting going on. We are here in studio. We also have Jim and joining

studio. We also have Jim and joining from all different sides of the world honestly. Um so it's great being here

honestly. Um so it's great being here today of course to kick off things.

We'll take a look what I think are some powerful data points from a recent EY study that was done in collaboration

with M.M. that sheds light on where the

with M.M. that sheds light on where the industry really stands today regarding on its AI journey. Tomaso, you you lead an AI company that helps asset and

wealth managers integrate this type of technology. What does it take to move

technology. What does it take to move beyond these AI pilot projects? So, so

we we work on the study of course together and it it is a little bit of a of a funny story because uh we live in a moment in which AI seems to be everywhere. Uh but yet we are stuck in a

everywhere. Uh but yet we are stuck in a paradox where a lot of companies are kind of play with it but not implementing. Um, I think that at the

implementing. Um, I think that at the end of the day, it's the typical, it's the famous sentence from Roya Mara, when there's a big technology shift, people always massively overestimate the impact

on the short term and underestimate the impact on the on the long run. So, I

think we're living through that. Uh,

although that's the, you know, the situation, there are a lot of companies that are actually way ahead. Um, we

support a lot of them. And I think that the the the key to go beyond the paradox is not trying to to experime I mean play around and experiment is is fine but you

got to nail down a couple of areas where you have found good traction good use cases there are numbers out there to demonstrate that and stick with that and

and then you plan to do more right uh often time when when there is a big change um companies try to do all at once uh and of course the the the

capacity capacity the focus capacity that a company that a team has to follow a project to bring it down to the ground to actually implement it then go in production is is always limited right

and so if you're playing with a ton of use cases all the time you struggle to you you you almost cannibalize one each other uh opportunities so the ones that have been successful it's it's because

they've been realistic they've been down to the ground they started small they test it out quickly they trust it and then they build it up from Um Jim, you've really you've led these

type of transformations efforts inside large organizations.

Where are you seeing the highest impact AI use cases right now in asset and wealth management?

Yeah, it's a it's a great question because I'm seeing use cases everywhere, but I would say that two of the highest use cases uh the first one would revolve around personalization. Um what's

around personalization. Um what's happening in the US asset management market is the model portfolio marketplace and direct indexing and portfolio customization is just

exploding in growth right now. And so if you're an asset manager, you have uh a larger number of permutations on portfolios than you've probably ever had ever had. And you know, maybe you're

ever had. And you know, maybe you're managing around a concentrated stock position or a client has expressed a preference for not being in a particular sector or they want a certain weight in

a geographic area. And what AI allows you to do is basically personalize portfolios and deliver them at scale and do it in a way that not only delivers a

better experience for the client but also delivers better risk management uh better compliance better cons. Um just a few days ago I was visiting with a client that uh has over a trillion

dollars on their platform and they talked about using spreadsheets to keep track of some of the customized portfolios. And as you think through

portfolios. And as you think through that, you think about all the challenges that creates and how AI can help solve that and create a better experience. So,

so that's the first one, portfolio customization. The second one revolves

customization. The second one revolves around the end advisor, the wealth management advisor. And I think about

management advisor. And I think about two segments. I think about the ultra

two segments. I think about the ultra high net worth advisor and that person probably has 25 to 30 clients and each client probably has four or five accounts and each of those accounts has

a different portfolio. Or I think about the affluent advisor who might have 150 relationships and four accounts per relationship for

about 600 different portfolios per account. What AI allows you to do is

account. What AI allows you to do is what I call connect the dots from the portfolio management to the asset allocation to the communication to the end client and do it in a way that

presents sort of a scalable solution on the communication front. And we're

seeing a lot of demand for that. And you

think about that like how do you communicate to 150 clients around 600 different portfolio options and get compliance approval for doing that and AI allows you to do that. So those are

two immediate ones where I see a lot of traction in the marketplace and a lot of opportunity ahead.

So if I if I may jump in here because Jim and I are working together many deals on this on this thing around these two topics. Um I think it's very

two topics. Um I think it's very important what Jim has said right because I think we started this conversation by looking at the uh if you will the framework and how we have to

say large companies are not maybe too tailored to to embrace a technological change so dramatic and and and and

profound as AI. But once they do the the the opportunities are around the corner and uh what Jim has said and and maybe Jim you you can elaborate even even

further there but if you think about it why is so important personalization because today I can buy a portfolio uh by myself building it by myself uh

with four ETFs without paying a commission to anyone. So as a client as a retail client as a affluent upper affluent private ultra network individual family office pension fund

endowments foundation whether you go full retail or full institutional why should I pay a fee to an asset or a wealth management company if I can do it

myself. So at the end of the day that is

myself. So at the end of the day that is what is it's that's where the demand originates from right clients are demanding for a more personalized solution. They're demanding for

solution. They're demanding for something that really mets meets their needs. Okay. And the industry, if we

needs. Okay. And the industry, if we have to be honest, because of the complexity of regulation, digitalization technology wasn't really able to cope. Yeah. Wasn't

really able to cope with that complexity very very well. But thankfully, we do have option now to to do better. And so

maybe Jim, I don't know if you want to jump in here again, but you mentioned spreadsheet. We I know who you were

spreadsheet. We I know who you were referring to cuz we were working on that the other day together and you wouldn't believe the day but uh but yeah but uh but it's a lot a lot a lot of

spreadsheet and um 1990s technology almost. Yeah, absolutely. And and you

almost. Yeah, absolutely. And and you know the funny thing is about the spreadsheets is that uh you know they're they were an effective tool and they are in some cases but they're not when you have a certain scale of uh clients and

most individual adviserss have enough clients that they should move away from that. I think that just back to an

that. I think that just back to an earlier point, this is kind of a little bit of what Tomaso was talking about when when you the reason people are moving to direct indexing is they want a portfolio that's personalized to them

and they want to under they want to own the underlying security so they can be tax efficient with how they basically manage their portfolio. So once you go

to that instead of owning one ETF you know you own 150 individual securities then what happens is you have to ask the question well how do I keep track of all that and how do I manage around that and

how do I have a taxaware portfolio and then when you go and you start to think about the endto-end process then you begin to say how do I communicate to my clients how their portfolio is doing now

that I have to comment on 150 securities versus one and that's where AI can be really helpful AI is super helpful at connecting the dots from investment

resource investment research to basically asset allocation to implementation to then how do I communicate to the client around how they're doing and that's where I think

there's a lot of aha moments when we're out in the marketplace and uh Tomaso it's been a fun journey and uh probably a bigger one ahead so Urus you have worked globally with so many wealth

managers at your position at EY what would you say are these underestimated barriers, whether we're talking technical, we're talking strategic or cultural, what is really holding firms

back?

Yeah, first of all, thanks for having me and greetings from the Middle East. Um,

the timing couldn't be better for that uh podcast today because I'm working here with a Valenture who engaged us to scale AI across the organization. So,

it's a great great timing and thanks for having me. Um f first of all I would say

having me. Um f first of all I would say we probably all acknowledge that AI goes and impacts you know wealth managers beyond the investment process. It really

goes front, middle and back office throughout the organization. And for us it's very important to establish clarity and structure you know the challenges the opportunities and also also the

barriers um our our clients have. And at

EY we love methodology. So for example this morning we went with our client through a very structured process where we looked at all the dimensions which we feel are relevant. For example we looked

at strategy. So if AI is a top of the

at strategy. So if AI is a top of the house priority, why do you have only 1% for example uh of your discretionary budget allocated to AI or if you want to

increase your client experience through AI um how do you use AI to improve client acquisition or to improve conversion rates of prospects for

example or if you say okay AI is a top of the house priority across your organization and the talent group so do you have the right skills in your organization

to kind of like or the AI enabled roles to implement your your AI strategy. So

this the spectrum is is is quite quite broad. I would say um if I would have to

broad. I would say um if I would have to call out you know what are the the biggest priorities or the biggest barriers I think that the first issue which we see is is certainly talent and

and culture. So so even the largest

and culture. So so even the largest firms they they struggle to attract and retain top talents um in that industry.

I think that's a that's a big issue when you want to drive innovation and also have a culture of adoption when it comes to AI. And the second also has been

to AI. And the second also has been gladly mentioned before is you know having having a having clarity on on your AI priorities and then map that into a road map to kind of like build it

out incrementally. I think those are

out incrementally. I think those are certainly the the biggest barriers. So,

so talent and setting the right priorities if I would have to summarize too.

And while you said that right now it couldn't be better timing, you're actually helping a wealth manager uh be being able, you know, to to scale this technology. Uh could you maybe provide

technology. Uh could you maybe provide uh an example of what you're doing regarding this because super interesting having a real life use case probably unfold right now, you know?

Yeah, obviously I have to maintain client confidentiality um which is uh which is clear but um our client here he has been investing for I would say more

over a decade into digitization getting text stack right getting data right and now it's really like how how to scale AI with a very particular focus

on on topline um so so it's it's it's around client acquisition converting clients uh converting prospects into clients clients, increase share of wallet um of clients. Um th those are

the priorities and and it was really interesting as as we also said before you know you can't look at AI in isolation or for a specific team or in a

specific silo. So we were essentially

specific silo. So we were essentially working with um all the departments of the organization from from HR to finance to risk to technology to data um to the

business leads uh front office people.

So we had in average probably 50 to 80 people um in our room because it simply touches every aspect of the organization. So

organization. So well that's that's great. It's it's

great having you know uh a real life use case unfold especially at the time that we're recording this. Um and I would like to ask you know Giovanni relating back to these barriers you know as an

wealth manage managers once they have identified these barriers that just mentioned can be cultural can be technological um what would you say a

truly resilient and scalable AI strategy looks like?

Yes. Uh but my opinion first and foremost asset and wealth manager has to uh to have a clear purpose and uh it's

important to uh to go beyond the uh the mere operational efficiency scope of the initial use cases and they have to be

focused also on value creation. It's

really important also to uh identify use cases application uh where the value is uh provided also

throughout new innovative services to end clients. So in in in one word AI

end clients. So in in in one word AI becomes a truly strategic levers at par with the other strategic levers uh that

included in the strategic plans of our uh wealth and asset manager. Having said

that is a truly scalable uh AI uh model and strategy is one that sets a northstar. Okay. And backwards it's

northstar. Okay. And backwards it's important to identify the right operating model, the right governance

model uh the the tech the tech stocks and also the use cases. It's uh so it's important that a scalable and resilient

AI uh strategy should encompass several aspects. Let me um outline some of the

aspects. Let me um outline some of the of these aspects. Uh initially it's really important to identify in a proper

way the uh use cases the um uh based on value on breath of and breath of usability to ensure the right business

case when scaling up um and it's also important that these use cases and the benefit can be matured by the concrete

KPIs the secondly ensure the right text It's really important that uh we can guarantee uh um

good results throughout a good quality of data and also it's important to put in place and to implement a scalable

architecture tech architecture. Um and

also important because not all the capabilities are uh internally uh to identify to choose the right partner to

work with uh in the case that capabilities are not met internally.

Third, adopt the right operating model.

And uh the last but not least, it's it's super important to build confidence in AI. So uh uh we suggest to our clients

AI. So uh uh we suggest to our clients to our to the asset wealth managers to start and to launch robust change

management program um to on board all the people all all of the organization who and because it's important that

everyone uh within the organization understand the real role that AI can bring or could bring uh to their work

and it's important also that that the population knows uh what skills need for

uh to to properly use it uh effectively.

In conclusion, a resilient and scalable AI strategy is one that looks at a a

bold mid-long uh term goal. Um and we uh we believe that throughout the right level of investment these uh the asset

and wealth management can achieve this this mid long-term goal. Definitely uh

scalable AI strategy involves everything from you know the people the technology the change management the use cases more importantly you have to have a clear objective as to where you're headed in

order to know how you're going to implement that technology and well once you integrate it how does a scalable AI value look like well I think that the

beauty about AI compared to traditional technology is that when you aim at of course what we're talking about here is uh implementing AI to support the investment process. So we have to make

investment process. So we have to make maybe make a step back. What does it mean? Okay, it's not just supporting

mean? Okay, it's not just supporting investment team because that's a very important thing but it's also supporting to deliver a portfolio to build portfolio at scale to industrialized personalization to industrialize the

personalization of reporting and the storytelling. So there's a whole

storytelling. So there's a whole spectrum that goes from um coming up with that investment idea down to building a portfolio and then telling a story about the portfolio. So in order

to scale, so how do you start small and build it up is we all know that portfolios that an an asset and a wealth management company manages it's they're not they're not all the same all in one

big bucket. There's areas that you can

big bucket. There's areas that you can start with. You can start to play with a

start with. You can start to play with a little portion of your portfolios and then it's much easier to build it up.

The be the beauty about being a flexible technology compared to you know the old school rulebased system is that once you have set it up the basics which is the

basic uh integration uh then the technology can take care of the edge case much better. Okay. And you

know we can really see some tangible value coming up from being able to scale this technology. However, we do have

this technology. However, we do have some underestimated barriers to go through in order to be able to fully implement it. Yeah, sure. So, so yeah,

implement it. Yeah, sure. So, so yeah, I've been the past two years uh in New York and and worked with our US colleagues and and there there are multiple differences. I I think one one

multiple differences. I I think one one probably starts around my mindset. You

know, in in in the US, you see a lot of thinking around, hey, what what's the opportunity? What's the upside? Whereas

opportunity? What's the upside? Whereas

in Europe you first start with okay what could go wrong what's the risk what's the downside so there's just I would say a a difference in in terms of mindset

and then also you know access to to risk capital and venture capital um is a big difference relative to what I see what I see here I think in the US if you have a

a 10% chance of winning you you take it you know because if if the one guy out of 10 investment makes it you you will you will do very well and um in in the

in Europe or in other regions the risk tolerance and the access to risk capital is uh is is certainly is certainly is certainly different and it was very funny I've I've been here with a with a

firm um from from the US and and Silicon Valley VC funded um the the co and founder is 20 years old the the CTO is

is 16 year old they have after 14 months a valuation of $250 million and work for the US government. They work for like top firms in the US and uh we brought

them over and I maybe it's the wrong environment to talk about what they do.

Um but there's a lot of innovation going and a lot of creativity going on. Um

which I would say Europe and and and the Western world here is still a bit in in in sleeping mode if I may say so. But

yeah, massive differences in in terms of culture, mindset, and how to do business. But but I have to say that

business. But but I have to say that being European and working with clients, we have about 50 clients globally. Okay.

And about 20% of our business in the is in the US. So having that perspective, I think that what Europe has as a problem and then I want you I'm curious to know what you guys think.

In my perspective, the problem and the opportunity of Europe is that when we look at the application of AI in the financial services is definitely a different

framework of regulation. Of course,

that's that's going to impact, but also it's a geographical divided market.

France is not Germany, which is not Spain, which is not Italy, which is not uh Switzerland is not but it's not definitely not Switzerland, which is not Austria. And and although geographically

Austria. And and although geographically speaking everything is kind of very close but these are separate markets with separate dynamics and especially when we thought think about asset wealth

management how vertically how much vertically is inte it pays off to be vertically integrated from the production to from the manufacturing to the distribution and

how much the financial advisor are tied to the banks. um that's a difficult environment to scale uh and to bring innovation broadly as in the US where it's just one big market. Of course, you

have advantages as well cuz you have your network which is closer by to you um which is uh which you can control which you provide tools so you can facilitate adoption on the other side.

So I think there's twofold maybe Jim I don't know you've been leading at Wells Fargo Advisors a lot of complexity there. So what what's your take on this?

there. So what what's your take on this?

Well, in in my view like all the complexity sometimes can paralyze big institutions and the best thing an institution can do in thinking through problems like this is just to get

started. You know they say uh you know a

started. You know they say uh you know a long journey starts with a single step.

It's just it's all about it doesn't have to be perfect. Let's develop a use case and let's get started. And as they do that, they'll they'll basically discover, wow, we had a lot of learnings

from this. Let's try another use case.

from this. Let's try another use case.

And I think that's the opportunity, especially for large institutions. It's

you're better off starting and improving things as you go than never starting.

And how can these large organizations, you know, as Tomaso previously said, uh it's kind of hard at the beginning, especially for a large organization, to have a complete new approach towards a

technology. How can can one of these

technology. How can can one of these large organizations get started? Yeah, I

think they can get started by just picking a small use case and doing a PC, doing a pilot, um getting used to the team and the technology, having some

success and then expanding. You know,

often times, you know, large enterprises can become a little siloed or a little bureaucratic. And you know, back in the

bureaucratic. And you know, back in the late 90s, there was a very fashionable book called The Innovator's Dilemma by Clayton Christensen. And it was all

Clayton Christensen. And it was all about how small firms tend to disrupt big firms because big firms sort of get stuck in their own way and they don't challenge the status quo. And that the

solution for big firms were to take, you know, three or four people and put them off the side and have those people disrupt the existing enterprise. Well,

basically AI does that disruption for you in a positive way. And so the way a big institution can do it is not to solve, you know, huge problems on day

one. solve small problems on day one and

one. solve small problems on day one and iterate and you know compound your success by doing another one and another one. The key to it though is speed,

one. The key to it though is speed, speed of execution and compounding very quickly and uh and starting and uh that's where I think uh this team would like to help.

Yeah, really get started you know as you said with maybe a pilot a small use case and then of course getting beyond the

pilot paradox as uh the EY paper states.

Um and well we talked about a little bit the differences between the U and the US market and how they can get started. Uh

Tomaso you helped several companies now get started Jim as well uh with the well their AI road map. Can you maybe share a

recent example? Yeah and I love how you

recent example? Yeah and I love how you say help to get started. It's so true.

So a lot of the work that we've been doing uh Jim and I and the team together was really getting started but I think that you have the same which most of the

time is just getting started understand what's the perimeter etc. So I mean the application is uh it's pretty clear uh there's three big areas in which asset

and wealth management companies are benefit from AI apart from a lot of operational stuff but that's like improving efficiency which is it's like a it's a trend it's happening it will

keep happening but it's not so transformative what's really driving top growth topline growth is supporting the investment process giving more tools

more information to the investment team so that they can see better and cut through the noise. We live in an area where we are bombarded by data every day. So having tools there helps to make

day. So having tools there helps to make smart and quick and faster and more informed investment decision. Pretty

intuitive. Second area is the personalization demand which is massive.

Okay. And the third area is reporting telling a story being able to uh craft a narrative around the portfolio that it's compelling for the final client that it's really providing something. Yeah,

wow. this is was this was designed for me and it makes sense and it's coherent.

So the practical application we we we came across are along these three lines.

Okay. And so these take different formats. It takes supporting investment

formats. It takes supporting investment committee at the strategic and tactical level down to providing very deep insight into the uh investment process.

It's creating discretionary personalized mandate DPMS. It's it's helping companies supporting companies to do model portfolio delivery. This is

particularly relevant in the US where asset management company are providing model portfolio for the financial adviser to take the burden off of them having to build a portfolio. Um it's

supporting the advisory activity within wealth management creating proposal but again with the scope of doing all of these activity at scale. So I think that's that's the massive difference.

Portfolio optimization tool reporting tool they've been around many many years. The difference is that because

years. The difference is that because this tool were very rigid and rule-based and not as flexible as what we have now with AI you can only do take tackle one

at a time. Here is the scale factor. I

believe this is the massive change on thing is the scale you can achieve by automating thing. Now I don't want to

automating thing. Now I don't want to fall into the agentic uh buzzword but that's kind of kind of where everything uh uh end ups. Yeah, those are some

concrete use cases that we we can relate to for regarding the implementation of AI and once you successfully scale these use cases, that's where the real value

lies. And maybe you you are a wealth

lies. And maybe you you are a wealth management leader for why you work with you know global companies. You you've

seen this several times. Um how would you measure the business value of AI and not just cost savings and efficiency metrics but what is where does the real

value lie? Sure. I I think most

value lie? Sure. I I think most important is that firms shouldn't expect a very quick return on investment. Um

I've seen many like shiny business cases with an ROI or an RO AI within 3 months or 6 months or 12 months or so. I think

that's that's the wrong perspective certain firms have. I think we we all acknowledge that AI is a is a long-term play. It's like shifting from typing

play. It's like shifting from typing machines to PC or coming from you know fire in the in in the in the stone age time to to electricity. So this just

takes a little a little bit of time. Um

then the other thing I think it's important that AI should support business objectives and business strategy of a firm which are different for for any firm. So so I think there is

no um I would say value in AI itself.

there's only value if AI does support business strategy and and and business business objectives and and then it's all about the nuances every firm has you know and um for example here in in the

region is all about it's all about growth so so if you want to double your your AOM or you double your assets on the reporting it's about okay how can I

for example increase my my prospect conversion rates or how can I deepen the client relationship and increase my share of wallet or if I want to shorten

the time to market for for new products, how can AI support all of this? So, so I think there's a lot of um value in AI, but it should always be be linked to

business priorities and metrics and then see how how AI can support those those priority and I think it goes across topline, it goes across bottom line, it goes across client experience, it goes

across the entire enterprise.

C can I bring a paradox to the table because what what U said made me think about a thing uh a topic most of the time when we when we engage with companies they're afraid but now that

there is AI everybody's going to do the same right I actually think that whether you look at you know across the the B business function actually AI is

bringing more diversification and and it allows you to actually personalize your offering way more because otherwise wise you had to have very rigid process that were very

similar to one another you struggle to yes the human experience might have been slightly different but at the end of the day how much can you scale that very little and so you ended up offering the same thing across the board so I don't

know what's your take on this but I actually feel that it's the opposite way around it's not that now that there is AI everybody's going to do the same thing it's the opposite now that there is AI you're actually going to be able to offer something different much better

and and and have have a greater competitive edge and I mean it's an unavoidable step Like said, it's like going from the typing machine to the computer. No, no, it's it's serious.

computer. No, no, it's it's serious.

But, but again, it takes a it takes a different angle than thinking about it's going to be the repetitive things all in. Agree. Agree. Uh AI application, AI

in. Agree. Agree. Uh AI application, AI adoption is growing as the is growing the understanding of the people within

the organization. In my opinion, the new

the organization. In my opinion, the new fronture is um addon to the uh the application the usual application to

automatize or digitalize the current process. The new fronture is to

process. The new fronture is to reimagine to reimagine the process to reimagine innovative services to end clients throughout AI. That's the new

fronture in my opinion because otherwise you can use you can you can't leverage AI as a competitive

uh weapon no for compared to the other clients. Yeah, I think yeah it's

clients. Yeah, I think yeah it's definitely an unavoidable step. Uh yeah,

Jim, what were you saying? Well, I was going to say I think that's a great point because when you think about any company, you know, every company has some competitive advantage or or secret sauce and the the evidence of that

advantage is in their data. And so when you look at like the data and you apply AI to the data of that company, what AI really does is it amplifies the

competitive advantage that that company has. And so uh I I just think it's uh

has. And so uh I I just think it's uh it's an accelerating journey that people have to get on really right away because

the disruption cycle you know used to be five to seven years but with AI it might be one to two to three years and so it's how do you amplify all the great stuff

you already have at your enterprise level that that is so sorry go ahead no I fully agree I think while while the

technology ology is is as we say you know general purpose technology the real differentiator is is your data and and how you interact with the LLMs and the reasoning and all of that and and I

think not going into AI is simply not an option um I think that's a very dangerous play um given given the implications AI has across the business landscape so yeah fully fully concerned

with what has been said real differentiator for me is not only data but also human knowhow Okay. So, uh

change management program are critical are essential are instrumental to better figure out the future application of of AI and and to give a practical thing

like in in in in where this is happening for real think about two use cases. One,

you know, we're supporting a a very well-known wealth management company to do their model activity, model portfolio activity, mean what's what's in Europe called discretionary personalized mandates. They have a great team within

mandates. They have a great team within the investment process. They have taking care of the investment process. Okay.

Super talented individuals. Um now

before that value remained at their top strategy. Yeah. Top uh vehicle, top

strategy. Yeah. Top uh vehicle, top funds and that was it. Mhm. They

couldn't trickle down to the individual portfolio because I mean how how do you do it? you need to hire 500 people,

do it? you need to hire 500 people, 5,000 people to get the idea, understand it. It doesn't make economic sense. With

it. It doesn't make economic sense. With

AI, you can preserve that investment uh unity which is made of their data, their capability of crunching the data and the human touch because there's a human processing involved as well. But then

you can scale it down down the line to the portfolio. The same thing happens

the portfolio. The same thing happens when you tell a story. You might have a good let's say you know a lot of company invest a lot in reporting in telling a story being having a strong narrative you might have somebody that's really

good at doing that how much do you scale it and then is it just the same story all in all what if you can that if you can take that capability both data human together and you streamline it down to

the individual personalization so it creates a very good competitive edge and in a market that I do agree with Jimmy is moving way faster than years ago

Uh, it's better to get started sooner. Yeah,

definitely. It's it's better to get started sooner and it's an unavoidable step for companies to keep a competitive edge. I think this is a good point to

edge. I think this is a good point to call it a wrap for our first episode of Beyond the Noise. Thank you so much for to Joavanni, Tomaso, Jim. Uh, it was

definitely a great conversation, super great insights today and I really appreciate you guys. uh well having a a bit of your time over here. I know

there's some busy busy schedules out there. Um and of course to our listeners

there. Um and of course to our listeners slash uh watchers of the podcast, if you'd like to explore more about, you know, M.M's work, you can check out our

know, M.M's work, you can check out our website at www.mm.m.ai.

You can explore some of the actual use cases that we discussed here today. Or

if you have any other, you know, questions, you can reach out to us directly over there. If you found this conversation valuable, well, reach out and you can also share Beyond the Noise

with your network. So until then, thank you so much and until next time. Thank

you very much. Thank you.