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(Image source: Manulife)
Manulife Financial Corporation (Toronto), one of the world’s largest life insurance companies with approximately 37,000 employees and more than C$1 trillion in assets under management through its Manulife Wealth and Asset Management business, is industrializing its artificial intelligence capabilities as part of a broader strategy that management describes as an AI-powered insurance company.
The initiative follows the selection of insurance company Akka (San Francisco) to provide the runtime infrastructure for a new enterprise AI platform, along with AdaptiveML as a reinforcement learning engine to dynamically optimize growth and manage large-scale AI use cases. Jody Wallis, global chief AI officer at Manulife Financial Corporation, said the initiative reflects a fundamental shift in how the company thinks about artificial intelligence, moving from isolated experiments to a disciplined, value-driven approach to responsibly embedding AI across the enterprise.
For the insurance industry, Manulife’s move highlights how AI is rapidly moving from experimentation to enterprise infrastructure. While many carriers have launched AI pilots, far fewer have begun building the governance, compute management, and operational platforms needed to run hundreds of AI applications within their core workflows. The strategy of Manulife, one of the world’s largest life insurance companies and a global asset manager, suggests that the next stage of AI adoption in insurance will be defined not by individual use cases, but by the ability to operationalize AI as a foundational capability across the enterprise.
Jodie Wallis, Global Chief AI Officer, Manulife Financial Corporation (Click to enlarge)
“Driving AI value is one of the five key pillars of our revamped enterprise strategy,” Wallis said. “Before 2025, it was an enabler of business strategy, and now we believe it is an integral part of business strategy.”
This change reflects the magnitude of AI’s potential impact on insurance company operations. Wallis describes AI as a technology that can reshape nearly every aspect of a business, from customer experience and distribution to underwriting and fraud detection.
“AI has the potential to influence everything we do,” she comments. “AI is changing every part of our business and is good for our customers, colleagues and stakeholders.”
10 years of AI investment
Manulife’s AI program dates back nearly a decade. The company began investing in artificial intelligence capabilities in 2016, initially focusing on machine learning applications such as trend modeling, price analysis, and fraud detection.
For several years, these efforts primarily supported analytical decision-making rather than operational processes. Between 2016 and 2023, insurance companies introduced approximately 70 AI use cases.
The advent of generative AI has dramatically accelerated development.
“In 2024 and 2025, we deployed 140 AI use cases,” Wallis said. “This equates to approximately 70 units per year. We plan to deploy another 200 units in 2026.”
This shift represents both a rapid increase in the speed of adoption and a shift in the way AI is used. AI systems are increasingly being integrated directly into operational workflows, rather than operating primarily as offline analysis tools.
“As AI becomes part of larger business processes, it becomes a core operational capability,” Wallis explains.
From experiments to infrastructure
The decision to choose Akka to build and support its enterprise AI platform grew out of Manulife’s efforts to prepare for its rapid expansion.
Before choosing a technology partner, the company first asked what features such a platform would need.
Wallis identified three priorities that will shape the platform’s design.
The first was the need for a standardized developer experience. As AI development expands across the organization, Manulife wants to ensure consistent engineering practices and governance controls.
“We are moving from a small group of experts deploying AI solutions to a broader group of colleagues building the solutions,” she says. “We need a consistent developer experience that supports the development of reusable, high-quality, and responsible AI solutions.”
The second requirement reflects the operational nature of modern AI systems. Many applications now run directly within customer-facing or internal workflows, creating new requirements for reliability and responsiveness.
“If AI is going to be part of how you serve your customers and employees, you need to treat it like any other high-availability platform,” Wallis says. “This means low latency, high availability, and complete visibility into what’s happening across the system.”
The third factor was computational efficiency.
Large-scale language models and other advanced AI systems rely heavily on GPU-based computing infrastructure, which is expensive and scarce.
“For years, we thought computing costs would continue to fall,” Wallis said. “For LLMs, we have reached a point where that assumption no longer holds true.”
Therefore, Manulife was looking for a platform that could optimize its use of computing while balancing performance, financial cost, and environmental considerations. The company already operates one of the most cloud-centric IT environments in financial services, with over 80% of its applications running in the cloud.
Responsible AI at scale
Rapid deployment is not possible without safeguards. Wallis emphasizes that Manulife is expanding its governance capabilities in parallel with its AI development. Manulife’s Responsible AI Principles enable the company to deliver value from AI to customers, colleagues and society.
All AI solutions are evaluated based on the company’s model risk management framework, which evaluates systems based on criticality and determines testing and validation requirements.
Manulife AI Principles. Source: Manulife. (Click to enlarge.)
“We don’t take governance lightly,” Wallis said. “Every dollar we invest in deploying AI solutions, we also invest in AI safety.”
The company also employs various technological techniques to improve the reliability of its generative AI systems and reduce the risk of model failure.
One such method is to use one model to generate an answer and use another model to verify it. Another approach involves restricting AI systems to specific corporate knowledge sources rather than allowing unrestricted access to external data.
“You can also tell the model to ignore everything it has learned except for a defined set of documents,” Wallis explains.
Manulife also conducts adversarial testing, running the system against known prompt attacks and exploitation scenarios collected in open source repositories.
Preferred usage example
Wallis highlights several areas where AI is expected to have a major impact.
Distribution is one of them. In Asia alone, Manulife works with approximately 106,000 agents and advisors.
AI tools can help agents prepare for meetings, understand customer needs, and tailor communications.
Underwriting means new opportunities.
Life insurance underwriting often involves large amounts of unstructured information, such as physician statements, test results, and other medical records. Before you can perform an underwriting analysis, you must first organize a lot of information. “AI allows us to distill what is relevant and structure information more efficiently,” says Wallis.
Manulife is Canada’s first life insurance company to use AI in underwriting. The company recently introduced a redesigned electronic application and an enhanced version of its proprietary AI underwriting engine, MAUDE (Manulife Automated Underwriting Decision Engine).
Similar capabilities are emerging in claims processing, particularly in markets where Manulife offers health insurance products. AI can analyze non-standard claim documents and extract relevant information needed for automated adjudication.
More broadly, Wallis describes a growing category of applications focused on intelligent document processing.
“Give us any document in any format, anytime, anywhere,” she says. “With AI, you can understand what’s going on, extract what’s important, and move forward with automated parts of the process.”
AI for the workforce
Manulife is also implementing AI tools in-house.
These include virtual assistants that help employees handle tasks such as human resources inquiries, procurement requests, and IT support. The company uses AI to assist software engineers with code development, testing, and environment provisioning.
Wallis refers to this category of applications as “AI for technology.”
Encouraging employees to adopt AI tools is also a priority. Manulife introduced workshops known as “prompt-a-thon.” In this workshop, teams will examine their daily workflows and experiment with AI prompts designed to improve productivity.
“We sit down with people and ask them, “What’s on your calendar today? What’s on your to-do list?” Wallis explains. “Then we look at how AI can help us complete these tasks more easily.”
These efforts contributed to widespread adoption across the organization. By 2026, more than 70% of Manulife employees will be using AI tools regularly.
From data-driven to AI-driven
Insurers have been working towards becoming data-driven organizations for the past decade, and the next step could be operationalizing data through AI embedded in core processes.
Wallis suggests that AI is moving beyond analytics into underwriting workflows, customer support and internal operations, a transition already underway at Manulife.
“We talked earlier about being data-driven,” she says. “Today we’re going to talk about leveraging AI.”
Manulife selects Akka for enterprise AI platform
