4 ways supervised AI can help ensure your M&A isn't DOA

Today's independent advisory firms face a convergence of challenges, including demographic shifts, technological leaps and an imbalance of supply and demand. How effectively RIAs use artificial intelligence to fuel their M&A can make the difference between performing at their best and being left in the dust.  

Consider: The number of SEC-registered advisors hit a record 15,396 in 2023, according to the latest Investment Advisory Industry Snapshot. Advisor demand also hit record highs with 56.7 million clients — more than 90% of them individuals — using their services in that year. RIA mergers and acquisition activity was up 5% in the first half of 2024 compared with the same period last year, according to DeVoe & Co. — with the greatest growth activity occurring in the largest RIAs. Yet close to 40% of advisors plan to retire over the next decade, according to Cerulli Associates — far more than plan to enter the industry.

Taken together, these data points suggest we are seeing more demand for advisory services with fewer advisors to serve it, that more of those advisors are independent RIAs, and that some are turning to M&A as a way to grow. 

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AI is already playing a role in RIAs' organic growth. But supervised AI tools — not to be mistaken with generative AI solutions — particularly excel when it comes to crunching lots of data and finding unexpected patterns, and can be deployed to provide crucial insight during mergers and acquisitions. The best piece of advice I could give to firms looking to merge or participate in a sale is to leverage AI not during a merger or acquisition, but well before. 

Here are some ways these tools can spot opportunity and reduce risk — ideally long before a deal closes. 

Improve accuracy of valuation

By studying data from multiple sources tied to a firm's offerings, supervised AI can help identify clients most likely to align with services such as tax planning, charitable giving, comprehensive estate planning, private market investments and insurance. 

Using data that the acquiring firm can obtain during diligence, AI can also render hyper-personalized engagement plans for each acquired client before the deal closes. This allows the acquirer to better understand the incremental revenue potential and related valuation, enabling more accurate pricing of the deal, forecasting of future profitability and tailoring of post-acquisition strategies. 

READ MORE: How to educate an AI model: What financial advisors should know

Identify, quantify and address client attrition risk

Data that an acquiring firm can obtain during due diligence can also be used to study client behaviors: Supervised AI tools can use third-party data to form a more complete picture of each client's propensity to leave their advisor in the case of a merger. 

Supervised AI's parameters would typically include analyzing the past 12 months of cash flows, open and closed accounts activity and the type of accounts within a client's household. These insights can help the acquiring firm understand the hidden attrition risks that may be lurking within the verifiable AUM reported during diligence. 

Prioritize existing leads pipeline

Available AI tools can intake existing leads from both parties and enrich them with third-party data to provide the firm with more precise insights for each prospect. 

For instance, has a prospect gone through a life event, changed a job, donated to charity, sold a business or received equity in a privately held company? AI can then translate these and more enriched data insights into a prioritized list of leads for the new combined firm in order to pinpoint the potential for new clients over the next three, six or 12 months after the deal closes.   

READ MORE: 7 insider RIA M&A trends, from mini-mega to equity-culture revolution

Expand wallet share with combined services

The best deals are worth more than the sum of their parts. If each firm has unique strengths, the combined client pool might present an opportunity to capture a greater share of wallet. AI can study the combined firm's ideal client profile, analyzing current assets and cash flows, looking at money-in-motion events and demographic data for each client in order to predict held-away assets ripe for consolidation.

AI can do the heavy lifting across the entire client base with speed and precision, looking for areas where the combined firm can serve clients more completely than the merging firms did individually. The result: a clearer picture of the held-away asset- gathering potential, prioritized by client, to predict net new assets that could be gained over the next several months.  

For RIAs, navigating the next few years will take nerve, opportunism and investment. With AI helping to read the tea leaves and point the way toward profitability, I am confident many of these new RIAs entering the market today can seize the opportunities presented by both organic growth and M&A activity to become industry leaders.