Automotive Firm Reduces Dev Effort by 45%

Overview

A global automotive manufacturer's legacy application environment slowed software delivery and held back modernization. Poor code quality, limited testing coverage, and security gaps were slowing development and also increased rework. Tech Mahindra stepped in with an AI-driven software delivery transformation in which it paired AI-assisted engineering with modern development practices to help the manufacturer move faster and build better software.More

A global automotive manufacturer's legacy application environment slowed software delivery and held back modernization. Poor code quality, limited testing coverage, and security gaps were slowing development and also increased rework. Tech Mahindra stepped in with an AI-driven software delivery transformation in which it paired AI-assisted engineering with modern development practices to help the manufacturer move faster and build better software. Development effort dropped by 45%, freeing engineering teams to focus on cloud-native development and software-defined vehicles.

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Industry Challenge

The client, a global automotive manufacturer with operations across multiple continents and a broad portfolio of vehicle brands, faced several challenges in its software delivery processes, including:

  • Legacy Application Challenges
    Poor code quality, limited unit testing, and security and compliance gaps caused delays and rework.
  • Scaling Delivery Without Additional Headcount
    Teams had to support more products and microservices with the same resources.
  • Limited Modern Technology Expertise
    Limited in-house expertise slowed cloud migration and modernization initiatives.
  • Manual Testing and Reporting
    Manual testing and reporting reduced productivity and limited experienced resources for higher-value work.

Our Approach and Solution

Tech Mahindra partnered with the automotive manufacturer to design and implement an AI-driven software delivery transformation framework that modernized engineering practices and improved development effectiveness.

AI-Assisted Software Development

GitHub Copilot and enterprise-grade LLMs supported coding, code modernization, testing, design assistance, and technical research.

End-to-End Developer Assistance

AI capabilities supported developers across the development lifecycle, from initial planning through implementation and testing.

Cloud-Native Application Development

Modern application frameworks and cloud platform services enabled scalable, efficient software delivery.

Software-Defined Vehicle Support

The modernized engineering approach supported the company’s transition toward software-defined vehicles and cloud-connected services.

Business and Community Impact

Lower Engineering Effort

Development and unit-testing activities saw approximately a 45% effort reduction.

Faster Delivery

Improved engineering efficiency enabled faster delivery of new features and services, allowing teams to focus on higher-value innovation.

Improved Software Quality

AI-assisted design and research capabilities improved overall solution quality and supported cloud migration.

Greater Operational Efficiency

Automated reporting, testing, and insights generation cut manual effort, improved productivity, and sharpened decision-making.