To maintain its commitment to clinical excellence, the client identified the need to modernize legacy operational processes and address technical debt within its engineering ecosystem. The primary challenges included:

• Manual operational bottlenecks: A reliance on manual, legacy-driven processes—specifically an Excel-based scheduling mechanism—constrained accuracy, scalability and operational resilience.
• Engineering quality gaps: Multiple code repositories exhibited inadequate unit test coverage, widespread code duplication and persistent "code smells," which hindered long-term maintainability.
• Capacity and timeline pressures: The need to deliver new functionality within aggressive timelines, coupled with constrained engineering capacity, made it difficult to balance rapid feature delivery with strict coding standards.
• Business impact: These fragmented processes diminished overall operational efficiency and extended the time-to-market for critical business requirements.

Strategic modernization pillars

• AI-assisted development: Integration of GitHub Copilot (GHCP) to streamline core engineering tasks, including boilerplate coding and automated unit test generation.
• Agentic AI support: Implementation of Agentic AI capabilities to automate documentation enhancements and reduce manual analysis during feature development.
• Quality remediation: A systematic effort to increase unit test coverage and eliminate code duplication and smells across repositories to strengthen the health of the application portfolio.

The shift to an AI-led engineering model has delivered significant, measurable gains in productivity, quality and clinical confidence:

• High AI adoption and trust: The program achieved a 79% adoption rate across targeted groups, with a 57% acceptance rate for GenAI suggestions.
• Productivity uplift: The initiative delivered a 6% productivity improvement, reflected in a higher "say-to-do" ratio and sustained team velocity.
• Accelerated time-to-market: Feature deployment cycle times were reduced by 7%, enabling the organization to respond more quickly to evolving clinical and regulatory needs.
• Operational resilience: Improved throughput allows the organization to manage increasing authorization and diagnostic volumes without compromising clinical rigor.
• Faster clinical decisioning: Intelligent triage and decision support are now routine, allowing diagnostics and utilization decisions to be executed faster and more consistently.

Looking forward, HCLTech plans to:

• Scale AI practices: Extend Copilot-enabled development and documentation workflows to a broader set of modules and repositories.
• Continuous modernization: Address remaining legacy processes to ensure the wider application landscape remains aligned with the organization's digital transformation roadmap.