For several years, major financial institutions have experimented with artificial intelligence through limited applications such as analytics tools and customer service chatbots. A new phase of adoption is emerging, one in which AI systems are integrated directly into operational workflows and can execute tasks across enterprise platforms.
Canadian insurance and financial services company Manulife is among the firms moving in this direction. The company is working to introduce agent-based artificial intelligence into its internal infrastructure, enabling automated systems to participate in routine business processes and support decision-making across departments.
To enable this shift, Manulife is building its capabilities around a runtime environment designed for agentic AI, a type of technology that can interact with multiple software applications and data sources to complete multi-step tasks. The initiative forms part of a broader strategy to streamline operational workloads and improve internal productivity.
According to the company, its artificial intelligence investments could deliver more than US$1 billion in business value by 2027, largely through improved efficiency and automation of repetitive processes. While Manulife has been exploring AI for years, the current strategy places greater emphasis on embedding the technology into everyday operational systems rather than using it only as a supplementary tool.
The insurer has also expanded the use of generative AI across its workforce. Company disclosures indicate that more than 35 generative AI applications are already running in production, with plans to nearly double that figure to around 70 in the coming years. In addition, approximately three-quarters of the company’s global workforce now uses generative AI tools in some capacity.
Bringing AI into Operational Workflows
Insurance operations typically involve managing large volumes of structured data, including policy records, claims documentation, underwriting assessments, and financial reporting information. These datasets often pass through several teams and systems before decisions are finalised.
Such complex workflows present opportunities for automation. Manulife’s new platform will enable internal teams to deploy AI agents that can interact with the company’s systems and datasets. Unlike conversational chatbots that respond to individual prompts, these agents are designed to complete sequences of tasks across different tools and digital environments.
For instance, an AI agent could gather information from multiple internal databases and compile summaries for employees who review claims or prepare financial reports. By automating the process of locating and organising information, the technology aims to reduce the time staff spend on preparatory tasks before making business decisions.
In recent years, many organisations have experimented with generative AI, primarily for activities such as writing content, generating code, and summarising documents. Industry analysts say the next challenge is transforming those capabilities into systems that support real operational processes within large enterprises.
A 2024 global survey by McKinsey & Company found that about 65% of organisations now use generative AI in at least one business function, a sharp increase from roughly one-third the year before. However, the research also highlights that most deployments remain limited in scale, with only a small percentage fully integrated into enterprise-wide operations.
AI Deployment in a Highly Regulated Industry
Introducing AI into financial operations presents additional complexities. Banks and insurance companies operate under strict regulatory frameworks that require transparency in decision-making and careful management of sensitive data.
Systems used for underwriting, risk evaluation, and financial analysis must be auditable and explainable, meaning organisations must maintain clear oversight of how automated decisions are generated.
A Deloitte study on artificial intelligence in financial services notes that institutions are increasing investments in governance frameworks, risk management tools, and monitoring systems as they scale their AI initiatives. The goal is to capture efficiency gains while maintaining compliance with regulatory expectations surrounding accountability and fairness.
Manulife says its new platform incorporates governance and security controls designed to monitor how AI agents access data and interact with internal applications. These measures are intended to ensure transparency in automated processes and maintain alignment with internal policies and regulatory requirements.
Such safeguards are particularly important in insurance, where automated tools often support processes related to claims management, compliance reporting, and financial documentation.
Growing Interest in AI Agents
The growing interest in AI agents stems from their potential to automate large volumes of administrative work. Financial institutions manage extensive back-office operations, including claims processing, policy administration, and internal reporting, all of which involve repetitive data-handling tasks.
AI systems capable of collecting and organising information from multiple systems could reduce manual workloads and allow employees to focus on higher-value activities.
Other financial organisations are also exploring similar technologies. Several banks in North America and Europe have begun experimenting with AI agents for applications such as fraud detection, research support, and operational analysis.
Research from Accenture suggests that AI-driven automation could eventually reduce operational costs in financial institutions by up to 30%, depending on the complexity of the processes being automated.
Despite the potential advantages, scaling AI into core business systems carries risks. AI models can produce inaccuracies, and automated workflows may propagate errors if proper oversight mechanisms are not in place. As a result, many organisations are gradually rolling out these technologies, often beginning with internal tools before expanding their use to customer-facing services.
Manulife’s effort to embed agent-based AI into its operational environment reflects a broader shift in enterprise technology adoption. As companies move beyond early experimentation, the focus is increasingly on integrating AI into the systems that underpin everyday business operations.
Whether such systems can consistently deliver reliable outcomes while meeting regulatory requirements will likely determine how widely AI agents are adopted across the financial sector in the years ahead.
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Bio: Ugochukwu is a freelance journalist and Editor at AIbase.ng, with a strong professional focus on investigative reporting. He holds a degree in Mass Communication and brings extensive experience in news gathering, reporting, and editorial writing. With over a decade of active engagement across diverse news outlets, he contributes in-depth analytical, practical, and expository articles exploring artificial intelligence and its real-world impact. His seasoned newsroom experience and well-established information networks provide AIbase.ng with credible, timely, and high-quality coverage of emerging AI developments.