The Hidden Work Around Financial Advice: How AI Is Reshaping Advisor Capacity and Workflow

[00:00:06] Manal Ali: Good afternoon, everyone, and thank you for joining us today for this session, The Hidden Work Around Financial Advice, How AI is Reshaping Advisor Capacity and Workflow. I'm Manal Ali, Senior Sponsored Content Specialist at Wealth Professional, and I'm thrilled to guide you through a practical, no-fluff conversation about where AI is genuinely useful in advisory work, and where it isn't. We're going to look at how Canadian advisors are actually reducing the operational brag in their practice without weakening trust, compliance, or client experience. So, before we dive in, just a few housekeeping notes. Feel free to fire away any questions in the Q&A box during the webinar. We will do our best to answer all of them during the Q&A portion at the end. And this webinar is being recorded, and the recording will be emailed to all registrants following today's session. So, without further ado, I'd like to introduce our speakers.

[00:01:07] Lewis Dyson: Thanks, Manal. I'm Lewis Dyson, Product Marketing Manager at Maximizer. We're a CRM company specifically for financial advisors, and I spend a lot of my time talking to, talking with advisory teams about how they're using technology in their practice. Over to you, Taylor.

[00:01:30] Taylor Campbell: Well, hello everybody! I'm very excited to be here today. I have been working in the tech space as a salesperson for about 6 years as of this Friday, with a specific focus on financial services in the last 2. What I spend most of my time on is actually helping financial services teams fix the gap between having a CRM and using it effectively in their practice. So today, I get to walk through some of the practical ways that teams are simplifying their processes and getting real value out of their systems with AI.

[00:02:04] Manal Ali: Amazing, we're lucky to have access to the both of you, thank you. So, let's get into it. Louis, every advisor I speak to seems to start in the same place, this kind of feeling of being busier than they've ever been, but not necessarily more productive where it matters. So where's this time actually going, and what can AI realistically do about it?

[00:02:27] Lewis Dyson: Yeah, thank you. Yeah, that's a great place to start, Manal, and it's the number one thing I hear, and I'm sure a lot of the audience today can relate to this. You know, advisors are busier than they've ever been. But that doesn't, you know, necessarily mean that they're more productive in areas that matter most, so that would be things like client relationships and delivering strategic advice. And that's the tension we're gonna get into, today, so… here's what I've found from, you know, talking with advisory teams across Canada. Most advisors are losing work, losing time to work that is invisible. So there's the kind of things that clients don't tend to see. And here are some examples of where advisor time is actually going.

[00:03:20] Lewis Dyson: First, we have pre-meeting prep, so… before, you know, a quarterly review, annual review, most advisors are opening up multiple tabs, your CRM, your custodian portal, recent emails, your financial planning software. And stitching everything all together by hand, you know, that usually takes around 30 to 45 minutes of work before the client even walks in the door or picks up their phone. The second area is, you know, everything that happens after the meeting, so post-meeting documentation. There's another 30 minutes or so of work that, you know, isn't accounted for, so that's things like writing up notes from the meeting. Sending follow-up, emails, or updating your CRM notes, logging things for compliance. Third, you know, and on that note of compliance, third is, is compliance itself, so KYC reviews, documentation, keeping everything, audit ready. And the fourth bucket is follow-up admin, so that's just things like reminders, Chasing documents, scheduling things, reporting. And status updates, you know, it's the stuff that…fills the gap… gaps between meetings. None of it actually earns client trust, none of it actually moves relationships forward. And it's… and AI can really take a chunk off of your plate, in these areas, which is exactly what we're… we're going to get into today.

[00:05:03] Lewis Dyson: So, what can AI do for you today? So, when I look at the advisory teams that are, you know, getting this right, their approach tends to break down into three parts pretty naturally, so… They are automate the tasks. use AI for the insights, but keep the relationships human. So, let me just walk through what each of those looks like in practice. So, the first area there, automation. This would be the things, like the repeatable, low-risk stuff, things like meeting summaries, AI note-takers. KYC reminders, Document filling, calendar scheduling. It's the kind of work where the answer is usually the right… the answer's usually the same every time, and the advisors I talk to who've automated this are usually… they're usually a little bit annoyed that it took them so long to do it. The second area, you know, is… is really interesting… is where things get really interesting. So this is using AI for insights. So that's things like segmentation. Life event detection. So, you know, being able to predict when there's a change in someone's life, life event.

[00:06:32] Lewis Dyson: Flagging clients who, you know, maybe you haven't spoken to in a while, or maybe, you know. are showing signals that they're at a potential risk of, of moving elsewhere. So, the key thing that I see teams, using this in a really effective way is, you know, that AI tells you what to look at, so… it's really good at signaling, finding connections, hidden connections, and telling you what to do about it. So that distinction, you know, really matters. And the third is… you know, keeping the human in the loop, so… there are certain things where AI You know, really shouldn't go, so… For example, things like sensitive topics, or, you know, Discovery compensation is financial planning. You know, say a client calls you about a spouse passing away, or they're going through a divorce. you know, these are conversations that earn trust, and AI really has no business being anywhere near that. And from what I've seen of the advisors who are using AI well, There's a very clear line. On where… they're very clear on where that line sits. And now I'm gonna pass it over to Taylor for some more about AI's limitations.

[00:07:59] Taylor Campbell: Thanks, Lewis. So, so far, what we've covered is where AI can show up in your practice, but it's really important, and I would argue just as important, to be clear on where it doesn't belong. So, in three buckets here, the first is job security. What we're seeing in the market is that AI removes your operational drag, but it's not about replacing advisor judgment. The firms that are doing this really well aren't cutting headcount, they're just reallocating their time more efficiently and more effectively. Advisors are able to spend more time with their clients and go even deeper into those relationships. The second is client relationships. AI shouldn't sit between an advisor and a client. The trust that your clients place in you isn't something that you can just hand off to a model. That part needs to stay entirely human. And third, sensitive conversations and final advice, touching back on what Lewis had mentioned. It's a hard line. When it comes to nuanced decisions, emotional conversations, or delivering advice, you, as the advisor are the subject matter expert. It's important that you stay human and have that voice with your clients, and there's no gray area here.

[00:09:16] Taylor Campbell: So with these boundaries in place, let's take a look at where advisors are actually using AI in a meaningful way. So, beyond tools like ChatGPT and basic note takers, there's 5 areas where I'm consistently seeing AI show up with my clients in Canadian advisor practices right now. The first is meeting transcription and summaries, and it's a really common entry point, I'd argue to say probably the most common entry point. Note takers over the last even year have gotten significantly more advanced and easier to use. They're now accurate enough to use in a client setting. Second is pre-meeting context. AI can pull together a full client brief ahead of your meetings, combining your CRM data, your custodian info, past notes, emails, planning tools, all into one view. So that you can then have the third structured document extraction. It's a big one. Pulling key fields out of things like your insurance policies, custodian PDFs, or statements used to be fairly unreliable, and it's advanced quite a bit in this last year. It's actually now very good, saving teams a lot of manual effort.

[00:10:31] Taylor Campbell: Fourth, and this is probably my favorite one to speak about, I'm a little biased here, but is the CRM data search with timeline summaries. Being able to ask your CRM a question in plain English and get back a summary built only from your client data. There's no extra digging, no clicking through records, just to get a summary. And fifth, segmentation and next best actions. So, AI is able to now surface which clients to reach out to, when to do it, and why, based on signals that already exist in your day-to-day interactions that live in your CRM. So, these things that we're talking about now, in 2026, aren't just theoretical anymore. They're practical, day-to-day use cases that teams are already adopting.

[00:11:20] Lewis Dyson: Thanks, Taylor. And before Taylor takes us into some specific workflows, I just want to pause on something that we can't skip over, and that's compliance and data. So… one of the advisors we work with has a line which I think really resonated with me, and he said that AI is an amplifier, so… it amplifies the good, and it amplifies the bad, and that's exactly right. If your underlying data and processes are in good shape AI accelerates everything. If they're not, AI just… you know, if your data is a mess, AI will just amplify the mess. So… Let's talk about what good looks like on the compliance side, and then we'll get into some practical examples.

[00:12:14] Manal Ali: Hey, yes, Lewis, that's the perfect brief. So, can you kind of walk us through the data and compliance side? What should every Canadian advisor in this audience be asking before they put any AI tool, like, near their book?

[00:12:33] Lewis Dyson: there are 3 questions I'd encourage everyone on this call to ask before they put AI near their book of business. You know, there aren't… these aren't technical questions, they're more business questions. First question is, where does my data live? So, what we're seeing is Canadian data residency matters, so… We all know that, you know, advisors face, regulations and restrictions about where client data can be processed. So, in plain English, what that means is, if your client's data is sitting on a server in another jurisdiction, like, say, the US, That's potentially a question for your compliance officer. Second question is, what is my data used for? Is it being used to train the AI platform itself, you know, or is it being used for fine-tuning, or is it what we call inference only? So that means that AI… Uses the data to generate a response, but that's it, nothing more.

[00:13:43] Lewis Dyson: So, the honest test here is, if an AI platform can't tell you, in plain language, whether your data is being used to improve their product assume the answer is yes, and always ask for it in writing. And the third question is, who can see it, and how is access being logged? So…Audit… you know, staying audit-ready shouldn't be enough for… it should be a natural byproduct of your day-to-day work, so… because, you know, this is something that regulators ask about, you know, not necessarily what… which AI model you use, but who touched the data and when? So, if your vendor can… answer… These three questions on the screen, clearly and in writing, you're in a good place to start having some more conversations.

[00:14:37] Taylor Campbell: And those are questions, Louis, that…if I'm thinking about even how I personally use AI in my day-to-day, those are the questions I'm asking. And in Canada, we have a really interesting landscape. There's…financial services teams who are able to just pick their own tools and not have to worry too much about head office and approvals there. There's teams who really need to go through their head office to make any type of decision when it comes to tech. So, I think just putting those three questions that you mentioned in practice up front, making sure that we're using those daily great, great practice for… for bringing AI in. But for the teams, and I work with certain head offices, we're thinking about governance inside the firm as well. So depending on where you fall in this bucket, you might have to answer through head office, or there might be some folks from head offices on this call. And let's talk about what that actually looks like inside of firms. At high level, the teams who are doing this really well are putting very clear boundaries in place.

[00:15:38] Taylor Campbell: First, around how AI is being used. It means being explicit about which workflows it's a part of, which tools are being approved, what data is being used, and sometimes even which client segments it's applying to. Not everything needs AI, and not every use case should just be turned on by default. And second, boundaries around data access. Not all client data is equal, it shouldn't be treated that way. The best teams are defining tiers. What's safe to use in AI-supported workflows, and what needs to stay completely restricted. And third, again, both professionally and personally, always having clear boundaries around human oversight. For anything client-facing, review and sign-off should be the default. AI can support your process, but it shouldn't be your final voice. And I'll give you a quick example of what design looks like here. Inside of Maximizer's AI hub called IQBoost, there's an anonymizer feature that strips out client-identifying details before any AI-generated summary is created. And whether you're using Maximizer or a different solution, that's really the standard you should be looking for. Vendors that are designing for Canadian data sovereignty Not trying to work around it.

[00:16:57] Lewis Dyson: No, that's a great point, Taylor. And now, you know, let's get into the part that I think everyone's been waiting for. Taylor, with those guardrails in place, what are advisors actually doing with their AI, in their day-to-day right now?

[00:17:15] Taylor Campbell: Yeah, it's a great question. I've been working with clients for, again, almost 6 years now, and I'm seeing, 3 different things happening, and not just in theory anymore, they're truly happening in practices now. Workflow 1 is in pre-meeting prep, or your rapid context building, heading into meetings with your clients. Before AI, the classic advisor night before routine was opening 5 tabs. Your CRM, your custodian portal, last meeting notes, recent emails, planning software, texting your assistant. Just to piece together the context that you need to have an effective meeting the day after. And that's about 30 to 45 minutes of work for a single meeting. What's changing now is that all of that information gets pulled into a single auto-assembled client record. The advisor still reviews, still applies their judgment, but you're not taking that time and doing the assembly work anymore.

[00:18:16] Taylor Campbell: We've seen this translate to roughly 30 minutes per meeting saved. Multiplied across a full review cycle, that becomes material very quickly. And I'll give you a real example of this. An advisor that we worked with joined a new firm and inherited 62 client accounts that he had never met. He had a couple of weeks before retention conversations started, and he used the timeline summaries within Maximizer to get up to speed quickly, and was able to retain 100% of those clients. And that's the real unlock here. AI that only works off of your client data, not the open internet, and that's what makes this safe inside of a Canadian practice. So if you would like to get started this week, I would say pick your next 3 client meetings and actually time you how long it takes to prep per meeting manually. That'll be your baseline, and where you know that you can start doing some additional time saving.

[00:19:15] Taylor Campbell: The second workflow that I'm seeing my clients use is in their post-meeting documentation, and even for myself, I'm seeing this. And it's where we're seeing the biggest immediate time savings. So, your meeting happens, a note-taker captures that conversation, and AI generates a structured summary that then all flows back into your CRM record, and your compliance log updates automatically. The advisor then spends 5 minutes reviewing and signing off, instead of 30 minutes writing their notes from scratch, trying to remember what happened in that meeting, potentially missing something really important that you discussed. And I want to be so clear here. Advisor is still involved. It's not a set-and-forget method. It is a set and confirm. And what's really going to add value to this is if your note-taker and your CRM are actually connected and working hand-in-hand. If you want to test this, what I would say is, write in your next 5 client meetings. Compare the AI-generated summary to what you have written. You'll know very quickly whether or not that's usable in your practice.

[00:20:21] Taylor Campbell: The third and final workflow that we'll discuss today is segmentation and next best action. I'm sure you've heard of this in the industry, but it's your Monday morning question. You open up your computer, you're drinking a coffee, and you want to start your week with understanding what clients you haven't talked to in 60 days. How many of them are actually showing signals that something is changing? AI can help surface that list using signals that, again, already look at data that you have inside of your CRM. Things like your last contact date, balance changes, calendar activity, sometimes even custodian updates. The key point here is the advisor is still deciding who to call. AI is not reaching out on your behalf, it's just prioritizing your attention. And this works, again, really nicely when your data is connected. Your CRM has to be your source of truth. Otherwise, your AI's working with half of a picture, and you end up with half answers. And, I'm sure that as advisors, you all know, a bad recommendation will cost your client trust faster than no recommendation ever would. So if you want to test this workflow this week, ask your CRM one simple question. Who have I not contacted in 60 days? If you can't get that answer cleanly in under a minute, then your first project likely isn't AI, it's probably your data hygiene.

[00:21:48] Manal Ali: Those are 3 really great practical examples, thank you. So, before we kind of go into questions from the audience, I thought I'd put… we do have a few rapid-fire questions for both of you. So, what's the single biggest mistake that you see firms making when they roll out AI?

[00:22:09] Lewis Dyson: Yeah, I think I can take this one. So, I'd say rolling out AI before fixing the data underneath it. I know we keep coming back to it, but it's the thing that we see trip firms up time and time again. you know, AI is an amplifier, so if your CRM is… Half populated, or your processes aren't documented properly. Ai doesn't paper over that, it just…amplifies it, so I think it's really important to get that foundation right first.

[00:22:50] Taylor Campbell: Yeah, and Louis, I'll piggyback on that one with you as well. I know that with AI, it's really easy to get really flashy right away, but I think the question that needs to be answered is, is this AI tool solving the what we have the most pain around. So, starting with that documentation, with that clean data, we want to make sure we start there. So, you get really quick wins with low risk, and then your team gets to trust everything that comes after that.

[00:23:20]Manal Ali: Got it, yeah, that's a great distinction. So, how do advisors introduce AI without their clients feeling like, you know, a sense of they're being handed off to a machine?

[00:23:31] Taylor Campbell: Yeah, I'll start… I'll start with this one, Louis, but, I had a conversation with a client recently, and she was asking me about, like, same thing, how do I use AI in my practice, but also have my clients know that this is happening. So, my response is always just tell them, be candid with your clients. Most of them don't mind that AI is helping you prep for their meetings. What they mind is being surprised later down the line. Your disclosure will always beat them discovering it in a surprise moment later down the line. what this client and I had discussed is, when you're onboarding documents, why don't you put in a short line about what tools you're using and AI that you're using, and that they can be aware of in that journey with you? Lewis, what do you think?

[00:24:20] Lewis Dyson: Yeah, absolutely, and just to… you know, echoed that, like, AI should never really… It shouldn't show up in those important moments that build trust, so…It can do the heavy lifting for you, it can do things like prepping for meetings, or the documentation after the meeting, but the actual meeting itself is still human, the advice is still human, the strategy is still human, so… That client relationship is yours, you're the subject matter expert, and you shouldn't blur that line, you know, make…Make sure your clients know that you have them.

[00:24:59] Manal Ali: Got it. And Taylor, you did give us some really great assignments, but if an advisor watching this can do one thing this week, what should it be?

[00:25:08] Taylor Campbell: Yeah, well, giving assignments, that's what I like to do on my.

[00:25:11] Manal Ali: But I would say…

[00:25:13] Taylor Campbell: If it's me, and I'm an advisor, I would…look at my top 3 clients, and I would actually just call them and have a conversation with them about how you're thinking about putting AI into your practice. You're gonna learn more from your top 3 clients, engage that temperature, more than even having a conversation with me. So, that's where I would start. Chat with your clients about it, see what their… what their temperature is there. Lewis, what do you think?

[00:25:39] Lewis Dyson: Yeah, have a conversation with your top 3 clients about how you're thinking about using AI in your practice, and you'll probably learn a lot from those conversations.

[00:25:54] Manal Ali: Got it. So, we've had some questions come through, and let me just get into that. So, if a person… the firm only allows one specific AI chatbot, and it isn't integrated with email, CRM, etc, but still limited, not good at extracting, interpreting CRM data, how do we then keep up?

[00:26:18] Taylor Campbell: My recommendation, as somebody who works with teams that have to report to head office is to have those conversations with your head office, and to understand what AI tools you want in place, you can do that pre-work for them as well. So those questions that Louis had asked earlier about, those three questions, where does my data live, what's going to happen with my data, and who has access to it, as well as some of those government… governance questions. Do the work up front. You can present that to tech… probably your business tech consultants at head office. But they need to hear from you, if there's something that you think will work better, and do that pre-work for them. And then, if you have an AI tool. oftentimes, the person that you're working with at that company is… is, the person who's responsible for having those conversations anyways. That's their skill set. So I would involve them in that conversation as well. Louis, do you have anything to add there?

[00:27:16] Lewis Dyson: No, yeah, I think you answered that really well.

[00:27:20] Manal Ali: And so, I think for advisors, financial advisors, it's probably top of mind. So, should you use general-purpose AI tools, or focus first maybe on finance-specific AI tools?

[00:27:33] Lewis Dyson: I think… I have just something to say on this, Taylor, before I let you, if you had a response, but…This is, like, a really, really good question. What we're seeing now is a real shift from, kind of, general-purpose AI to what we call vertical AI, so that's, like industry-specific AI tools. And some of the benefits of using those more specific, you know, financial advice… financial services AI tools is, you know, it is more specific. It speaks your language.

[00:28:12] Manal Ali: Yeah.

[00:28:13] Lewis Dyson: That context around…you know, why you may be asking a specific question, so it knows to tailor the response to you. So, you know, I would definitely say there's a lot of benefits to using more specific AI tools, like, and just to give you an example, you know, again, so the AI tool that we have on our platform, IQBoost. That is really only, the data that it is analyzing is the data inside your CRM. It's kind of… it might sound less powerful to do it that way, but when you restrict the data it's being trained on, it actually enables you to give you more Specific, personalized responses. So, in, you know, a relationship, an industry where relationships are so important that's definitely, like, a big plus than, say, if you were to go onto ChatGPT, you would get, kind of, more generic advice.

[00:29:21] Manal Ali: Taylor, did you want to add to that at all?

[00:29:24] Taylor Campbell: Yeah, it's… again, it's a great question about the general purpose AI or finance-specific. And following Lewis, you're… you're always going to get the most value out of something that's built and tailored towards you. We're also in a really interesting time where there's not…there's a lot of general AI tools, but they're not all specific to finance. What I think is the more important thing is keeping data Canadian. That's a really, really hot topic for a lot of my clients right now. A lot of even their clients are concerned where their data is, so I would say, first, look Canadian. Second, if you can get it personalized to your line of business, then that's where I would go next, but Canadian first.

[00:30:13] Manal Ali: Great, thank you for that. Yeah, I think those are really important distinctions, and we do have another question. So, they're going into the enterprise chatbot we have is localized and not connected to the cloud, for example, not used For training data, the firm still has a policy not to upload any sensitive client information. So I know, Taylor, you went into this a little bit, but… so they're saying this limits the usefulness of the AI and constrains us from automating anything client-related. And what would you be… what would your thoughts be on that?

[00:30:45] Taylor Campbell: Yeah, my thoughts are that sensitive client data is… would be my number one priority here. And to anonymize that data before you use any type of AI to give you any type of response. My thoughts on it are you can still get very useful, good answers, even if your data is anonymized and stripped, and it should be. Especially if your head office is giving you those rules and putting those in place. There's a reason that they've done that. They've done their due diligence, and this is the decision that they've come to. So I think it's a… it is a fine line of, hey, this is what the firm wants, and also, we do need to respect our client data sensitivity. Now, as far as it limiting the usefulness of the AI, I would be curious to know, and again, get in touch with me after, whoever asked this question, feel free to get in touch with me after this webinar, because I would be curious to know what exactly you're not finding the benefit and the value of through AI, even if we anonymize that data. Louis, what do you think?

[00:31:54] Lewis Dyson: No, yeah, I think you've hit the nail on the head there. Nothing to add.

[00:31:59] Manal Ali: Great, and another question is, is AI something only larger advisory firms can realistically implement? I think there's probably a cost factor, or is there a path for smaller firms and solo practitioners?

[00:32:14] Taylor Campbell: I think there's a huge benefit for anybody looking at this. Actually, some of the people who spend the most time on admin are the smaller teams that I work with. So if we can try and reduce, again, some of that operational drag by using really great AI tools put in place. I think everybody can benefit from that.

[00:32:34] Manal Ali: And what do the regulators actually say about AI and use in client-facing contexts? Are there rules yet? Or, like, broader rules, or is it still the Wild West a little bit?

[00:32:47] Lewis Dyson: Yeah, I like that analogy of the Wild West. It still is, you know, AI seems to be everywhere, but it still is in its infancy in a lot of ways. Ciro hasn't published any AI-specific rules yet that I'm aware of, but the, you know, existing documentation…obligations, you know… Staying audit ready, that kind of thing still applies. And I guess, you know, the question you have to ask yourself is if you wouldn't be comfortable showing… regulate how a decision was made, then don't use AI to make that decision.

[00:33:36] Manal Ali: And we do have one more. So, how do you handle the situation where AI produces an output that's wrong? Like, who is liable in that sort of situation?

[00:33:50] Taylor Campbell: I would say, because I've experienced that before, where I've asked for output and it's been wrong, I typically regenerate a response, but I'm always in control of that. I'm not just sending out responses and copying and pasting and doing that. It's my responsibility, just like it's the advisor responsibility, to review that answer and make sure that it's accurate before you send it out to your clients.

[00:34:15] Lewis Dyson: Yeah.

[00:34:16] Manal Ali: I want to add… yes, sorry.

[00:34:17] Lewis Dyson: Just to add, you know, like we say, that it's really important to keep the human in the loop. AI should never be a replacement for actual financial advice, or it shouldn't give financial advice, Directly, but it is a health tool nonetheless. And it just helps you To… to do what you do. In, like, a more scalable way.

[00:34:45] Manal Ali: Okay, we have another one come in. So, yeah, this one is also a top of mind for myself, because there's so many vendors now out there. What should we actually be looking for when evaluating AI vendors, beyond kind of the three questions Louis mentioned?

[00:35:05] Lewis Dyson: Yeah, I'd say… Look for vendors who can explain their data handling. In plain English, you know, without the need for hiring a lawyer. Look for… Design choices that… that…protect your clients. No, so what we were talking about, like, things like anonymization, security, access controls, audit logs, And as Taylor was saying, you know, ask for references from other Canadian advisory firms, you know, see what other teams are doing, and what tools they're using, and…yeah, if… if you… if you're not hearing anything, supporting that, I think that… that tells you… tells you some.

[00:36:00] Manal Ali: And…Not that the fear factor is quite strong. So, another question we've had come in, just because in terms of job security, will AI give investors more power to invest on their own then? Because, you know, we have the rise of DIY, and, like, could this even reduce the demand for financial advisors?

[00:36:21] Taylor Campbell: I can… I can speak to that, because I work with advisors who… listen, the advisor space itself has been highly competitive for years, right? You're either investing.

[00:36:31] Manal Ali: There's a dearth, yeah.

[00:36:33] Taylor Campbell: Exactly! There's always been high in that space. We've even seen, right, again, in the last 5 years, and I'm sure everybody's gonna groan when I say this, but, Wealth Simple. That's a tool that's come up, and the clients that I'm working with haven't found that they're losing too much business to Wealth Simple, because…

[00:36:52] Manal Ali: That's a good starting point, yeah.

[00:36:53] Taylor Campbell: Exactly. They're building those relationships with their clients. I think what we always need to remember at the end of the day. You are the trusted financial advisor in their life. You're not just somebody who wants to handle money, and you're just asking your clients for that. You're the person who they call when they're anxious, when they are nervous, when there's really big life events happening for them. That's your opportunity to really build some goodwill with your clients. And from that, what we see, and what my best advisors see, is not only do they have these long-term relationships with their clients, where they then ask them to invest more, and ask them for more policies, but they're also opening up their network and saying, hey, I had a really great experience with this advisor, you should also go to them. So I think as long as, as an advisor, at the end of the day, what you're focused on is your clients and how you best serve them. That's the heart of your business. That's how you're going to retain your clients. And sure, there might be people who look at this AI and they do whatever, but. I think that the relationship's at the heart of it, and if you have those strong relationships, I don't think there's gonna be too much impact that way, but that's… that's just my opinion.

[00:38:07] Manal Ali: Yeah. Louis, did you want to add to that?

[00:38:09] Lewis Dyson: No, I 100% agree. Like, it is a hot topic right now, you know the impact that AI is going to have on any number of industries. And I do think, like, as Taylor was saying, that it's just gonna make, that… Trusts. That much more important, and… I think, at the end of the day investors are gonna really want to invest in something that they can trust, so… yeah, I just think it just makes it that much more, critical.

[00:38:48] Manal Ali: Yeah, and I think in financial services, people don't realize, or they forget, there's a huge succession thing coming along. There is a bit… actually a huge need for financial advisors, so, you know, for at least this industry, the fear factor should be low. So I'm just gonna ask one final question before we wrap. We have, should I disclose my use of AI to all clients, and to what extent? I just thought that was a little bit important.

[00:39:14] Taylor Campbell: Yeah, I think getting back to that, example I used earlier of my clients She was really trying to understand how to do this. I think it's really important to disclose the use of AI to your clients. And to what extent? You don't have to sit them down and walk them through your computer and your day-to-day, what you do. But I think if you have a clear understanding, and you should, of what AI tools you're using, where that data's sitting, again, going back to those three questions, and you have really great guardrails in place for how you're using AI, and you're confident in those answers. I think a 2-3 sentence or even small paragraph in onboarding documents, and in your meetings with your client… your existing clients, and just letting people know this is how I'm using AI in my business, I think that's… that's really candid, and I think your clients will appreciate that more than anything.

[00:40:08] Manal Ali: Okay, amazing. Sorry, Louis, did you… you're good with Taylor.

[00:40:12] Lewis Dyson: Yes.

[00:40:12] Manal Ali: Yeah, yeah. Right. Yes, thank you both so much. I think we're extremely lucky to have been on this. I think it was a great, session, so… Thank you to our speakers for sharing your expertise on where AI is actually useful in advisory work, and more importantly, where it isn't. So, you will all receive a recording of the webinar, and have a wonderful day, and thank you so much for joining us.

[00:40:38] Taylor Campbell: Thanks, everyone, so much! Chat later!