The wealth management industry is fully embracing AI, working to identify best practices that define how to use it both effectively and responsibly.

When ChatGPT took the world by storm back in 2022, the few financial advisors who jumped on the bandwagon mainly used it to write emails, articles, and social media posts.

Flash forward to today, where more than 40% of advisors are also using various AI apps to save time transcribing meeting notes, onboarding clients, and generating leads.

In the next few years, a slew of new AI-based apps will automate many time-consuming back-office tasks, and a few may even be able to take over certain portfolio management activities.

Some of these tools are available today. When properly configured with an appropriate data structure and security safeguards, they can dramatically increase operational efficiency and give advisors more time to focus on business development and client service.

Turning meeting notes into to-do lists

Some of the earliest AI apps served as notetakers for recorded meetings and phone calls. Their functionality was mainly limited to generating transcripts and summaries.

These apps have since evolved to generate follow-up tasks that were traditionally conducted by advisors or their support staff. 

Take Zeplyn, for example. According to co-founder and CEO Era Jain, the app not only transcribes meetings or virtual sessions, but it can also analyze the content to document a client or prospect’s key personal and financial details, investment goals, life events, and personal preferences.

“With the advisor’s approval, Zeplyn can automatically enter these details into the client’s or prospect’s CRM record and generate tasks assigned either to the advisor or specific members of their team, which can help increase back-office efficiency,” says Jain.

Geoffrey Moore, chief information officer at Valmark Financial Group, anticipates the integration of note-taking apps with financial-planning applications and onboarding tools.

“For example, I’ve been closely following an announcement about an integration that will enable note-taking app FinMate AI to move information captured during client and prospect interactions directly into PreciseFP, a data management app. PreciseFP will then be able to export this data directly into financial-planning applications like eMoney, RightCapital, and MoneyGuidePro,” says Moore.

Extracting and analyzing data

Moore is also a strong advocate of AI data-extraction tools that capture and analyze information stored in various documents. These apps are particularly well-suited for aiding in product evaluation and selection.

Valmark monitors insurance policies through its Policy Management Company (PMC). In the past, PMC staff had to manually input original illustration data into the company’s core systems. Now, AI can extract the data and automatically feed it to the appropriate team members for review.

AI is also helping Valmark’s insurance marketing team keep up to date with product information from multiple insurance carriers. By placing all of the documents into an AI repository, the team can simply ask the AI questions and get answers with citations based on the content in the system, rather than have to conduct time-consuming searches.

“For example, the team recently was asked to answer a client’s question, ‘Can I deduct my long-term-care policy premiums?’ The AI was able to quickly provide the answer and display the referenced documents so our internal expert could feel confident in their response,” says Moore.

Optimizing portfolio management

Some pundits predict that AI-driven investment tools will be the main source of advice for self-directed investors by the end of the decade. Other research suggests that many investors are open to allowing AI to aid advisors in managing their portfolios.

Predictions aside, AI-driven tools that can fully handle discretionary oversight of clients’ investments are still in their relative infancy.

Wipro is a diversified IT company that provides AI solutions and other services to different industries, including financial services. Its WealthAI platform can integrate a client’s identified financial objectives, risk tolerance, and investment-policy constraints with real-time values for their account holdings to determine if rebalancing or reallocating may be necessary, according to Ritesh Talapatra, the sector head of Wipro’s Capital Markets and Insurance business.

“For example, if a client has certain preferences for risk and volatility stored in CRM or in the firm’s portfolio management platform, WealthAI can analyze market movements and trigger automatic rebalancing in real time, or alert the advisor that it has identified a need for rebalancing and get the advisor’s approval before the adjustment is made,” says Talapatra.

Ben Olsen, founder and CEO of Beemo Automation, adds that beyond making allocation calls, some of these tools are evolving to provide context for portfolio adjustment.

“AI large language models (LLM) can be trained to analyze the fundamentals and trading movements among underlying holdings within a client’s portfolio and then generate commentary the advisor can use to explain why they’re recommending reallocations at the portfolio level or increasing or reducing exposure to certain securities,” says Olsen.

Over the past several years, many investment firms and turnkey asset management programs (TAMPs) that serve financial advisors have also been vigorously exploring the use of AI in both strategy development and in some elements of portfolio management.

William Hubbard, a quantitative money management analyst consulting with Flexible Plan Investments, a TAMP serving advisors and their clients, points out that forward-thinking investment firms are integrating AI’s analytical power while preserving the human judgment essential to effective portfolio management.

In a recent article, Hubbard writes, “The capabilities AI brings to the investment management industry are impressive. Through natural language processing, AI systems can digest and analyze thousands of unstructured documents—earnings calls, regulatory filings, executive interviews—at speeds no human team could match. These systems can uncover subtle relationships among variables that might otherwise go unnoticed, helping portfolio managers identify opportunities and risks with greater precision. Risk management has advanced through AI’s ability to simulate countless market scenarios, providing deeper insights into potential vulnerabilities within portfolios.”

He adds, “Artificial intelligence is rapidly reshaping how financial professionals analyze data, assess risks, and construct portfolios. Like the revolutionary tools that changed the art world, AI is a powerful instrument—but it remains just that: a tool. As van Gogh’s brush served his vision rather than creating it, AI supports the expertise of investment professionals.”

Creating customized workflows

A growing number of packaged AI tools can assign tasks, create workflows, and generate outgoing communications based on analysis of incoming client emails. According to Olsen, these processes involve integrating AI LLMs with off-the-shelf automation tools like Microsoft Power Automate or Zapier.

As an example, he describes a workflow triggered by a client’s email to their advisor requesting a change to an IRA beneficiary.

“The workflow feeds the email to the AI LLM, which then directs the automation tool to enter the beneficiary change request into the client’s CRM record, and automatically generates the tasks needed to fulfill the request and assigns them either to the advisor or to a member of their support team. The workflow can also create an outgoing email that the advisor can send to the client acknowledging their request,” says Olsen.

While Beemo Automation specializes in developing these kinds of integrated workflows, Olsen says that technically proficient advisors or operations personnel can build many of them in-house.

“We’re seeing more and more firms build their own internal automations to fill gaps in their software stack, especially when it comes to generative AI, which can be applied across so many parts of the business. The nice thing about tools like Power Automate is that they’re relatively easy to learn and use without requiring extensive programming knowledge,” says Olsen.

Streamlining compliance reviews

One of the biggest bottlenecks for marketing teams at advisory firms are compliance reviews of marketing and advertising materials. A particular piece may need to undergo several rounds of revisions as reviewers chop out language or performance data that may be misleading or promissory.

This challenge is especially acute for smaller, independent RIAs that lack full-time advertising compliance reviewers and must rely on a designated compliance officer to review these materials.

A new generation of AI-driven marketing review tools is emerging to address this challenge.

Warrant is one of them. According to founder and CEO Austin Carroll, Warrant’s AI agent is fully trained in SEC, FINRA, and state-specific compliance and disclosure requirements as well as best practices in financial marketing.

Marketing teams or advisors can simply upload drafts or proofs of scripts, videos, images, podcasts, email messages, social media posts, disclosures, client agreements, or other client communications into the app.

“Warrant then reviews these submissions against the policies the firm has established either for national distribution or for their specific state. It then generates a report card flagging potentially non-compliant language and suggests alternative language. It can also provide suggestions based on past submissions,” says Carroll.

While Warrant and similar apps don’t replace compliance or legal officers in advertising reviews, they speed up these processes by providing a streamlined platform for submitting materials and eliminating language that could result in multiple revision rounds.

“And since Warrant stores all approvals and submissions, it can serve as a tool for providing documentation of these cases should the firm ever be audited,” says Carroll.

Data modernization: The key to making the most of AI’s capabilities

For advisory firms to make the most of the new generation of AI tools, they must be committed to data modernization, says Talapatra.

In its most simplistic form, this means moving client data into a centralized data repository where AI LLMs can access it.

“In many offices, data is siloed across different applications, making it difficult for AI agents to access. True data modernization requires firms to centralize all static and real-time client and market data from CRM systems, portfolio management applications, and client communication systems into a central, secured data reservoir, whether it’s in a mainframe or in the cloud. Once it’s there, AI agents can access this data to build the workflows needed to execute various portfolio management and client service tasks,” says Talapatra.

Matt Reiner, a managing partner at Capital Investment Advisors, agrees.

Over the last year, his firm built an agentic framework prototype using multiple AI agents to handle various tasks.

“For example, one agent was focused solely on our investment committee research. Another agent read portfolio statements, and another was focused solely on our Salesforce data. We also developed a centralized query system that can route requests to the appropriate agents to respond, whether it’s asking for a portfolio review or to take meeting notes,” says Reiner.

“But one thing we’ve learned throughout this process is that AI is only as good as your data is structured. Without structured data, AI fails you and leads to frustration. So, we shifted to creating a data warehouse instead of an agentic system, for now.”

The need for data governance

Most advisors understand that they shouldn’t be feeding confidential personal identification information (PII) into “open” AI LLMs like the free version of ChatGPT.

But most of the AI apps mentioned do require access to client-specific data on the front end to complete their tasks. That’s why advisory firms must fully understand how AI apps they’re considering using protect PII from exposure to LLMs and comply with national, state, and international privacy standards such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

Most AI apps use some kind of anonymization process that removes clients’ names, account numbers, and contact information from queries before feeding them into the LLM.

However, keeping track of these processes can be complicated when a firm is using multiple AI apps that access PII from a centralized data repository.

“That’s why it’s critical for firms that want to use AI apps to adopt a data governance policy that clearly specifies not only which data will and will not be fed into the repository, but who in the firm has access to different tools. For example, client service people might be given access to AI note-taking tools but prevented from using portfolio management apps,” says Talapatra.

Advisors can no longer ignore AI’s game-changing potential

Reiner believes that the wealth management industry must not only fully embrace AI but also work together to identify best practices and shape regulations that define how to use it both effectively and responsibly.

“This is not a time to sit back and let the story unfold on its own. We must be proactive with this technology, helping the story unfold for the betterment of our clients and teams,” says Reiner. 

“If advisors can fully harness the potential of AI to handle a lot of our routine investment management and back-office tasks, this will free up more time for us to focus on having quality personal interactions with our clients—which is our moat in an ever-evolving technological world.”

The opinions expressed in this article are those of the author and the sources cited and do not necessarily represent the views of Proactive Advisor Magazine. This material is presented for educational purposes only. This article does not imply endorsement of any company, product, or service mentioned.