By Liam Hanlon
For the past two years, financial advisors have done something they have never done before: adopt new technology.
According to a University of Chicago study conducted in 2023 that looked across 11 professions, advisors had the lowest adoption of AI at work and at home. Fast forward to today, and according to WealthManagement.com, over two-thirds of advisors are now using AI in their daily workflows.
So, what happened?
Well…technology started to solve a real problem. That problem was time.
Advisors were spending far too much time on maintenance and administrative work, and not nearly enough time with clients.
AI meeting assistants, like Jump, helped solve this. Primary research shows adopters save two to three hours per day, or roughly 300 hours per year.
Nearly 90% of advisors claim they use that saved time on revenue-producing activities, such as delivering client service, holding prospecting meetings, and marketing or promotion.
The Mission Evolution: From Reclaiming Time to Enhancing It
This shift has driven new thinking across the industry, and it has also pushed firms to rethink their mission.
The question we used to ask was: How can we use AI to help advisors reclaim their time?
That mission is not over, but we have largely succeeded so far.
Now the question is: How can we use AI to enhance the time we have returned?
The 200 to 300 hours per year that advisors have gained by adopting this technology, and are now spending with clients and prospects.
How can we help them succeed in every critical interaction?
We believe the next great frontier of AI in wealth management will focus on this problem. At its core, it is about building an engine to enhance organic growth.
Organic Growth: Two Paths to Expansion
Organic growth can be achieved in two distinct ways: going wider or going deeper.
Going wider means increasing the number of interactions you have with clients and prospects. Jump has indirectly enabled this by returning time to advisors. There are also tools such as Finny, SmartAsset, and others that focus directly on this category by generating more opportunities.
Going deeper is different. It does not increase the number of interactions. Instead, it improves success in every interaction.
Case Study: Prospecting, Wider vs Deeper
Let’s use prospecting to compare the two approaches.
If a firm has 100 prospecting meetings per year, converts 30% of those prospects to clients, and each prospect represents $500K, that results in $15 million in AUM.
To drive organic growth, the firm can go wider, go deeper, or do both.
Option 1: Going Wider (More Meetings)
Going wider means increasing the number of prospects.
For example: increase from 100 to 127 prospects while maintaining a 30% conversion rate, at $500K per client. That produces roughly $19 million in AUM growth.
Option 2: Going Deeper (Higher Conversion)
Going deeper means keeping the same number of prospects, but improving conversion.
For example: keep 100 prospects, but increase conversion from 30% to 38%, still at $500K per client. That achieves the same result: roughly $19 million in AUM growth.
The Difference: The Cost of Growth
The growth outcome is the same. The difference is the cost.
The cost of 27 additional meetings is 27 hours away from the existing book of business. It is 27 more hours of meetings, preparation, and follow-up.
The cost of improving conversion can be close to zero, if done correctly.
The challenge is that it has traditionally been far easier to find more prospects than it has been to increase conversion rates.
But that is changing.
The Answer: Conversational Intelligence
We believe the answer lies in conversational intelligence.
Conversational intelligence is intelligence derived from the context of a real conversation between an advisor and a client. For the first time, we can capture that data, interpret it, and act on it, to inform what an advisor should say or do in order to improve outcomes.
You are going to hear a lot about conversational intelligence over the next few years. It will become a buzzword.
But it is important to clarify what it is, and what it is not.
What Conversational Intelligence Is Not
It is not structured metadata about a meeting, such as:how long the meeting was, how many meetings happened, who attended.
What Conversational Intelligence Actually Is
It is the details inside the conversation itself, including: who brought up a topic, how long they discussed it, how many questions were asked, when in the conversation it was introduced, etc.
That level of detail reveals: trending topics / objections, signals and opportunities, propensity and susceptibility and what advisors can say or do to increase the likelihood of success
So What’s Next?
Gathering the data is almost as important as what you do with it, but today that challenge is much easier than it was even a year ago. AI meeting assistants that sit on calls can capture the conversation itself, including full transcripts, along with metadata about what happened in the meeting.
What you do with that data, and how you prove it creates a measurable impact on outcomes, is far more complicated.
Doing that well requires advanced expertise, including behavioral science, linguistics, and rigorous statistical modeling. It requires PhDs to work alongside subject matter experts to create models that can understand the nuance and complexity of what impacts how or why a client makes a decision.
Jump is taking a scientific approach to this problem.
We use algorithms to identify features, or conversational variables, that could influence outcomes.
Then we use machine learning models to determine the direction and size of that impact, and even more advanced models to understand the scale of the effect across a broader advisor population.
Buying A Better Advisor in Every Critical Interaction
What you can expect from this industry over the next two years is execution against that future.
The goal will be clear: make advisors better in every critical interaction that matters.
But as this market accelerates, assessment of this technology will be of the utmost importance. Not every solution will deliver real outcomes, and not every provider will be able to prove impact with rigor.
Leaders will have to determine where to invest, and which strategy makes the most sense for their firm
Liam Hanlon is Head of Insights at Jump AI, an artificial intelligence assistant for financial advisors.
