By Mela Seyoum in Financial Advisor IQ featuring Shane Cummings, CFP®, AIF®, Wealth Advisor & Director of Technology/Cybersecurity
Key Takeaways
- AI is reshaping advisor training and performance tracking. Platforms are using client data and meeting insights to evaluate and guide advisors.
- Human judgment remains essential. Mentorship, empathy, and context cannot be fully replicated by AI systems.
- Data quality and oversight are critical risks. Poor inputs or incomplete data can lead to misleading recommendations and performance evaluations.
Information from notetakers, financial planning tools and CRMs is being used by AI to track advisors’ performance and create potential client recommendations.
While some emerging artificial-intelligence platforms are trying to scale the way advisors are trained and evaluated using the troves of client data available, other industry players say that advisors and firm leadership should exercise caution and have human checks.
Among the platforms offering client and performance insights for advisors is Jump, which launched in 2024 as a meeting assistant and in March announced expanded capabilities.
The insights offered on Jump include the ability to measure client versus advisor talk time, identify what types of assets are discussed in client meetings, and highlight common concerns or questions that clients raise, said Parker Ence, Jump’s chief executive officer and co-founder.
Firm leadership can also take a larger view of the insights and performance across the firm and can ask the platform questions on topics such as which questions clients are bringing up in meetings and see the progression over time, Ence said.
Since Jump announced its new capabilities last month, 6,000 users have tapped the growth function that converts client conversations into insights and advisor performance evaluation, according to Ence.
While these AI platforms will be useful for identifying the things top performing advisors are doing right and communicating that to other advisors, mentorship is still important in training advisors to help them understand more nebulous qualities such as firm culture, said Shane Cummings, a wealth advisor and director of technology and cybersecurity at registered investment advisory firm Halbert Hargrove.
“What we’re really trying to do here is up-level all advisors across the industry to perform like a very top producer in the space,” said John Connell, CEO of Focal AI.
Focal also has AI notetaking and meeting-prep capabilities and offers performance insights through feedback after client calls to help advisors improve in areas such as active listening, deepening client relationships and structuring conversations, Connell said.
Another AI platform trying to “accelerate the learning curve” for advisors is WealthStream, said Dan Daum, the firm’s CEO and co-founder.
“Judgment exists in very few people within a firm, and it’s usually the people that have decades of experience … but it’s not really scalable to bring on newer generations of advisors,” Daum said.
These AI platforms offer an array of recommendations. At Jump, for example, an advisor might get a recommendation to follow up on an opportunity for referrals or manage a held-away account, Ence said.
WealthStream, for example, might recommend that a client who is a new business owner should set up a solo 401(k), or that a recently divorced client should have their estate plan updated, Daum said.
Focal and Wealthstream also have both advisor and manager views on the platforms.
Since these tools offer managers the ability to see advisor performance across the firm, there’s some potential concerns from advisors that all the work they do may not appear in a documented, measured way that can be fed into an AI platform, Cummings said.
“You might have a client phone call that takes an hour, you might have a task that takes five minutes. If somebody’s looking at a dashboard and they’re just doing this in bulk all the time every day, there might be a tendency to lump a lot of this together,” Cummings said.
Not every client meeting can be transcribed with AI, with some clients still wanting to meet in person and perhaps not being comfortable with having a meeting recorded, he said.
Those differences in client preference may also create discrepancies across different advisors based on their book of business.
“There’s still some friction there in terms of not all client meetings are created equal,” Cummings said.
He added that, for now, these types of AI tools are on the long-term radar for advisors at Halbert Hargrove, which says it has $4.2 billion in assets under management across 11 locations and 28 advisors.
The demand will also likely come from the leadership at wealth management firms, since they will be able to see what advisors across the firm are doing on a high level, Cummings said.
For advisors using these AI platforms, it’s important to ensure that good quality data is being fed into it, otherwise it could provide conflicting recommendations, said Jennifer Maruca, president of wealth management consulting firm Platform Value Group.
Regardless of the data quality, advisors need to perform checks to make sure that the insights and recommendations they are getting are in line with what they know about a client and the source of information that AI is drawing from, Maruca said.
With the advent of AI tools that can provide more recommendations and some coaching, training for advisors may need to more heavily lean into soft skills such as empathy and listening to further craft an advisor’s judgment, Maruca said.
