Highlights From The Advise AI Conference 2024

The wealth management industry stands at the precipice of an AI revolution.

AI has the potential to redefine every aspect of how financial advisors track, communicate, and serve their clients. This was the unmistakable message at the recent Financial Planning's Advise AI 2024 conference, where industry leaders gathered to explore the transformative impact of artificial intelligence on financial advice – from client meetings to email to compliance – and provided actionable insights for advisors aiming to maintain their competitive edge.

As someone who's closely tracked the intersection of technology and financial advice for years, I can attest that this isn't just another passing trend. The conference underscored a pivotal shift in thinking about AI's role in our field.

Contrary to fears of automation replacing advisors, the focus is now on a new form of human–machine partnership. AI is poised to enhance advisor capabilities – with a flood of startups positioning their tools as AI sidekicks – and able to leverage vast amounts of data to quickly generate personalized insights and deliver advice at scale. The primary goal being to increase efficiency so that advisors can concentrate on their core strengths: relationship building and strategic guidance.

Understanding the implications of AI in wealth management is no longer optional – it's a necessity for the future of advisory practices. As we delve into the future of our industry, the question isn't whether AI will transform wealth management, but how swiftly and profoundly it will do so. The time to prepare and adapt is now.

The Dynamic Duo: Human Advisors And AI

"Think less Terminator, more Ironman," quipped industry guru Michael Kitces. "We're not replacing advisors with AI; we're creating 'cyborg advisors' who can leverage technology to provide superior service and advice."

This sentiment echoed throughout the conference, with speakers emphasizing AI's role in augmenting human capabilities rather than replacing them. From streamlining operations to enhancing client relationships, AI is proving to be the Swiss Army knife of wealth management technology.

Michael also emphasized that AI's primary value in wealth management is enhancing advisor productivity and efficiency, particularly in areas like meeting notes and client engagement. He recommended that vendors use the term "expediting" to describe the value of their AI tools rather than "automating", as advisors need to maintain oversight and personalization over their processes.

For most advisory firms, the challenge isn't one big data issue but rather hundreds of small data problems. He noted that AI could potentially help normalize non-standardized data across different platforms, but that many firms just need better data warehousing solutions before they can leverage AI to generate useful insights.

Kitces cautioned software vendors about leading with "AI" in their marketing strategy, as it can be polarizing to up to 2/3 of advisors they surveyed. He suggested focusing on specific use cases and benefits to advisors' businesses, as they care more about results than the underlying technology used.

Many AI vendors also promote that their tools can provide personalization at scale for advisors, but Kitces downplayed the importance of this. "[T]he whole nature and flavor of what we do is every client is unique and special and different, and we have a deep relationship with them. I don't need you to create personalization at scale."

Other speakers disagreed with Kitces, including Sindhu Joseph of CogniCor Technologies, Inc., who defined personalization as having knowledge of the opportunities and risks for each client and delivering this in a proactive manner in the right context.

Data: The New Oil In The Wealth Management Engine

If AI is the engine driving an industry-wide transformation, data is undoubtedly the fuel. Michael Djurdjevic of InvestCloud echoed the importance of data centralization, noting that a unified data warehouse is crucial for AI to work its magic across multiple systems and data sources.

However, as Kitces pointed out, many firms are still grappling with the basics.

AI can significantly enhance advisor capabilities in areas such as predictive alerting, advisory intelligence, and business intelligence, potentially guiding advisors through client life events and generating personalized investment proposals, according to Djurdjevic.

Best practices for AI implementation include starting small with key performance indicators, measuring what matters most to the firm, and enabling advisors to personalize their use of AI.

While the potential of AI in wealth management appears significant, Djurdjevic maintained that success ultimately depends on the quality and centralization of a firm's data. As AI tools continue to evolve, firms with well-organized data infrastructures will be better equipped to leverage these technologies for improved efficiency and client service. (See The Journey Towards Data-Driven Wealth Management)

Efficiency: AI's Middle Name

One of the most tangible benefits of AI in wealth management is its impact on operational efficiency. Andree Mohr from RIA Integrated Partners shared that they deployed new account-opening processes using technology from Invent that saved her firm the equivalent of 2 full-time employees.  It required around 6 months of hard work from both teams, but Mohr emphasized it was worth the effort since it also allowed the team to create personalized experiences for clients by providing markers along the way.

Meanwhile, Nick Graham reported that Cambridge Investment Research saved a staggering 40,000 hours across their advisor base after deploying AI-enhanced meeting tools from Jump and Zocks.  These apps were chosen for their ability to meet compliance requirements and provide personalized choices for advisors.

Graham highlighted the importance of getting everyone comfortable with the new technology in order to achieve the benefits.

These efficiency gains aren't just about cutting costs. As Erik Allison of EA Wealth noted, tools like AI-powered transcription services are freeing up advisors to focus on what really matters: building meaningful client relationships. (See Engaging Advisors Through AI-Powered Collaboration)

The Personal Touch: AI-Enhanced Client Relationships

Despite fears of AI depersonalizing client interactions, the conference speakers painted a hopeful picture. Sindhu Joseph of CogniCor Technologies highlighted how AI is enabling more tailored and effective communications by integrating CRM data, portfolio analysis, and client behavioral profiles.

There were a number of vendors at the conference offering client meeting support software besides CogniCor, including Jump and Zocks. Just 2 years ago, the Advisortech map that Michael Kitces and I produce had only 4 products in the Client Meeting Support category, with only one, Pulse360, offering meeting automation. And the AI Assistant category didn't even exist! But since the launch of ChatGPT and the boom in generative AI technology in November 2022, these 2 categories have expanded to 19 applications!

This surge in meeting transcription applications can be attributed to significant advancements in Large Language Models (LLMs), particularly in speech recognition and Natural Language Processing (NLP). These improvements include more accurate phoneme recognition, better handling of diverse accents and languages, enhanced noise filtering, and superior contextual understanding – all of which have dramatically increased transcription accuracy while reducing the computational resources required, making real-time transcription both more reliable and economically viable.

All of the meeting support tools launched for advisors offer a similar set of features designed to enhance the personal touch in client relationships, such as surfacing action items and follow-up opportunities, as well as integrations to the top CRMs to extract data, push updates, and create tasks.

During her demo of their Advisor Copilot product, Joseph showed the platform's ability to identify life events and changes in client circumstances, such as the birth of a child, allowing advisors to reach out with timely, personalized outreach.

Era Jain from Zeplyn described AI tools that help advisors quickly access past conversation details, enabling more personalized follow-ups. "It's like having a perfect memory of every client interaction," Jain explained. "AI isn't replacing the personal touch; it's enhancing it."

What is the best way to deploy innovative AI tools? Joseph suggested that enterprise firms should be more cautious and avoid rushing to launch their own AI pilot projects.  Because the technology is moving so fast, whatever they are testing could be obsolete by the time they are ready to go into production. She recommended sticking with established vendors (like CogniCor wink, wink) who are able to keep pace with the rate of change in AI.

Jain and her co-founder both worked on speech recognition projects when they were at Google and brought this expertise to their startup.  They are positioning Zeplyn to be more than a notetaker by also offering workflow automation, form filling, and client engagement features.  This will be a difficult path for them as the functional overlap between applications in client meeting support, AI assistants, workflow support, and CRM categories is already becoming confusing. (See Text and Chat: How AI is Reshaping Client-Advisor Communications)

Navigating The Regulatory Maze

As with any technological advancement in finance, AI comes with its share of regulatory challenges. Daniel Bernstein from MarketCounsel clarified that while AI use is generally allowed under current regulations, firms need to develop comprehensive AI use policies to stay ahead of the curve when it comes to AI compliance.

Sid Yenamandra from Surge Ventures emphasized AI's role as an efficiency tool for automating compliance tasks. He highlighted the potential for AI to handle the estimated 17,000 regulatory events that need tracking annually. Yenamandra also introduced the concept of an "AI use policy", suggesting firms should inventory their AI use cases and conduct vendor due diligence.

Vall Herard from Saifr stressed the importance of high-quality data for effective AI models, drawing a comparison to traditional financial models to emphasize the need for robust model risk management practices. Herard also discussed the potential for AI to enhance marketing compliance, citing capabilities to catch 90–95% of compliance issues in content generation.

AI also has the potential to greatly reduce turnaround time for compliance reviews. LLMs will be able to review not only graphics and text but also audio and video data, which will enable much faster deployment of compliant marketing content. This could eliminate one advantage of independent advisory firms, as larger wealth management companies will be more nimble and responsive to advisor requests.

Bernstein noted that while there are no specific AI regulations for compliance, firms are expected to understand and manage their AI risks. He addressed how AI-generated content, such as meeting transcripts, fits into existing regulatory requirements for books and records. He advised firms to have clear policies on how they use AI-generated information.

All panelists discussed the need for thorough due diligence on AI vendors and tools. They emphasized the importance of understanding data sources, assessing model risks, and regularly testing AI systems to prevent drift and ensure continued accuracy.

We recommend that firms document their evaluation process and create a simple tracking tool to help with side-by-side comparisons. This will also help when justifying decisions to senior management, boards, and teams.

The discussion touched on practical challenges, such as handling errors in AI-generated meeting notes and the complexities of using AI for tasks like asset valuation in client communications. The panelists agreed that while AI offers significant benefits, it requires careful implementation and ongoing oversight.

Throughout the conversation, the panelists balanced optimism about AI's potential with caution about its risks, emphasizing the need for firms to be proactive in developing policies, conducting due diligence, and maintaining oversight of AI systems in compliance processes. (See Heavy Lifting: Leveraging AI to Drive Actionable Insights in Wealth Management)

Best Practices For Selecting The Right AI Use Cases

This panel included Michelle Feinstein, Vice President and General Manager of Global Financial Services at Salesforce, Samuel Deane, a financial advisor from Deane Wealth Management, and Amanda Lott, Head of Wealth Planning & Innovation at JP Morgan.

The consensus recommended taking a strategic approach to AI implementation, starting with a clear strategy that aligns with specific business outcomes and prioritizes compliance, data safety, and privacy. They advocated targeting 2 or 3 high-impact use cases rather than attempting to implement many solutions simultaneously. Key criteria for choosing AI applications included their potential to significantly boost advisor productivity, drive revenue opportunities, and address top pain points.

Some of the high-priority use cases the panel recommended include:

• Client Onboarding
• Summarizing Client Interactions
• Generate Sales Script Generations
• Client Meeting Prep And Summaries
• Enriching Lead Data To Improve Prospecting
• Summarize Research Documents And Earnings Reports
• Mining Knowledge Bases Of Internal Policies & Procedures

Successful AI integrations, according to the panelists, hinge on several factors: data readiness and quality, feasibility of implementation, and compatibility with existing systems like Salesforce's AI stack. The discussion underscored the importance of change management, boosting advisor receptivity to the new technologies, and ongoing education to ensure smooth adoption. Recommendations included forming an AI steering committee, leveraging partnerships with existing vendors for execution support, and maintaining a patient, iterative approach to testing and refinement.

By focusing on these best practices, wealth management firms can more effectively harness AI to transform advisor workflows and elevate client service to higher levels. (See AI’s Judgment Day: How ChatGPT-4 is Reshaping Wealth Management)

The Future: AI As A Democratizing Force

Looking ahead, the potential of AI in wealth management seems boundless. Michael Wilson of Orion painted a picture of AI systems that can integrate vast amounts of data to provide holistic financial advice. Swati Bairathi of direct indexing vendor 55ip suggested that AI could democratize access to sophisticated investment strategies, making them available to a broader range of clients.

Orion's focus has been on building AI capabilities to enable internal integration and connectivity between different systems and data sources, Wilson explained. This will allow them to provide more personalized solutions through CRM integrations.

In a similar fashion to how engagement tools create meeting summaries, Orion will be able to generate smart summaries to help advisors communicate actions to clients, such as reasons behind portfolio rebalancing, Wilson said.

Wilson emphasized the importance of adapting communication styles and language based on individual client personalities and preferences. This helps strengthen the advisor-client relationship and improve investment decisions. (See The Oppenheimer Effect: AI’s Chain Reaction in Wealth Management)

Give Them What They Want: Unlocking Client Insights With AI

AI should not be seen as a replacement for human advisors, but rather as a powerful tool to deepen client connections and understanding.

Carrie Nelson, CEO of Atlas Point, shared her personal story of using her company’s behavioral assessment tool, Financial Virtues, to improve communication with her own financial advisor. This experience highlighted how AI-powered insights can reveal and address communication gaps to create stronger advisor-client relationships.

Building trust with advisors is crucial when it comes to AI adoption, according to Brooke Juniper, CEO of Tifin Sage, which is an application designed to help advisors develop personalized advice and make more data-driven decisions. She believes that AI systems should be transparent and explainable, allowing users to understand how they work and where their insights come from. Juniper advocates for surfacing source documentation and providing insights into the underlying data and models.

Andrew Smith Lewis, CEO of Alai Studios, introduced Lydia, an AI companion for advisors focused on behavioral finance. Smith Lewis described Lydia's ability to remember advisor personalities and client interactions, which creates more personalized and engaging client experiences. He emphasized that AI should not focus solely on automating processes but rather on augmenting the capabilities of human advisors.

Juniper believes that successful AI adoption requires support from leadership. When firms view AI integration as an imperative and encourage experimentation, adoption rates increase. She points to Morgan Stanley as an example of a firm that has successfully integrated AI into its operations by fostering a culture of innovation and embracing the potential of AI.

Organic growth will become an increasingly important topic in the wealth management industry, particularly for independent RIAs, Nelson said. As more private equity money flows into the RIA space, firms will need to find sustainable ways to grow their businesses. She believes that AI can play a crucial role in driving organic growth by enabling advisors to identify and convert prospects, deepen relationships with existing clients, and efficiently manage their practices.

The future of AI in wealth management is one of increasing verticalization, Smith Lewis proposed, where AI solutions are tailored to the specific needs and requirements of the industry. He believes that generalized AI platforms like ChatGPT, while powerful, cannot fully address the complexities of wealth management. Smith Lewis anticipates the development of bespoke AI solutions that are specifically designed to meet the unique challenges and opportunities faced by advisors.

The panelists all spoke about practical considerations for AI adoption, including the importance of user-friendly interfaces, integration with existing workflows, and a top-down implementation approach. They agreed that AI should be positioned as a companion to advisors, not a replacement, and that successful implementation requires a shift in mindset from transactional interactions to a focus on how and why AI can enhance the advisor-client relationship. (See AI Unleashed: Augmenting Human Experience at Scale)

AI Implementation And ROI For Advisors

The "Innovators Fireside Chat" panel at Advise AI brought together three leaders of companies that won innovation awards to discuss their experiences developing AI solutions for advisors and the key factors driving success in AI implementation.

Andrew Altfest, CEO of FP Alpha, highlighted the shift from back-office automation to AI tools that support client-facing tasks. He noted that previous advisor technology investments focused largely on back-office efficiency, but AI now presents opportunities to streamline and enhance activities like client communication, portfolio construction, and new client onboarding. Altfest believes that this shift is driven by advisors' desire to automate tasks that previously required significant human input, allowing them to focus on higher-value activities like developing deeper client relationships and expanding their service offerings.

He cautioned, however, that the rapid pace of AI development makes it challenging for advisors to keep up. What might have been considered cutting-edge technology a few years ago can quickly become outdated, requiring advisors and technology providers to constantly adapt and innovate.

Brad DeLoatche, Chief Product Officer of digital marketing firm Snappy Kraken, focused on the importance of AI in driving both better client outcomes and increased operational efficiency. He emphasized the need for AI solutions that can help advisors truly understand their clients' needs and preferences, allowing for more personalized advice and engagement. DeLoatche believes that AI can also play a significant role in streamlining operational tasks, allowing advisors to focus on building relationships and delivering value to clients.

He shared Snappy Kraken's experience with their AI-powered compare tool and summary generator, which helps advisors create personalized narratives for clients by pulling in data from various sources. He highlighted how they leveraged generative AI to improve the language and tone of these narratives, making them more engaging and relatable for clients.

Brian Wallheimer, Editor-in-Chief at Financial Planning, noted that AI can be a powerful tool for advisors to "spend time on the right things", focusing on core activities like building relationships and providing high-value advice. He also pointed out that AI is enabling more competition in the industry, as non-traditional players like banks and direct-to-consumer platforms are increasingly using AI tools to offer wealth management services. This means that advisors will need to actively adapt and embrace AI to maintain their competitive edge.

The panelists discussed the potential impact of AI on advisor fees and business models. As AI enhances advisor efficiency and enables the delivery of more sophisticated services, the industry may see a shift in how advisors are compensated. They agreed that technology providers need to be transparent about the value they deliver and ensure a fair sharing of that value with advisors. (See AI-Powered Sales Enablement for Wealth Management with Devon Drew)

Leveraging Generative AI To Write Stuff!

One tip when using generative AI for writing marketing content is to train it to write in your voice, Susan Theder, Chief Marketing and Experience Officer at digital marketing platform FMG.  Advisors can accomplish this by uploading a few pieces of their writing and then asking the AI to describe their writing voice, tone, style, and personality. Because everyone has slightly different styles depending on what is being written, this process should be repeated for each type of output that's used frequently (e.g., a blog, a social media post, a newsletter, a client email, etc.) Advisors can create a document to save these descriptions for each content type and use it in the prompt every time they ask AI to write something for them.

Another suggestion from Theder to improve the quality of the output from generative AI tools is for advisors to clearly articulate their role and goal in the prompt, along with what they're asking it to write. For example, if writing specifically to clients impacted by Hurricane Milton, the tone would be very different from one used in a monthly newsletter. Using full sentences to clearly describe the desired outcome ensures the AI fully understands the context. And when describing a role, advisors should elaborate beyond simply identifying as a financial advisor by including details like age, specialization, and anything else that would help the AI better reflect their voice. (See From ChatGPT to Amazon Q: Are You Ready To Welcome The New AI Generation?)

Conclusion: Embracing The AI Revolution

As the wealth management industry embarks on this AI-powered journey, one thing is clear: Those who embrace this technology thoughtfully and strategically will be best positioned to thrive. The future of wealth management is here, and it's a collaborative effort between human expertise and AI capabilities.

In this brave new world, the most successful firms will be those that find the right balance between leveraging AI for efficiency and maintaining the human touch that clients value. As we move forward, the question isn't whether AI will transform wealth management, but how quickly and effectively firms will adapt to this new reality.

The rise of the advisor's AI sidekick is upon us. Are you ready?