Bridging the AI Confidence Gap Between the C-Suite and Advisors

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The rise of artificial intelligence in wealth management is not just a trend – it's a transformation. AI has been a part of the advisor’s toolkit for years, but the landscape is rapidly shifting. Today’s AI tools, particularly those powered by advanced machine learning and large language models, offer capabilities that go far beyond the automation of routine tasks.

This doesn’t come without firms facing challenges. According to a recent study by global talent recruiting company Upwork, there’s a notable gap between the enthusiasm of executives for these technologies and the confidence of the employees who are expected to use them.

C-suite leaders are overwhelmingly optimistic, with 96 percent expecting AI to boost productivity across their companies. But the reality on the ground tells a different story: 77 percent of employees report that AI has actually added to their workload. They find themselves spending more time reviewing AI-generated content, learning to use these tools, and, in some cases, taking on additional work as a result of AI implementation.

How can wealth management firms ensure that AI tools are genuinely enhancing productivity rather than becoming a burden on financial advisors?

Understanding the new AI landscape

The AI tools of the past were designed to automate repetitive, rule-based tasks. They made processes like retirement planning more efficient, but they operated within well-defined parameters. Today’s generative AI, however, is a different beast. It can tackle complex, non-deterministic tasks – things that were previously thought to be the exclusive domain of human judgment.

For financial advisors, this means that AI is now capable of enhancing decision-making processes, improving client interactions, and streamlining operations in ways that were unimaginable just a few years ago. But with this increased power comes a greater need for understanding and trust in these tools.

Involving advisors in the development process

In my experience, one of the most effective ways to ensure that AI tools meet the needs of advisors is to involve them in the development and beta-testing process from the outset. We successfully employed this approach in our partnership with a large national RIA.

From the beginning, advisors were deeply involved in shaping an AI-driven tool designed to enhance their firm’s portfolio mapping process. Setting clear expectations about what the tool could – and couldn’t – do at each stage of development was crucial. This transparency built trust and allowed for quick adjustments to address any concerns, setting the stage for a smoother rollout when the tool was ready for broader use.

Addressing the confidence gap

After the development phase, the focus shifted to implementing the AI tool in a way that aligned with advisors’ day-to-day needs. For many advisors, the introduction of AI can initially seem like a solution looking for a problem – an additional layer of complexity rather than a simplification.

To counter this, it was essential to identify specific areas where AI could alleviate the most significant pain points, such as automating routine tasks, enhancing the accuracy of portfolio analyses, or streamlining communication processes.

In the case of our large RIA partnership, the AI tool was designed to reduce manual errors, save time, and offer flexibility in client portfolio matching. The success of this pilot program demonstrated tangible benefits, highlighting the importance of involving frontline users in the deployment of AI tools, making them feel empowered rather than overwhelmed.

The importance of training and support

Implementing AI is not a “set it and forget it” proposition. Advisors need thorough training that goes beyond the mechanics of the technology, addressing how AI fits into their broader workflow and supports their professional goals. While this training might initially require some extra effort – such as learning new tools and refining outputs – it is crucial in mitigating the challenges reported by employees who feel that AI has added to their workload.

By investing in effective training and well-designed software, advisors can move past the initial learning curve and experience significant time and work savings. The goal is to ensure that any upfront effort quickly translates into enhanced efficiency and productivity, making the adoption of AI a net positive for advisors.

Transparency remained key during the training phase. Advisors needed to know exactly what the AI tool was supposed to do and how its success would be measured. Clear metrics were established to gauge success, such as reduced response times or increased time spent on higher-priority tasks.

In the pilot with the large RIA, the impact of proper training and support was evident. The AI-driven tool for document extraction and portfolio mapping resulted in significant time savings of $35,000 per year, a 90 percent success rate in data extraction (up from 50 percent in the first month), and a reduction in turnaround time from one week to just 12 hours. These outcomes were not just due to the technology itself, but also because of the investment in training advisors and providing ongoing support to address any issues that arose.

Maximizing the benefits of AI

The true potential of AI lies in its ability to enhance operational efficiency and scale advisory businesses. By automating low-value tasks, AI frees up advisors to focus on what they do best: building relationships and providing personalized financial advice.

But it’s important to remember that AI is not a silver bullet. It won’t fix broken processes or solve all of a firm’s problems overnight. To maximize the benefits of AI, firms need to have a clear understanding of what they want to achieve and how AI can help them get there.

For example, one of the most common pitfalls we see is the assumption that AI will magically solve all data-related issues. In reality, AI is only as good as the data it’s fed. That’s why it’s critical to have good data practices in place and to continuously refine these practices as the technology evolves.

The potential of AI in wealth management is undeniable, but realizing that potential requires more than just adopting the latest technology. It requires a thoughtful, user-centered approach that starts with understanding the needs of the people who will be using these tools daily. By engaging advisors in the process, providing thorough training, and setting realistic expectations, firms can bridge the gap between C-suite optimism and frontline reality.

Gabe Rissman is the co-founder and president of YourStake.

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