Serving millions of clients globally through insurance, wealth, and asset management businesses, Sun Life operates in one of the most highly regulated environments — where trust, data integrity, and operational resilience are non-negotiable. As the organization accelerates its transformation into a more data-driven and AI-enabled enterprise, those realities shape every architectural and strategic decision.

In part one of this three-part interview, Chris Goodale, VP of Enterprise Data and Analytics Enablement at Sun Life, speaking with Jason Sturgess of Denodo, outlined how the organization is evolving its data strategy, enabling AI across the enterprise, and building a truly data-centric culture at global scale.

Part two examined how those foundations are translating into concrete GenAI wins and customer-facing innovations.

In this concluding installment, the conversation turns to two questions many enterprises now confront: how semantics underpins trustworthy, scalable AI, and how Sun Life avoids the common trap of AI pilots that fail to deliver sustained business value.

Why semantics sit at the center of Sun Life’s data and AI strategy

As Sun Life continues to scale analytics, GenAI, and emerging agentic capabilities, Goodale is clear that technology alone is not the differentiator. Instead, he points to a foundational layer that often receives less attention but plays an outsized role in success: semantics.

At its core, semantics provides clarity and consistency. By establishing explicit logical definitions, Sun Life reduces ambiguity and ensures that data is interpreted the same way across the enterprise. That shared understanding, Goodale notes, is essential not only for traditional analytics but also for GenAI and agentic systems, where a common language improves accuracy and builds trust in outcomes.

He describes what he calls the “Rosetta Stone effect,” a reference to the ancient artifact that enabled scholars to decipher Egyptian hieroglyphs by presenting the same meaning across multiple scripts. In the enterprise context, a robust semantic layer acts as a universal translator between business subject-matter experts, technologists, and AI agents, allowing each to communicate effectively using a shared language. In a global organization with diverse stakeholders, this alignment becomes critical.

Semantics also plays a central role in governance and compliance. Explicit data definitions support stronger classification, security, and auditability, which are increasingly vital capabilities in a complex and fast-evolving regulatory landscape. Beyond controls, semantics powers advanced analytics by providing the context AI systems need to understand and use data effectively.

“It goes beyond just the technical controls,” Goodale says. “It provides context, and that context is critical for AI systems to understand and to utilize data effectively and ultimately produce the best possible outcomes.”

In short, semantics is not a nice-to-have. “They’re really essential for ensuring data quality, consistency, and usability across our organization,” he notes, particularly as Sun Life evolves its data strategy and embraces new technologies.

Moving AI from pilot to production and value

Despite growing excitement around generative and agentic AI, many organizations struggle to move beyond proofs of concept. Goodale acknowledges the challenge, noting that the majority of AI pilots fail to deliver measurable ROI. He adds that this is an area where Sun Life has invested significant time and effort.

The company’s approach begins with balance. Sun Life combines top-down focus on large-scale transformational opportunities with bottom-up innovation. These initiatives are defined upfront with strong stakeholder engagement, clear outcome articulation, and cross-functional teams that address success factors holistically — before any work begins.

To ensure the right investments, all AI initiatives are evaluated through a formal value realization framework. This framework guides prioritization and measures impact in both pilot and production stages. “Value is the backbone of how we prioritize and invest in our AI work,” Goodale states.

That value may take many forms, including productivity gains, cost savings, sales opportunities, improved client experience, or often a combination of all four.

Crucially, Sun Life does not view technology readiness as sufficient on its own. Goodale emphasizes the importance of “AI-ready data,” particularly for generative and agentic use cases, supported by foundational investments in data infrastructure. Executive sponsorship, cross-functional partnerships, and strong collaboration between strategy, technology, and finance teams further increase the likelihood of success.

Change management, upskilling, adoption strategies, and communication are treated as first-class priorities. “A really strong, intentional focus on adoption is key to optimizing both the projected and the realized value of our AI initiatives,” he says.

Rather than pursuing AI everywhere at once, Sun Life deliberately allocates resources to initiatives with the highest potential impact, only when all conditions for success are met. This disciplined approach, Goodale believes, is what enables the organization to consistently move AI from concept to production.

A multidimensional path forward

Looking ahead, Goodale does not frame Sun Life’s journey as chasing the next breakthrough technology. Instead, he returns to the balance — between innovation and fundamentals, speed and responsibility.

“We’re committed to harnessing the power of AI and investing in our data to drive innovation and improve outcomes,” he says. That commitment is grounded in strong data management fundamentals, robust governance, and intentional change management.

He cautions that many AI initiatives fail because they focus too narrowly on technology enablement, overlooking the broader dimensions required for success — business case alignment, operational readiness, and responsible production management.

Sun Life’s approach, by contrast, is deliberately multidimensional and multifaceted. While advancing new technologies, the organization remains focused on using them responsibly and effectively in the service of its core mission.

As Goodale puts it, the goal is not innovation for its own sake, but impact: “to fulfill our purpose of helping people achieve lifetime financial security and live healthier lives.”

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