Manulife moves AI agents into core financial workflows

Manulife’s Bold AI Play: $1 Billion Bet on Agentic Automation in Insurance

After years of cautious experimentation with artificial intelligence, large financial institutions are now shifting toward operational deployment—and Canadian insurance giant Manulife is leading the charge. The company is investing heavily in agentic AI systems designed to autonomously execute business workflows, marking a significant evolution from simple chatbots to intelligent digital workers.

The insurer announced its ambitious plan to generate over US$1 billion in value by 2027 through productivity gains and workflow automation. This isn’t just another pilot program—Manulife is building a comprehensive runtime platform that will allow AI agents to interact with internal systems, complete multi-step tasks, and assist in decision-making processes across the organization.

Currently, Manulife has more than 35 generative AI use cases in production, with plans to expand to approximately 70 within the next few years. The company reports that around 75% of its global workforce already uses generative AI tools in some capacity, demonstrating the rapid adoption of these technologies within the organization.

From Pilots to Production: The AI Evolution in Financial Services

The insurance industry presents a perfect testing ground for agentic AI. Every day, millions of policy documents, claims records, underwriting assessments, and financial reports flow through multiple systems and teams before reaching final decisions. These complex, data-intensive processes create ideal conditions for AI automation.

Unlike traditional AI tools that respond to single prompts, agentic systems can complete sequences of tasks across different software platforms. For example, an AI agent might automatically gather data from multiple internal databases, analyze patterns, and generate comprehensive reports for claims adjusters or underwriters—tasks that typically consume hours of human labor.

This shift represents a broader industry trend. According to McKinsey’s 2024 Global AI Survey, 65% of organizations now use generative AI in at least one business function, up from just one-third the previous year. However, the survey also reveals that only a small fraction of these deployments have reached full production across large parts of businesses, with most remaining limited to pilot projects or specific departments.

Navigating the Regulatory Minefield

Financial institutions face unique challenges when deploying AI at scale. The insurance sector operates under strict regulatory oversight, requiring robust controls around data usage, decision transparency, and auditability. Systems used for underwriting, risk analysis, or claims processing must meet rigorous standards for explainability and fairness.

Manulife’s platform includes comprehensive governance and security controls designed to manage how AI agents interact with internal systems. These safeguards track decision-making processes, monitor data usage, and ensure operations comply with company policies and regulatory requirements. Such measures are particularly crucial in insurance, where automated systems often support processes tied to claims management and regulatory reporting.

Deloitte’s research on AI in financial services indicates that banks and insurers are significantly increasing investment in model oversight tools, internal AI policies, and risk review processes as they expand automation. The challenge lies in balancing efficiency gains with regulatory expectations around accountability and fairness.

The Business Case for AI Agents

The potential benefits of agentic AI in insurance operations are substantial. Claims processing, policy management, internal reporting, and customer support all involve repetitive tasks that require staff to gather and synthesize information from multiple sources. AI systems capable of autonomously collecting and organizing this information could dramatically reduce manual workload.

Research from Accenture’s Banking Technology Vision report suggests that AI-driven automation could help financial institutions reduce operational costs by up to 30% over time, with much of the benefit coming from speeding up routine tasks and improving data handling accuracy.

Other financial firms are pursuing similar strategies. Banks across the US and Europe have begun testing AI agents for fraud detection, internal research tasks, and customer service operations. The common thread is using AI to handle time-consuming analysis and data collection, freeing human employees to focus on higher-value activities.

The Path Forward: Risks and Opportunities

The transition from AI pilots to operational systems carries inherent risks. AI models can produce errors, and automated workflows can amplify mistakes if not properly monitored. This risk explains why many financial firms are adopting gradual rollout strategies, starting with internal tools before expanding to customer-facing systems.

Manulife’s approach demonstrates how large enterprises are testing the next stage of AI adoption. The critical question will be whether these systems can deliver reliable results while meeting stringent regulatory requirements. If successful, AI agents could become integral to financial operations, handling routine work that previously required large teams of staff.

As companies move beyond early experiments, the focus is increasingly on making AI technology work within the everyday systems that run large organizations. The insurance industry, with its complex data flows and regulatory requirements, serves as a proving ground for whether agentic AI can deliver on its promise of operational transformation.

The next few years will reveal whether Manulife’s billion-dollar bet on AI automation represents the future of financial services or an ambitious experiment that pushes the boundaries of what’s possible with current technology.

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