Advisors and home-office executives at banks have increased their use of artificial intelligence (AI), for both assisting with data review and also directly in the portfolio construction and asset allocation process. While AI-related tools have yet to hit widespread adoption in the banking space, leading industry players are leveraging AI to offer a more customized and irreplicable experience for the advisor, according to The Cerulli Report—U.S. Private Banks and Trust Companies 2025.

While just fewer than half (42%) of bank advisors report current use of AI capabilities within their practice, this number is anticipated to increase to more than three-quarters (77%) within the next two years. Private banks, however, cite significantly higher levels of AI usage with more than half (56%) already using AI assistance to some degree and 80% anticipating integration in the next two years.

The breadth of AI capabilities at banks ranges from text-based large language models to real-time investment data analysis backed by complex data science to make high-conviction buy and sell recommendations. AI is being used in portfolio construction both directly via AI-powered asset management strategies and indirectly with more AI-powered risk management advice and optimization tools.

“Advisors may be able to leverage AI-enabled tools to further customize client accounts, meeting needs and goals, risk tolerances, and liquidity requirements, as well as optimizing tax outcomes,” says Matt Zampariolo, research analyst. “When properly controlled, offloading this type of analysis to machine learning can free advisors to spend more time face-to-face with clients, marketing and prospecting, engaging in more human-driven planning projects such as estate planning and wealth transfer, and deepening existing relationships,” he adds.

While the benefits of increased investment and adoption of enhanced technology systems are clear, many banks still run into hurdles. Year after year, high associated costs continue to be a primary factor preventing the widespread adoption of technology within bank wealth management departments. More than half (55%) of executives cite cost as the top factor holding their firm back from technology adoption, followed distantly by advisor experience, or lack thereof, and data security risk concerns.

“Smaller community and regional banks generally lack the size of the private banks and larger superregionals, making large investments in enterprise-level advisor-facing and other technologies a significant challenge,” says Zampariolo. “However, the case can be made that early adopters of AI will be at the forefront of growth in the years to come. Cerulli believes that nimble bank practices should evaluate their current and prospective integration of third-party AI tools that may lead to enhanced advisor productivity and client outcomes.