When it comes to the transformation taking place at his business, John Hancock CIO Kartik Sakthivel (pictured) describes a broad-based approach centered on embedding artificial intelligence and automation across core business functions.
One effort focuses particularly on improving how products are distributed and how preliminary assessments of life insurance inquiries are generated at scale. As competition intensifies and customer expectations shift, the ability to deliver faster, consistent sales processes becomes increasingly material to growth and operational efficiency.
At the front end, tools such as the firm’s GenAI-powered Quick Quote are designed to give advisors faster initial assessments and streamline the purchase journey. This is paired with a wider enterprise push, where roughly 70% of Manulife (John Hancock’s global parent company) employees are already using internal AI tools to reduce manual work and focus on higher-value judgment. The combination, Sakthivel says, reinforces discipline while enabling the business to scale without proportionate increases in cost or complexity.
“Responsible use of AI is now part of how we work every day,” he said, positioning it not as an experimental capability but as embedded infrastructure. The emphasis is less on isolated innovation and more on consistent, repeatable deployment across workflows. This shift allows performance improvements to compound over time, particularly as models and processes mature.
External partnerships play a central role in executing this strategy, particularly as the firm seeks to balance speed with architectural control. Sakthivel says the company prioritizes modular, cloud-native, API-first solutions that integrate cleanly into its broader technology stack. This approach reduces vendor lock-in and allows the organization to evolve components independently as business needs change.
Selection criteria are intentionally rigorous. Providers are evaluated based on demonstrated outcomes, including case studies and referrals, alongside their ability to solve clearly defined business problems. “What we look for is to ensure that any external partner helps us solve the business problems our business partners and customers are looking for us as technology professionals to solve,” he said.
Security and compliance requirements are equally stringent. Partners are expected to meet certifications such as SOC2 and ISO, support data residency requirements, and provide strong encryption, auditability, and access controls. Interoperability is another key consideration, with an emphasis on standards-based APIs, event-driven compatibility, and the ability to operate within existing cloud environments.
The engagement model reflects this discipline. Relationships typically begin with proof-of-concept initiatives that progress to tightly scoped pilots aligned to specific business metrics. Joint playbooks, defined KPIs, and clear exit criteria ensure accountability on both sides. Scaling decisions are contingent on meeting performance thresholds and completing comprehensive security and compliance reviews.
“We deploy joint playbooks, set success KPIs, and establish clear exit criteria,” he said, underscoring a structured approach to innovation that minimizes risk while accelerating time to value.
Integrations across systems remains one of the more persistent challenges across the insurance industry. Disparate systems and workflows create the risk of fragile connections that can undermine reliability and scalability. To address this, the firm adopts an event-driven architecture supported by workflow engines and AI, enabling more resilient orchestration across the value chain.
This architectural shift is complemented by a strong focus on change management. Sakthivel emphasizes that technology adoption depends on frontline trust and humans-in-the-loop where appropriate, particularly when AI is introduced into decision-making processes. Co-design and testing with business teams, phased rollouts, and targeted training are used to build confidence and ensure that new tools are effectively embedded into day-to-day operations.
“Change management and adoption are equally critical,” he said, noting that performance dashboards are used to link technology initiatives directly to business outcomes. This visibility helps reinforce the value of transformation efforts and supports continuous improvement.
Governance structures are designed to match the scale and complexity of the transformation and to meet applicable laws and regulations. Technology leadership sets architectural standards and security guardrails, while data and AI leaders oversee model lifecycle management, fairness, and explainability. Business leaders play a central role in defining priorities and success metrics, ensuring that technology investments remain aligned with commercial objectives.
“All transformations are fundamentally about partnerships and collaboration between the business and technology teams,” Sakthivel said. “This alignment extends across product, engineering, risk, compliance, and change management functions, each contributing to the broader transformation agenda.”
The result is a coordinated operating model supported by a shared playbook. Value-backed pilots, strong governance, and a platform-first mindset allow successful innovations to scale across products and geographies. At the same time, the emphasis on reuse and standardization helps capture efficiencies across the enterprise.
“Our momentum comes from a shared playbook,” he said, pointing to the combination of disciplined execution and cross-functional collaboration. “And at the end of the day, strong executive leadership support ultimately helps enable and empower AI transformation.”
