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GitHub Copilot Saving in IT Projects

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 Like other AI-assisted tools for developing applications, GitHub Copilot has become part of our lifecycle of developing IT solutions. It is not a question of whether Git Hub Copilot brings value to a developer or DevOps. The question is, what is the impact of quality attributes, effort and budget-wide?

This article assesses the impact of GitHub Copilot from a budget perspective. The intention is to identify how much the cost of building and running a cloud solution is reduced when the technical team has access to GitHub Copilot.

 

Over the internet, a lot of studies have focused on developer experience, the quality of the outcome, and how much of the code generated by GitHub Copilot ends up being used in production. To estimate the impact of the required budget to develop a greenfield cloud application running on Microsoft Azure, we used these studies as a reference, calculating the impact of an AI-assisted tool in the day-to-day work of a technical person.

The existing public studies that are available in February 2025 converge to the following conclusions:

Effort

  • Development effort reduced by up to 45% for entry-levels, with an average of 30% across entry-mid-senior levels
  • The number of completed tasks increased by 6-8% and pull requests increased by 8.6%
  • Code review effort decreased by 15%
  • Productivity boost by 30%, saving around 0.5h per day

Quality

  • The success build rate increased by 84%
  • Readability 3.62%, Reliability 2.94%, Maintainability 2.47%, Conciseness 4.16 increased
  • The number of line of code without errors increased by 13.6%
  • Increase with 56% of functionality that passed the unit tests

Experience

  • The acceptance rate of GitHub Copilot suggestion is 30%
  • Search time of technical people reduced by 54%
  • The level of stress of the team decreased

, and many other savings and metrics and numbers.

GitHub Copilot is impressive. It helps the technical team build, test, and run applications that run on-premises or in the cloud.

To assess potential business savings, we need to understand the technical team's shape and the time spent in meetings, reading documentation, and writing or debugging code.

Based on the above information, we know that the potential savings using GitHub Copilot are around 60% of the total time a technical person spends in IDEs and another system where GitHub Copilot can assist in the Building and Operating (Run) phases. The saving is expected to be higher in the Build phase (60%) and lower in the Operating phase (40%).

Two more variables impact the total savings per IT project using GitHub Copilot:

  • The team shape also contains people who don’t use GitHub Copilot (e.g., PM, scrum master, enterprise architect)
    • Budget allocation per phase. The general split is 15-20% in design and assessment, 40-50% in the build, and 30-40% in the Operate phase.

This allocation and split can vary depending on the project type, industry, methodology, etc.

Considering all the information that we have until now, we have the following split and savings generated by GitHub Copilot, for an IT project that is budgeted for $1.2M

 

Project Phase

Typical Budget Allocation (%)

Budget Amount ($)

GitHub Copilot Savings ($)

Assessment & Design

15–20%

$180k – $240k

$0 (No impact)

Build

40–50%

$480k – $600k

$86.4k – $108k (18% savings)

Operation

30–40%

$360k – $480k

$25.2k – $33.6k (7% savings)

 

The total amount of savings generated by GitHub Copilot for a $1.2M project is around $110k—$140k, representing 9.3-11.8% of the total budget allocated. The savings increase to 18-19% in the Build phase when more technical work is delivered and decrease to 6-7% during the Maintenance phase.

The Assessment & Design phase covers work related to requirements gathering, architectural design, stakeholder workshops, and risk and compliance assessment. For these activities, the support delivered by GitHub Copilot is limited. Specialized tools based on rule engines or AI models can be used for this purpose.

In the Build phase, the savings increase to 18-19% by delivering work related to building the actual software, upgrading existing solutions and packages, unit testing, testing, integration and the initial IaC and CI/CD pipeline setup.

The Operation phase provides a potential saving of around 7% during deployment, operational support, bug fixes, security updates and feature enhancements. Feature enhancements and bug fixing are two dimensions that can increase the saving when GitHub Copilot is used.

There is a high potential to increase savings for strictly regulated industries, like life science, where heavy documentation needs to be provided or for LOB applications. GitHub Copilot also has a high impact during team rotation, becoming the AI-assistant for the new team members, decreasing the ramp-up period.

When the potential savings provided by GitHub Copilot are compared to the Copilot Business ($224 per year) and Enterprise subscription ($468) costs, there is no question that Copilot adds value. AI-Assistant should be part of the toolset together with learning paths and training materials on how to use them at full potential.  

 

We can conclude that GitHub Copilot can potentially reduce the cost of IT projects by 9.3-11.8% and improve quality, efficiency, and productivity. The savings can go up to 18-19% in the Build phase and 6-7% in the Operation phase. AI-assistant tools like GitHub Copilot have become standard tools used by the technical team.


Useful links and resources:

  1. https://arxiv.org/pdf/2406.17910

  2. https://github.blog/news-insights/research/does-github-copilot-improve-code-quality-heres-what-the-data-says

  3. https://www.faros.ai/blog/is-github-copilot-worth-it-real-world-data-reveals-the-answer

  4. https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-code-quality

  5. https://github.com/customer-stories/emirates-nbd

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