The inaugural ADVISE AI conference, held earlier this month in Las Vegas, was dedicated exclusively to artificial intelligence in U.S. wealth management, reflecting the growing industrywide interest in the topic.
At the conference, I heard from advisors who are laser-focused on how AI can help them better serve their clients and run their businesses more efficiently. While such focus on quick wins is understandable, it has also led to a cripplingly fragmented tech stack across wealth management. At this important juncture in technology decision-making, it’s important to reflect on the larger evolving tech ecosystem.
While this can seem overwhelming for decision-makers and users, anchoring on a single ground truth can offer clarity in this sea of complexity: It’s all about the data.
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While this is true across all layers of the tech stack, here I’ll focus on AI meeting admin tools, which cover things like meeting transcription, summarization, task identification and integration with CRM.
Advisor interest in this category is high, and for good reason: All advisors want to spend less time preparing for client meetings and more time actually engaging with clients. Currently, advisors typically spend 16% of their time on non-value-added meeting admin, according to research by Michael Kitces.
While reduced admin time is its own reward, AI meeting tools offer an even bigger benefit: Time spent with clients grows AUM.
Large language models offer a step-change in the ability of technology to extract actionable insights from meeting transcripts. AI tools can effectively summarize conversations, highlight themes and trends over time and pinpoint tasks and follow-ups. These insights can be saved in CRM systems with greater accuracy than manual advisor note-taking and with zero incremental effort. This builds a valuable knowledge repository that can be mined using simple natural language queries. Giving advisors instant access to prioritized summaries of questions, interests and concerns that clients have expressed over time is the foundation of scaling personalized advice.
Expanded contexts
But while there are clear benefits to be gained from transcribing and summarizing meetings, there are other important contexts that are key to assessing client needs and optimizing next best actions.
Consider the content in the following data sources:
Emails. Most meetings begin with an email between advisor and client. The meeting catalyst — who initiated it and why — is an important piece of context. Analyzing catalysts over time can help advisors become more proactive and systematic about how they engage with clients.
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Calendar. Focusing exclusively on meetings that are transcribed misses other valuable context about how time is invested in a practice, including internal prep, social/entertainment events, travel time, staff development and more.
Files. Whether it’s proposals that are saved in Word/Powerpoint, commentaries on market/product developments, or all manner of newsletters and marketing communications, client engagements often involve some type of enterprise content. The right AI tools can gather and save pre and post-meeting admin artifacts.
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AI and alts
While meeting admin tools will yield narrow wins, these adjacent data sources are essential when advisors or home offices want to pursue opportunities and trends across their entire book of business. Take the example of an advisor who wants to start offering their clients access to alternative investments. While alts can offer valuable diversification benefits, they also introduce complexity into the equation.
AI tools that are connected to data sources across the enterprise can identify which clients have expressed interest in alts and what attributes they’re looking for; identify which of the available products meet clients’ primary criteria for sector, liquidity, diversification and other features; rank the relevant products based on criteria of the advisor’s choice (liquidity, fees, minimum order size); understand what additional compliance requirements must be fulfilled before putting these complex products in a client’s portfolio; and draft a narrative to help advisors position the top products in a way that will resonate with their clients. (Critically, the results generated by the tool must contain links to source materials across the firm so advisors can validate and refine the analysis.)
Data rules
It’s easy to be overwhelmed by the number of new AI tools, but decision-makers will make better choices if they keep data front-and-center. Remember that AI is only as good as the data that it’s able to leverage. Tools that have access to more of your firm’s data will generally perform better.
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Also keep in mind it’s not necessary to increase the sprawl of applications on advisor desktops to reap the benefits of AI. Existing vendors are leveraging tooling in the cloud to add these capabilities to their platforms. Because these platforms have critical data, the AI solutions they create will likely yield better results with a lower total cost of ownership.
The data of wealth management firms is scattered across portfolio management, CRM, financial planning, proposal generation, billing and other systems. While tools like Microsoft’s 365 Copilot unify data across desktop tools, firms will want to use AI over their entire data estate. Before investing in AI tools, firms need a roadmap for unifying all their data. Most importantly, make sure that the roadmap is grounded in use cases that help advisors grow and scale their practices.
The potential of gen AI tools to reduce meeting admin is clear, but the playbook for how it will transform the wealth management industry has yet to be written.
Firms can position for success by having a clear vision — across IT and the business — of the differentiated processes that drive value for the firm and what data and insights can accelerate and augment those processes. This will provide a north star to guide and prioritize investments in a rapidly evolving technology landscape.
