AI notetakers' sentiment analysis tools could help advisors improve client meeting skills

The baseline value proposition for an AI notetaker is pretty clear. The tool logs into a meeting between the client and advisor, transcribes the dialogue that takes place, and runs that text through a Large Language Model (LLM) that summarizes the meeting, identifies relevant takeaways, and notes action items that need to be followed up on. Which means that (1) the advisor themselves is not burdened with taking notes during a meeting (and they don't necessarily need to bring a second person in the meeting as a notetaker); (2) the meeting notes will be more likely to capture all of the relevant points than if the advisor had (often distractedly while talking to the client themselves) taken notes themselves or "brain-dumped" into a Word document after the meeting; and (3) much of the manual work necessary for drafting a meeting follow-up email to send to the client is eliminated, since the advisor can simply paste in the bullet-pointed meeting summary and add their own personalized messaging around it before sending it to the client.

Just from that functionality alone, it's fairly easy to see why AI notetakers have taken off (e.g., the most recent Kitces Research on Advisor Technology found that around 40% of independent advisors have adopted meeting support tools in one form or another, barely two years after their widespread release). The time spent on preparing and following up for meetings can take a significant amount of time – often one hour of before/after time per one hour of meeting time itself, according to the latest Kitces Research on Advisor Productivity – and that time savings can open up more hours for deeper planning or more client-facing time.

But once an AI notetaker has listened to and transcribed a client meeting, there are other things that it can do with that transcript besides creating a meeting summary and identifying follow-up tasks. A meeting transcript is a rich trove of data that can be analyzed in various different ways, including not just for the concrete details about what went on in the meeting, but also for a more qualitative evaluation of how the meeting went. Which can at least in theory make an AI notetaker useful both for managing their various meeting-related tasks and for providing insights that can help advisors hold better meetings.

Which is important, because client meeting skills are crucial but sometimes underappreciated for advisors looking to improve their craft. It's simple enough to say that advisors should do things like make eye contact, listen actively, avoid talking too much, and keep the conversation moving so it doesn't go past its end time, but in practice most people are better at some of those skills than others, particularly in a client meeting situation when the advisor is more focused on keeping up with the conversation than on managing the meeting structure. But meeting skills can be improved through repetition – as the meeting cadence gets more comfortable for the advisor, they can think a little more each time about which areas they're trying to improve on, and actively work to build those skills until they've become second nature and the advisor can move on to the next skill. And that improvement can come much faster, and be much more effective, when there's some kind of feedback mechanism by which the advisor can see specifically which parts of each meeting they did well with, and which areas they need to focus on to improve. As Ericsson, Prietula, and Cokely's classic 2007 Harvard Business Review paper "The Making Of An Expert" put it, "experts are always made, not born" – and the true key towards becoming an expert in anything is through "deliberate practice" with feedback and coaching on how to better do the things that stretch one's current capabilities.

But few advisors get a chance to receive this feedback. If they're lucky, they might start their career in a paraplanner or associate advisor role where they can sit in on meetings, observe the more experienced lead advisor and how they manage the meeting, and eventually take over portions of the client meetings themselves before going fully solo in meetings with their own clients. But even then, there might not be a chance for the lead advisor to give meaningful feedback in the hectic period between doing post-meeting follow up and starting preparation for the next meeting. And many advisors may not even get a chance to sit in on meetings before being thrown into client meetings on their own, giving them no avenue for feedback at all other than their own gut feeling after the meeting is over (which might give a good sense of whether or not the meeting went well overall, but doesn't provide much in the way of useful specifics beyond that).

Which is why it's at least interesting that AI meeting note tools like Jump and Zocks have built meta-level meeting analytics tools that can help advisors assess certain key aspects of their meeting performance – and which have been arguably underutilized as training tools for advisors looking to improve their meeting skills.

One of the simplest of these meeting metrics is talk time: How much on average, or during a particular meeting, did the advisor spend talking during meetings versus the client? The ideal number for this might vary from meeting to meeting and client to client, but in general if the advisor's meeting conversations all tend to be one-sided with the advisor delivering information, they might need to remind themselves to step back and listen on occasion. And for deeper analysis, Jump and Zocks (as well as some general-purpose AI notetakers like Fireflies) also include sentiment analysis tools, which analyze the meeting transcription for words or phrases with positive or negative connotations to give an impression of the overall tenor of the meeting. As shown in the sample screenshot from Zocks below, AI notetakers can track client sentiment in real-time throughout the meeting, allowing advisors after the fact to pinpoint the times when their clients' sentiment rose or dipped during the meeting.

And as Jump's recent Financial Advisor Insight Report found after analyzing thousands of anonymized meetings and their sentiment data, the client's sentiment at the beginning of a meeting can vary widely based on market conditions, global news, or events in their own lives – but as the chart from the report below shows, that sentiment tends to meaningfully improve by the end of the meeting.

The report also found that certain advisors were more able to consistently improve client sentiment throughout their meetings by showing awareness of and validating clients' emotions, limiting the advisor's own speaking time so clients can express their concerns and feel heard, and emphasizing the client's own goals and relationships rather than investments and the markets. With these findings, Jump developed an "Advisor Emotional Intelligence Index" based on key factors like talk time, the number of open-ended questions asked by the advisors, the level of empathy expressed in their statements, and the amount of emotional awareness demonstrated by the advisor, all of which are correlated with a greater intra-meeting lift in client sentiment.

All of these tools could be valuable for advisory firms for at least one of two different purposes. At the individual advisor level, these metrics can provide something of a running scorecard for the advisor's meeting skills. In the spirit of "what can be measured can be improved", advisors who want to focus on key metrics like talk time or sentiment improvement during a meeting can use these tools to keep score and provide continuous feedback that they can use to improve. Jump and Zocks each have their own literal meeting score metrics that can help gamify the experience (e.g., encouraging the advisor to top their best score from one meeting to the next).

And at a firm level, these tools can help managers see which of their advisors may be more skilled at emotional intelligence than others. This can have all kinds of implications, from salary and bonus decisions to assigning incoming clients to identifying which advisors would be the best choice to train or share best practices with other advisors at the firm.

It's worth noting that not every skill related to success in client meetings can be quantified and analyzed by AI notetakers today. Meeting note tools only analyze the transcript of the meeting, meaning they focus solely on the words said out loud. But there's a whole vocabulary of unspoken language that goes back and forth between advisors and clients during a meeting, from posture and body language to eye contact to tone and inflection to allowing for moments of silence and reflection – none of which gets captured in an AI notetaker. The technology is therefore only one part of what can be a more holistic effort by advisors to self-assess their meeting skills.

But the key point is that, for advisors who are using notetaking tools like Jump or Zocks, these tools exist in the technology that they're already using – there's no need to buy an extra piece of technology when it's already a part of the AI notetaker. Although many advisors may see them as add-on features outside the tools' core notetaking function, or may only use them sporadically to check on individual meetings, it could be worth a closer look at how they can be used more systematically to help the advisor improve their client meeting skills. In a world where communication is being rapidly taken over by AI, the most successful advisors will be the ones who can develop deep and lasting human connections with their clients – and while AI can't build those connections itself, it can perhaps ironically be a useful aid in giving feedback so advisors can learn how to do it themselves. 

This article first appeared on the Nerd’s Eye View at Kitces.com at https://kitc.es/advisortech-feb2026, and has been reprinted here with permission.

Ben Henry-Moreland 

Ben Henry-Moreland is a Senior Financial Planning Nerd at Kitces.com, where he specializes in writing and speaking on financial planning topics including tax, practice management, and technology. He also co-authors the monthly Kitces #AdvisorTech column. Drawing from his experience as a financial planner and a solo advisory firm owner, Ben is passionate about fulfilling the site’s mission of making financial advicers better and more successful.

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Michael Kitces

Michael Kitces is Head of Planning Strategy at Focus Partners Wealth, which provides an evidence-based approach to private wealth management for near- and current retirees, and Focus Partners Advisor Solutions, a turnkey wealth management services provider supporting thousands of independent financial advisors through the scaling phase of growth.

In addition, he is a co-founder of the XY Planning Network, AdvicePay, fpPathfinder, and New Planner Recruiting, the former Practitioner Editor of the Journal of Financial Planning, the host of the Financial Advisor Success podcast, and the publisher of the popular financial planning industry blog Nerd’s Eye View through his website Kitces.com, dedicated to advancing knowledge in financial planning. In 2010, Michael was recognized with one of the FPA’s “Heart of Financial Planning” awards for his dedication and work in advancing the profession.

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