2X FASTER ANDROID DEVELOPMENT WITH AI-POWERED DELIVERY FOR A LEADING MULTI-BRAND RESTAURANT OPERATOR

A focused six-week AI initiative made Android development 2x faster while introducing development and QA agents, unit testing practices, and a foundation for scaling AI-assisted delivery across engineering teams.

INTRODUCTION

Improving engineering productivity requires understanding where AI can make a measurable difference in existing development workflows.

A leading global retailer wanted to increase delivery velocity and quality across a complex legacy environment. Slow solution definition and development processes, limited lifecycle metrics, and low AI adoption were affecting team performance.

CI&T mapped upstream and downstream processes and prioritized AI use cases that could demonstrate measurable results within six weeks, starting with development and quality assurance.

By introducing development and QA agents Powered by CI&T Flow, alongside unit testing practices and workflow adjustments, the team tested AI capabilities directly within day-to-day delivery.

The initiative made Android development 2x faster and provided practical learnings to support broader AI adoption across the organization.

THE CHALLENGE: CLOSING THE GAP BETWEEN DELIVERY SPEED AND QUALITY

Legacy code, slow development processes, and limited lifecycle metrics were holding back delivery performance. The organization needed to increase engineering velocity while improving quality and creating a stronger foundation for scalable delivery.

Complex Legacy Environment

Legacy code increased engineering complexity and made development more time-consuming, creating challenges for both productivity and quality.

Slow Development Processes

Solution definition and development activities were taking longer than expected, limiting the team's ability to increase delivery velocity.

Limited AI Adoption and Visibility

AI adoption across the team was still low, while the organization lacked consolidated metrics across the development lifecycle to clearly measure performance and identify improvement opportunities.

THE SOLUTION

CI&T mapped upstream and downstream processes to identify where AI could create measurable improvements within a six-week period.
Rather than introducing multiple changes at once, the initiative prioritized a focused set of agents and engineering practices that could demonstrate value and provide learnings for future expansion.

AI-Assisted Development
and QA

Development and QA agents were introduced to support engineering and testing activities across the delivery process.

Senior engineers from one team also used the agents while delivering within the team's existing scope, helping validate their impact in day-to-day development.

Unit Testing for New Features

Unit testing was introduced for new features to strengthen quality earlier in the development process and support more reliable delivery.

This helped combine productivity improvements with stronger engineering practices rather than focusing on speed alone.

Workflow Adjustments

The initiative also identified adjustments needed across upstream processes to help teams make better use of AI agents.

These learnings provided a clearer path for improving the agents and extending the approach to additional teams.

THE IMPACT

The initial six-week implementation demonstrated measurable gains while identifying opportunities for broader adoption.

FASTER ANDROID DEVELOPMENT

Android development became 2x faster

Development and QA agents supported day-to-day delivery activities

Senior engineers tested the approach within the team's existing delivery scope

IMPROVED ENGINEERING PRACTICES

Unit testing introduced for new features

Development and quality activities supported within the same AI-assisted approach

Workflow improvements identified across upstream and downstream processes

FOUNDATION FOR SCALING AI

Initial agents validated through real delivery activities

Opportunities identified to expand agent usage across the broader team

Additional agents and workflow improvements planned for future phases

TECHNOLOGIES USED

POWERED
BY FLOW

CI&T Flow Enterprise AI Management System supported the integration of intelligent agents into development and quality workflows, helping teams improve productivity, strengthen engineering practices, and establish a foundation for broader AI adoption.

ENGINEERING AGENTS

AI-powered agents designed to support software development and quality assurance activities, helping teams accelerate execution while maintaining engineering quality.

AI-ASSISTED ENGINEERING

An approach that combines AI capabilities with existing engineering practices, allowing teams to introduce automation gradually, measure its impact, and expand adoption based on real delivery results.

UNIT TESTING

Unit testing practices were incorporated into new feature development to strengthen quality earlier in the lifecycle and support more reliable software delivery.