With over 95,000 advisors, more than 150 years of operational history, and a digital footprint that reaches North America, Europe, and Asia, Sun Life stands among the world’s largest life and health insurers. Its push toward a modern, AI-enabled enterprise highlights both the ambition and the scale required for data and analytics innovation.

In part one of this series, Sun Life’s VP of Enterprise Data and Analytics Enablement, Chris Goodale, outlined how the company strengthens data foundations, governance, and culture to support generative and agentic AI.

In this second installment of his conversation with Jason Sturgess, Regional VP and General Manager at Denodo, Goodale explores Sun Life’s early GenAI wins, customer-facing innovations, and the company’s balanced approach to modern data architecture.

Sun Life’s early GenAI successes

When asked to highlight notable use cases, Goodale reflects on how quickly the organization embraced GenAI and how rapidly it began to deliver material value. “We jumped into the GenAI game back in 2023, and it’s been quite a ride,” he says. One of the first major milestones was an internal AI chatbot trained on HR and operational guidelines. Its speed to market and enterprise-wide adoption set the tone for future AI work.

Soon after, Sun Life launched Sun Life Asks, a global internal chatbot housed securely within its ecosystem. Since its debut in November 2023, usage has skyrocketed. “We’re talking over 3 million queries with about 20,000 visits every week,” Goodale shares. Employees use it for brainstorming, summarizing documents, coding, writing, goal setting, and more — freeing time for higher-value work. Further, Sun Life is now deploying AI to transform frontline and customer-facing workflows.

“For instance, we’ve launched tools that help our advisors in Canada efficiently and securely summarize client meetings,” Goodale says. The outcome is less paperwork and more meaningful client conversations.

Specialized teams are also experimenting with custom chatbots for compliance and actuarial functions, alongside exploring GenAI–assisted summarization of complex physician statements, a potential game changer for underwriting.

A flagship win: The Group Benefits Plan Sponsor Dashboard

When Sturgess asks which initiatives he takes special pride in, Goodale points to a product that illustrates the powerful impact of enterprise-wide collaboration. He highlights Sun Life’s self-service “Group Benefits Plan Sponsor Dashboard,” calling it “a fantastic example of how collaboration between our enterprise teams and our business groups has created tangible value.”

The dashboard provides plan sponsors with on-demand, actionable insights into their benefits programs. More importantly, it elevates data from informational to strategic.

“It’s not just about presenting data. It’s about transforming that data into actionable intelligence,” Goodale says. The dashboard empowers sponsors to understand member needs, optimize benefits, and deliver better health outcomes — creating value for employers, employees, and Sun Life alike.

“It embodies what we’re striving for,” he adds, “using our technology capabilities and data expertise to create solutions that make a real difference in people’s lives.”

Regulatory readiness and strategic data management

Turning to architectural strategy, Goodale notes that Sun Life’s data management approach is shaped by both regulatory rigor and future-looking design. As a Canada-headquartered insurer, Sun Life places strong emphasis on compliance with the Canadian Office of the Superintendent of Financial Institutions’ (OSFI’s) E-21 guideline, which sets expectations for enhanced data risk management and operational resilience.

“One of our top priorities is ensuring compliance with OSFI’s E-21 guideline,” he says. But he also notes that Sun Life views the standards as broadly applicable best practices—not simply regulatory obligations.

“Well-managed, properly governed data with good hygiene is fundamental to new and emerging technologies like generative AI and agentic,” he explains. Without high-quality data, enterprises cannot fully capitalize on transformational AI opportunities.

As a global organization, Sun Life continually adapts to a shifting regulatory landscape.

“Our focus on data management isn’t just about meeting the current regulatory requirements,” Goodale says. “It’s about positioning Sun Life for future success in an increasingly data-driven world.”

Centralized or decentralized?

When asked whether enterprises should centralize or decentralize their data architecture, Goodale says: “It’s a false choice. I don’t think it’s an either-or thing necessarily,” he notes. Instead, Sun Life aims for a balanced operating model that blends enterprise consistency with business-unit agility.

“We set standards and build foundational capabilities once for the enterprise,” Goodale explains. This centralization drives scale, efficiency, and common practices. At the same time, local teams retain the flexibility to tailor insights and applications to their markets and clients. “This decentralized aspect allows for agility and specialized focus where we think it’s needed most,” he explains.

Ultimately, Sun Life’s data strategy comes down to balance. By building capabilities centrally and deploying them across the enterprise, the company gains the consistency, efficiency, and scale needed for durable data foundations, says Goodale.

By giving business groups the latitude to apply those capabilities locally, it preserves the agility, specialized focus, and market responsiveness required to serve clients effectively.

Concluding, he says that it is a hybrid model — centralized where it drives scale and decentralized where expertise and flexibility matter most — and this positions Sun Life to remain nimble as the business, technology, and data landscape continues to evolve at speed.

CDO Magazine appreciates Chris Goodale for sharing his insights with our global community.