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Artificial intelligence (AI)-powered meeting tools represent one of the most rapidly adopted applications of generative artificial intelligence (GenAI) for financial advisors, with 91% using a generic or industry-specific solution. Early movers are already leveraging AI to prepare for meetings, capture structured notes, generate CRM-ready summaries and flag potential suitability issues — all with data safety and security in place.
While we’ve observed that many wealth management firms are comfortable bringing on a compliant version of a chatbot like ChatGPT to help with ideation, most are not yet ready to let the GenAI “robots” move money or write financial plans. They are, however, interested in advisor productivity. That’s where meeting AI can help.
Why Meeting AI is a must-have
Overnight client experience advantage. Meeting AI helps advisors prepare for meetings (with AI-generated premeeting briefings), be more present during meetings (thanks to AI handling live data capture), and deliver postmeeting follow-ups (through CRM updates, recap emails, task management and financial planning software). The benefits are clear. Clients highly value “trustworthy” advisors who “listen to and understand their goals.”1
Immediate advisor time savings and improved advisor experience advantage. Advisors spend up to a third of their time on premeeting and postmeeting preparation, follow-up, and documentation. An advisor once described this tedious work as “the bane of my existence.” Advisors using these systems save between one to three hours per workday,2 reducing administrative slog by 50%-90% (depending on their customization level).
Increasing advisor productivity advantage. Advisors can recapture anywhere from 250 to 750 more hours per year, which they can use for client acquisition and relationship building. Add up this time over three years, and the exponential time savings is staggering.
Compounding data and insights advantage (for enterprise leaders). AI meeting platforms can extract data from across large samples of meetings and client interactions, giving enterprise leaders new visibility and opportunities to deepen client relationships.
Accelerated new advisor training cycle and firmwide adoption of best practices. These platforms can apply automatic AI scorecards or analysis of client interactions (based on the firm’s definition of best practices or ideal agendas), helping new advisors ramp up faster and reinforcing best practices.
Given the cumulative benefits outlined above, the sooner advisors adopt meeting AI, the more they are set to gain from compounding advantages.
“Advisors, on average, spend more than 2 hours ‘behind the scenes’ for every 1 hour in client meetings. Automating those behind-the-scenes tasks with AI is like adding hours back into the week – a true force multiplier for advisory teams.” – Michael Kitces
The “why” is abundantly clear. So now, you might be wondering, “Should I build or should I buy?”
When building might make sense
Developing an AI meeting note generator might seem as simple as just getting some speech-to-text models, running the outputs through an LLM to summarize notes and tasks, and setting up a way to save the notes to CRM. But there’s so much more to it.
Here are a few scenarios where building AI internally may make sense:
- When the project will bring a key core competency that enables strategic differentiation
- If there is substantial downside risk associated with not owning it in-house
- In the event that vendor solutions can’t be configured to meet your needs
With the vast number of factors that would go into building a solution, there is a high likelihood that the technology will outpace the product. In fact, a RAND Corporation report highlights that 87% of these types of AI projects do not reach deployment, underscoring the challenges of turning a promising prototype into a scalable product.3
When buying might be a better option
Building meeting AI is likely the wrong choice for 99% of enterprise wealth firms. Here’s why:
- What seems cutting-edge today might not be in five months.
- It’s deceptively expensive to ship and maintain tools of this nature.
- Delaying time-to-value while you build is probably not worth the wait.
Even for well-resourced firms, building internal AI tools can be a major undertaking. While custom solutions may be well-received at launch, the rapid pace of innovation means they can quickly lag behind more agile, off-the-shelf alternatives.
Building is possible, but speed, scalability, and ongoing feature evolution must all be factored into the equation.
Don’t Wait
For most of us, the age of AI kicked into full gear around the launch of ChatGPT. From that point on, many of us realized that generative AI would change everything.
In the wealth management industry, meeting AI is proving to be a great starting point for enterprise wealth management teams to gain instant productivity. Acting quickly offers the additional compounding of long-term productivity and data advantages.
Whether you’re planning to build or buy, the key is to act swiftly. This capability will be table stakes in the next 12-24 months, and those that delay risk falling behind.
Parker Ence is the CEO and co-founder of Jump, a provider of AI solutions for financial professionals.
1. https://www.theamericancollege.edu/knowledge-hub/research/what-do-clients-want-from-financial-advisors
2. https://jumpapp.com/blog/how-jump-saves-advisors-time-survey-results-are-in
3. https://venturebeat.com/ai/why-do-87-of-data-science-projects-never-make-it-into-production/
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Advisor Perspectives welcomes guest contributions. The views presented here do not necessarily represent those of Advisor Perspectives.
