Bank executives see ample opportunity in agentic AI, but getting the most out of the technology means rethinking how it’s incorporated into business processes.
AI agents can “truly transform operations,” said Ted Paris, SVP, head of analytics, intelligence and AI at TD Bank U.S. However, Paris said executives across the industry are paying attention to reports about AI project failure rates, which has pushed him to carefully consider how to incorporate AI so it becomes successfully implemented in the company's environment.
“I reflect on that a lot,” Paris said. “It’s not an issue of getting the model wrong. It’s more about how well you’re integrating these things into your business processes so that you get adoption and usage.”
AI represents a “massive opportunity” for TD Bank, Raymond Chun, group president and CEO of its parent company TD Bank Group, said in the company’s Q4 2025 earnings call in December. The bank implemented approximately 75 AI use cases generating $170 million in value in 2025, he said. It expects AI to drive $200 million in incremental value in 2026.
“We are prioritizing our AI investments with use cases focused across categories such as customer acquisition, customer insights and risk management,” Chun said during the call.
As the bank scales AI, leaders have worked to involve the full ecosystem and understand how employees and customers will be interacting with the tools, Paris said. While the bank is implementing an AI strategy that includes traditional and generative AI, it’s also assessing, testing and building its agentic capabilities.
TD Bank added a generative AI virtual assistant in its U.S. contact center. The Knowledge Management System handles questions from agents and provides consistent responses in order to reduce client hold time on calls and resolve issues faster while lowering the amount of times problems are escalated to supervisors, TD Bank told CIO Dive in an email.
When bringing in AI agents, Paris said it’s critical to involve partners, including risk, legal, IT and change management, to help ensure “our colleagues are ready for this.”
“You have to start from the beginning in thinking through: How are we going to adopt this? What’s it going to look like in its full production environment, where people are going to have to engage with it?” he said.
Crafting an AI agent strategy
Financial services firms can employ different AI agent strategies, Paris said. One involves finding tasks that can be easily automated and inserting agents into those processes. Banks “can get a lot of benefit out of that,” Paris said.
But another way forward is to rethink business processes to take an “agent-centric approach,” Paris said.
“That means: Let me now pretend like I had these agents from the beginning as opposed to simply plugging them into a process that was designed for a different type of work environment,” Paris said. “Some of these work processes were created years and years ago.”
Tools for enterprise AI agent design and deployment have matured to a point where financial services firms will take greater advantage of the technology this year, according to Accenture’s Top Banking Trends for 2026.
Indeed, there are multiple types of AI agents banks can consider that serve different purposes, Paris said, such as administrative agents to help automate routine tasks, task-oriented agents to execute actions within workflows or orchestrator agents that help dictate what’s happening with other agents.
“You need them all, with anything you’re doing,” Paris said.
Morgan Stanley’s wealth management business, for example, is building super agents to serve as an orchestration layer, Jed Finn, head of wealth management at Morgan Stanley, said earlier this month during the UBS Financial Services Conference.
Meanwhile, Citi updated its AI platform Citi Stylus Workspaces last year to include agentic AI capabilities, enabling employees to condense multi-stage workflows into a single, automated process and gather insights from large datasets.
Regardless of a bank’s approach to incorporating AI agents, vendors are zeroing in the technology’s potential in financial services.
Software provider Creatio recently released six AI agents for banking that focus on specific revenue generation and operational efficiency tasks. Salesforce unveiled Agentforce for Financial Services last year, while AWS launched mortgage approval agents powered by Amazon Bedrock, a generative AI platform.
Looking ahead, Paris said he wants to lean in on creating end-to-end projects.
“This is not like the traditional world, in which you build a model and you can just deploy it,” Paris said. “There’s far more involved in the ecosystem and, to get the most out of it, you really want to make sure you’re completely rewiring it, not just automating individual bits and pockets.”
