mindtalks artificial intelligence: How to Overcome Advisor Resistance to AI-Generated Insights – Barron’s – picked by mindtalks

Today’s clients increasingly expect more personalized financial advice and offerings, including products that cut across wealth, insurance, and banking and that align with their social values. But advisors appear to be struggling to meet these expectations. In a recent survey conducted by our firm, more than half the investors we canvassed said that the financial advice they receive from their advisor was too generic.  

​​This has heightened the need for wealth management firms to embrace technology, including artificial intelligence, to arm their advisors with data and tools that will improve the quality of advice and how it is delivered. And yet when it comes to exploring AI to help advisors onboard new clients and provide more relevant advice to existing ones, most firms are stuck in low gear, with few putting the technology at the core of their business. Indeed, less than one-third of the wealth managers we surveyed are scaling AI across their businesses.

Part of the problem is getting buy-in and engagement from financial advisors who need convincing that insights created by AI will be actionable in their day-to-day roles. In some instances, recommendations provided by AI don’t match clients’ immediate needs or stages of life. Or an advisor may receive an alert indicating that she should initiate a client conversation about a particular product or service, but without an explanation as to why the recommendation is being made. Advisors also resist cumbersome processes. For example, one wealth manager’s systems require advisors to open more than 10 different screens to view various AI-generated recommendations.    

Many of these AI speed bumps can be overcome with the right organizational approaches. Here’s what wealth managers need to do to ensure that AI-driven insights are useful and embraced by financial advisors:

Focus on data. Obtaining client data from third-party external sources such as relationship maps and data providers, and then integrating it with internally held data, are key to building a robust AI model. Armed with data such as online browsing histories, life stages and events, consumer buying habits, board memberships and affinities, wealth management firms may determine that a particular client is interested in cryptocurrency or ESG investments, for example. And by introducing such investment options, the advisor avoids the client seeking out a competitor’s offerings.  

Collaborate. Treat AI as a team sport and remove existing silos between business functions. To ensure that AI-generated insights are relevant and useful, the firm’s data scientists need to be in the same room working closely with the analytics, business, sales, marketing, and advisor teams. This will help the data scientists more closely align the AI model to business needs. And it will give advisors a deeper understanding of the attributes used in the model upon which the recommendations are built.

Enable feedback. Building a feedback loop—which allows the advisor to provide insights back to the data science team—can help sharpen AI-driven insights. Suppose that prior to a client meeting, the advisor’s dashboard notes that there is an opportunity to offer a mortgage to the client. Following the meeting, the dashboard should provide a basic checklist of questions such as “Did you meet with the client?”, “Did the client accept the offer?”, “Was the recommendation helpful?”, and “Why or why not?” Providing this kind of feedback to the data scientists will help them improve the AI model and enhance the relevancy of future recommendations. These feedback loops can be created either in house or with the help of a vendor.

Measure results. It’s vital to assess whether the AI-generated insights translate into greater client engagement. Are the insights helping to grow the investment portfolio? Are they facilitating the sale of other suitable investments that the client hasn’t previously accessed, such as private placements or investment pools? Careful analysis is necessary to determine whether, for example, a sale of a particular product can be attributed to the model or from other sources the advisor researched on her own.

Adopting new technology is seldom easy. Wealth managers are making investments in AI but are still trying to successfully funnel the results down to the advisor level. To fully realize the value that these tools offer, insights must be specific, actionable, and easy for advisors to understand and use. Scaling these tools across the enterprise remains a challenge for many firms. In our experience, it requires a combination of executive commitment, willingness to adopt a culture of innovation, an ability to collaborate across silos and the right blend of data scientists and other talent.

Scott Reddel

Photo Illustration by Staff; Courtesy of Accenture

Scott Reddel leads Accenture’s North America Wealth Management practice. He brings 15-plus years of experience partnering with wealth management, asset management and private equity firms on strategy, M&A, new business launch and transformation agendas. He serves as executive lead for Accenture’s top wealth transformation client initiatives, and drives partnership engagement with the wealthtech and innovation ecosystem. He is based in New York.

Keri Smith

Photo Illustration by Staff; Courtesy of Accenture

Keri Smith leads Accenture’s Applied Intelligence Financial Services practice for the U.S. Northeast and is a leader in their Cloud First business group. She is responsible for defining how Accenture uses data, artificial intelligence (AI), automation and analytics to reimagine business and accelerate time to value for financial services clients. She has served clients across multiple industries including financial services, life sciences and private equity. She is based in New York.


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