When wealth managers think about AI they should frame the decision on how it can make their business more successful, and a lot of that is about whether clients get a better service.

This news service covers various ways in which artificial intelligence/machine learning changes wealth management. Readers want guidance. Of course, in a fast-changing situation, certain predictions must carry health warnings.

In a move to shed light on the topic is Scott Lamont, director, Consulting Services, F2 Strategy. This news service knows F2 Strategy well (we covered its recent important purchase of Oakbrook Solutions – see here). As always, we invite readers to jump into debate and if you disagree with an article, or want to add to it, let us know! The usual editorial disclaimers apply to views of guest writers. Email tom.burroughes@wealthbriefing.com

In recent months, the buzz around generative artificial intelligence (AI) and its various possible applications to the wealth management industry has become near-inescapable. While the headlines are abundant, the media may not be equipping advisors and firms with sufficient answers to understand how AI can solve real business problems. 

To help family offices and registered investment advisors (RIAs) who find themselves hesitant – wanting to get out on the field and enter the AI arena but unsure of how to play the game – I suggest walking it back to the essential aims of the business.

Knowing that engagement is a stronger driver of client satisfaction than portfolio performance, advisors and firms are constantly assessing how frequently, and through which means, they are connecting with the end client. On a larger scale, businesses must also continually evaluate time use, identifying instances of optimal resource allocation as well as areas where tasks can be streamlined for greater efficiency. The end goal is to free up more time for advisors to engage with their clients. 

Here are three key areas of focus for firms to consider where AI can help create more touch points with the client, supporting family offices in driving efficiency and growth.

1. The advisor’s day-to-day client engagement
When advisors consider how to use these new tools in their daily operations, they can begin by looking at tasks that are difficult and time-consuming to perform independently. This assessment can help them identify opportunities for generative AI tools to ease the burden. If AI is able to take the necessary information and create the desired output, then the advisor should seize the opportunity to delegate.  

Though person-to-person communication is uniquely human, AI can play a powerful supporting role when it comes to some of the digital aspects of a client communication strategy and an advisor’s ability to communicate personably at scale. For example, ChatGPT and other Open AI platforms can assist in drafting thought leadership, newsletters, blog posts, and weekly client and prospect emails. 

When it comes to engagement, AI can play a role in supporting advisors by relieving them of other tasks, enabling them to prioritize and devote more time to client interaction. For example, there are AI tools that can put together a quarterly performance review much faster than an advisor can, offering analysis based on performance return data combined with market data. By effectively integrating AI into their operations, advisors and their firms can unlock substantial time resources. This enables them to prioritize tasks that rely on their distinct human abilities, while AI takes care of the more routine and automated aspects.

For example, Conquest Planning uses AI technology to help their advisors create more efficient and personalized financial plans for clients and prospects, freeing up the advisors’ time while also allowing them to deliver personalized advice at scale. 

2. Creating operational efficiency and business growth
Machine learning and data analytics can be incredible tools for firms to gain a deeper understanding of how to enhance their business operations. Firms have access to all sorts of data: what areas of the business they are spending the most time on; how long it is taking to open accounts, to move money, or to rebalance portfolios; and even how much revenue a firm might be receiving from spending time with a particular client. 

However, firms with poorly organized data may encounter difficulties in fully harnessing the potential of such analytics. Without the right data in the right place, machine learning tools won’t be able to deliver meaningful insights about a given business. The first step here is for firms to examine their data’s infrastructure and ensure that it is set up properly, subsequently enabling these tools to glean valuable insights into how the business is running and pinpoint potential areas for improvement. 

Business intelligence (BI) tools like Qlik, Tableau, and Microsoft Power BI are all great resources. But the challenge of data organization remains. In order to fully take advantage of BI tools, firms must lay the groundwork with a robust data structure. By doing so, they enable these tools to efficiently streamline operations, address crucial questions, and steer the business toward impactful enhancements.

It's also key to make sure the right questions are being asked. One must learn how to give these tools information so they can provide the correct answers in response. Similar to how a person must learn to ‘speak ChatGPT’s language’ to avail themselves of its full benefits, users of BI tools must also develop proficiency in communicating with these tools. 

3. Data privacy and regulatory issues
Compliance should always be a primary concern in our industry, and this holds true when considering the integration of generative AI. Over the next year or two, it will be crucial to closely monitor data privacy and regulatory issues as significant trends. Firms need to have clarity on the destinations of their clients' data and shore up the security of those platforms. Consequently, we are witnessing firms such as Morgan Stanley opting to license ChatGPT's software, which prevents their clients' information from being transmitted outside the firm's secure environment.

While bringing AI solutions in-house is a step toward ensuring data integrity in emerging tools, it is only the tip of the iceberg. As this trend evolves, wealth management firms must remain abreast of regulatory shifts and identify ways of exerting control over information within their organizations. This includes keeping up with evolving compliance requirements related to AI and actively shaping data governance practices to ensure regulatory compliance.

For example, firms may want to implement a protocol that requires advisors to review any external messaging generated by AI. This ensures that every client engagement communication undergoes human advisor scrutiny prior to dissemination. 

Whether a firm adopts these tools today or a year from now, the writing on the wall is clear. AI and machine learning are here to stay, having already demonstrated their ability to deliver highly valuable use cases for family offices and RIAs. And though there will always be those who are concerned that robots are coming for their jobs, I am optimistic that AI has more good than evil to offer our industry. It is not going to steal your job; the person (or firm) using AI effectively is going to steal your job. 

To that end, advisors and firms would be wise to focus on learning the languages of AI and BI tools and thinking ahead about the possible associated regulatory implications. This way, when – not if – the time comes to implement, they will already be well on their way.