Large financial institutions have been locked in a slow-moving transition from AI experimentation to operational deployment — and pressure to show concrete returns is mounting across the sector.
Manulife, the Canadian insurance giant, is now deploying agent-based AI systems directly into its core internal workflows. Unlike the chatbot-style tools that defined the first wave of enterprise AI adoption, these agents are designed to execute sequences of tasks across multiple software environments and datasets without requiring step-by-step human instruction.
The company says it expects its AI initiatives to generate more than US$1 billion in value by 2027 through productivity gains and workflow automation, according to the announcement. That figure sits at the center of a broader operational strategy, not a side project.
From Pilots to Production
Manulife currently has more than 35 generative AI use cases in production and plans to expand that figure to approximately 70 in the coming years. The firm also reports that around 75% of its global workforce already uses generative AI tools in some form. Those numbers signal a deployment pace well beyond the pilot-stage activity that still defines most of the industry.
The new runtime platform is built to support what the company calls agentic AI — systems capable of interacting with internal data sources, collecting information across multiple tools, and preparing outputs for employees handling case reviews or internal reporting. The practical effect is a reduction in the time staff spend gathering information before making a decision.
Insurance operations are well suited to this kind of automation. Policy records, underwriting assessments, claims data, and financial reports typically pass through several systems and teams before any decision is reached. That structure creates natural entry points for agents that can move information across those boundaries without manual intervention.
Governance as a Condition, Not an Afterthought
Operating in a heavily regulated sector means that deploying AI at scale is not purely a technology problem. Systems touching underwriting, risk analysis, or claims management must remain auditable and explainable under existing financial services rules. A Deloitte study on AI in financial services notes that banks and insurers are increasing investment in model oversight tools, internal AI policies, and risk review processes as automation expands.
The platform Manulife is building includes governance and security controls designed to track how agents interact with internal systems, monitor data usage, and ensure operations stay within company policy. Those safeguards are not optional in this environment — they are a condition of deployment.
The broader industry context adds weight to that challenge. A McKinsey 2024 Global AI Survey found that roughly 65% of organisations now use generative AI in at least one business function, up from about one-third the prior year. Yet only a small portion of those deployments have reached full production across large parts of their respective organisations, with many still confined to specific teams or limited pilots.
The company said it intends to continue expanding the number of agentic use cases deployed across its operations as the platform matures.
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