Technology and Trust: Future-Ready Advisors' Blueprint for Success

[00:00:00] David Kitai: Good morning and good afternoon, everyone. Thank you for joining us from across the country today for this exciting webinar, Technology and Trust, Future-Ready Advisors' Blueprint for Success. My name is David Kitai. I am the Senior Editor at Wealth Professional, and I am thrilled to guide you through what promises to be a deep discussion on how advisors can balance efficiency with empathy, ensure smooth adoption, and identify the technology that truly elevate the client experience. Before diving into today's session, I wanted to go over some housekeeping notes very quickly. There will be a Q&A session held at the end of our webinar today. There is a Q&A box at the bottom of your screens. Please feel free to fire away any questions into that box during the webinar, and we will do our best to answer all of the questions during the Q&A period. There will also be a few polls that will pop up during the webinar. You will see those pop up in your screens. Please participate. This is a really valuable way to have your voice heard. And finally, this webinar will be recorded and the recording will be emailed to all registrants following today's session. Without any further ado, I will get to introduce the first of our speakers who can pass it on to the rest. And you can learn a little bit more about each of them, starting with Richard Owen, the CEO and president of Buckler. Richard, please.

[00:01:45] Richard Owen: Good morning. Good afternoon, everyone. Richard Owen. From Buckler, we produce AI-powered compliance and cyber management software. Background is all wealth management across a couple of banks and a couple of asset managers, and happy to walk you through some ideas that we have at Buckler.

[00:02:10] Gabe Karkanis: And hello, my name is Gabe Karkanis. I'm the Senior Director of Product at Maximizer. At Maximizer, we have a CRM offering focused towards financial services. We work with several sub-verticals within financial services. And I've been with the company for a couple of years now, helping them to really shape the roadmap, what we're building and really charting the future for CRM. And I'll hand it over to Peter next.

[00:02:38] Peter Wloka: Great. Thanks, Gabe. I'm Peter Wloka. I am the CRO of Buckler, working with Richard and team. We're super excited to be able to demonstrate exactly what we're doing for the financial advisor. Technology is table stakes now. It's not just a factor of you really need to embrace technology in order to be able to excel. So I have a long background in, in with the financial advisor as well. I've been a financial advisor and I've been working with financial advisors for almost 30 years now. So we'll turn it over to David.

[00:03:14] David Kitai: Thank you so much, Peter. And to come back to you, because of some of that experience you've already been highlighting, you know, you've worked with so many Canadian firms. And as you say, you know, technology is table stakes, but this industry has not always had the best job, you know, adopting this. So why in your experience has adoption kind of historically lagged? And what is finally driving this major change in adoption behavior?

[00:03:40] Peter Wloka: Great question. It's, you're right. Adoption has lagged significantly from other industries. Advisors operate inside a highly regulated, fragmented, low margin, but very much relationship driven industry that's packed with a lot of legacy systems, which is why we created Buckler. There's a lot of systems that are antiquated, using PDFs and spreadsheets. And we think we can definitely disrupt that. To continue, it's a very risk-averse culture. People are concerned with change. And then I think there's the technology firms have done a pretty poor job of integrating into the pools of the different firms. It's super hard to do. I think what's forcing faster change, and I think I'm jumping ahead, but really, I think that's been the biggest problem, is that you've got the average age of the advisor is close to 60. They have some concerns with adapting new technology, and as do their firms. And so you've got to make sure that the technology that is being... potentially looked at fits their requirements. Makes sense?

[00:04:54] David Kitai: It does indeed and I believe we are moving on to a poll that's coming up next. Yeah, perfect. So the poll question here that should be appearing on all of our participant screens here. It's not a small question. What is your firm's biggest challenge with technology today with the options of adoption resistance, too many systems, compliance risk, or hard to prove return on investment. Please take a moment to fill this in. We should be getting our results momentarily. From my own experience of interviewing financial advisors on a day-to-day basis, it's a case to be made for each of these answers.

[00:05:35] Peter Wloka: It's, again, an ongoing challenge, but very curious to see where we're going to end up. David, do you mind if I just continue the conversation? Because I think you asked... uh you know what's historically lagged but then i didn't answer the question around you know what's finally driving change yes um you know and i think it's just a mixture of of competitive uh pressures from fintechs from you know smaller firms uh we've heard it you know after our 400 or so meetings with advisors in the states it's really it seems to be larger firms move more slowly So I think it's the wealth symbols, et cetera, that have really... helped with change. COVID digital era acceleration, I think technology has been a major beneficiary of. Fee transparency, CRM3, which I know is a tough word for everyone. Regulatory operational demands, big new vendors, including AI tools, and client expectations. I think everything that we've seen is that younger clients want digital. And those are some real concrete examples.

[00:06:48] David Kitai: Okay. Thank you so much for that. And we now have some results from our poll here. And Peter, I would love to get a little bit of your thought. We have too many systems at 30% just ahead of adoption resistance at 27%, followed by hard to prove ROI at 23% and compliance risk at 20%. So pretty even across the board, but with Too many systems taking the lead there. Your thoughts?

[00:07:13] Peter Wloka: And it's true. I mean, a lot of the systems don't necessarily integrate. It's like just plugins. What you really want to have is technology that will seamlessly integrate into the data lake of each firm. I think that's super important. So yeah, I mean, people just don't know where to go. Some firms provide technology to their advisors. Other firms allow their advisors to pick and choose. And it would be super hard as an advisor to figure out which ones are most important. So, I mean, I think that's on the firms. I think that's on the technology providing it to the firms. But I also think it's on the advisors. It's kind of like the Nike old ad of just do it. You need to take technology. This is not, it's table stakes now. This isn't just something that it's a nice to have. It's a must have. Just do the thing. But in doing the thing, and this is where I'm going to bring Gabe in, you know, there can be some mistakes made. So, Gabe, could you share from your perspective what some of the most common mistakes you see when firms roll out this new technology and specifically with CRMs? And how can advisors and firms kind of avoid some of these mistakes?

[00:08:23] Gabe Karkanis: Yeah, I think that's a great question. One of the biggest mistakes is trying to use the CRM out of the box without tailoring it to your firm's specific workflows. That typically can lead to frustration because the CRM ends up feeling like it's built for someone else. Secondly, really around skipping integrations. So without investment data, insurance policies, and other important financial back office data being brought and flowing into the CRM, you'll never really get that full client picture. And that's when people don't use the tool as much as they could. And lastly... Many advisors don't take advantage of automation. If your CRM isn't triggering tasks for things like client onboarding, for KYC updates and reviews, for due diligence, it's really leaving time on the table. And so I think those are really three of the biggest challenges that I see with adoption of CRM in particular.

[00:09:16] Gabe Karkanis: And if we shift to the next slide, I think most CRMs were not designed for financial advisors. Most of them are off the shelf, general purpose CRM tools. A maximizer is quite different. We're building CRM to work the way that advisors work. And in our opinion, a good CRM should really do three things, should help you build better relationships by surfacing personal, timely, contextual insights, as well as key investment and insurance data, and other data that's flowing into the CRM, it should really save you time by reducing repetitive admin and simplifying these daily tasks. And lastly, should really help you run your practice or run tasks on autopilot through smart configurable workflows and triggers. An example of this would be from onboarding all the way to KYC renewals. We're actually launching a workflows and automation system that helps break these complex processes into tasks, assigns them across your team, and keeps everything moving forward without the manual task tracking mechanism. So this is about designing a CRM that works for you, for the advisor's firm, and not against you. So then you can focus on your clients and not the operations.

[00:10:20] David Kitai: Okay, so we are now coming up to our second poll of the webinar. And we're going to see how our audience feels about confidence with their CRMs and how it actually helps them grow their business. And thank you, Gabe, for the insights before. I think that sort of leads very neatly into this question. So this one's a little more straightforward. Very confident, somewhat confident, not at all or not sure. It's, again, one of these questions. And I feel like this cuts across so many industries, across so many different sides of tech where the tool is the tool there to just be a tool. Or is it there to genuinely help what you're trying to do? And it is always really interesting to hear what advisors have to say on this broad question, especially when it comes to CRMs. So we've got the results coming up momentarily. And here we are. Very confident is taking the lead here at 41%, followed by somewhat with the not at alls and not sures taking 14% each. Even combined at 28, that sits at the lowest of our three. So Gabe, your thoughts on our poll results here?

[00:11:32] Gabe Karkanis: Yeah, that's really interesting. I mean, there's a few people that are very confident and the rest, which are somewhat and not at all. I think if we add up the more negative side of things, I mean, that's really common. It usually means the CRM isn't fully designed around your day-to-day workflows. We often hear advisors say they're spending more time trying to organize themselves than actually getting things done. That's where automations and workflow design can really make a difference. In the end, your CRM should really feel like a teammate and not just a to-do list.

[00:12:07] Gabe Karkanis: Okay, on to the last slide here. So here at Maximizer, we're building towards a vision where your CRM becomes your operating system for advice. And what does that mean? Essentially, with our upcoming workflows and automation release that we are working on, you'll be able to set triggers like, um, when a lead becomes a client, when a KC is many days expired, or when a document is uploaded from a back-off custodian system, etc., or a new change in holdings, it can then launch multi-step workflows, assign tasks dynamically all the way across your team from the admin assistants to the advisors to other people on the team. And it'll complete those tasks one at a time. Things like compliance steps, onboarding calls, document prep, all tracked automatically. It's sort of like running your practice on autopilot, but with the full control. And I would say the real magic now really happens when this combines with our data integrations with systems like Cresis, custodian systems, back office financial systems, insurance carriers. As that data is flowing into the platform and triggering these audited workflows that can be spread across your team, that's really where automation can really begin to add immense value to the firm. So that means when a client calls, you don't just have their contact info ready. You have essentially their.

[00:13:38] David Kitai: Thank you, Gabe. I mean, this is a fascinating kind of where the industry is at now, how things are shifting, what your firm is doing to achieve shifts. I'd like to bring in Richard here to talk to a CEO about what's to come. And I hope, Richard, you can share a little bit of insight. Look ahead. What technology do you see shaping advisors most and why?

[00:13:57] Richard Owen: Yeah, I think the, actually Gabe said a couple of times, it's about automation. The biggest challenge I think, again, we talk to a lot of firms, a lot of advisors, is the amount of work they have to do that is not client-facing or revenue-generating. So that, I talked to one advisor probably about a month ago and he figures it's... between 60 and 70 percent of his time is on non-client facing non-revenue generating admin back office tasks and you know platforms like maximizer can automate a huge amount of that so the automation the way we look at ai is is ai is just a way to automate processes um it is not i don't think it's ever going to go you know for the for the science fiction fans out there i don't think we're going to get you know skynet anytime soon. But I think the power of AI is to automate those non-critical processes, and non-critical meaning an advisor doesn't need to do them. The machine, the AI can actually do that much more efficiently. It can do it 24-7. And where we look at it is, okay, so if I can use AI to drive a lot of that automation, where is that going to leverage my business or where is that going to impact my business? And what we've seen, I think, to date, is we've seen incremental changes.

[00:15:18] Richard Owen: And incremental changes basically is using something like ChatGPT or a large language model, an LLM, to help me write better emails, help me create better presentations, help me draft a document, read this document and summarize it for me. Those allow me to do emails, I don't know, 10% quicker. I've still got to write the email. The LLM can help me write a better email. That's something I'm already doing. You can make it more efficient by making it better, but that's not going to change the way I can scale my business, sustainably scale my business. So we look at sort of what's been done now, that initial rollout of those LLMs can help me do things faster. Again, emails, note-taking, et cetera. But those are fairly generic. And another thing that Gabe mentioned was out of the box. And a lot of those LLMs are out of the box. There's very few wealth management specific AI tools. That's something that we're obviously spending a lot of time doing. And I think when AI catches up with wealth management, that's when it's going to get really interesting because a lot of those incredibly manual time consuming processes in wealth management. So regulatory compliance, portfolio management, monitoring, predictive analytics around portfolios or clients. You could do them manually. It's going to take a long time to do all those calculations in real time. If you have the AI doing that, that is going to free up a huge amount of time. And I don't think it's ever,

[00:16:53] Richard Owen: I don't think it's ever going to get to the point where it's completely autonomous. But I think AI can do a lot of that heavy lifting in the background, serve that information up to the advisor in a really meaningful, focused way. So the advisor essentially is going to be at a decision point really efficiently. So if you think about something where we spend a lot of time around regulation, instead of looking at every security on a daily basis to see if there's a material change, serve me up the material changes today. Send it an email with a five, give me a bullet point on each one, and then let me click through that and see which clients own it. I still have to do it as an advisor, but the AI can organize that data. Whether it comes to portfolio management or regulatory or predictive analytics around clients, which would be more on Gabe's side. All of those things can be automated by the AI. The reason it's not being done at scale to date is, again, it's that out of the box. There is really no specifically built AI out of the box. And to understand wealth management and understand AI and understand the practice of what an advisor is doing, is a pretty rare combination if you have that kind of Venn diagram or those three things. And, you know, Copilot does a great job and ChatGBT does a great job, but they don't actually understand necessarily what an advisor is doing on a day-to-day basis. So I think it's the AI, they need to be built expert systems in AI. That includes things like neural networks and AI agents or agentic AI. Deep decision making,

[00:18:23] Richard Owen: Deep learning on the AI that is trained on market data and wealth management data that then can be used by advisors to automate those incredibly time consuming processes and get deployed in the background, that's where we see the biggest change for an advisor is a lot of these processes in the background are going to strip away a lot of those time-consuming components of their day-to-day business. From my perspective, it makes no sense. I mean, if it's like a branch manager to scroll through pages and pages and pages of trades, the AI can actually identify the trades that are potentially suspicious that need to be reviewed and serve up those nine trades to the branch manager, for example. So I think the question about kind of where is it going to go, I think you're just going to see more and more AI being deployed in the back end, which is going to free up the advisors on the front end to spend less and less time doing manual processes that don't generate revenue, don't allow them to spend more time with their clients. Some advisors spend more time on the golf course. But it basically allows them to scale. That's a fascinating view of the future, Richard. And I love the way you sort of take those incremental changes, small, you know, again, marginal pieces of evolution that cascade into something that is completely transformative. And there's an interesting kind of analogy that I think we sort of talked about before, which is the analogy of email and with that coming kind of like.

[00:19:59] David Kitai: Digitization of documents eventually moving into DocuSign and all these other things where potentially for an advisor, you know, if they were sort of working at the adopt the early adoption of email, this was an annoying fiddly process that went on in their back end that or that they now had to start integrating into their work. Now email and all the pieces that come with it are absolutely essential to the functioning of any advisor or any professional in almost any, in any setting. Um, I wonder if there is a historical analogy to this moment that you would highlight.

[00:20:28] Richard Owen: Yeah, I mean, emails are perfect. I mean, I can remember the first email I got, and it was a bit mind-blowing. But I think the analogy here would be, I don't think any advice, I certainly wouldn't want the AI writing my emails and sending them for me. I just sent out 8,000 emails this morning under my signature. Yeah, I'm not. No, thanks. But the AI generating the email and presenting it to you, these are the four emails you want to send today. I can review those and then release them. That saves me a couple of hours. So I think there's, again, it goes back to the AI can get the advisor to the decision point. They can do 95% of the work. And then the advisor still has to make that decision. That's where the value is of having an advisor, is they get all the information, the advisor is still the one that actually pushes the button, that makes that decision. And email is a perfect example. I mean, if the AI could do that, serve up here are the four emails that need to go today, they've been drafted, please review and release. Perfect.

[00:21:28] Richard Owen: So I think the changes that the advisor is going to see is going to be a lot of different places. It's something that Gabe was talking about as well around KYC. Just organizing tasks, organizing who in your team is going to do those tasks, creating content for you to review. We focus a lot on the kind of due diligence and investment process. All of those are very, very, very manual processes that the AI can do just infinitely faster, 24-7, incredibly low cost. And that, again, frees up the advisor to do things that actually generate revenue, build on client relationships or play more golf. Kind of up to them, but that's how you scale. So yeah, I think the email analogies is perfect, but it's AI is not going to replace an advisor. It's fundamental. It's a human relationship, but AI is going to allow advisors to manage clients much, much more efficiently and spend more time doing what they love, which is building client wealth, working with clients, and building a business.

[00:22:29] David Kitai: Yeah. And I would emphasize on behalf of all advisors; there are a lot of relationships that are built and maintained on the golf course. So.

[00:22:34] Peter Wloka: Correct. Correct.

[00:22:35] David Kitai: Not knocking the golf course. No, no, no, no, no. But so look, thank you for, for again, a really fascinating overview and thank you to each of our panelists for, for what has been a really interesting kind of opening salvo of this discussion. We're going to shift into the Q&A. I have a few questions for you folks,

[00:22:53] David Kitai: And then we'll also go into our audience questions. I would say to our audience, please, I see a few questions coming in. Can we please add a few thoughts, questions, concerns, ideas that you may have that you want to contribute to this conversation in that box there. To begin, and I'll have each of you gentlemen go kind of one at a time on this one, what is one adoption mistake, and you get to pick one, that you think firms should avoid?

[00:23:17] Richard Owen: Do you want me to go first? Please. Sure. I think it's the let's try and fix huge problems first. Let's go for the big problems first. As opposed to kind of that incremental, and I'm, again, not knocking, you know, note-taking. Great. Now, note-taking, like, tick that one off. That's not a huge disruptive change. I think a lot of firms are trying to sort of build the perfect solution rather than a good solution and evolve over time. You know, one of the things that a lot of technology firms do is you start off with a minimum viable product. So just start with something. Start with something and build from there. And a lot of firms that we talk to are trying to kind of do the moonshot out of the gate. And AI is changing so fast. The technology is changing. The business is changing so fast that you're going to spend two years building a product or building a solution. And by the time you've launched it, you're two years behind and you're solving something that doesn't need to be solved anymore. So I think for a lot of firms,

[00:24:17] Richard Owen: It's that we want to go fast, but we want to get it right. It doesn't have to be perfect, but I think it's the mindset to change to get it out there, evolve. It's going to change. It's not going to be perfect out of the gate, but you've got to start somewhere and build on it. So I think just a lot of firms are trying to build kind of the moonshot as opposed to just get something in market that you can actually work and evolve.

[00:24:44] Peter Wloka: Yeah. Gabe or Peter? Oh, sorry. No, no. Go right ahead. Rich and I are attached to the hip. I would say. Really, the biggest thing is, we've talked about it a lot, is identifying what the solution is and then going to go find it. Don't try and build it on your own. We've seen that. Studies have shown that 60% of the time, a self-built technology solution fails. It's more like 90% of the time that a third-party solution fails. Advisors will understand 90% of the time it succeeds. The opposite. 60% fail, 90% succeed. Advisors have a tough time firing themselves, so they buy mutual funds and ETFs and packaged products and things like that. So when a firm fails, and we've seen some recent CEO exits, and we've had some challenges in the technology space on self-built products. So I think it's super important to make sure that it's... personalized, as we've all said,

[00:25:41] Peter Wloka: But make sure that we're solving for identifying what the issue is, not a massive, let's do platform 3.0. It's part of that solution but having something that is specific and measurable and definitely personalized for a particular problem.

[00:25:54] David Kitai: Okay. And Gabe, giving you the last word here on one adoption mistake that you think the firm should avoid.

[00:25:58] Gabe Karkanis: Yeah, there's a big list of adoption mistakes that I think I've just seen over the years. I've also started my career as an advisor as well and had three years of a homegrown TRM that was really custom built and really effective because we had full control over it. But if we just try to boil it down to really one tip around adoption, I think for me, it would come down to not just using a checklist. And if you have a CRM that kind of checks all the boxes, then you... buy it and you think everything's going to be great. There's more complexity with that, especially when there's organizational change and people potentially have to change their patterns to use a CRM. And so I think the tip I would give to really avoid that is really include different roles in the CRM selection process when you're attending these demos with these providers. And I'm speaking specifically to CRM, and this could be used for other tools as well. So if you bring in the people who will be using the tool to help you evaluate if it's going to fit into their workflows or if it's not going to fit into their workflows and just gauge their reaction and take their opinion in as you make that decision, I think they can help you spot some tools that won't have successful adoption ahead of time. So really incorporating the different users in ahead of time and helping to identify which ones require too much change within their organizations to use it.

[00:27:06] Gabe Karkanis: Now there are tools like for example with Maximizer that you can actually customize it quite heavily to wrap around your workflow. And some of our competitors, the extremely large general purpose CRMs can also be customized and tailored to fit around your process and your people. But the difference is you may pay hundreds of thousands of dollars for those customizations for some of those bigger general purpose CRMs, as opposed to one where you can do those customizations on your own. So I think I kind of boil this into two tips, really incorporate all the roles and the stakeholders in the tool selection process, the people that will be using it especially, and make sure that the system, if it doesn't match up perfectly to your workflows, do you have the power to customize that tool to wrap around your workflow so that you don't have to wrap your business around the tool? That's where we see the adoption challenges. That is fascinating. The tail wagging the dog, not to maybe use the most overused metaphor in wealth technology, but that seems to sort of to sit at the crux of what you're saying. And again, I think it's a fascinating and really important insight to pull out. Gabe, I want to stay with you with this before we go back down the line across our panelists. You know, as we've kind of outlined, advisors exist in relationship-based business, and that relationship is such a key underlining of trust between themselves and the clients. And, you know, we've seen this in things like the Edelman Trust Barometer. There is more trust placed in relationships than there is in tech platforms, automation, especially when something seems, you know, behind many gates of back-end whatever.

[00:28:45] David Kitai: And I'm showing my own novice with technology here. But how can advisors, Gabe, navigate that difficulty of protecting the trust that lies in a human relationship while introducing digital and tech-based automations that are essential to the next stage of efficiency in their practice?

[00:28:59] Gabe Karkanis: It's a very important question, and it's extra timely with AI just surging and taking off and becoming commonplace. I think back to a story and I'm not sure I'm going to get these details right, but I believe it was a software engineer from Samsung. And he had put in some company information into a chat GPT prompt and sent it in there. And then on different people's accounts, they could actually get that Samsung sensitive data because it was somehow used for training the model further. So, I mean, I always go back to this example that when you send something and like you send text over the internet, especially if it's an LLM tool, um, it's almost the same thing as sending a direct email to the company with all the sensitive information and they may post it somewhere. It may become available, maybe used in training. So this is instrumental. And I think we might see a couple big catastrophic mistakes that happen over the coming years with people providing data to the wrong providers that are not necessarily as trustworthy as they make themselves out to be. So it's really paramount. And we've talked to... hundreds of our own advisors using the CRM. And we really saw this was a big concern too. And so I think each company can handle this differently. And what we do is we have our locally hosted infrastructure within Canada with AI models deployed in our own infrastructure. That way, we're not sending things off to external LLM and AI technologies. So that makes things really secure from our standpoint.

[00:30:18] Gabe Karkanis: But I think if people aren't already having this top of mind, it absolutely should be because a breach with this sensitive information could be really a devastating thing for the firm. Peter, do you mind going next on this one?

[00:30:31] Peter Wloka: Sure. How advisors, in your view, can protect trust while introducing automation? Yeah. I mean, there's a couple. I think a lot of this we've already talked about, but I mean, transparent communication. Explain exactly what you're doing, how you're using it. An example is an AI note taker. I was at a physio appointment a couple of weeks ago, and they just used it because they wanted to spend the time with me and then see if they missed anything, they can use that. So that's super important to have that transparent communication. But also creates the number two example, which is a personalized touch. You're really spending the time, you're really there for the person that you're helping. So that's super important. Number three is like explain where the data security is. Where are you getting your data from? You know, is this an open LLM or is this something that's a proprietary built AI function like ours at Buckler is?

[00:31:21] David Kitai: Okay. And Richard, if I can bring you in for some final thoughts here on navigating the difficulty of maintaining trust and introducing automation.

[00:31:27] Richard Owen: Yeah, I think I would always say that, you know, if it's internal process, and I agree with Gabe on the privacy on LLMs, there's all kinds of horror stories. You can ask ChatGPT, it'll tell you what the horror stories are. I think for any kind of automation or any kind of process, if it's internal, if it's around your team, I think, you know, it's a lot easier to adopt. You know, people are not going to be maybe as freaked out about getting an automated note or an automated email. I think anytime your interface is with a client, I think the advantage of using AI for automation is to give you more time to actually have a relation, build a relationship with the client. So I would actually do less automation client facing. I think clients value that personal touch from an advisor and then spend the time and leverage AI on those internal processes that the client's not going to see and they don't particularly care, frees up more of your time to actually build those relationships that are going to add the value and add the revenue at the end of the day. That's what it's about is building revenue. So it's kind of minimize the or don't go overboard on the automation to the client. Uh automate the back end that they don't see um just frees up much more capacity and actually just gives you more capacity um so that would be my advice was was focused on the back end rather than everything on the front end because that can get a little uh uncomfortable I think for some clients. No, fascinating. Thank you, Richard. Um, final question in in this little piece before we move into some of the audience questions.

[00:32:54] David Kitai: And please, we've got some space for our audience. We've got a few great questions coming in and a few others. So I would just encourage anyone else with thoughts and questions to please enter them. But I'll start with Peter on this one. And I'll ask that we keep it to one line answers. Now, this is the lightning round.

[00:33:08] David Kitai: If you had one piece of advice, Peter, for an advisor struggling with tech adoption today, what would it be?

[00:33:12] Peter Wloka: Okay, so I supposed to be quick. I mean, you have to engage in part of your strategy. It's table stakes, period. You need to do it.

[00:33:18] David Kitai: Beautiful. Okay. I mean, that's lay down the gauntlet and yeah, this is, it has to be done. Richard, your one line of advice?

[00:33:23] Richard Owen: I would say try using AI for at least one part of one thing, one thing per day. Write an email with chat GPT once a day. It's the easiest way to learn it, easiest way to get comfortable with it, easiest way to... That's not a one-line answer, but anyway. Use one piece of AI once a day. How do you get to Carnegie Hall?

[00:33:37] David Kitai: Gabe, a final one line of advice on those struggling with tech adoption today.

[00:33:39] Gabe Karkanis: Yeah, I'd say stay simple where possible. If a spreadsheet can solve the problem, stick with it. But as that hits the walls, as the team grows, then... upgrade the technology at that point in time. So don't over-engineer too early.

[00:33:50] David Kitai: Okay, fantastic. Well, thank you for that. And now we're turning to our audience questions here. And again, there's still space to ask them as needed. But first question that's come in here, is AI being integrated to be self-contained within these tools, or are they connected to the publicly available systems?

[00:33:59] David Kitai: And then in parentheses, considering personally identifiable information and security and compliance advisors deal with. Public tools are not necessarily secure. They advise against including sensitive data during use. Maybe Gabe, if you want to start with that one.

[00:34:10] Gabe Karkanis: Sure. And I guess I actually already partly answered this. So I think you definitely want to be careful about this. Read the privacy policies, get your IT or your technical person involved as you're making these decisions so that you can understand how these companies, what they're doing with your data, where it's being stored, where it's being sent. Absolutely, you can host. Some of this stuff like we've done. We've used some of the existing models and our own infrastructure to do this, or we're not just sending stuff to the servers in the USA, for example. But sometimes that may not be a concern if you're doing a task where there is no PII. I think the most important thing is knowing what you're okay with in a particular use case and then getting the right technology solution for that particular use case with their security demands.

[00:34:47] David Kitai: Thank you. Peter or Rich, do you have any... thoughts on that security question.

[00:34:51] Peter Wloka: Richard, I'll leave this one to you.

[00:34:53] Richard Owen: Sure. Yeah, it's really important. I think it was Gabe that said, you know, whatever you send, it's like sending it as an email. It really is. The kind of the horror stories out there about whether it's source code, let's load it into ChatGPT and say, okay, you've just basically mailed that to everyone on the internet. So I think there are some things that are... If you just think it through logically that are fairly safe, if you wanted to interpret an analyst report on Apple, like give me this in 150 words, it's public document, it's public information. I would be very, very careful around uploading a client statement. Very different, and that information is now public. So I think the privacy, just be very careful about how you're using it. Unless you know exactly where that data is being stored and how it's being processed. I would not trust any of the public LLMs with anything that is not generic public information. Just... It's, it, you don't know, well, you know, that's just going out to the web. So, yeah, I would, I would go back to, yeah, check with your IT department, be very careful about it. Most of the applications, you know, with, with Maximizer, with us at Buckler, our AI is private and proprietary. It does not go to the public website. We do not pull public data and the data stays private. That's, that's the easiest way to control it. But yeah, I think just be careful and, and, you know, do your research.

[00:36:21] David Kitai: So the other question that I've got coming in here, automation of workflows relies heavily on information brought into a CRM via plugins. This often requires manual updating, filling in relevant details, deduplicating entries. A key issue appears to be the non-scalable underlying architecture in industry-leading CRMs.

[00:36:39] David Kitai: A lot of clicks, a lot of extra navigation, limitations on use. It might work great for two to three people, but it falls apart when the business grows to six-plus people. And workflows shift to pool tasks on more flexibility. Phrase more as a comment than a question, but this issue of scut work, data entry, this may be becoming more labor intensive than the tasks that it was meant to make more efficient. How do you folks engage with that issue and how do you address it in what you're providing to advisors?

[00:37:13] Gabe Karkanis: I can take this here. I mean, very interesting question, by the way. I feel like whoever asked this really has gone down this road. They've seen a few of the pain points and, you know, we're working through some of these pain points with some really clever solutions right now. So if I just quickly summarize, the question is about as you're bringing data into the CRM, it looks like the person who asked this question may have some more of a manual upload process from what I'm understanding. And then there's things like duplicates and then it begins to fall apart when you're trying to assign a task. But let's say, what if... your admin assistant is on vacation and then the other admin assistant is not given this task because they're not assigned to it. So a couple of interesting things here. I'll just mention two things. The first one is as we're building out our technology and our synchronization engines for pulling data in from external sources like custodian systems, holding dealers, investment carrier, sorry, insurance carriers and other tools, we are developing a pretty sophisticated mapping strategy where you can, it really looks for things along the lines of birth dates,

[00:38:14] Gabe Karkanis: First name, last name, fuzzy matching. And it does a pretty good job of matching up as much as possible. And this is with automated synchronization, bringing data into the CRM. So not necessarily the manual upload process. So that's how we're tackling that. And then of course, as good as the algorithms are, there's always going to be a few records that weren't able to be harmonized. So that's where you got to just resolve that in a manual interface. But that should only be a couple per week, not overkill. So if you're having a lot more than that, then that's where I think there maybe is some opportunity for improvement in that synchronization pipeline that you might have going. But on the other side of things is the workflows. And this is really interesting. So with our system we're developing, you can set up roles within Maximizer. Let's say administrative assistants, or let's say client scheduling, and you can add multiple people to those quote unquote roles. And so at that point in time, it's less focused on one person being in the office, because let's say the administrative assistant, let's just say his name is John, is out on vacation. Well, then that task gets dropped for two weeks. So that's how we're handling those two things. Like we have some synchronization logic and an interface where you can handle the duplicates as well as automated synchronizations with custodian systems, holding dealers, et cetera. And lastly, connecting those to workflows with the role-based assignments. So that's one of the breakthroughs that we're championing on the workflow side of things.

[00:39:32] Gabe Karkanis: As far as we're aware, very few other CRMs actually provide that. Anyways, that was a bit of a long answer, but a very interesting question.

[00:39:40] David Kitai: No, it was a fascinating answer. Richard, anything to add on this one?

[00:39:43] Richard Owen: No, I think Gabe is definitely the expert on that one.

[00:39:45] Peter Wloka: Okay, beautiful. Maybe I can just jump in. I mean, obviously we're not a CRM, but we do aggregate notes for teams as well. So you can upload, download the notes into Buckler so that you could share it as a team. We do some very similar things.

[00:39:53] David Kitai: Gentlemen, unfortunately, we are coming to the end of our time. I just want to say thank you so much for these absolutely fascinating insights and just bringing, again, this range of experience, wisdom, and insight into this discussion, which I know is going to go on for a long time. We're going to keep having conversations like these, but I always really value them. And thank you to all of our attendees. Really, really appreciate your time, your questions. A reminder that the recording will be shared with you. Thank you for your attendance and have a wonderful rest of your day.