Manulife Bets on AI to Transform Its Financial Processes

Manulife Commits to AI Automation

In the world of large financial enterprises, the adoption of artificial intelligence has long been limited to specific projects, often focused on data analysis or enhancing customer support. However, a new era is emerging, where AI is set to play a more central role in business operations. Canadian insurer Manulife exemplifies this transition by integrating AI agents into its internal processes.

Manulife is developing an execution platform specifically designed for agent-based AI, a technology that enables systems to perform tasks across various software tools and data sets. This initiative is part of a broader strategy aimed at automating high-volume tasks and improving decision-making within the company.

In a recent announcement, Manulife expressed its ambition to generate over $1 billion in value by 2027 through AI, focusing on productivity gains and workflow automation. The company, which has been investing in AI for several years, is now concentrating on a deeper integration of this technology into its daily operations. Currently, more than 35 generative AI use cases are in production, with a goal of reaching around 70 in the coming years. Approximately 75% of its global employees are already using generative AI tools in various forms.

AI at the Core of Operations

Insurance companies, like Manulife, manage enormous volumes of structured data, ranging from policy information to claims records, underwriting assessments, and financial reports. This data often flows through multiple systems and teams before a decision is made, creating an environment ripe for automation. Manulife anticipates that its new platform will enable teams to deploy AI agents capable of interacting with internal systems and data.

Unlike traditional chatbots that respond to simple queries, these AI agents are designed to perform complex task sequences across different software tools and workflows. For instance, an agent could aggregate data from several internal systems and prepare summaries for employees tasked with reviewing cases or drafting reports. The goal is to reduce the time staff spend gathering information before making a decision.

Over the past two years, many companies have experimented with generative AI tools for tasks such as writing, coding, or document synthesis. Experts believe the current challenge is to transform these capabilities into systems that can support operational work in large organizations.

Regulatory Challenges in Financial Systems

Financial institutions, due to the strict regulatory oversight that governs them, face additional hurdles when attempting to deploy AI in production. The systems used for underwriting, risk analysis, or investment decisions must be audited and explainable, making governance and monitoring essential for any AI deployment.

A Deloitte study on AI in financial services highlights that banks and insurers are increasing their investments in model oversight tools, internal AI policies, and risk review processes as they expand automation. Organizations are seeking to balance efficiency gains with regulatory requirements for accountability and fairness.

Manulife clarified that its platform includes governance and security controls to manage how AI agents interact with internal systems. These controls aim to track how decisions are made, monitor data usage, and ensure that systems operate in accordance with company policies. Such protections are crucial in the insurance sector, where automated systems often support processes related to claims management and regulatory reporting.

The Benefits of AI Agents

The appeal of AI agents lies in their ability to reduce manual work in vast administrative operations. Claims processing, policy management, internal reporting, and customer support often involve repetitive tasks requiring staff to gather data from various sources. AI systems capable of collecting and organizing information within systems could allow employees to focus on other tasks.

Other financial companies are exploring similar approaches. Banks in the U.S. and Europe have begun testing AI agents for fraud detection and internal research tasks. In many cases, the goal is to assist employees with time-consuming analyses or data collection.

Research from Accenture's Banking Technology Vision report suggests that AI-driven automation could help financial institutions reduce their operational costs by up to 30% over time, depending on the processes involved. A significant portion of the benefits comes from accelerating routine tasks and improving data processing accuracy. Transitioning from pilot projects to operational systems carries risks. AI models can produce errors, and automated workflows can amplify mistakes if not monitored. This risk is one reason many financial companies are adopting phased deployment strategies, starting with internal tools before expanding to customer-facing systems.

Manulife's plan to deploy AI agents in its operations illustrates how large companies are testing the next step in enterprise AI adoption. The crucial question will be whether these systems can deliver reliable results while meeting regulatory expectations. If so, AI agents could become an integral part of financial operations, managing routine tasks that once required large teams of personnel.