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Transforming SDLC with the AI Flywheel Effect

energy & utilities AI

Industry

Manufacturing

Challenge

Limited AI knowledge and fragmented adoption of tools like Copilot, along with a lack of training, slowed down development and created inconsistent usage.

Results

The new AI solution resulted in 100% daily Copilot usage and a 40% time savings, with 89% of developers rating productivity gains as moderate to excellent.

Technologies That Powered the Transformation

SharePoint, GitHub Copilot, Opsera, Azure DevOps (ADO)

89% of Developers Rated Productivity Impact as Moderate to Excellent

A significant majority of developers agreed that Copilot matched or surpassed their expectations regarding AI-assisted development. Furthermore, 89% rated the tool’s impact on their productivity as moderate to excellent, highlighting substantial value and increased output.

 

40% Time Savings

Developers reported time savings of 40% from using the new AI tools with coaching. These efficiency gains translated into faster delivery cycles and more consistent code quality.

 

100% Daily Copilot Usage by Developers

Every developer in the organization integrated Copilot into their daily workflow, as verified by continuous Opsera metric tracking. This widespread adoption ensured that the full suite of Copilot’s capabilities could be leveraged to enhance productivity across all teams.

 

100% Perceived Usefulness of Copilot

100% of developers surveyed reported that they found Copilot to be helpful in their work. This unanimous response demonstrates the practical value that Copilot added to the team's coding process and collaboration.

 

Ai in energy & utilities-1

Overview

A Fortune 500 company transformed its software development lifecycle by embedding GitHub Copilot into the SDLC cycle. The combination of Copilot, Opsera, dashboard capabilities and an AI coach empowered developers to fully adopt AI. rSTAR's comprehensive solution automated tasks, streamlined workflows, reduced development time, and enabled teams to deliver higher-quality code faster. It fostered a culture of continuous improvement through the AI flywheel effect. Developers achieved unprecedented levels of productivity as they fully leveraged the advantages of AI-powered development tools with the support of the coach. Best of all, developers went from skeptical, occasional users to firm believers in AI-powered code development and testing.

Business Problem & Stakes

  • Limited AI Familiarity and Confidence:
    Limited AI knowledge left developers lacking confidence and unable to fully leverage tools like Copilot.
  • Fragmented and Inconsistent Adoption:
    Copilot was primarily leveraged for code generation, not code reviews, leading to a fragmented experience.
  • Barriers to Widespread Usage: 
    Developers underutilized the platform’s full capabilities because they lacked training.
  • Lack of Centralized Resources and Processes: 
    Manual troubleshooting steps and scattered documentation slowed Copilot onboarding.

 

rSTAR Solution

  • Accelerated Copilot Integration:
    GitHub Copilot was embedded into 80% of developer workflows, leading to exceptional productivity gains.
  • AI Integration Throughout SDLC:
    Integrating Copilot, Azure DevOps, and Opsera streamlined performance monitoring, efficiency and accuracy.
  • AI Coach and Knowledge Base:
    Centralized Knowledge Base combined with training sessions and AI coaching increased developer use and efficiency.
  • Structured Learning for Prompt Confidence:
    Structured training enhanced developers’ confidence in using AI prompts and reinforced code hygiene best practices.

 

Download The Case Study

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