AI agents are starting to take on a more direct role in how financial advice is delivered, as large banks move into systems that support client interactions.
Bank of America is now deploying an internal AI-powered advisory platform to a subset of financial advisers, rolled out to around 1,000 financial advisers, according to Banking Dive. The move is one of the clearer early examples of how AI is being used in core banking roles, where systems support decision-making in real time.
The platform is based on Salesforce’s Agentforce, which enables the creation of AI agents to handle tasks. It is designed to help advisers handle client queries and prepare recommendations. It can also help manage daily workflows. According to Banking Dive, the system is part of a wider push among major banks to test how AI agents can work alongside human staff.
Bank of America has been expanding its use of AI in its business. It’s said its virtual assistant Erica handles work equivalent to about 11,000 employees, while 18,000 software developers use AI coding tools that have improved productivity by around 20%.
AI agents move to financial decision-making
The approach differs from earlier deployments of AI in banking, which focused mainly on chatbots or internal productivity tools. In those cases, AI was used to answer simple questions or automate routine tasks. The newer systems are built to handle more complex work, including analysing client data.
Firms like JPMorgan, Wells Fargo, and Goldman Sachs are also testing AI tools aimed at improving productivity and helping staff in client-facing roles, though these efforts vary and are not always focused on advisor-specific AI agent systems. While each bank is taking a different approach, the common goal is to increase output without expanding headcount.
Banks report gains in how quickly advisers can access information or prepare for meetings, based on industry reporting and early deployment feedback. Yet there are ongoing concerns about accuracy and oversight, especially when AI systems are used to suggest financial decisions.
Some analysts remain cautious about how quickly AI is changing banking. Wells Fargo analyst Mike Mayo wrote that recent developments have yet to produce major new products, describing the current phase as “a little boring from a product standpoint”.
Human oversight
Bank of America’s rollout stands out because of its scale. Financial advisers sit at the centre of the bank’s relationship with clients, particularly in wealth management. Introducing AI into that role suggests a growing level of trust in the technology. It also shows a willingness to let it influence how advice is formed and delivered.
When dealing with complex financial decisions or high-value clients, industry executives acknowledge AI is unlikely to completely replace expert roles, particularly in complex financial workflows where context and judgement matter.
This hybrid model is becoming more common in the sector. Firms are treating AI as a part of the workforce, with staff expected to work alongside systems day-to-day.
Progress’s limits
There are also practical challenges. AI systems depend on clean, structured data, which is not always easy to achieve in large organisations. Integration with existing tools can take time, and staff may need training to use new systems effectively.
Regulation adds another layer of complexity. Financial institutions must ensure that AI-driven recommendations meet compliance standards and explain decisions if questioned by regulators. This requirement may limit the amount of autonomy provided to AI systems, particularly in areas like lending or investment advice.
Some estimates imply that up to one-third of banking jobs, or parts of those roles, could eventually be handled by AI. The introduction of AI agents into advisory roles raises questions about how the job itself may change. If systems can handle more of the analytical work, advisers may spend more time on client relationships and less on preparation. Over time, this could shift the skills required for the role.
Reliance on AI introduces new risks. Errors in data or model output could affect recommendations, and over-reliance on automated systems may reduce critical review by human staff. The issues are still being studied as deployments expand.
Bank of America’s rollout offers a view into how an AI transition may play out. It shows a large institution testing how far AI can be integrated into everyday work. As more banks follow a similar path, the focus is likely to shift to how AI can be managed once it becomes part of core operations.
See also: Visa prepares payment systems for AI agent-initiated transactions
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