MetLife Asia Boosts Efficiency by Integrating Azure DevOps into Workflow

In an era where technological advancements dictate the pace of industries, MetLife Asia has taken a significant step forward by integrating Azure DevOps into its software development cycle. This strategic move has not only improved efficiency but also enhanced the quality of development and deployment processes. The transformation was spearheaded by MetLife Asia’s CIO, Siew Choo Soh, who revealed that with the adoption of Azure DevOps, the time for automated tests and builds has dramatically decreased from nearly eight hours to under an hour. This impressive reduction in time is just one of the many benefits of transitioning to a DevOps framework. The integration of regularly scheduled scans has also played a crucial role in improving code quality and promptly addressing vulnerabilities, saving approximately 15 minutes per build per developer and enabling earlier error detection.

Expanding Services Across Regions

MetLife Asia provides vital insurance services in several countries, including Australia, China, India, Japan, Malaysia, and Vietnam. Their development efforts primarily concentrate on crafting Java and ReactJS mobile applications for essential functions such as policy registration, profile updates, policy inquiries, and claim submissions. Additionally, they have developed applications that support value-added services like scheduling doctor appointments and facilitating direct video calls with doctors. Previously, MetLife relied heavily on manual testing processes, a practice that resulted in significant inefficiencies, with builds taking as long as eight hours and errors often being detected only after deployment. This hampered their ability to meet growing customer demands quickly and accurately, thereby necessitating a revolutionary change in their development cycles.

By adopting Azure DevOps, MetLife aimed to address these inefficiencies and streamline their software development lifecycle. The goal was to standardize build pipelines to include a continuous integration and deployment (CI/CD) approach, allowing for more scalable and efficient deployments across regions. Azure Boards played a critical role in ensuring efficient project management, while Azure Repos provided scalable Git repositories for source code management. Azure Pipelines automated the build and deployment processes, simplifying the task of deploying applications to the target environment seamlessly. This unified approach not only improved productivity but also significantly bolstered application deployment security by adhering to standardized, automated processes.

Enhancing Development Security Operations

The integration of Azure DevOps into MetLife’s workflow significantly enhanced their development security operations (DevSecOps). Developers aimed to align with industry standards regionally to ensure their applications met rigorous security requirements. Before Azure DevOps, the growth in application size and number challenged scalable and secure development. The new DevOps framework offered a comprehensive solution, automating quality checks and integrating security models into the build pipelines, ensuring higher reliability and consistency in deployments.

Teams adopted a test-and-learn approach to refine processes, making standards implementation seamless. The full adoption of Azure DevOps took six months, granting end-to-end traceability. This enabled MetLife to better protect customer information, access real-time updates and performance trends for releases, and make faster, more informed decisions. Soh emphasized the company’s commitment to evolving its infrastructure and processes, showcasing a proactive approach to growth in a competitive industry.

The successful Azure DevOps integration has made MetLife Asia a leader in the insurance industry’s tech evolution. Leveraging Azure DevOps, MetLife has increased efficiency and quality in their development cycles, paving the way for innovation and improved customer satisfaction. This strategic move underscores MetLife’s commitment to a secure development environment while enhancing services to meet ever-changing customer needs.

Explore more

Is Bad Data Architecture Stalling Your AI Ambitions?

The corporate landscape is littered with the wreckage of ambitious artificial intelligence projects that were doomed from the start because they were built upon the shifting sands of legacy data systems rather than a rock-solid architectural foundation. While the allure of generative models and autonomous agents captures the imagination of the executive suite, the practical reality of implementation often reveals

Enterprise Software Valuation – Review

The digital infrastructure underpinning the global economy has undergone a radical transformation as enterprise software moves beyond simple automation toward predictive, AI-integrated environments. This transition marks a departure from the legacy models of the past decade, placing a spotlight on how 191 US-listed firms with market capitalizations over $2 billion are being appraised. Current market sentiment focuses on the financial

Why Human Systems Are Essential for Successful AI Integration

The global rush to integrate artificial intelligence into every facet of business operations has led to a paradoxical situation where massive financial injections often result in stagnant growth and technical obsolescence. Across the globe, organizations are pouring billions into advanced algorithms, yet many find that these investments fail to deliver a measurable return. The prevailing assumption that a more powerful

The UN Establishes Global Framework for AI Governance

Secretary-General António Guterres has emphasized that while national actions are essential, global coordination remains indispensable to prevent a regulatory race to the bottom in AI development. This statement resonates deeply as the world faces a critical juncture where the speed of technological advancement consistently outpaces the slow-moving gears of traditional bureaucracy. In 2026, the proliferation of large-scale language models and

Can AI Balance Economic Growth With Global Risks?

The silence of a high-tech laboratory often masks the thunderous impact of its outputs, but today that impact is felt in every coffee shop and boardroom across the planet where silicon chips are redefining human capability. More than a billion individuals have now woven generative models into the fabric of their professional and personal existences, creating a momentum that moves