Streamlining Advisor Productivity & Client Engagement using Automation & AI

Most firms and advisors would rather spend their time on client-facing activities or in areas that drive New Revenues or operational efficiencies than on manual but necessary tasks.

From client meeting prep and reading financial documents to follow up and creation of deliverables, AI offers innovative solutions that enable advisors to deliver exceptional service while minimizing manual efforts.

This session will explore the AI-assisted solutions that exist that can automate many of these time-consuming areas so the advisors' focus can be more on relationships, planning and providing more robust offerings to clients,  driving greater revenues and creating more efficiencies.

Discover how AI technologies can revolutionize client-focused workflows, empowering advisors to focus on what truly matters.


Transcription:

Shell Black (00:10):

All right, so I know we have a packed little session here, so I want to get us going. So let's just jump right into introductions. I'll lead, so I'm Shell Black, president and founder of Shell Black. We're a Salesforce Implementation and Consulting Partner that specializes in wealth management. We help wealth firms grow, scale and optimize using Salesforce technologies. Over the past 14 years, we've worked with over 400 firms of all sizes, from a handful of advisors to the big platform aggregators, including 40% of the Barons top 20. So if you have Salesforce, looking at Salesforce, or maybe you need some support in your M&A activity, come see my team. We're on the exhibit hall booth 308.

Michelle Feinstein (00:52):

Good afternoon everyone. Michelle Feinstein. I represent Salesforce, so I run wealth and asset management and setting the strategy of where we can invest in technology and for this topic infuse AI into the flow of work. Really excited about the conversation today and some of the best practices that we can share with you all in terms of what we're hearing from other firms and how they're prioritizing the use of AI for their advisors and their staffs.

Amanda Lott (01:17):

Hello everyone. My name is Amanda Lott. I'm the Head of Wealth Planning and Innovation at JP Morgan Private Bank, and I am focused on scaling wealth planning and advice through thought leadership, advisor education and digital innovation. Prior to this role, I was a Financial Planning Expert that helped other advisors, and prior to that I was an advisor. So I think that background has allowed me to uniquely connect the dots so that we can deliver better client and advisor outcomes.

Samuel Deane (01:43):

Hi everyone. I'm Samuel Deane. I'm the Founder and President of Deane Wealth Management. We're an independent investment advisor for startup founders and tech professionals. We specialize in equity compensation, financial planning, investment management, and tax planning. I started my firm in 2018 at 26 years old with no clients, no assets, no revenue, no anything, and fortunately, with a $15,000 technology spend, I was able to spend a wealth management firm from scratch. So as a millennial, I have a very close relationship with technology and it certainly shaped my vision for my business and how we plan to implement technology into our wealth management firm. And so I'm happy to be here today to share a little bit about my experience and hopefully you guys can take away something. Thanks for having me.

Shell Black (02:41):

Thanks everyone. To get us started, let's do a quick poll of the audience. How many of you by a show of hands are using AI today in your company? Oh, wow. We have some pretty good adoption. So those folks that raise their hands, does compliance know what you're doing? All right, that's great. Now that really kind of helps us kind of get an idea of what's in the room. I know this was covered a little bit in the prior sessions, but we've got a slide coming up that uses these terms, so I just want to make sure everybody's kind of on the same page. Three types of AI, predictive AI, I'll start with that one. Predictive AI let should be proactive with your clients rather than reactive. So that could be understanding what's the next best action you should take with your client whether, hey, this is a good time to send an email, or maybe this is a good time or opportunity to recommend a new product or service.

(03:31):

Generative AI, I think we've seen a lot of this at the conference so far that's creating new content from other sources of information that could be summarizing a client meeting or maybe helping you craft an email. And the last one is autonomous AI may not have been talking about this that much, but that's AI that does not require any human interaction. So I guess the use case I always think about is maybe an agent or a chatbot that can guide a client through a service request from start to finish and resolve it without any human interaction. So predictive, generative, and autonomous AI. And with that, let's get started with our questions for our panelists, and I think it'd be great if we just grounded, where are wealth firms today with ai? And I think I'm going to leverage Michelle for this first question. And the question is, what trends are you seeing and where are wealth firms today on their AI journey?

Michelle Feinstein (04:26):

Sure. So I sit in a unique sea because I get to meet with many firms like all of you, all throughout the year and not just in America, but across the globe. When I think about the three types of AI that you described, most of those firms have been dabbling with predictive ai and I would say in the first part of the year, learning about generative ai. And so they started to accelerate defining use cases and they were working with lots of different folks in different lines of business across their organization, but all they were doing was starting to generate a lot of use cases but not actually moving them to the next stage. As we've gotten into the second half of the year and their teams have gotten more comfortable, they've started to bring compliance to the table and they're getting permission to start executing and putting these proof of concept use cases into production.

(05:12):

And I would say there's a bit of a race, which use cases should we implement first. So there's been a bunch of firms that have said, oh, we have 200 use cases or 300 use cases. That's ridiculous, right? No one's going to execute that many. Most of the firms I'm talking to are getting it down to five or less, and they're very simple. They're employee facing because employee facing is safer, not a hundred percent safe, but safer. And it's usually in the areas of sales productivity, service productivity, advisor productivity or content creation. I would say in wealth management, we've been moving slow, but we're starting to accelerate and a lot of that is still we're waiting for what's going to happen as it relates to regulation and rules and making sure that no one gets fined. So that's my point of view where we are with the trends.

Amanda Lott (05:59):

So I can answer this from how JP Morgan as an organization is thinking about this specifically asset and wealth management under Mary Erdoes, which I think our line of businesses kind of leading the charge on AI. We're really, we think our employees are really the best drivers of innovation. They are the ones who are in the trenches and know all the no joy work that they do every day, and the ones who are most motivated to find technology solutions to help them do it easier. And so we've empowered our employees and given them large language models so that they can experiment and explore. And we have AI days and we just had our second one last week, we had 70 use cases that were presented and ultimately made it down to finalist round. I think 17 were presented to our operating committee. And so we are having these conversations every single day. One quote that we heard, we invited Harvard's D Cubed, I think it stands for Digital Data Design Group. They came and spoke to us last week, and one of the things that I thought was so interesting that she said was innovation is 30% technology, 70% organizational change. And so technology does not drive innovation. Humans do.

(07:14):

You can't just have five humans that are responsible for the innovation, for your, I mean, I'm thinking about a 300,000 person firm, but we are asking every single employee to think, how can you do your job differently? And I think the firms that are not bringing their entire employee base along and really empowering them to drive and see how they can change are the ones that are probably going to move slower.

Michelle Feinstein (07:43):

Could I add a comment to that real quick?

Shell Black (07:44):

Absolutely.

Michelle Feinstein (07:44):

 I also think the firms that are accelerating like yourselves are just naturally weaving AI into the employee's daily lives. So as an example at Salesforce, we've weaved it into Slack. It's pretty amazing. You come in every day and just say, recap every Slack message, you could have thousands. The click of a button, it gives you a summary. So that's an example of it starts to get them thinking of other ideas.

Amanda Lott (08:07):

Yes. I mean whether you want to create a little, you're not going to get all advisors to go at once. And so we're like, well, we have our advisor meeting and we have the prompt of the week and explain what it is. You've got to serve it up and you'll have those early adopters, but you're going to have to bring everybody else along.

Shell Black (08:22):

Thank you for that. Michelle and I were just at Dreamforce, a big conference last month by Salesforce, and one of the takeaways I had out of that was don't do AI. Just to do AI. There really needs to be benefits. And I think Samuel, I think we'll start with you on this one, but how does both the advisor and the client benefit from AI?

Samuel Deane (08:44):

That's a really great question. Before I dive in, can I get a show of hands for folks who've been an advisor at least since 2010? Okay. I was still in college at that time.

Amanda Lott (08:57):

Yeah, subtle burn, right?

Samuel Deane (09:02):

I'm sure you guys remember around that time, that's when betterment and some of the roboadvisors were coming into fruition. And I'm sure you guys remember it was a pretty common thing to hear how AI is going to be replacing financial advisors and those sorts of things. Fast forward a decade and a half later, we realized that that's not actually the case. Roboadvisors in fact served a demographic that weren't being served by financial advisors to begin with. If anything, I think roboadvisors complimented financial advisors in a way that we could now offer portfolio management a low cost approach. Today, I think you are hearing something very similar with AI, with the risk of AI and potentially the risk of AI and it potentially eliminating the need for financial advisors. And similarly, I don't necessarily think that's the case. If anything, I think AI is actually replacing a lot of the back office and administrative functions and even honestly can augment what a CSA really does and what that role actually looks like.

(10:18):

I think AI will increase efficiency by automating routine tasks, streamlining compliance tasks, streamlining portfolio management, providing better data insights, whether that's related to the client's financial life or whether that's related to the client from a behavioral perspective. I also think that AI can significantly help advisors provide more value by expanding our service offering. I don't remember who exactly came out with the study. It could have been Charles Schwab, I think it was in 2023 a year ago. And it talked a lot about how the traditional financial advisor is sort of staring away from being an investment manager and gearing more towards being an integrated life and wealth coach where technology and AI is giving us the opportunity to do things that we weren't doing before, whether that's tax prep and tax planning, whether that's offering, whether offering lending as a service and bringing private banking in-house, whether that's estate planning, whether that's helping clients with mortgages and those sorts of things. And so I think ultimately AI helps advisors be more productive, which indirectly benefits the client, but also gives us the opportunity, like I said, to expand our service offering to be more valuable to our clients. And that extends beyond just money and wealth and portfolio management.

Amanda Lott (11:47):

That's what robos did, right? Robos put pressure on portfolio management costs and that if you were going to add value as an advisor, you had to do more than. And so I think if it's just, we're going to keep leveling up. So if you want to be a really great advisor, like the Microsoft gentleman this morning was saying, using AI tools so that your advisor's like, wow, they're so on top of it. They give me proactive, customized advice. And so I think it's just going to not replace, but augment and force everyone to level up in order to continue to differentiate themselves. I think in general, if advisors have less of the no joy work, they're going to be happier. If you have less no joy work, would you? I would. And so that means happier advisors, happier clients, you're able to either serve more doing the same offering or expand that offering.

(12:41):

And then I think of, I'm going to give you two examples of how we're thinking about it at JP Morgan, and one is how can you use AI to have a more consistent client experience, particularly when you have more than one advisor, right? So how can you prompt, okay, you should be talking to this client about this topic at this time. That advisor may or may not have brought that up because either it's not in their skillset, it wasn't on their radar. And so that helps as you grow and have advisors who have different experience levels be able to offer a consistent client experience. The third thing is then how do you take your best advisors or your advice experts or your investment experts, and this is maybe crude, we scrape their brains, get all their best thinking and put it at the fingertips of all of your advisors.

(13:33):

So we've done that for all of our planning thought leadership, we've ingested it so advisors can ask questions like, when would I use a DAF versus a private foundation? My client just inherited an IRA from her 87-year-old mother. What do RMDs look like? And then serve it up with the thought leadership that we have on it. That's huge. And so I think another example on leveraging your best advisors, we also know how our best advisors respond to emails, engage in certain client interactions. So how are we taking that so that you're leveling up those advisors who may be less experienced, so ultimately your client gets a better deliverable.

Shell Black (14:13):

Thank you for that. So we've talked a little bit about where AI sits with wealth management. We've talked about some of the benefits. I'd like to get into some use cases. And so I'm going to use this slide to help kind of facilitate this next question. The question being, what are the top AI use cases our audience should be thinking about? Michelle, do you want to get us started on this one?

Michelle Feinstein (14:33):

So I think, look, we've all sat through many of the sessions today. A lot of these were brought up I think in wealth management, and this definitely came up as Dreamforce. The number one use case is meeting summarization, client interaction summarization, and I'll give you a wonderful use case that we both saw. So I was on a panel with RBC Wealth and Greg Belzer and team got up there and they did a demonstration. So if you think about summarization, a lot of firms are doing it at the account level or the meeting level. They went and approached it at the household level and they came up with that use case because they went and they interviewed their advisors and said, what is your number one pain point? And their number one pain point was gathering all the interactions that have happened across that household in a year.

(15:15):

In a year. Do you know how long that would take? So what did they do? They created literally an AI easy button that says household summary. They demonstrated it, and now they can see across categories. Who in the household has a financial plan? When was the last high value touchpoint with that client? What were the interactions? How do I need to reach out to them? I mean, they have a laundry list of activity. Now what's interesting is they also said sources. So the advisor can click that button and see where did the information come from in the LLM? And they did that purposely to build confidence. Yes. So in addition, I'll point out another use case that really wasn't talked about a lot today, and it goes back to the unstructured data example. And so unlocking policy and procedures, if you think about where do all the phone calls come from, right?

(16:00):

Advisors might be calling into the home office or to an assistant or someone trying to understand how to do something or if they're allowed to do something. We have a couple of firms feeding their policy and procedure documents into Salesforce, into the CRM using some of our RAG and our LLM capabilities and creating a collaborative user interface so that those advisors can just ask the system. So those would be a couple of the use cases. I point out the last comment I want to make is autonomous agents. No one's talked about it a lot. We talked about it a lot at Dreamforce. So if you think about the three types of AI, autonomous agents being probably the next best opportunity and probably a little scary for our industry, but this is where imagine you had an org chart, half of your team is human, half of your team are autonomous agents. So you're actually allowing them to get some of the work done for you in the areas of service in the areas of advisor productivity. An example, you want to update beneficiaries. Why can't the autonomous agent do that? You can still put guardrails around it and rules. This is where we're going next. There was a stat by Forrester. There's actually a great research piece out there that by 2026, more than 80% of wealth management firms will be autonomous agents.

Shell Black (17:17):

Amanda, JP Morgan, any use cases you can think about?

Amanda Lott (17:21):

I would say the three that I would add to what Michelle mentioned, I mean thought leadership is a big one. We spend hours creating thought leadership and what you can do, just giving it a really beefy outline and saying, I need you to give me a 1200 word article that I can put on the webpage and then be able to say, okay, give me a 200 word summary of this, do more of this. That has saved hours of time already. So that one I think is really, that was a very easy one for our advice SMEs to link onto. The other one is just document summarization. So whether that's estate planning documents, tax returns, letters of intent for business owners, executive comp statements, anything that would take a lot of time to review it manually can be, I mean you probably won't get it to a hundred percent, but maybe you could get it to 80 or 90%. And then I have a human take it over the finish line and take something that might've taken an hour down to 10 minutes. So we're seeing a lot of opportunity there. And then the last one would be on compliance JP Morgan named this year we are on track to do 50% more know your client files with 20% less people. And so that's huge.

Michelle Feinstein (18:35):

Compliance, that's an untapped opportunity. So also with Salesforce, we're absolutely prioritizing compliance as a theme. One of the early use cases with AI we rolled out was transaction disputes. So having the AI analyze what's happening and creating potential responses or related attachments to that transaction dispute, but also making sure if you need to route it to a compliance person that you can do that.

Samuel Deane (18:58):

Totally love those responses. As an advisor, I could say that the top three use cases I have with AI are meeting prep and summary thought leadership. And there was another one you mentioned that slipped in my mind.

Amanda Lott (19:13):

Like client emails,

Samuel Deane (19:15):

Client emails too, but mostly thought leadership and,

Michelle Feinstein (19:21):

Around financial planning.

Samuel Deane (19:22):

Yeah, I would say so to survey the audience really quickly, many of you are financial advisors. How many of you are financial advisors at an RIA that manages more than one to 200 million in a UM and less than one to 200? Okay. So I think as a boutique shop, I'm one of those advisors that manage less than a hundred million. And as a boutique shop, I think it can easily get overwhelming to think about all the different ways that we need to remain competitive in this landscape. And I can say that as you scale the things that Michelle and Amanda are talking about and referencing become much more important. But as a smaller advisor with under one to 200 million in assets, you don't necessarily have to go out and build an in-house solution. It can be easy as leveraging software that's already leveraging AI to make your life pretty simple.

(20:23):

And so one of my favorite use cases that I mentioned is meeting prep and summary to be able to have a tool, create an agenda that's based off of prior conversations you've had with this client to create a summary of the meeting that is for compliance purposes, but that you can also share with clients so that you're on the same page. And to then also create a follow-up email that you can send to the client. That's pretty much high level of what those detailed summaries look like. That saves hours. I don't know about you all, but starting my firm from scratch at 26, my biggest fear was not doing something right and getting a $50,000 fine from compliance, that will completely put me out of business. And I am really focused on having super detailed notes so that number one, I can always have something to go back and reference. And number two, that's something I know I won't get in trouble for compliance, that's easily a task that can take me 30 to 45 minutes after every meeting with a platform like Jump or Zox or Vega Mines, that cuts that down to five to 10 minutes. And so something like that, that's essentially depending on the type of team you're building, that's essentially the function of a CSA role or a junior advisor for $2,000 per year.

(21:51):

Talk about ROI. So to think about those types of cost savings and the type of productivity that you can have with ai, super impactful. We talked a little bit about compliance and streamlining compliance. If you run an RIA and you're a solo advisor that archiving communications across all aspects, social, email, text and so forth, it's something that we have to do even as a solo advisor, as silly and redundant as that is because you're the one communicating, it's still something we have to do to have a platform that can easily do that. Communications archiving for you. See if there's any red flags, that's a task that depending on how many text messages and emails I have in a given month could take me 20 minutes. I can leverage an AI platform like Archive Intel as an example to cut that down. It took five minutes if that. Where there essentially the platform is essentially doing the communications archiving review, and I'm just going in to review the review essentially imagine how much time hours that saves a solo advisor to be more productive, to be more attentive to their clients, to be more of an active listener versus a passive listener. It really shifts things when you're implementing ai and that's from personal experience, not from reading research reports and those sorts of things.

Michelle Feinstein (23:20):

I think one of the opportunities we haven't talked a lot about is with financial planning, think of the data collection process associated to financial planning and you're getting all kinds of documents from a customer Excel spreadsheets or copies from notes that they've taken or stored away. Imagine you could consume that into the AI and it's the financial plan. It's not going to go as far as make a recommendation to the client, but maybe to you or the advisor. It's just helping you with, I like this term, the WTF, where to focus for advisors.

Amanda Lott (23:49):

That's what I thought when you said that.

Michelle Feinstein (23:50):

I know everybody thinks that.

Amanda Lott (23:51):

But with financial plan, yes, data entry, but where I go to a client experience, and so if I get a plan in place, how do I then nudge to the advisor, Hey, it looks like this client, they're still earning income, they aren't financially independent, you should talk about life insurance with them, or it looks like they look really good. You could also show them a scenario where they retire three years earlier and so automatically serve those things that right now you manually have to think of and have experience. And so again, that's like leveraging best advisors. What would they do given this fact pattern to increase that baseline client experience?

Shell Black (24:33):

That's awesome. I'm really loving the diversity of perspective on that. That's wonderful. But as the moderator, I'm going to push this forward and we need to talk about data because data is what drives AI. So to help kind of facilitate this one, we've got yet another slide, but my question is how are firms solving for fragmented data in making it actionable? Michelle, you want to start?

Michelle Feinstein (24:57):

Alright, so we'll start.

Shell Black (24:57):

Yep.

Michelle Feinstein (24:58):

So this is a visual we've used, I've used it many times with customers and it's about the data maturity curve. And as you've all heard today, I mean data is the foundation of any good AI experience. And in wealth management we've had 30 to 50 years of creating what some of us would call a single source of truth. But underneath what is that single source of truth, right? It's messiness, right? We have a lot of proprietary systems, legacy tech stacks, siloed organizations. We love to acquire each other and then we don't blend the organizations together very well. And so this becomes a paralyzing factor for firms in order to move forward and become the firm they want to be, right? So what you're looking for is here at level one connected internal data between one and two. This is where most wealth firms are these days.

(25:45):

When I ask people to raise their hands and say, where are you in the spectrum? They're usually somewhere between one and two or in the middle of two. So let me ask this room, where do you think your firm is? How many of you are at level one? How about level two? How about level three? See, it goes down and that's okay, right? So a lot of us here at Salesforce and many other organizations we're trying to help accelerate you. So for instance, at Salesforce you may have heard of something called agent force. What is that? That's basically the whole collection of capability from data cloud to agents and AI in the flow of work to prompt builders and agent builders. But we're trying to accelerate this innovation and then we're giving it out to the universe. We're giving it to partners and firms and saying, you don't have to build this from scratch.

(26:31):

Start leveraging our tools so that you can get to level three and level four, level three. Using predictive AI, helping you be more proactive, less reactive, tell me what's going to happen next. Tell me a change in Michelle's behavior, anticipate her needs, give me the next best actions to recommend. The more exciting spot is when we get into that generative AI, and if we had one more pillar, it would be autonomous. This is going to allow your firms to double or triple your growth the size of your books because you're going to have AI working on your behalf and you don't have to add to staff to do it.

Amanda Lott (27:07):

So on the data side, it has to be stored digitally to be able to leverage this and think of how much information is stored in notebooks, in Outlook, calendar appointments, unstructured data. Yeah, unstructured data. It's a word doc. And so even getting it into a digital format that can be leveraged by some of these tools, that's a big step for many firms to take. And so what we have found is you also have to show the value to the advisors of why should they take some of their precious time to get that data into good order. And so I think that's a little where we've found using FOMO of like, here, look what I was able to do and how much time it saved me because I had loaded all this information about my clients was living in the right ecosystem instead of in my personal files.

(28:00):

So then it served up meeting agendas for me. It created meeting notes. I think you have to incentivize them to get the data in good order. And then the other part is you have to make sure that data stays clean. So I'll give you an example of one of the chatbots we've built where we've ingested all of our planning thought leadership and advisors can query questions. Think of all your thought leadership and when a new tax law comes out, annual exclusion goes up, retirement law changes, secure Act 2.0, how do you make sure that the content that's been ingested that then advisors are then getting answers from and relying on those answers to give advice to their clients is up to date. And so it has forced us to get a lot better about our thought leadership content creation process, renewal process. How do we flag things that need to be updated so that they don't become stale? And then I also think, what is that one source of truth? I've got one net worth on my client's KYC or their suitability form. I've got another net worth in their plan. I've got another net worth on their estate planning balance sheet. Which one do you use when you're trying to decide what insight serve up to them?

Michelle Feinstein (29:14):

Yeah, I'll just add, that's a great point. So I think a few years ago all of you were sitting there saying, what should be that single source of truth? Is it the custodian? Is it the CRM? Is it the financial planning tool? Is it something proprietary? And I think there was a perception that all the data had to be moved in order to be normalized and reconciled and actioned upon. That's not the case anymore. Right? Now you can have some of your data in the CRM, you can let some of your data sit in other source systems, whether it's databricks, snowflake, your proprietary platforms, but now you can map the data and the data can be reflected on a client engagement level and a relationship level in many different source systems. So I think our world has become much more open architecture and we're all starting to play much nicer together and we're flowing that data because it benefits all of us.

Amanda Lott (30:00):

Yes.

Shell Black (30:01):

I was going to make a quick comment on something or expand on what Amanda said, and that's the value to advisors. Going back to that RBC wealth demo where they were doing the kind of a household summary, the value to the advisors, and I won't use WTF because it's not as cute, but I used W-I-F-M, it's like acronym day, what's in it for me? So what was in it for the advisor to use? Where I'm getting at, what was in it for the advisor is if they were fastidious and accurate and good discipline about where they put the data and how complete the data, the accuracy jumped tremendously in the generative AI in that household summary. So they connected the dots real quickly. RBC called it the aha moment.

(30:45):

That presentation where it's like, okay, if I use the tool as design and I can give it the information, I want to have a much better product. Any other comments on this one before moving on? Good. All right. Yeah, I think we got that one. Got one covered pretty well. Alright, I think we'll just leave this up, but I think one of the topics that always come up is the problem with hall hallucinations and bias. It makes great news stories and we're all familiar with it. And we were talking a little bit about guardrails. And so that really gets into my next question and that's how firms use AI innovation safely. Samuel, want to start us off on that one?

Samuel Deane (31:25):

Sure. I think the number one priority at least for us is to really communicate with clients and provide transparency around AI. In my opinion, clients should know when we're using AI to provide financial planning recommendations or portfolio management recommendations, right? There should be certain disclosures that talks about the risks and benefits with implementing AI. Even something as simple as a meeting prep, like jump for one. I've actually never had a client question, and that's something that's pretty common with advisors is how do your clients feel about this random note taker being here? And I was a little insecure about it at first, never had a client ask about it. I actually brought it up to a client and their response was, no, it can stay. It actually prevents, it allows me to actually pay attention and focus on what you're saying and not take notes.

(32:24):

And I'm like, well great. It allows me to do the same thing for you. And that way we're both very present in our meeting and we are maximizing on active listening rather than trying to multitask. I also think that we as advisors need to take responsibility for the decisions that AI gives us. So an example of that would be maybe I am okay with the AI tool that I'm using, generating a meeting agenda. That's not something that I will need to intervene on. I'm okay with trusting AI and the output that it gives me for a meeting agenda or follow up email or something like that. But there are certain functions where I may want to intervene and have the final say like trading for example. So I'm okay with a platform saying, Hey Sam, based on this client's IPS and risk tolerance and your conversations and those sorts of things, here are some recommendations that we think you should implement.

(33:29):

That at least gives me to the points that we made earlier that at least gives me a starting point to say, okay, well I like this recommendation or I don't like this recommendation or this recommendation makes sense. But I know this person does not want to invest in anything related to Elon Musk. And so I think it's incredibly important to identify, and Michelle's going to expand on this, but I think it's incredibly important to identify areas where you're okay with AI giving you that final output. And God forbid it's wrong, it's low hanging fruit, it's not the end of the world, but where the stakes are more serious and significant, you definitely want to have some human intervention there. And I think that's what I think about when I think about using AI safely with our clients.

Amanda Lott (34:16):

Yeah, I was thinking about the human in the loop aspect and you should absolutely have a human in the loop where the risk of getting it wrong is high to the firm, to the advisor, to the client. It's interesting, I have maybe a different take on the level of transparency required to the client when you're using these AI tools. And the analogy that came to my head when you were talking about what responsibility do we have to let a client know that this investment proposal was created with the help of AI? We don't do that today when you use Morningstar to help you make an investment proposal. And so I think there are degrees. So if you have someone recording your meeting and listening in and taking notes that I feel like is less, more black and white to me, but when you're using it as a tool to help run your practice more efficiently, I think it's a little more gray. And I don't think you necessarily need to let the client know how the sausage is made, but I think I totally agree with you, the higher the risk, the human in the loop, but also AI, many times it won't get it a hundred percent right? You don't get it 90% of the way there. And so having that human who can take it across the finish line and ultimately we're relying on our advisors to use their judgment just like they would with any other tool.

(35:43):

We're just making it easier for them to hopefully make an even more informed decision. And then the last thing I would say is whether you're a larger firm and building some of these things yourself or you're working with other vendors, you want to make sure they have people who are obsessing about this day in and day out and have the right people on it. We have found that having this kind of combo of your traditional product owner, your tech, but then an SME, because to tell you, does this chat bot answer this question of how does this RMD work? If I inherited it from my 84-year-old mother, my tech partner can't answer that, I can answer that. And so you have to have someone who's going to be able to validate it from an advisor perspective to know if this is good or not.

Michelle Feinstein (36:30):

One comment to that I would say is one of the firms I was talking to, in fact, many of them, they do hire outside consultants to sit alongside their teams that are strategizing on AI. And one of the things they do is they create an AI blueprint where in the lifecycle of the advisor journey is AI appropriate, where AI would add value, drive a better outcome, solve an advisor pain point. And so when you start going along everything from prospecting to converting them into a client, to funding the account to creating the financial plan, all of a sudden your blueprint starts to light up with AI use cases that could save a lot of time, increase productivity, and it helps you focus yes, where to begin.

Shell Black (37:06):

Thank you for that. Alright, I think we got one more question and we're going to use this slide to help facilitate that.

Amanda Lott (37:14):

Again, look at that.

Shell Black (37:15):

So yeah, we actually might have time for q and a. So I think at this point it's what are the things you should be thinking about as you walk out of here and how do you get started? And the question's going to be, what advice would you give a firm that is starting with their AI journey? And I'm actually going to kick us off on this one and I'm going to steal some of Samuel's comment here. I had the same thought is look at your existing tech stack and see what AI features you might already own before you go out and buy the nice shiny penny that you find. And if I looked at our own tech stack, we've got Salesforce, I know that's a shocker.

(37:50):

Google apps and Asana, all of them are SaaS platforms that have AI features that we can enable. Go back a couple of slides and think about those use cases. Do they map to those features that you already own out of the box, right? So think of all the AI features out of the box, think of the use cases and is there something you can leverage there? So again, the question being what advice would you give a firm that's getting started on their AI journey? Who would like to start on this one?

Michelle Feinstein (38:13):

I'll go next.

Shell Black (38:14):

Okay.

Michelle Feinstein (38:14):

I think we've talked about a lot of them today, but I think if you're someone that's in the decision seat of deciding are you going to build, buy or partner for AI or the development of an LLM or prompt builders, you've got to say to yourself, are we going to add some distinct value by doing this ourselves right now or should we start by outsourcing and testing and use some of these out of the box solutions first and then later on revisit whether you should build an LLM. So that would be number one. I think number two is know your business outcomes, interview different personas around your organization. Don't try to solve for everybody right out of the gate. Maybe you start with advisors and the service teams and then you can expand it over time. And again, I'll just underline that point of start with fewer use cases, three to five, that's it.

Samuel Deane (39:03):

I would say if you're a solo advisor, I would challenge you to probably even boil that down to one use case only because, not because Michelle's incorrect or anything, only because we wear so many hats as a solo advisor, I would say to be clear on your firm's goals and pick one area of your business that you want to improve. So whether that's content creation, sales, client service or productivity, they are platforms that is leveraging AI in each of those areas. So I'm going to quickly give an example of each. So content creation could look something as simple like in the last session, talked about it, but creating a YouTube video, having a transcription service, transcribe your video into a Word document and then using something like ChatGPT to create that transcription into a blog, that's a one-time effort that you can put that content out there across different modalities, right?

(39:58):

If we're talking about client servicing, leveraging a platform like SOA Finance, which gives advisors the ability to assist clients with mortgage loans, with lending in general where that platform is using AI bots to quickly save time and costs on helping clients complete mortgages, whether that's productivity, we talked about jump a lot with pre-meeting prep and summaries and those sorts of things. So pick an area that you want to improve in and maybe depending on how invested you are, maybe you do one area per quarter and you really just focus on that particular area in that quarter. But pick one area, find you can go on kit's famous FinTech map and find a solution that falls in that area. And that is how I would start my journey into ai. If you haven't started yet, again, this is mainly for firms who don't necessarily have the capability or need the capability to build a solution in-house. Like I said, there are several vendors that are leveraging AI. You just want to make sure that they're actually leveraging AI and not just saying it because that's pretty common also.

Shell Black (41:13):

Anything else on that one? Good. Alright, we actually have about four minutes. We can do a q and a question. We actually ended on time. Anybody want to ask our panelists a question? Got one upfront.

Audience Member 1 (41:26):

What's your favorite way of giving the staff members incentive to get clean data into a repository?

Amanda Lott (41:35):

I think it's show them what they get when they do that. And then you have, we pilot with advisors who are forward thinking and willing to try things out and then we get them to speak about the innovation, not us because it's so much more credible coming from their peers. I think you have to show them the value first and that probably also means start small, whether it's one use case and figure out it could be the thing that's taking them the most time. It could be the thing that sucks the most joy out of their day. And if you find those use cases where they're like, oh my gosh, and then you've built credibility to do the next thing.

Shell Black (42:17):

I'll just say real quickly kind of that with them what's in it for me? RBC was saying that they started with the use case that they did, which was that household summary because he said advisor payroll was the biggest expense in their P&L and if they could save those advisors time, there would be ROI. Right? So again, how is that going to benefit them and kind of pull that back to that audience. Another question. We've answered it all. I don't know. Alright, so with that, I really enjoyed the diversity of perspectives that this panel has brought. Can you me thank our panelists for coming out today?