BMO announced on November 6, 2026 the introduction of AI Agent Assist for employees handling Canadian clients’ credit card transaction inquiries at its Virtual Connect contact centre, with targets of cutting resolution time by 20–30% for eligible inquiries and automatically processing about 40% of eligible credit refund requests.
Virtual Connect receives more than 500,000 such calls each year, and each currently takes an average of 12–14 minutes to resolve. Against that baseline, the deployment focuses on the preparation work inside employee-assisted calls. The assistant is designed for internal use, bringing relevant transaction information and guidance directly to team members so they spend less time navigating systems.
Both targets are limited to eligible interactions and are objectives rather than reported contact-centre results. For qualifying inquiries, BMO says clients could receive their credit as soon as the same day, making faster refund processing a conditional client outcome rather than a universal turnaround commitment.
How AI Agent Assist Supports Employees
AI Agent Assist combines AI-enabled transaction matching, policy retrieval and case preparation for employee review. BMO intends the consolidated workflow to reduce searches across systems, manual tasks and the need to place clients on hold while staff navigate those systems. Supplying information and guidance at the point of use is also meant to help employees respond more quickly and support consistent service.
The design uses a human-in-the-loop principle. Client-service employees remain accountable for reviewing AI-generated information and making appropriate servicing decisions. The technology handles information gathering and preparation, while responsibility for the decision affecting the client stays with people.
Reusable Capabilities and Enterprise AI Goals
BMO is designing the capabilities behind AI Agent Assist for reuse across other client experiences and business processes. The contact centre therefore provides a high-volume setting for components the bank intends to carry into later agentic AI applications, extending the work beyond a single service process.
Greg Brown, BMO’s Head of Enterprise AI Transformation and Chief Digital Officer for Personal and Business Banking Canada, described the work as a broadening from individual use cases to AI at scale, with clear guardrails and people accountable for every decision.
Brown also linked that approach to protections and oversight as AI agents play a growing role in financial services. Security, privacy and human oversight are central to BMO’s approach as it builds its own capabilities and creates conditions for broader AI experiences.
The work supports BMO’s stated enterprise-wide goal of generating more than $1 billion in AI-enabled revenue growth, productivity improvements and operating efficiencies by 2030. The figure covers the bank’s broader AI ambition, rather than a value assigned to AI Agent Assist alone.
The immediate test is whether AI-supported case preparation can shorten eligible calls and automate some eligible refund requests while employees retain responsibility for servicing decisions. Because the underlying capabilities are designed for reuse, the deployment links BMO’s service-level targets to a broader operating question: whether operational gains, oversight and human accountability can remain together when the technology is applied to other processes.
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