Large financial firms have spent years testing artificial intelligence in small projects, often limited to data analysis or customer support tools. The next phase appears to involve something more operational: systems that can take action in business workflows. Canadian insurer Manulife is moving in that direction as it works to deploy agent-based AI systems inside its internal operations.
The company is building these abilities with a runtime platform designed to support agentic AI, the type of system that can carry out tasks in different software tools and datasets. Manulife said the effort is part of a broader plan to automate high-volume work and assist internal decision making in the business.
In a company statement announcing the project, the company said it expects artificial intelligence initiatives to generate more than US$1 billion in value by 2027 through productivity gains and workflow automation. The insurer has been investing in AI for several years, but the current push focuses on integrating the technology more deeply into day-to-day operations. Manulife has already been expanding its internal use of generative AI tools. The company said it currently has more than 35 generative AI use cases in production and plans to expand that number to about 70 in the coming years. It also reported that around 75% of its global workforce already uses generative AI tools in some form, according to company disclosures.
Moving AI to operations
Insurance companies handle large amounts of structured data. Policy information, claims records, underwriting assessments, and financial reports often move through several systems and teams before a decision is made. These processes create an environment where automation tools can assist with tasks like document review and internal reporting. Manulife said its new platform will allow teams to deploy AI agents that can interact with internal systems and data. Instead of responding to a single prompt like a chatbot, these agents are designed to complete sequences of tasks in different software tools and workflows.
For example, an AI agent might collect data from several internal systems and prepare summaries for employees who are reviewing cases or preparing reports. The goal is to reduce the time staff spend gathering information before making a decision.
Over the past two years, many companies experimented with generative AI tools for tasks like writing, coding, or summarising documents. Analysts say the next challenge is turning those abilities into systems that can support operational work in large organisations.
A report from McKinsey’s 2024 Global AI Survey found that about 65% of organisations say they now use generative AI in at least one business function, up from about one-third in the previous year. However, the same research notes that only a small portion of those deployments have reached full production in large parts of the business, with many still remaining limited to pilot projects or specific teams.
AI inside regulated financial systems
Financial institutions face extra hurdles when they try to move AI into production. The sector operates under strict regulatory oversight, which requires strong controls around data use and decision transparency. Systems used for underwriting, risk analysis, or investment decisions must be auditable and explainable. That environment makes governance and monitoring central to any AI deployment. A study from Deloitte 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 they expand automation. Organisations are trying to balance efficiency gains with regulatory expectations around accountability and fairness.
Manulife said the platform includes governance and security controls intended to manage how AI agents interact with internal systems. The controls help track how decisions are produced, monitor how data is used, and ensure the systems operate in company policies. Such safeguards are important in insurance, where automated systems often support processes tied to claims management and regulatory reporting.
The case for AI agents
The appeal of AI agents lies in their ability to reduce manual work in large administrative operations. Claims processing, policy management, internal reporting, and customer support involve repetitive tasks that require staff to gather data from different sources. AI systems that can collect and organise information in systems may allow employees to focus elsewhere.
Other financial firms are exploring similar approaches. Banks in the US 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 analysis or data collection.
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, depending on the processes involved. Much of the benefit comes from speeding up routine tasks and improving the accuracy of data handling. The move from pilots to operational systems carries risks. AI models can produce errors, and automated workflows can amplify mistakes if they are not monitored. That risk is one reason many financial firms are adopting gradual rollout strategies, starting with internal tools before expanding to customer-facing systems.
Manulife’s plan to deploy agent-based AI in its operations shows how large enterprises are testing the next stage of enterprise AI adoption. The important question will be whether these systems can deliver reliable results while meeting regulatory expectations. If they can, AI agents may become a regular part of financial operations, handling routine work that once required large teams of staff.
As companies push beyond early experiments the focus is on making technology work inside the everyday systems that run large organisations.
(Photo by Joshua)
See also: Agentic AI in finance speeds up operational automation
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