バンク・オブ・アメリカがAIエージェントを銀行業務に導入

AI agents are starting to take on a more direct role in how financial advice is delivered, as large banks move beyond internal tools and into systems that support real client interactions.

Bank of America is now deploying an internal AI-powered advisory platform to a subset of financial advisors, 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 rather than back-office tasks or limited pilots. It also reflects a broader shift across the industry, where AI is moving from basic assistance to systems that can 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 advisors 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 rather than operate as standalone tools.

Bank of America has been expanding its use of AI across the business. The bank has said its virtual assistant Erica handles work equivalent to about 11,000 employees, while all 18,000 of its software developers use AI coding tools that have improved productivity by around 20%, according to Banking Dive. These figures give a sense of how widely AI is already embedded across different parts of the organisation.

AI agents move closer to financial decision-making

This 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 and suggesting next steps.

That shift brings AI closer to the core of financial decision-making. Instead of acting as a support layer, the technology is now embedded within the advisory process itself.

Other large banks are moving in a similar direction. The same Banking Dive report notes that firms such as 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 at the same rate.

Early data suggest these tools can improve efficiency, though results vary. In some cases, banks report gains in how quickly advisors can access information or prepare for meetings, based on industry reporting and early deployment feedback cited by Banking Dive. At the same time, there are ongoing concerns about accuracy and oversight, especially when AI systems are used to suggest financial decisions.

A wider pattern is emerging across financial services. Many institutions are investing in AI, but they are doing so in a controlled way, often limiting deployment to specific teams or use cases. The goal is to test how the technology performs in real settings before expanding further.

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,” according to Banking Dive.

Human oversight remains central

Bank of America’s rollout stands out because of its scale and placement. Financial advisors 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.

At the same time, the system is not replacing advisors. Instead, it is meant to work alongside them. Human monitoring remains an essential part of the process, particularly when dealing with complex financial decisions or high-value clients. Industry executives also acknowledge that AI is unlikely to completely replace expert roles, particularly in complex financial workflows where context and judgement still matter.

This hybrid model is becoming more common across the sector. Rather than removing people from the loop, banks are trying to combine human judgement with machine-generated insights. Some firms are starting to treat AI as a part of the workforce rather than a tool, with staff expected to work alongside these systems on day-to-day tasks.

Progress comes with limits and trade-offs

There are also practical challenges. AI systems depend on clean, structured data, which is not always easy to achieve in large organisations with legacy systems. 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. They must also be able to explain them if questioned by regulators. This requirement may limit the amount of autonomy provided to AI systems, particularly in areas like lending or investment advice.

Despite these constraints, banks are starting to move beyond experimentation and into operational use, even if progress remains uneven. Some estimates imply that up to one-third of banking jobs, or parts of those roles, could eventually be handled by AI, though timelines remain unclear.

The introduction of AI agents into advisory roles also raises questions about how the job itself may change. If systems can handle more of the analytical work, advisors may spend more time on client relationships and less on preparation. Over time, this could shift the skills required for the role.

At the same time, reliance on AI introduces new risks. Errors in data or model output could affect recommendations, and overreliance on automated systems may reduce critical review by human staff. These issues are still being studied as deployments expand.

What sets the current phase is not just the technology, but where it is being used. Moving AI into frontline roles suggests that banks regard it as a tool for shaping outcomes rather than simply improving efficiency behind the scenes.

Bank of America’s rollout offers a view into how that transition may play out. It shows a large institution testing how far AI can be integrated into everyday work, while still keeping human oversight in place.

As more banks follow a similar path, the focus is likely to shift from whether AI should be used to how it should be managed once it becomes part of core operations.

See also: Visa prepares payment systems for AI agent-initiated transactions

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