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What the documented deployments show
The examples are concentrated around GitHub Copilot, often alongside tools such as Visual Studio, Visual Studio Code, Azure DevOps, or GitHub Enterprise. That pattern matters: these deployments generally add assistance to existing engineering environments rather than replace the development workflow.
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Reported work ranges from code suggestions and unit testing to updating older code, generating web components, and helping engineers use unfamiliar scripting languages. Evidence varies just as much. Some reports describe an internal survey or evaluation; others are vendor-hosted customer stories or brief summaries in a roundup. Their figures should be read as claims about the named organization and measure, not as an industry benchmark.
Company-reported deployments and outcomes
Hitachi: coding and unit testing within existing frameworks
Hitachi adopted GitHub Copilot as part of an effort to promote internal AI use and improve system-development productivity. Its stated primary use cases were coding and unit testing, integrated with existing development frameworks. The company also established a practitioner community to share knowledge. Microsoft’s Hitachi customer story says an internal evaluation began in October 2023 and recruited about 200 participants for three to four months. Hitachi used a survey organized around the SPACE framework, with responses across six performance measures.
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In findings attributed to Hitachi’s internal evaluation and survey, 83% of users said they completed tasks faster. Hitachi reported average productivity gains of 10% to 20% in coding and unit testing, reaching 30% in some cases. Separately, in a validation application, Hitachi reported that combining Copilot with its Justware approach raised the code-generation rate from 78% to 99%. That validation-app result is distinct from the broader survey findings; neither is an independent, cross-company benchmark.
HP: assistance across code creation, review, and older projects
HP’s deployment began with a GitHub Copilot Business trial and expanded to broader use with GitHub Enterprise. In a Microsoft customer story, HP describes developers using inline suggestions and chat in supported development environments and command-line interfaces, alongside Azure DevOps and Visual Studio. Work included writing and reviewing code, updating older code, and solving problems on new projects. The story says several thousand developers were active daily, and characterizes productivity as increased without giving a standardized quantified result.
HP Senior Manager, Enterprise Digital Services Evan Scheessele described the rationale this way: “To stay competitive, we knew we had to embrace AI, and from a developer’s perspective, we wanted to make that easier, giving our developers the tools and resources they need to create and collaborate efficiently, unlocking new value and new speed. GitHub Copilot was the answer.” This is HP’s perspective in a Microsoft-hosted customer account, not an independent evaluation.
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According to Microsoft’s Lumen story, the company piloted GitHub Copilot with nearly 600 engineers in Bangalore, India, before expanding it to a global population of 2,400 engineers. The deployment used Azure DevOps, Visual Studio, and Visual Studio Code. Lumen describes using suggestions to troubleshoot and to work with less familiar scripting languages, including Terraform, ARM, and Bicep.
Lumen managers reported faster troubleshooting and more efficient onboarding. Senior Software Engineering Manager Nikita Rathore said: “Autocomplete, in particular, saves developer time. It gives suggestions for multiple solutions with different code complexity, resolving issues that used to take half a day in less than an hour.” That is a company-reported example, not a measured result established across the global engineering population.
Trimble: reducing repetitive work and context switching
Trimble’s Copilot story focuses on repetitive work, fragmented knowledge, and context switching, and describes integration with GitHub Actions. In its customer story, GitHub reports Trimble figures of 1,000 developer hours saved per day and an average of 30 minutes saved per developer per day. Trimble also reported that web-component output changed from one per week to five per day. The accessed story does not show a publication date, so these figures should not be assigned a year.
Trimble Distinguished Engineer and Principal Architect Jeff Doolittle said: “We want to help developers reach that flow state. Copilot helps reduce the cognitive burden. If a developer is trying to remember how to do something and then the answer is right at their fingertips, that’s fantastic.” These are vendor-published customer claims, not results from a controlled comparison across companies.
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Other examples in Microsoft’s roundup
Microsoft’s July 2025 roundup includes software-development examples involving Bancolombia, BNY, HP, Infosys, and LambdaTest. The roundup says Bancolombia reported a 30% increase in code generation; it describes BNY’s Copilot adoption and reports that more than 80% of BNY’s developer community relied on Copilot daily. It also says LambdaTest integrated Copilot into its workflow and reported a 30% reduction in development time.
These are concise roundup summaries rather than a review of each company’s underlying evaluation. The reported percentages have different definitions, populations, and methods, so they cannot be used to rank organizations or treated as evidence of a common effect.
What a broader study adds—and what it does not
GitHub’s May 13, 2024 article on research with Accenture developers describes a combination of a randomized controlled trial, DevOps telemetry, a company-wide adoption analysis, and a survey. Its figures cover distinct kinds of evidence and should not be collapsed into one productivity claim.
- In survey responses, 90% of participants said they felt more fulfilled in their job with Copilot, and 95% said they enjoyed coding more with its help.
- The article says more than 80% of participants successfully adopted Copilot, with 67% using it at least five days per week. It separately reports an average use frequency of 3.4 days per week.
- The satisfaction percentages are survey responses. The article’s RCT and telemetry concern other measures; the satisfaction figures are not themselves controlled-trial productivity results.
These findings are reported in a GitHub-published account of the GitHub and Accenture study. GitHub is the product vendor, so the study should be attributed accordingly rather than presented as independent of the product.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteHow to compare AI-development deployments
A useful comparison starts with what was deployed and what was measured—not with the largest percentage in a customer story. Check five dimensions:
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- Workflow: Was the assistant used for code completion, unit tests, legacy-code changes, unfamiliar languages, or broader engineering tasks?
- Rollout: Did the organization begin with a limited pilot, and what population did it later include?
- Integration: Which IDEs, source-control platforms, or DevOps tools were part of the deployment?
- Evidence method: Is the finding from a controlled trial, telemetry, an internal survey, or a company story?
- Outcome: Does the report measure task speed, output, adoption, satisfaction, or another result—and over what period?
The public accounts discussed here do not provide a standardized, same-method comparison across all organizations. A reported increase in code generation, a survey response about task speed, and a daily-use figure answer different questions. They are useful for understanding implementation patterns, but they do not establish that another team will see the same outcome.
Why this is not a verified list of exactly 36
The title’s number cannot be substantiated as a canonical roster from the cited material. Microsoft’s roundup is a larger, updated collection of customer stories, and the sources described here document selected deployments rather than a fixed set of exactly 36 cases. The examples are real organizational deployments reported by the companies or their publishers; they are not a verified complete 36-case list.
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