GenAI-powered engineering modernization for a leading financial

GenAI-powered application modernization for a leading financial enterprise

Enabling an AI-led engineering model that modernizes software delivery, boosts developer productivity and accelerates innovation

5 min read

Overview

The client is a leading financial services and insurance enterprise serving the US market at an enterprise scale. The enterprise operates in a highly regulated environment where security, resilience and customer trust are paramount. To compete in a digital ecosystem, the client’s strategic focus is community leadership, continuous innovation and financial empowerment to enhance the member experience through digital transformation, improving engineering productivity by adopting GenAI platforms and tools.

The Challenge

To maintain its competitive position, the client recognized the need to modernize legacy applications, reduce technical debt and deliver new modules within aggressive market timelines.

Key challenges included:

Engineering quality gaps: Several code repositories had low unit test coverage, code duplication and limited SDLC automation, affecting time to market, maintainability and overall quality.
Capacity and timeline pressures: Limited engineering and SDLC automation and delivery efficiencies made it difficult to accelerate feature delivery while maintaining strict coding standards.
Business impact: Fragmented engineering practices reduced overall operational and cost efficiency and extended the time to market for critical business requirements.

The Solution

HCLTech enabled the client to transform its software delivery lifecycle by integrating GenAI-assisted tools and modernizing legacy application development and testing.

Strategic GenAI industrialization and AI infusion into SDLC workflows

HCLTech conducted a comprehensive Value Stream Mapping (VSM) assessment to identify opportunities to automate and remove inefficiencies and identify high-impact/AI-infusion initiatives. The transformation followed a pilot to scale to sustain approach, enabling phased implementation based on the VSM findings. To strengthen governance and visibility, mandatory GenAI usage tracking was embedded directly in Jira through standardized prompt labels.

Key value enablers

AI-assisted development: Integrated GitHub Copilot (GHCP) to streamline core engineering activities, including automated code generation and unit test generation, code reviews and vulnerability remediation. GHCP also accelerated testing cycles by automating test generation and intelligent regression selection.
Agentic AI support: Implemented Agentic AI capabilities to improve documentation quality and reduce manual effort across analysis, code review and code generation activities.
Framework upgrades: Delivered structured Java framework upgrades, including Spring Boot enhancements, Struts‑to‑Spring migration and service dependency updates.
Enabling rapid AI adoption: Provided specialized GitHub Copilot to around 2,000 engineers, with agentic and prompt engineer training to ensure engineering teams were aligned with the customer GenAI roadmap.
AI infusion governed through heatmaps: Utilized to track usage across various "hubs" and drive targeted interventions where needed.

The Impact

The shift to an AI-led engineering model delivered significant and measurable gains in productivity, quality and scalability.

Engineering adoption: Achieved 76% GenAI adoption by engineers with a 73% suggestion acceptance rate, reflecting strong confidence in AI-assisted tools.
Productivity gains: Achieved a 14–18% productivity improvement in development activities and a 12–14% improvement in testing across multiple hubs, enabling teams to deliver more user stories without increasing capacity.
Shorter time to market: By delivering more stories per sprint, the team increased speed to market and was able to take on an expanded project scope.
Enterprise scalability: Created reusable GenAI patterns and engineering assets that scale across multiple business lines, reducing duplication of effort.

Beyond The Numbers

The success of this engagement was driven by the rapid cultural and technical adoption of GenAI. HCLTech’s Value Stream Mapping (VSM) approach enabled quick identification and prioritization of high-impact use cases, positioning AI as a strategic accelerator rather than just a productivity tool. This transformation created a more agile and predictable development environment, allowing teams to absorb increased scope without additional overhead while establishing a sustained engine for continuous improvement.