Among the emerging crop of Generative AI-powered tools being offered to advisors are note takers.
But don’t let that description fool you: These are more than just digital stenographers.
What began as simple transcription services is quickly evolving into sophisticated meeting assistants that capture not only what was said, but also the sentiment behind it, according to a new study from industry consulting firm The Oasis Group and AdvisorEngine Inc.
"These aren't just note takers – they're meeting assistants," explains John O'Connell, founder and CEO of The Oasis Group. “It’s quickly gone from just capturing the transcript to now, ‘OK, help me analyze this conversation.’”
The first-of-its-kind study into Generative AI tools for financial advisors tested them with a script of a client scenario drawn from daily advisor practice. The study measured a dozen factors, including transcription, accuracy and intuitiveness of use. (Generative AI, simply explained, uses algorithms to analyze content and create new content.)
The study looked at six AI note-taking apps currently available on the market for advisors: Jump, Zocks, Mili, Zeplyn, FinMate and GReminders.
What Oasis Group’s research found was that all these tools deliver impressive accuracy in capturing factual data – most achieving accuracy rates of 95% or higher – but still struggle a bit with the human nuances of client interactions.
“The greater value of these tools at the moment is how they free financial advisors and their staff from time-consuming tasks such as transcription and organizing follow-up activities,” said AdvisorEngine CEO Rich Cancro.
“It allows an advisor to focus on the prospect or client instead of taking notes,” Cancro said. “So now they can fully participate in the conversation – that in itself makes the dialogue more accurate, because they're fully engaged. The depth of the conversation will be better.”
Cancro explained that AdvisorEngine conceived and sponsored the study with The Oasis Group, partly to gain a better understanding of the potential AI software partners it can work with, and partly to provide investment advisors with an objective, rigorous analysis of emerging AI tools.
“There’s a lot of industry interest in AI, and we wanted to initiate and support research that fleshes out how well AI performs in certain ways to aid firms,” said Rich Cancro, founder and CEO of AdvisorEngine. “AI Note Takers allow advisors to focus on the client instead of taking notes. So now they can fully participate in the conversation – that in and of itself makes the dialogue more accurate, because they're fully engaged throughout the conversation with prospects and clients. The conversation will be better.”
Facts versus feelings
The study revealed a significant gap in how AI tools handle factual versus emotional content.
When processing numerical data such as portfolio values, income figures, and specific financial goals, almost every AI note taker demonstrated precision. However, the technology consistently missed opportunities to flag personal moments that could strengthen client relationships.
In one test scenario involving a hypothetical couple planning for a new baby, the AI tools accurately captured financial discussions about college savings through 529 plans. Still, none identified the opportunity to send a congratulatory gift when the baby was born — something even a junior client relations assistant would naturally notice.
"They're great at picking up specific action items tied to financial advice. They struggle with picking up the soft skills stuff," O'Connell observed.
This limitation raises questions about whether over-reliance on AI could potentially weaken the human connections that form the foundation of successful advisory relationships.
From transcription to prediction
The evolution of these technologies has been remarkably swift.
What began as transcription tools focused on capturing verbatim conversations just a year ago has rapidly expanded to include meeting summaries, identifying action items, and now sentiment analysis.
Some solutions can even track speaking patterns, showing which participant dominated the conversation.
“These tools will now tell an advisor if the meeting was positive or negative,” O’Connell said. “Who talked more in the meeting? It will track the most active person in the meeting: the husband spoke this percentage of the time, and the wife spoke this percentage of the time. If you're an advisor, you can learn if you are effectively engaging both spouses.”
Cancro said the next evolution of these tools will be their deeper integration with CRM systems to automate meeting preparation and suggest topics for future client conversations.
The technology is moving toward what the wealth management industry has long sought but rarely achieved: genuine "next best action" capabilities that recommend appropriate follow-ups based on client circumstances, sentiment and life events.
Operations, compliance impacts
O’Connell and Cancro note that the advent of these technologies is changing operational structures within advisory firms.
Traditionally, advisors would either take notes during client meetings or dictate notes afterward to support staff who would then enter the information into CRM systems.
With AI handling the note-taking function, advisors can remain fully present in conversations while capturing more comprehensive information.
“It's a significant level of prospect management efficiency and personalization because the information is being captured better and the tedious follow-up can be automated through deep integrations with modern CRM’s,” Cancro said.
Relying on human assistants is not exactly a foolproof process, O’Connell pointed out.
“The advisor is expecting the CSA to capture all the notes and the CSA may not capture every key point, because they may not have the knowledge of the client relationship or the knowledge of the client's prior interactions to capture all those notes really well.”
The study also highlighted important considerations regarding data security when using AI note-taking tools. Firms must understand where client data is stored, O’Connell said, whether it's used to train AI models, and how to protect sensitive information.
O'Connell shared a cautionary example of an advisor who uploaded client financial statements to free AI tools without realizing the data could potentially be used to train the underlying models – a significant security and privacy concern in a highly regulated industry.
What’s next
O'Connell suggests that firms should push technology vendors to improve the capture of soft skills data while continuing to involve humans in reviewing AI-generated outputs. This human-in-the-loop approach ensures technological efficiency doesn't come at the expense of relationship quality.
He also expected that these tools will reduce operational costs for firms in the long run, specifically the need for support staff to attend client meetings.
“It's a force multiplier for an individual,” O’Connell said. “Do I think that this technology is going to get so good that I can do a meeting by myself with it running on my phone and not have to pay for the cost of two or three people sitting in that meeting to help me? Yes. That cost will go away, 100%.”
Cancro said these tools should also solidify the expectation that the advisor remains focused on clients, rather than being bogged down in laborious tasks.
“Advisors should be spending their time on deepening their existing client relationships to turn clients into referral machines as well as doing activities to create prospects and ultimately converting prospects into clients,” Cancro said. “In an ideal world, their technology tools are handling tasks along with their operations and client success teams. They should be highly focused on engaging with their clients and prospects and automating as much as they can downstream.”
