Building an AI-Ready Enterprise: How Sun Life Is Enabling Trusted Innovation at Global Scale
Sun Life operates across Canada, the U.S., the U.K., Ireland, and fast-growing Asian markets. That reach means one thing for leadership: data is now a core business lever for client experience, risk, and how AI gets deployed responsibly.
Chris Goodale, Vice President of Enterprise Data and Analytics Enablement at Sun Life, is leading that shift. His directive is simple and hard at the same time: enable data and AI across every line of business, without spinning up disconnected projects that stall at pilot.
From multi-industry experience to an enterprise-wide mandate
Goodale joined in 2024 after roles spanning financial services, retail, consumer packaged goods, and cloud. He and his team support all business units and geographies, giving practitioners the capabilities and processes to deliver outcomes at scale.
His remit spans three pillars: enterprise enablement for data and analytics, enabling GenAI and agentic AI as shared capabilities (not a siloed lab), and enterprise data governance. That last one is especially tough across structured and unstructured data in a global business.
Evolving the data strategy for GenAI and agentic AI
To keep pace with AI, Sun Life is tuning its data strategy around five essentials managers will care about:
- High-quality, diverse data: GenAI needs large amounts of clean, relevant data or the output won't be useful.
- Governance and compliance: Raise the bar to meet ethical and regulatory expectations, especially for sensitive insurance and financial data.
- Accessibility and integration: Reduce data silos so models can reach the right sources without friction.
- Security: Protect proprietary and client data with stronger controls as AI use grows.
- AI-literate culture: Help teams understand where data and AI create value, and how to apply them to decisions and client outcomes.
The takeaway for managers: budget for data quality, governance, and access like you would for any revenue-critical system. Models are only as good as the foundations you build for them.
Right tool, right problem
Sun Life treats GenAI as an enabler, not the default answer. The operating principle: use the right technology for the job, measured by outcomes.
That means start with the problem, define the decision or process you're improving, then select the method-GenAI, traditional ML, analytics, or automation-that delivers the result with clarity and control.
From provisioning GenAI services to coding co-pilots
This approach is already live across the organization. Teams have automated the provisioning of GenAI services to speed solution delivery while staying compliant.
- Test automation and synthetic data: GenAI is used to generate test cases and realistic test data, improving time to value in development and QA.
- Developer productivity: Engineers are using coding co-pilots like GitHub Copilot to reduce cycle time and ship with higher confidence.
The throughline: modern AI capabilities paired with a stronger data strategy produce tangible, safe wins for clients and internal teams.
What a data-centric culture really takes
For Goodale, the opportunity is bigger than the obstacles. Success comes from collaboration with the business and meeting each group where they are.
There is no one-size-fits-all playbook in a company of this scale. Each group gets a plan that maps to its goals, maturity, and regulatory context.
Partnerships, upskilling, and change management
- Deeper partnerships: Co-create use cases, data products, and KPIs with the business so the work ties directly to outcomes.
- Upskilling: Invest in hands-on training so teams can use data responsibly and make better decisions with AI. For role-based options, see Complete AI Training by job.
- Change management: Communicate clearly, show live demos, and share quick wins to build momentum and real adoption.
Global alignment, local relevance
One of the biggest opportunities: create global consistency while respecting regional rules. Standardize principles and controls; adapt the execution to local regulations.
Leaders can anchor their programs to trusted frameworks like the NIST AI Risk Management Framework and ISO/IEC 42001 for AI management systems. These give teams a common language for risk, governance, and accountability.
What managers can do this quarter
- Name accountable owners for your most critical datasets and define data quality targets for each.
- Stand up an AI intake and triage process with clear criteria: value, feasibility, sensitivity, and risk.
- Implement a "data access by design" pattern: minimize movement, log access, and enforce least privilege.
- Catalog high-value unstructured sources (documents, emails, knowledge bases) and set rules for PII and retention.
- Pilot two low-risk GenAI use cases (e.g., test case generation, internal knowledge search) and measure cycle time saved.
- Roll out coding co-pilots to a defined cohort with guardrails, metrics, and a 30-day feedback loop.
- Publish a simple AI policy: approved tools, dos and don'ts, review steps for sensitive data.
- Launch a practical learning path for managers and practitioners focused on decision quality, not theory.
The bottom line
Sun Life's playbook is clear: treat data as a strategic asset, apply AI where it actually moves the needle, and build trust into the process from day one. Do that, and scale stops being the blocker-and becomes the advantage.
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