Executive Summary
For financial advisors, maintaining accurate and comprehensive client meeting notes has long been a core – albeit time-consuming – component of effective and compliant practice management. While many advisors acknowledge the value of documenting what was discussed and agreed upon in client meetings, in reality, there is a difficult tradeoff between taking accurate notes while also staying fully present in sometimes emotional or complex conversations (or alternatively, 'brain dumping' notes after the meeting, which relies on the advisor remembering everything discussed in the meeting clearly enough to write an accurate meeting note). But with the rise of AI-powered meeting note tools, advisors may no longer need to choose between these competing priorities. Yet, even as these tools promise to transform how advisors handle meeting notes and follow-up tasks, they also introduce new risks around data privacy and output accuracy that fiduciary advisors must thoughtfully manage.
AI meeting notetakers seek to eliminate the inherent tension between focusing on client conversations and capturing detailed notes by automatically transcribing the meeting dialogue and summarizing key points and follow-up items. These tools – whether general-purpose (like Fathom, Fireflies, or Zoom's AI assistant) or industry-specific (like Jump, Finmate AI, or Zocks) – aim to generate structured, actionable records of meetings that advisors can use for compliance, follow-up, and CRM documentation. When functioning properly, these tools can (at least in theory) not only improve advisors' efficiency by automating much of the follow-up work from each meeting, but also improve the quality of advice and implementation by helping ensure that nothing slips through the cracks.
Still, these benefits come with tradeoffs. First is the risk of the AI tool inaccurately transcribing or summarizing the meeting. Although most AI tools are highly accurate in transcribing meeting dialogue (with many achieving near-perfect dictation), they can struggle in other ways. For example, unlike human notetakers, AI models generally don't detect sarcasm, emotion, or nonverbal cues – limitations that can result in critical misunderstandings, such as incorrectly recorded recommendations or missed follow-up items. Advisors must therefore be vigilant in reviewing and editing AI-generated notes to ensure accuracy and completeness, particularly when tasks are auto-generated and delegated downstream to a team. Moreover, advisors can improve AI effectiveness by using clear and unambiguous communication and confirming key decisions during meetings (both of which are best practices even when the advisor isn't relying on AI-generated meeting notes).
Data privacy and management also present risks for advisors using AI meeting note tools. Because AI tools often capture and process sensitive client information, advisors must carefully evaluate how these platforms store, share, and secure that data. Some tools offer more privacy-conscious options (like user-controlled data exports or real-time note generation that doesn't store audio) while others may ingest data from outside sources like CRMs and planning software to enhance their capabilities. Regardless of the feature set, advisors should seek client consent before using AI recording tools, especially in states that require dual-party consent for meeting recordings, and assess whether a tool's data practices align with their firm's security and compliance standards. Conducting due diligence on which data is collected and how it's used can help advisors match a tool's capabilities and data collection practices to the requirements needed for it to actually do what the advisor needs it to.
Ultimately, the emergence of AI meeting note tools reflects a broader evolution in how technology can support financial planning professionalism. These tools offer advisors – particularly solo practitioners or those without extensive support teams – a more cost-effective way to improve documentation and reduce operational friction. But like any tool, their effectiveness depends on how well they are implemented, reviewed, and integrated into an advisor's workflow. All of which means that advisors are best served with a cautiously optimistic approach toward AI notetakers, with both a clear understanding of the tools' limitations and a recognition of how they could, if used properly, raise the bar for the quality of advice they give. Because, as with all planning tools, the goal isn't just efficiency – it's about delivering better outcomes through more thoughtful, human-centered advice.
Why Meeting Notes Are Important
When a financial advisor has a meeting with a client, it's generally a good idea for the advisor to keep a written record of that meeting for posterity. This helps the advisor meet their legal, ethical, and compliance obligations and enables them to give more effective, personalized advice over time.
From a compliance standpoint, RIAs are required to both establish and enforce a written code of ethics that, among other things, lays out the standards of business conduct that their advisors must adhere to, including the fiduciary duty to act in the client's best interest. Written records of client meetings that detail any recommendations given by the advisor, as well as any analysis or rationale in support of those recommendations, can help advisors demonstrate to their firm (and regulators) that they've abided by their firm's code of ethics.
Client meeting records also play a key role in mitigating professional liability. As trusted professionals whose clients rely on them to make important financial decisions, financial advisors are accountable for the advice they give. This means that if an advisor fails to exercise due care in making a recommendation to a client, and that recommendation ultimately causes some financial harm, the advisor can be held financially – and legally – liable for the damage done. In such cases, written meeting notes can serve as critical evidence in court or arbitration proceedings. Compared to memory alone, these notes are far more reliable in defending against disputes over what was discussed or recommended during meetings that might have occurred months or even years in the past.
Finally, keeping written records from a meeting can support the advisor's ability to provide meaningful and diligent advice on an ongoing basis. Infrequent meetings with lengthy agendas can make it hard to remember all the relevant meeting takeaways – from immediate action items and topics that need to be revisited in future meetings to personal asides the client may have casually mentioned (like a favorite restaurant, which can inspire a meaningful year-end appreciation gift). Capturing these details in written form is extremely useful for organizing the key information about what went on in the meeting, recording what needs to happen next, and recalling what should be included in future meetings – all of which are invaluable for advisors to provide their clients with the most relevant and valuable advice during and after each meeting cycle.
Components Of Good Meeting Notes
Given the importance of written meeting records – not only for keeping meeting information organized to provide better advice for clients, but also for providing documentation for compliance and liability protection purposes – it's effectively a requirement for advisors to make written notes of their meeting, even though it isn't technically required by any statute. Having thorough records makes it much easier to demonstrate the advisor's compliance with state or SEC regulations, as well as the additional standards upheld by professional organizations like CFP Board for their certificants. Without the written record provided by meeting notes, there's no tangible proof of the advisor's compliance.
But at the same time, client meeting notes need to include some specific components to fulfill these goals.
First, meeting notes should accurately summarize what was discussed in the meeting, and any recommendations the advisor made. This information must be both clear and correct, both to help the advisor remember the meeting topics later and to serve as a reliable record for compliance and liability protection. Essentially, it's best to treat a meeting note as if it might one day come up in court as evidence of what the advisor said – because it really might!
Second, meeting notes should capture any follow-up action items that the client and advisor need to take. Advice is only as valuable as the client's ability to act on it, and meeting notes serve as the bridge between the moment an action is agreed upon and the advisor and client's follow-through afterward. Including a list of follow-up tasks in the meeting note makes it clear which actions the advisor needs to complete themselves, assign to their team, or follow up with the client to keep the all-important momentum going.
Third, there must be a secure system to record and store client meeting notes. It should make the information easy to retrieve and reference in the future, whether by the advisor themselves, their compliance department, or in the hopefully rare cases where they're needed as evidence in a client dispute. And since meeting notes often contain sensitive personal and financial information, it's important that they're protected by a system that's secure enough to prevent the client's information from being accessed, either by external threats (like hacking) or internal vulnerabilities (like weak system access controls).
Taking Good Notes Vs Maintaining The Connection With Clients
As advisors gain experience building their technical knowledge and client communication skills, they also develop their ability to take effective meeting notes.
Some advisors who were fortunate to start their careers in a paraplanner or associate advisor role, where much of their jobs involved taking notes in client meetings so the lead advisor could fully focus on the conversation, may have gotten lots of practice learning to take good meeting notes. And hopefully, many of those advisors eventually transition into lead advisor roles themselves, where they can focus on talking with the client while delegating note-taking to newer team members.
But many advisors spend at least part of their careers juggling both roles – trying to focus their attention on the conversation at hand during client meetings while taking notes that will help them remember that conversation after the fact. And as many advisors can attest, it's very difficult to stay mentally engaged in a client conversation – not only listening to what the client is saying, but also paying attention to nonverbal cues, body language, and other subtle ways that people interact that go beyond the words themselves – while capturing everything in clear and organized notes for follow-up and compliance purposes.
Pitfalls Of The 'Brain Dump' Method For Writing Meeting Notes
In the moment, few advisors want to compromise their connection with the client, especially during emotionally charged or vulnerable conversations. But as powerful as those moments can be, human memory tends to fade, and it often requires many reminders to recollect what was said hours, weeks, months, or years later. As a result, many advisors have adopted a 'brain dump' approach: taking only minimal notes during the meeting to the extent that it can be done unobtrusively and without breaking the conversational thread with the client. Then, once the meeting is over, the advisor writes down as much as they can recall – typically in a notepad, Word document, or CRM notes field – while the conversation is still fresh.
While the brain-dump method has been a 'good enough' solution when no one else is available to take notes, it's hardly ideal.
First of all, it requires dedicated time immediately after the meeting to write or dictate notes. And if the advisor doesn't have time to finish a full note before they need to head off to another meeting or focus on other tasks, they may not remember everything they need to complete a comprehensive note later.
And even when there is enough time to take notes after the fact, doing so via brain dump doesn't guarantee that the advisor will remember everything discussed or recommended during the meeting, especially after a longer meeting that covers many topics. Which can lead to misremembered discussions, missing follow-up items, and a failure for the advisor and client to be on the same page about what was recommended and agreed upon. All of which can create misunderstandings that stall implementation, or, in the worst-case scenario, escalate into a client dispute that ends in arbitration or litigation.
AI Meeting Note Tools: Eliminating The Tradeoff Between Client Connection And Comprehensive Notes?
Despite its shortcomings, brain dumping remains a common way of taking client meeting notes, simply because there haven't been other viable options for solo advisors or those who don't have a second person available to take notes during the meeting itself. This is at least part of the reason why the new crop of AI-powered meeting notetakers, which emerged over the last two years, has become one of the first types of AI tools to gain widespread adoption among advisors.
The 2024 Kitces Research on Advisor Productivity found that around 18% of advisory teams use some type of AI meeting notes tool. While this is far from a majority, it still represents a sizable foothold, especially given that AI notetakers have only been available on a widespread basis for less than two years. Advisors are roughly split between general-purpose meeting note tools (like Fathom, Fireflies, and Zoom's embedded AI assistant) and those developed specifically for financial advisors (like Jump, Zocks, Finmate AI, and many others in the Client Meeting support category of the Kitces AdvisorTech Map). But in both cases, the core value proposition is the same: eliminating the need for advisors to take notes during meetings – or brain dump their notes afterward – by automatically transcribing the advisor-client dialogue and summarizing it into an organized set of key takeaways and action items.
At first glance, AI meeting note tools seem to eliminate the conundrum for advisors between being fully present in client conversations and capturing comprehensive meeting notes for posterity. With a tool that can essentially fill the role of an associate advisor focused solely on note-taking, advisors can immerse themselves in the conversation without worrying about forgetting any key details or follow-up items, which, in turn, benefits the client in two ways.
First, it allows the advisor to commit their full attention to the conversation, including the subtle nonverbal cues that might call for deeper follow-up questions. That presence can help the advisor get to the heart of what the client's question or concern is really about. The clearer the advisor's understanding, the more precisely they can tailor their advice – not just to the more superficial concerns, but to the heart of what really matters to the client.
Second, a comprehensive, automatically generated meeting note improves the chances that all meeting follow-up items will be acted on. Rather than relying on memory alone, the advisor can be reasonably certain that everything will be captured by the meeting note software. Which makes it easier for the advisor to stay on top of tasks that need to be performed, helping clients to keep moving toward their goals – whether that's starting their retirement distribution strategy, reallocating their portfolio, or implementing tax planning strategies. The more efficiently the advisor can move these actions forward, the sooner the client can shift their attention to the next priority.
In short, the potential of AI meeting note tools isn't just about greater efficiency in capturing meeting notes. They can also – at least in theory – improve advisors' ability to work in their clients' best interests by taking comprehensive notes without distracting the advisor from the conversation itself. Which is particularly relevant for advisors who still rely on the brain-dump method of capturing meeting takeaways, for whom AI meeting note tools can raise the bar on the professionalism of their advice at a small fraction of the cost of hiring an associate advisor or assistant.
Risks Of AI Meeting Note Tools
As attractive as the potential upsides of client meeting note tools are, it's equally important to consider the potential downsides as well.
There are risks inherent in letting technology take over any job function. For example, financial planning software can help advisors model future projections and compare different planning scenarios. But if the advisor misunderstands how the software's calculations work, or if any of the client's information is entered erroneously, then by the dictum of "Garbage In, Garbage Out", the output (and therefore the advisor's recommendations) could be erroneous as well. Which makes it important to carefully check any AI-generated output before it's put in front of the client – because ultimately, recommendations aided by technology are still the advisor's responsibility.
The same applies to AI meeting notetakers. Although these tools can help advisors save time and give better advice when everything works correctly, the advisor remains liable for any errors that result from the software's output – whether the issue originates from an error in the software itself or from how the advisor uses it. And because client meeting notes play such a central role in helping advisors serve their clients effectively, it's extremely important for those notes to be accurate, regardless of whether they're taken by a human or AI.
Example: Adam is an advisor who meets with a new client, Brianna, for the first time over video. Brianna has accumulated a large position in her employer's stock over the years, and during the call, Adam and Brianna discuss whether she should sell the stock to diversify her investments. She ultimately decides not to sell because of the large amount of capital gain income it would incur.
Unfortunately, however, the call's audio recording became somewhat garbled during the discussion about the employer stock, and Adam's AI notetaker captures the decision as Brianna agreeing to sell the stock.
If Adam reviews the AI-generated meeting notes and takeaways, he will likely catch the mistake, remember that the decision was to not sell the stock, and edit the meeting note and follow-up tasks to correct the record. But if he simply assumes that everything is correct and sends the AI-generated follow-up tasks to his team, they may end up selling the employer stock that wasn't supposed to be sold, triggering an irreversible capital gains tax bill for the client.
From a fiduciary standpoint, then, it's important to understand where AI notetakers can go wrong, how likely those issues are to occur, and what impact they might have if they do occur.
Meeting Note Accuracy
One of the most persistent concerns with generative AI tools such as meeting notetakers is accuracy: How well do they transcribe the meeting dialogue and summarize the conversation topics, takeaways, and follow-up tasks? A tool that eliminates the need for an associate advisor to take notes – or for the lead advisor to juggle note-taking and conversation – is only helpful if the notes it produces actually reflect what was said. Otherwise, if the advisor or their team members rely on inaccurate notes in their follow-up, they could end up taking actions that don't align with what the client and advisor actually agreed to in the meeting.
But while accuracy is obviously important, it's worth noting that "accuracy" can mean a number of different things when it comes to AI notetakers. In most cases, however, overall accuracy can be broken down into three separate components:
- Dictation accuracy, referring to how well the app transcribes what was said (and by whom) during the meeting.
- Summarization accuracy, referring to how well the tool condenses the full transcript into key takeaways and action items.
- Meaning accuracy, referring to the tool's ability to interpret unspoken communication during the meeting, such as body language, vocal inflection, or pace of conversation.
Dictation Accuracy
Overall, most AI notetakers perform well in terms of dictation. For instance, a research study performed by the consulting firm Oasis Group evaluated six advisor-specific AI notetakers (Jump, Zocks, Finmate, Zeplyn, Greminders, and Mili) and found that most transcribed a scripted 'meeting' with 100% accuracy. This is comforting, given the importance of accurate meeting transcription to AI-generated meeting notes: Since the meeting transcription is effectively the raw material that is processed by the AI model to distill into key points and action items, the accuracy of the transcription correlates directly with the quality of the AI-generated meeting notes (because again, "Garbage In, Garbage Out").
However, one tool (Jump), despite accurately transcribing the meeting text, had some issues with determining which parts of the conversation were spoken by which participants (which aligns with anecdotal feedback I've personally heard from advisors, who've mentioned misattributed dialogue as one of the few persistent accuracy issues with transcription).
This misattribution of meeting text can cause further issues downstream, especially with tools that automatically generate and assign follow-up tasks. If a task is tied to the wrong speaker, it could subsequently be assigned to the wrong person entirely. So while advisors can be fairly confident that their AI tools will accurately capture what was said, they may need to double-check who said what during the conversation to make sure nothing was misattributed, which could affect the accuracy of the notes themselves.
Summarization Accuracy
Most meeting note tools also tend to do very well at summarizing key points and creating follow-up tasks, though not necessarily with 100% accuracy. In the same Oasis Group study, the six tools captured key data points from the sample meeting with around 85% to 96% accuracy, while action item accuracy ranged somewhat lower, from 62% to 87%.
However, these numbers should be taken with a grain of salt. While these AI notetakers were less than 100% accurate, the study didn't compare the results with how a human notetaker would have performed at the same tasks. Humans aren't perfect at taking notes either, and it's certainly plausible that the AI tools' performance in summarizing the meeting and identifying tasks would be on a par with (or maybe even better than) that of the average human notetaker.
Although 100% accuracy is certainly the ideal, it's debatable what level of accuracy is truly necessary to serve clients well, given that most advisors aren't 100% accurate in taking notes themselves. It would be interesting to see more research comparing human versus AI notetakers, but in the meantime, an 85%+ accuracy rate in capturing key points seems reasonably in line with what a competent human could capture in real time. The somewhat lower accuracy score in action items, however, suggests that advisors may need to do more work in verifying the notetaker's AI-generated tasks, and adding any additional tasks based on their own recollection of the meeting that the notetaker may have missed.
Meaning Accuracy
Where AI meeting note tools clearly fall short compared to human notetakers is in interpreting meaning. AI can analyze only the words spoken in a meeting, which means that anything conveyed nonverbally – through facial expressions, body language, vocal tone, and cadence – will likely be missed. Which is a key gap in AI notetakers' abilities, since these nonverbal cues can often reveal our thoughts – intentionally or not – better than words can.
Humans, by contrast, are better at picking up on sarcasm, irony, or subtext that isn't explicitly stated or where the speaker's meaning is the opposite of the words they say out loud. For example, if a client makes an offhand joke about the cost of eggs, the AI notetaker might misinterpret it as their actually being worried about affording groceries, which would have serious implications for the types of recommendations the advisor might make.
In other words, any time an AI meeting note tool is required to interpret things said (or left unspoken), it runs the risk of either misunderstanding the meaning or missing chunks of nonverbal communication altogether.
The graphic below illustrates the three components of note-taking accuracy. Advisors generally don't need to worry much about whether an AI notetaker will transcribe their meeting dialogue accurately, as most tools handle dictation nearly flawlessly. And although AI notetakers are less than 100% accurate at capturing key takeaways – and less accurate still at noting follow-up tasks – they can still be expected to summarize at least as well as the average human notetaker. However, AI notetakers significantly lag behind humans in interpreting the meaning behind nonverbal or joking communication, to the extent that any such communication that the advisor wants to capture would need to be added manually to the meeting notes after the fact.
How Advisors Can Reduce The Risk Of Inaccurate Notes
Once advisors know where AI notetakers are most likely to slip up, they can focus their efforts on those areas to mitigate the risk of inaccurate notes or missed follow-ups.
Whether or not an advisor is using an AI notetaker, it's always important to communicate clearly and directly in client meetings. Doing so helps ensure that clients can keep up with the conversation and aren't overwhelmed by a flood of technical jargon or left confused by ambiguous language. However, clear communication becomes even more essential when using AI tools to capture the meeting dialogue in light of those tools' limitations in picking up on nonverbal or ironic language.
Statements and recommendations can be phrased as plainly as possible to make their meaning obvious to both the client and the AI notetaker. Consider the following two statements:
You may want to consider making adjustments to your spending to better achieve your financial goals.
I recommend that you make adjustments to your spending to increase your chances of achieving your financial goals.
At first glance, the two statements might seem to be saying the same thing. However, only the second statement clearly states the advisor's recommendation. The first leaves a significant amount of interpretation up to the listener – for example, whether the advisor genuinely believes spending adjustments would be in the client's best interest or whether it's just one scenario among many to analyze before making a final decision. And when the 'listener' is an AI notetaker, which by default takes the words it processes at face value, there's no guarantee that it will correctly interpret the statement as a recommendation – unless, as in the second statement above, the recommendation is made explicit by the advisor.
The same concept applies when the client says something that could be ambiguous. In these cases, asking clarifying follow-up questions (e.g., "Just to make sure I understand you correctly, is this what you're saying?") can help avoid any misinterpretation by the AI notetaker. Likewise, when there are follow-up tasks or action items to take after the meeting, advisors can clearly state what needs to be done and by whom so the notetaker can accurately capture those tasks.
Again, all of these techniques are already communication best practices for advisors, regardless of whether they're using an AI notetaker. The more that gets left up to interpretation in any scenario, the more likely it is that misunderstandings will arise, potentially leading the advisor and client to have different ideas about what was agreed on and who is responsible for which actions going forward.
Put differently, a good question for advisors to ask themselves when making recommendations is: "Would an AI model that's designed to take everything I say 100% literally correctly identify and summarize what I just told the client?" Not only will focusing on clear language during meetings help the AI notetaker interpret and summarize accurately (and reduce the time needed to review and edit notes afterward), but it can also help ensure the client understands everything that's said as well.
Lastly, and equally important, advisors can review AI-generated notes after each meeting (and before saving them to their CRM) to catch any obvious inaccuracies like misattributed statements or misinterpreted remarks. If the notetaker omitted any key meeting points or if there were nuances the advisor picked up from nonverbal cues, those details can be added manually. Effectively, this AI meeting note review takes the place of the post-meeting brain-dump process, except the advisor is editing and augmenting the AI-generated note rather than starting from scratch. Ideally, this results in a more accurate meeting note created in less time.
Data Management And Privacy
Another main area of concern around AI meeting note tools is how client information is used by the technology. These tools can interact with client data in multiple ways, and for advisors, it's a best practice to be aware of how their clients' data might be used so they can take steps to protect that information accordingly.
The first place where client data is exposed to AI meeting notes software is in the meeting transcript itself. Since the technology captures everything said during the meeting, any personal information the client says out loud is also 'heard' by the software and fed into its model to generate transcripts, meeting takeaways, and follow-up tasks. Since this is the core function of an AI notetaker, there's not really any way to avoid having the technology capture personal information once it's been spoken. For this reason, it's a best practice for advisors to seek their clients' permission proactively (ideally by obtaining their written consent) before using an AI notetaker during a meeting. (At least 10 states actually require that all parties in a call give consent for that conversation to be recorded, making permission not just a best practice but a requirement.)
Beyond the recording itself, however, it also matters how the software handles client data after it's captured, and how securely it's stored.
All major AI meeting note providers claim compliance with SOC 2, a third-party attestation that their cybersecurity practices meet certain industry standards for service organizations. In other words, it's unlikely that client information stored by an AI meeting note provider would be stolen due to a platform breach. However, the risk of exposure on a third-party platform is never zero. Which means it's generally best to make sure that any technology the advisor uses – including AI notetakers – has access only to the client data it needs for the job that the advisor needs it to do, without accessing or storing client data unnecessarily.
As shown in a survey from technology consultant Ezra Group of 10 different advisor-specific AI notetakers, tools can differ greatly in what types of data they store or pull in from other sources like the advisor's CRM, financial planning software, or even their email inbox. Some tools, like Zocks and Mili, don't actually record the meeting or publish a transcript after the fact; they simply create notes in real time during the meeting. (Finmate AI also has the option to disable recording.) Other tools, like Finmate AI and Zeplyn, do record the meeting but don't store any of the recordings on-platform. Instead, they allow advisors to export their meeting notes to their CRM with the option to either delete the recording or archive it on the advisor's own systems.
Still, many other tools are able to retain much more client data, including meeting transcripts, recordings, and even imported data from CRM and other systems, with the goal of serving as a central client intelligence hub rather than simply a note-taking app. This has become increasingly common as the AI notetaker category has become more crowded over the past year, and providers have sought to differentiate themselves by expanding beyond note-taking.
For advisors, then, doing due diligence means comparing the client information the provider needs to have against what it actually collects. If the intent is simply to use the tool as a note-taking app where the meeting notes will be archived to the advisor's CRM, it might make sense to consider a tool like Zocks, Mili, or Finmate. These allow for recording-free note-taking and store minimal client data on-platform, which result in less risk of client data being unnecessarily stored. For those seeking more robust functionality, platforms like Jump often allow firms to customize the data used or stored, which gives advisors the ability to limit exposure while still taking advantage of the tool's broader features. In any case, the goal should be to ensure that the tool collects only the amount of data needed for the job that the advisor needs it to do.
As a technology that has only been commercially available for a couple of years, generative AI – and AI meeting note tools in particular – have provoked a wide range of reactions among potential users. Some early adopters have enthusiastically dived in, excited by the possibility of eliminating some of the grunt work of meeting note-taking and follow-up, but perhaps not entirely aware of the potential risks around accuracy and privacy that AI notetakers introduce. On the other hand, some might remain skeptical of the technology, waiting until tools that create AI-generated meeting notes can do so with 100% accuracy – while perhaps not recognizing that even a human notetaker can't be expected to have perfect accuracy all the time.
Ultimately, then, the best approach – depending on which end of the spectrum an advisor is coming from – might be one of cautious optimism: cautious in that fiduciary advisors must ensure their meeting talking points and recommendations are captured accurately and that client information is appropriately safeguarded; and optimistic in recognizing how AI notetaker technology, when used in a way that takes advantage of its strengths and mitigates its shortcomings, can truly help many advisors raise the bar in efficiency, the quality of their advice, and client connection. Because while no technology is perfect for everyone, the fact remains that all financial advisors do need a notetaker in their meetings – whether it's themselves, another person, or an AI notetaker – and if advisors clearly understand the risks of AI solutions and how to manage them appropriately, they might find that the technology can do the job better (and faster) than brain dumping notes into their CRM themselves after each meeting!
