[{"data":1,"prerenderedAt":815},["ShallowReactive",2],{"/en-us/blog/measuring-ai-roi-at-scale-a-practical-guide-to-gitlab-duo-analytics":3,"navigation-en-us":39,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Paul Meresanu":702,"blog-related-posts-en-us-measuring-ai-roi-at-scale-a-practical-guide-to-gitlab-duo-analytics":717,"blog-promotions-en-us":753,"next-steps-en-us":805},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":20,"description":25,"extension":26,"externalUrl":27,"featured":13,"heroImage":17,"isFeatured":13,"meta":28,"navigation":29,"path":30,"publishedDate":20,"rawbody":31,"seo":32,"slug":15,"stem":34,"tagSlugs":35,"tags":37,"template":14,"updatedDate":19,"__hash__":38},"blogPosts/en-us/blog/measuring-ai-roi-at-scale-a-practical-guide-to-gitlab-duo-analytics.yml","Measuring AI ROI at scale: A practical guide to GitLab Duo Analytics",[7],"paul-meresanu",[9],"Paul Meresanu","AI investment starts with measurement. Building a successful AI-powered development platform begins with understanding actual usage, adoption patterns, and quantifiable business value — especially ROI from [GitLab Duo\nEnterprise](https://about.gitlab.com/gitlab-duo-agent-platform/).\n\nTo help our customers maximize their AI investments, we developed the GitLab Duo Analytics solution as part of our Duo Accelerator program — a comprehensive, customer-driven solution that transforms raw usage data into actionable business insights and ROI calculations. This is not a GitLab product, but rather a specialized enablement tool we created to address immediate analytics needs while organizations transition toward comprehensive AI productivity measurement.\n\nThis foundation enables broader AI transformation. For example, organizations can use these insights to optimize license allocation, identify high-value use cases, and build compelling business cases for expanding AI adoption across development teams.\n\nA leading financial services organization partnered with a GitLab customer success architect through the Duo Accelerator program to gain visibility into their GitLab Duo Enterprise investment. Together, we implemented a hybrid analytics solution that combines monthly data collection with real-time API integration, creating a scalable foundation for measuring AI productivity gains and optimizing license utilization at enterprise scale.\n\n## The challenge: Measuring AI ROI in enterprise development\n\nBefore implementing any analytics solution, it's essential to understand your AI measurement landscape.\n\nConsider:\n\n- **What GitLab Duo features need measurement?** (code suggestions, chat assistance, security scanning)?\n\n- **Who are your AI users?** (developers, security teams, DevOps engineers)?\n\n- **What business metrics matter?** (time savings, productivity gains, cost optimization)?\n\n- **How does your current data collection work** (manual exports, API integration, existing tooling)?\n\nUse this stage to define your:\n\n- ROI measurement framework\n\n- Key performance indicators (KPIs)\n\n- Data collection strategy\n\n- Stakeholder reporting requirements\n\n### Sample ROI measurement framework\n\n![Sample ROI measurement framework](https://gitlab.com/-/project/54775568/uploads/06da2f5c3a75197cd272aedb3d67a347/image.png)\n\n## Step-by-step implementation guide\n\nImportant: The solution below describes an open source approach that you can deploy in your own environment. It is **NOT** a commercial product from GitLab that you need to purchase. You should be able download, customize, and run this solution free of charge.\n\n### Prerequisites\n\n**Before starting, ensure you have:**\n\n- GitLab instance with Duo enabled\n\n- GitLab API token with read permissions\n\n- Access to configure GitLab CI/CD variables\n\n- Basic understanding of GitLab CI/CD pipelines\n\n### 1. Initial setup and configuration\n\nLet's set up the project environment by first cloning the repository.\n\n```bash\n\ngit clone https://gitlab.com/gitlab-org/professional-services-automation/tools/utilities/gitlab-graphql-api.git\ncd gitlab-graphql-api\n\n```\n\nThen push it to your own GitLab instance or namespace, and navigate to your project in GitLab to configure the CI/CD variables.\n\n### 2. Configure pipeline control variables \n\nControl which analytics pipelines run by setting Project CI/CD Variables. \nGo to **Project Settings → CI/CD → Variables** and add:\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| `ENABLE_DUO_METRICS` | \"true\" | Enable/disable Duo AI metrics pipeline |\n| `ENABLE_PROJECT_METRICS` | \"false\" | Enable/disable traditional project metrics pipeline |\n\n#### Example configurations:\n\n- **Duo Only**: `ENABLE_DUO_METRICS=\"true\"`, `ENABLE_PROJECT_METRICS=\"false\"`\n- **Both Dashboards**: `ENABLE_DUO_METRICS=\"true\"`, `ENABLE_PROJECT_METRICS=\"true\"`\n- **Disable All**: `ENABLE_DUO_METRICS=\"false\"`, `ENABLE_PROJECT_METRICS=\"false\"`\n\n### 3. Configure data ingestion\n\nConfigure the data ingestion in your `.gitlab-ci.yml` file. This creates raw project metadata shared across all metrics.\n\n#### Basic configuration variables:\n\n```yaml\nvariables:\n GROUP_PATH: \"gitlab-org/professional-services-automation\" # Your group path\n INCLUDE_SIMPLE_FIELDS: \"fullPath,name,description\" # Fields to fetch\n ARGUMENT_FIELDS: \"mergeRequests\" # Argument fields requiring additional config\n LIMIT: \"100\" # Projects per API call\n MAX_ITERATIONS: \"2\" # Maximum API calls (for testing)\n GITLAB_GRAPHQL_API_VERSION: \"0.1.0\" # API version from Package Registry\n```\n\n#### Configure argument fields for specific metrics:\n\nFor each metric you want to collect, define argument field variables:\n\n```yaml\n# Example: Merged Merge Requests\nARGUMENT_FIELD_1_NAME: \"mergeRequests\"\nARGUMENT_FIELD_1_FILTER_NAME: \"state\"\nARGUMENT_FIELD_1_FILTER_VALUE: \"merged\"\nARGUMENT_FIELD_1_RETURN_VALUES: \"count totalTimeToMerge\"\n\n# Example: Packages Count\nARGUMENT_FIELD_2_NAME: \"packages\"\nARGUMENT_FIELD_2_RETURN_VALUES: \"count\"\n```\n\n### 4. Configuring metrics aggregation\n\nAfter data ingestion, set up aggregation rules in `.gitlab/Schedule.gitlab-ci.yml` for each metric you want to generate.\n\n#### Example metric job configuration:\n\n```yaml\nprocess_average_time_to_merge:\n \u003C\u003C: *process_data_template\n stage: process_data\n variables:\n METRIC_NAME: \"9_Average_Time_To_Merge\"\n BUSINESS_LEVEL_START: 2\n BUSINESS_LEVEL_END: 4\n AGGREGATE_COLUMNS: \"mergeRequests_state_merged_totalTimeToMerge:sum mergeRequests_state_merged_count:sum\"\n NEW_COLUMN_NAME: \"average_time_to_merge_days\"\n NEW_COLUMN_FORMULA: \"mergeRequests_state_merged_totalTimeToMerge / mergeRequests_state_merged_count / (24 * 60 * 60)\"\n SORT_BY: \"average_time_to_merge_days\"\n FILTER_CONDITION: \"mergeRequests_state_merged_count >= 5\"\n```\n\n #### Required variables for each metric:\n\n| Variable | Description | Example |\n|----------|-------------|---------|\n| `METRIC_NAME` | Name of the metric (used in file naming) | \"9_Average_Time_To_Merge\" |\n| `BUSINESS_LEVEL_START` | Starting level for business hierarchy | 1 |\n| `BUSINESS_LEVEL_END` | Ending level for business hierarchy | 3 |\n| `AGGREGATE_COLUMNS` | Columns to aggregate with function | \"mergeRequests_state_merged_count:sum\" |\n| `SORT_BY` | Column to sort results by | \"average_time_to_merge_days\" |\n\n### 5. Run scheduled pipeline to configure analytics\n\nOnce configured, run a scheduled pipeline to generate your analytics:\n\n1. Go to **CI/CD → Schedules**\n2. Create a new schedule or run an existing one\n3. The pipeline will automatically:\n - Ingest data based on your configuration\n - Aggregate metrics according to your rules\n - Deploy dashboards to GitLab Pages\n\n### 6. Access your analytics dashboards\n\nAfter the scheduled pipeline completes successfully, GitLab Pages automatically deploys your dashboards:\n\n- **Project Metrics Dashboard**: `https://your-username.gitlab.io/project-name/existing-metrics/`\n- **Duo Metrics Dashboard**: `https://your-username.gitlab.io/project-name/duo-metrics/`\n- **Main Landing Page**: `https://your-username.gitlab.io/project-name/`\n\nThe main landing page automatically detects which dashboards are available and displays appropriate links.\n\nYou'll see:\n\n- License utilization: Total licensed users vs. active users (together with code suggestion analytics)\n\n![GitLab Duo Analytics Dasboard](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478265/nhbukcflhmghs5jatrip.png)\n\n- GitLab Duo Chat analytics: Unique Duo Chat users, average Chat events over 90 days, and Chat adoption rate\n- GitLab Duo engagement analytics: Categorizing Duo usage for a group of users as Power (10+ suggestions), Regular (5-9), or Light (1-4) based on usage patterns\n\n![Duo Chat Analytics - last 90 days](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478265/xgq05hh2ybzb8ugsxqza.png)\n\n- Usage analytics: Code suggestions by programming language (language coverage distribution), Code suggestions language performance analytics (accepted vs rejected rate)\n\n![User adoption view of Duo analytics](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478265/mu3dx5g2l2lki2ehlr2g.png)\n\n\u003Cp>\u003C/p>\n\n![Language performance analytics](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478267/xf0thn8sm4dlhoyyqg9i.png)\n\n- Weekly Duo Chat trends: Duo Chat usage patterns\n\n![Duo Chat daily usage trends](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478265/plhycnmewye3vp6vitqj.png) \n\n#### Dashboard features include:\n\n- **License Usage Analytics** - Track licensed vs active users\n- **Code Suggestions Analytics** - Monitor acceptance rates and language distribution\n- **Duo Chat Analytics** - View chat interactions and adoption rates\n- **User Engagement Analytics** - Categorize users by activity level\n- **Language Performance Analytics** - Analyze acceptance rates by programming language\n\n### 7. Understanding the APIs used\n\nThe solution leverages multiple GitLab APIs to collect comprehensive usage data:\n\n#### AI usage data API (aiUsageData)\n\n```graphql\n# Fetches individual code suggestion events\nquery: |\n {\n group(fullPath: \"your-group\") {\n aiUsageData {\n codeSuggestionEvents {\n event # ACCEPTED or SHOWN\n timestamp # When it happened\n language # Programming language\n suggestionSize # SINGLE_LINE or MULTI_LINE\n user { username }\n }\n }\n }\n }\n# Purpose: Tracks every code suggestion shown or accepted by developers\n```\n\n#### GitLab Self-Managed add-on users API\n\n```graphql\n# Gets licensed user information\nquery: |\n {\n selfManagedAddOnEligibleUsers(\n addOnType: DUO_ENTERPRISE\n filterByAssignedSeat: \"Yes\"\n ) {\n user {\n username\n lastDuoActivityOn\n }\n }\n }\n# Purpose: Identifies who has licenses and when they last used Duo\n```\n\n#### AI metrics API\n\n```graphql\nquery: |\n {\n aiMetrics(from: \"2024-01-01\", to: \"2024-06-30\") {\n codeSuggestions {\n shownCount\n acceptedCount\n }\n duoChatContributorsCount\n duoAssignedUsersCount\n }\n }\n# Purpose: Gets pre-calculated metrics for trend analysis\n```\n\n#### Service Ping API (REST)\n\n```bash\nurl: \"{GITLAB_URL}/api/v4/usage_data/service_ping\"\n# Purpose: Collects instance-wide usage statistics\n```\n## Putting it all together\n\nTo demonstrate the power of this integrated analytics solution, let's walk through a complete end-to-end implementation journey — from initial deployment to fully automated ROI measurement.\n\nStart by deploying the containerized solution in your environment using the provided Docker configuration. Within minutes, you'll have both the analytics API and React dashboard running locally. \n\nThe hybrid data architecture approach immediately begins collecting metrics from your existing monthly CSV exports while establishing real-time GraphQL connections to your GitLab instance.\n\n**Automation through Python scripting**\n\nThe real power emerges when you leverage Python scripting to automate the entire data collection and processing workflow. The solution includes comprehensive Python scripts that can be easily customized and scheduled.\n\n**GitLab CI/CD integration**\n\nFor enterprise-scale automation, integrate these Python scripts into scheduled GitLab [CI/CD](https://about.gitlab.com/topics/ci-cd/) pipelines. This approach leverages your existing GitLab infrastructure while ensuring consistent, reliable data collection:\n\n```yaml\n\n# .gitlab-ci.yml example\n\nduo_analytics_collection:\n stage: analytics\n script:\n - python scripts/enhanced_duo_data_collection.py\n - python scripts/metric_aggregations.py\n - ./deploy_dashboard_updates.sh\n schedule:\n - cron: \"0 2 1 * *\" # Monthly on 1st at 2 AM\n only:\n - schedules\n```\n\nThis automation strategy transforms manual data collection into a self-sustaining analytics engine. Your Python scripts execute monthly via GitLab pipelines, automatically collecting usage data, calculating ROI metrics, and updating dashboards — all without manual intervention.\n\nOnce automated, the solution operates seamlessly: Scheduled pipelines execute Python data collection scripts, process GraphQL responses into business metrics, and update dashboard data stores. You can watch as the dashboard populates with real usage patterns: code suggestion volumes by programming language, user adoption trends across teams, and license utilization rates that reveal optimization opportunities.\n\nThe real value emerges when you access the ROI Overview dashboard. Here, you'll see concrete enagagement metrics metrics which can be converted into business impact for your organisation — perhaps discovering that your active Duo users are generating 127% monthly ROI through time savings and productivity gains, while 23% of your licenses remain underutilized. These insights immediately translate into actionable recommendations: expand licenses to high-performing teams, implement targeted training for underutilized users, and build data-driven business cases for broader AI adoption.\n\n## Why GitLab?\n\nGitLab's comprehensive DevSecOps platform provides the ideal baseline for enterprise AI analytics and measurement. With native GraphQL APIs, flexible data access, and integrated AI capabilities through GitLab Duo, organizations can centralize AI measurement across the entire development lifecycle without disrupting existing workflows.\n\nThe solution's open architecture enables custom analytics solutions like the one developed through our Duo Accelerator program. GitLab's commitment to API-first design means you can extract detailed usage data, integrate with existing enterprise systems, and build sophisticated ROI calculations that align with your organization's specific metrics and reporting requirements.\n\nBeyond technical capabilities, our approach ensures you're not just implementing tools — you're building sustainable AI adoption strategies. This purpose built solution emerging from the Duo Accelerator program exemplifies this approach, providing hands-on guidance, proven frameworks, and custom solutions that address real enterprise challenges like ROI measurement and license optimization.\n\nAs GitLab continues enhancing native analytics capabilities, this foundation becomes even more valuable. The measurement frameworks, KPIs, and data collection processes established through custom analytics solutions seamlessly transition to enhanced native features, ensuring your investment in AI measurement grows with GitLab's evolving solution.\n\n## Try GitLab Duo today\n\nAI ROI measurement is just the beginning. With GitLab Duo's capabilities you can build out comprehensive analytics. With this you're not just tracking AI usage — you're building a foundation for data-driven AI optimization that scales with your organization's growth and evolves with GitLab's expanding AI capabilities.\n\nThe analytics solution developed through GitLab's Duo Accelerator program demonstrates how customer success partnerships can deliver immediate value while establishing long-term strategic advantages. From initial deployment to enterprise-scale ROI measurement, this solution provides the visibility and insights needed to maximize AI investments and drive sustainable adoption.\n\nThe combination of Python automation, GitLab CI/CD integration, and purpose-built analytics creates a competitive advantage that extends far beyond individual developer productivity. It enables strategic decision-making, optimizes resource allocation, and builds compelling business cases for continued AI investment and expansion.\n\nThe future of AI-powered development is data-driven, and it starts with measurement. Whether you're beginning your AI journey or optimizing existing investments, GitLab provides both the platform and the partnership needed to succeed.\n\n> Get started with GitLab Duo today with a [free trial of GitLab Ultimate with Duo Enterprise](https://about.gitlab.com/gitlab-duo-agent-platform/).","ai-ml",{"featured":13,"template":14,"slug":15},false,"BlogPost","measuring-ai-roi-at-scale-a-practical-guide-to-gitlab-duo-analytics",{"heroImage":17,"body":10,"authors":18,"updatedDate":19,"date":20,"title":5,"tags":21,"description":25,"category":11},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749662840/Blog/Hero%20Images/ai-experiment-stars.png",[9],"2025-09-09","2025-08-06",[22,23,24],"AI/ML","product","tutorial","Learn how to transform raw usage data into actionable business insights and ROI calculations with this in-depth tutorial.","yml",null,{},true,"/en-us/blog/measuring-ai-roi-at-scale-a-practical-guide-to-gitlab-duo-analytics","seo:\n config:\n noIndex: false\n title: 'Measuring AI ROI at scale: A practical guide to GitLab Duo Analytics'\n description: Learn how to transform raw usage data into actionable business\n insights and ROI calculations with this in-depth tutorial.\ncontent:\n heroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1749662840/Blog/Hero%20Images/ai-experiment-stars.png\n body: >-\n AI investment starts with measurement. Building a successful AI-powered\n development platform begins with understanding actual usage, adoption\n patterns, and quantifiable business value — especially ROI from [GitLab Duo\n\n Enterprise](https://about.gitlab.com/gitlab-duo-agent-platform/).\n\n\n To help our customers maximize their AI investments, we developed the GitLab Duo Analytics solution as part of our Duo Accelerator program — a comprehensive, customer-driven solution that transforms raw usage data into actionable business insights and ROI calculations. This is not a GitLab product, but rather a specialized enablement tool we created to address immediate analytics needs while organizations transition toward comprehensive AI productivity measurement.\n\n\n This foundation enables broader AI transformation. For example, organizations can use these insights to optimize license allocation, identify high-value use cases, and build compelling business cases for expanding AI adoption across development teams.\n\n\n A leading financial services organization partnered with a GitLab customer success architect through the Duo Accelerator program to gain visibility into their GitLab Duo Enterprise investment. Together, we implemented a hybrid analytics solution that combines monthly data collection with real-time API integration, creating a scalable foundation for measuring AI productivity gains and optimizing license utilization at enterprise scale.\n\n\n ## The challenge: Measuring AI ROI in enterprise development\n\n\n Before implementing any analytics solution, it's essential to understand your AI measurement landscape.\n\n\n Consider:\n\n\n - **What GitLab Duo features need measurement?** (code suggestions, chat assistance, security scanning)?\n\n\n - **Who are your AI users?** (developers, security teams, DevOps engineers)?\n\n\n - **What business metrics matter?** (time savings, productivity gains, cost optimization)?\n\n\n - **How does your current data collection work** (manual exports, API integration, existing tooling)?\n\n\n Use this stage to define your:\n\n\n - ROI measurement framework\n\n\n - Key performance indicators (KPIs)\n\n\n - Data collection strategy\n\n\n - Stakeholder reporting requirements\n\n\n ### Sample ROI measurement framework\n\n\n ![Sample ROI measurement framework](https://gitlab.com/-/project/54775568/uploads/06da2f5c3a75197cd272aedb3d67a347/image.png)\n\n\n ## Step-by-step implementation guide\n\n\n Important: The solution below describes an open source approach that you can deploy in your own environment. It is **NOT** a commercial product from GitLab that you need to purchase. You should be able download, customize, and run this solution free of charge.\n\n\n ### Prerequisites\n\n\n **Before starting, ensure you have:**\n\n\n - GitLab instance with Duo enabled\n\n\n - GitLab API token with read permissions\n\n\n - Access to configure GitLab CI/CD variables\n\n\n - Basic understanding of GitLab CI/CD pipelines\n\n\n ### 1. Initial setup and configuration\n\n\n Let's set up the project environment by first cloning the repository.\n\n\n ```bash\n\n\n git clone https://gitlab.com/gitlab-org/professional-services-automation/tools/utilities/gitlab-graphql-api.git\n\n cd gitlab-graphql-api\n\n\n ```\n\n\n Then push it to your own GitLab instance or namespace, and navigate to your project in GitLab to configure the CI/CD variables.\n\n\n ### 2. Configure pipeline control variables \n\n\n Control which analytics pipelines run by setting Project CI/CD Variables. \n\n Go to **Project Settings → CI/CD → Variables** and add:\n\n\n | Variable | Default | Description |\n\n |----------|---------|-------------|\n\n | `ENABLE_DUO_METRICS` | \"true\" | Enable/disable Duo AI metrics pipeline |\n\n | `ENABLE_PROJECT_METRICS` | \"false\" | Enable/disable traditional project metrics pipeline |\n\n\n #### Example configurations:\n\n\n - **Duo Only**: `ENABLE_DUO_METRICS=\"true\"`, `ENABLE_PROJECT_METRICS=\"false\"`\n\n - **Both Dashboards**: `ENABLE_DUO_METRICS=\"true\"`, `ENABLE_PROJECT_METRICS=\"true\"`\n\n - **Disable All**: `ENABLE_DUO_METRICS=\"false\"`, `ENABLE_PROJECT_METRICS=\"false\"`\n\n\n ### 3. Configure data ingestion\n\n\n Configure the data ingestion in your `.gitlab-ci.yml` file. This creates raw project metadata shared across all metrics.\n\n\n #### Basic configuration variables:\n\n\n ```yaml\n\n variables:\n GROUP_PATH: \"gitlab-org/professional-services-automation\" # Your group path\n INCLUDE_SIMPLE_FIELDS: \"fullPath,name,description\" # Fields to fetch\n ARGUMENT_FIELDS: \"mergeRequests\" # Argument fields requiring additional config\n LIMIT: \"100\" # Projects per API call\n MAX_ITERATIONS: \"2\" # Maximum API calls (for testing)\n GITLAB_GRAPHQL_API_VERSION: \"0.1.0\" # API version from Package Registry\n ```\n\n\n #### Configure argument fields for specific metrics:\n\n\n For each metric you want to collect, define argument field variables:\n\n\n ```yaml\n\n # Example: Merged Merge Requests\n\n ARGUMENT_FIELD_1_NAME: \"mergeRequests\"\n\n ARGUMENT_FIELD_1_FILTER_NAME: \"state\"\n\n ARGUMENT_FIELD_1_FILTER_VALUE: \"merged\"\n\n ARGUMENT_FIELD_1_RETURN_VALUES: \"count totalTimeToMerge\"\n\n\n # Example: Packages Count\n\n ARGUMENT_FIELD_2_NAME: \"packages\"\n\n ARGUMENT_FIELD_2_RETURN_VALUES: \"count\"\n\n ```\n\n\n ### 4. Configuring metrics aggregation\n\n\n After data ingestion, set up aggregation rules in `.gitlab/Schedule.gitlab-ci.yml` for each metric you want to generate.\n\n\n #### Example metric job configuration:\n\n\n ```yaml\n\n process_average_time_to_merge:\n \u003C\u003C: *process_data_template\n stage: process_data\n variables:\n METRIC_NAME: \"9_Average_Time_To_Merge\"\n BUSINESS_LEVEL_START: 2\n BUSINESS_LEVEL_END: 4\n AGGREGATE_COLUMNS: \"mergeRequests_state_merged_totalTimeToMerge:sum mergeRequests_state_merged_count:sum\"\n NEW_COLUMN_NAME: \"average_time_to_merge_days\"\n NEW_COLUMN_FORMULA: \"mergeRequests_state_merged_totalTimeToMerge / mergeRequests_state_merged_count / (24 * 60 * 60)\"\n SORT_BY: \"average_time_to_merge_days\"\n FILTER_CONDITION: \"mergeRequests_state_merged_count >= 5\"\n ```\n\n #### Required variables for each metric:\n\n | Variable | Description | Example |\n\n |----------|-------------|---------|\n\n | `METRIC_NAME` | Name of the metric (used in file naming) | \"9_Average_Time_To_Merge\" |\n\n | `BUSINESS_LEVEL_START` | Starting level for business hierarchy | 1 |\n\n | `BUSINESS_LEVEL_END` | Ending level for business hierarchy | 3 |\n\n | `AGGREGATE_COLUMNS` | Columns to aggregate with function | \"mergeRequests_state_merged_count:sum\" |\n\n | `SORT_BY` | Column to sort results by | \"average_time_to_merge_days\" |\n\n\n ### 5. Run scheduled pipeline to configure analytics\n\n\n Once configured, run a scheduled pipeline to generate your analytics:\n\n\n 1. Go to **CI/CD → Schedules**\n\n 2. Create a new schedule or run an existing one\n\n 3. The pipeline will automatically:\n - Ingest data based on your configuration\n - Aggregate metrics according to your rules\n - Deploy dashboards to GitLab Pages\n\n ### 6. Access your analytics dashboards\n\n\n After the scheduled pipeline completes successfully, GitLab Pages automatically deploys your dashboards:\n\n\n - **Project Metrics Dashboard**: `https://your-username.gitlab.io/project-name/existing-metrics/`\n\n - **Duo Metrics Dashboard**: `https://your-username.gitlab.io/project-name/duo-metrics/`\n\n - **Main Landing Page**: `https://your-username.gitlab.io/project-name/`\n\n\n The main landing page automatically detects which dashboards are available and displays appropriate links.\n\n\n You'll see:\n\n\n - License utilization: Total licensed users vs. active users (together with code suggestion analytics)\n\n\n ![GitLab Duo Analytics Dasboard](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478265/nhbukcflhmghs5jatrip.png)\n\n\n - GitLab Duo Chat analytics: Unique Duo Chat users, average Chat events over 90 days, and Chat adoption rate\n\n - GitLab Duo engagement analytics: Categorizing Duo usage for a group of users as Power (10+ suggestions), Regular (5-9), or Light (1-4) based on usage patterns\n\n\n ![Duo Chat Analytics - last 90 days](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478265/xgq05hh2ybzb8ugsxqza.png)\n\n\n - Usage analytics: Code suggestions by programming language (language coverage distribution), Code suggestions language performance analytics (accepted vs rejected rate)\n\n\n ![User adoption view of Duo analytics](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478265/mu3dx5g2l2lki2ehlr2g.png)\n\n\n \u003Cp>\u003C/p>\n\n\n ![Language performance analytics](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478267/xf0thn8sm4dlhoyyqg9i.png)\n\n\n - Weekly Duo Chat trends: Duo Chat usage patterns\n\n\n ![Duo Chat daily usage trends](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754478265/plhycnmewye3vp6vitqj.png) \n\n\n #### Dashboard features include:\n\n\n - **License Usage Analytics** - Track licensed vs active users\n\n - **Code Suggestions Analytics** - Monitor acceptance rates and language distribution\n\n - **Duo Chat Analytics** - View chat interactions and adoption rates\n\n - **User Engagement Analytics** - Categorize users by activity level\n\n - **Language Performance Analytics** - Analyze acceptance rates by programming language\n\n\n ### 7. Understanding the APIs used\n\n\n The solution leverages multiple GitLab APIs to collect comprehensive usage data:\n\n\n #### AI usage data API (aiUsageData)\n\n\n ```graphql\n\n # Fetches individual code suggestion events\n\n query: |\n {\n group(fullPath: \"your-group\") {\n aiUsageData {\n codeSuggestionEvents {\n event # ACCEPTED or SHOWN\n timestamp # When it happened\n language # Programming language\n suggestionSize # SINGLE_LINE or MULTI_LINE\n user { username }\n }\n }\n }\n }\n # Purpose: Tracks every code suggestion shown or accepted by developers\n\n ```\n\n\n #### GitLab Self-Managed add-on users API\n\n\n ```graphql\n\n # Gets licensed user information\n\n query: |\n {\n selfManagedAddOnEligibleUsers(\n addOnType: DUO_ENTERPRISE\n filterByAssignedSeat: \"Yes\"\n ) {\n user {\n username\n lastDuoActivityOn\n }\n }\n }\n # Purpose: Identifies who has licenses and when they last used Duo\n\n ```\n\n\n #### AI metrics API\n\n\n ```graphql\n\n query: |\n {\n aiMetrics(from: \"2024-01-01\", to: \"2024-06-30\") {\n codeSuggestions {\n shownCount\n acceptedCount\n }\n duoChatContributorsCount\n duoAssignedUsersCount\n }\n }\n # Purpose: Gets pre-calculated metrics for trend analysis\n\n ```\n\n\n #### Service Ping API (REST)\n\n\n ```bash\n\n url: \"{GITLAB_URL}/api/v4/usage_data/service_ping\"\n\n # Purpose: Collects instance-wide usage statistics\n\n ```\n\n ## Putting it all together\n\n\n To demonstrate the power of this integrated analytics solution, let's walk through a complete end-to-end implementation journey — from initial deployment to fully automated ROI measurement.\n\n\n Start by deploying the containerized solution in your environment using the provided Docker configuration. Within minutes, you'll have both the analytics API and React dashboard running locally. \n\n\n The hybrid data architecture approach immediately begins collecting metrics from your existing monthly CSV exports while establishing real-time GraphQL connections to your GitLab instance.\n\n\n **Automation through Python scripting**\n\n\n The real power emerges when you leverage Python scripting to automate the entire data collection and processing workflow. The solution includes comprehensive Python scripts that can be easily customized and scheduled.\n\n\n **GitLab CI/CD integration**\n\n\n For enterprise-scale automation, integrate these Python scripts into scheduled GitLab [CI/CD](https://about.gitlab.com/topics/ci-cd/) pipelines. This approach leverages your existing GitLab infrastructure while ensuring consistent, reliable data collection:\n\n\n ```yaml\n\n\n # .gitlab-ci.yml example\n\n\n duo_analytics_collection:\n stage: analytics\n script:\n - python scripts/enhanced_duo_data_collection.py\n - python scripts/metric_aggregations.py\n - ./deploy_dashboard_updates.sh\n schedule:\n - cron: \"0 2 1 * *\" # Monthly on 1st at 2 AM\n only:\n - schedules\n ```\n\n\n This automation strategy transforms manual data collection into a self-sustaining analytics engine. Your Python scripts execute monthly via GitLab pipelines, automatically collecting usage data, calculating ROI metrics, and updating dashboards — all without manual intervention.\n\n\n Once automated, the solution operates seamlessly: Scheduled pipelines execute Python data collection scripts, process GraphQL responses into business metrics, and update dashboard data stores. You can watch as the dashboard populates with real usage patterns: code suggestion volumes by programming language, user adoption trends across teams, and license utilization rates that reveal optimization opportunities.\n\n\n The real value emerges when you access the ROI Overview dashboard. Here, you'll see concrete enagagement metrics metrics which can be converted into business impact for your organisation — perhaps discovering that your active Duo users are generating 127% monthly ROI through time savings and productivity gains, while 23% of your licenses remain underutilized. These insights immediately translate into actionable recommendations: expand licenses to high-performing teams, implement targeted training for underutilized users, and build data-driven business cases for broader AI adoption.\n\n\n ## Why GitLab?\n\n\n GitLab's comprehensive DevSecOps platform provides the ideal baseline for enterprise AI analytics and measurement. With native GraphQL APIs, flexible data access, and integrated AI capabilities through GitLab Duo, organizations can centralize AI measurement across the entire development lifecycle without disrupting existing workflows.\n\n\n The solution's open architecture enables custom analytics solutions like the one developed through our Duo Accelerator program. GitLab's commitment to API-first design means you can extract detailed usage data, integrate with existing enterprise systems, and build sophisticated ROI calculations that align with your organization's specific metrics and reporting requirements.\n\n\n Beyond technical capabilities, our approach ensures you're not just implementing tools — you're building sustainable AI adoption strategies. This purpose built solution emerging from the Duo Accelerator program exemplifies this approach, providing hands-on guidance, proven frameworks, and custom solutions that address real enterprise challenges like ROI measurement and license optimization.\n\n\n As GitLab continues enhancing native analytics capabilities, this foundation becomes even more valuable. The measurement frameworks, KPIs, and data collection processes established through custom analytics solutions seamlessly transition to enhanced native features, ensuring your investment in AI measurement grows with GitLab's evolving solution.\n\n\n ## Try GitLab Duo today\n\n\n AI ROI measurement is just the beginning. With GitLab Duo's capabilities you can build out comprehensive analytics. With this you're not just tracking AI usage — you're building a foundation for data-driven AI optimization that scales with your organization's growth and evolves with GitLab's expanding AI capabilities.\n\n\n The analytics solution developed through GitLab's Duo Accelerator program demonstrates how customer success partnerships can deliver immediate value while establishing long-term strategic advantages. From initial deployment to enterprise-scale ROI measurement, this solution provides the visibility and insights needed to maximize AI investments and drive sustainable adoption.\n\n\n The combination of Python automation, GitLab CI/CD integration, and purpose-built analytics creates a competitive advantage that extends far beyond individual developer productivity. It enables strategic decision-making, optimizes resource allocation, and builds compelling business cases for continued AI investment and expansion.\n\n\n The future of AI-powered development is data-driven, and it starts with measurement. Whether you're beginning your AI journey or optimizing existing investments, GitLab provides both the platform and the partnership needed to succeed.\n\n\n > Get started with GitLab Duo today with a [free trial of GitLab Ultimate with Duo Enterprise](https://about.gitlab.com/gitlab-duo-agent-platform/).\n authors:\n - Paul Meresanu\n updatedDate: 2025-09-09\n date: 2025-08-06\n title: 'Measuring AI ROI at scale: A practical guide to GitLab Duo Analytics'\n tags:\n - AI/ML\n - product\n - tutorial\n description: Learn how to transform raw usage data into actionable business\n insights and ROI calculations with this in-depth tutorial.\n category: ai-ml\nconfig:\n featured: false\n template: BlogPost\n slug: 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and Anthropic: Governed AI for enterprise development","GitLab deepens its Anthropic Claude integration, bringing governed AI, access to new models, and cloud flexibility to enterprise software development.",[723],"Stuart Moncada","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776457632/llddiylsgwuze0u1rjks.png","2026-04-28","For enterprise and public sector leaders, the tension is familiar: Software teams need to move faster with AI, while security, compliance, and regulatory expectations only get more stringent. GitLab deepens its Anthropic Claude integration so organizations get access to newly released Claude models inside GitLab’s intelligent orchestration platform where governance, compliance, and auditability already run.\n\nClaude powers capabilities across GitLab Duo Agent Platform as the default model out of the box, across a variety of use cases from code generation and review to agentic chat and vulnerability resolution. If you've used GitLab Duo, you've already experienced how Duo agents automate workflows across the entire software development lifecycle (SDLC).\n\nThis accelerates the integration of Claude’s capabilities into GitLab, broadens how enterprises can deploy them, and reinforces what makes GitLab fundamentally different as a platform for software development and engineering: governance, compliance, and auditability built into every AI interaction.\n\n> \"GitLab Duo has accelerated how our teams plan, build, and ship software. The combination of Anthropic's Claude and GitLab's platform means we're getting more capable AI without changing how we work or how it is governed.\"\n>\n> – Mans Booijink, Operations Manager, Cube\n\n## The real differentiator: Governed AI\n\nWith GitLab, governance controls and auditing are built into the SDLC. When Claude suggests a code change through the GitLab Duo Agent Platform, that suggestion flows through the same merge request process, the same approval rules, the same security scanning, and the same audit trail as every other change. AI doesn't get a shortcut around your controls. It operates within them.\n\nAs GitLab moves deeper into agentic software development, where AI autonomously handles well-defined tasks, the governance layer becomes more important. An AI agent that can open a merge request, help resolve a vulnerability, or refactor a service needs to be auditable, attributable, and subject to the same policy enforcement as a human developer. That requirement is an architectural decision GitLab made from the start, and one that grows more consequential as AI agents take on broader responsibilities.\n\n## Enterprise deployment flexibility\n\nThis also expands how organizations access the latest Claude models through GitLab. Claude is available within GitLab through Google Cloud's Vertex AI and Amazon Bedrock, which means enterprises can route AI workloads through the hyperscaler commitments and cloud governance frameworks they already have in place. No separate vendor contract. No new data residency questions. Your existing Google Cloud or AWS relationship is the on-ramp. \n\nGitLab is now also available in the [Claude Marketplace](https://claude.com/platform/marketplace), allowing customers to purchase GitLab Credits and apply them toward existing Anthropic spending commitments – consolidating AI spend and simplifying how teams discover and procure GitLab alongside their Anthropic investments.\n\n## Advancing an agentic future\n\nGitLab's vision for agentic software development, where AI handles defined tasks autonomously across planning, coding, testing, securing, and deploying, requires models with strong reasoning, reliability, and safety characteristics. It also requires a platform where those autonomous actions are fully governed.\n\nAgentic workflows demand models with strong reasoning, reliability, and safety characteristics, criteria that guide how GitLab selects and integrates AI model partners. And GitLab's governance framework helps ensure that as AI agents assume more advanced development work, enterprises maintain full visibility and control over what those agents do, when they do it, and how changes are tracked.\n\n## What this means for GitLab customers\n\nIf you're already using GitLab Duo Agent Platform, you'll get access to Claude models and deeper AI assistance across your software development lifecycle, all within the governance framework you already rely on.\n\nIf you're evaluating AI-powered software development platforms, you shouldn't have to choose between advanced AI capabilities and enterprise control. This strategic integration is built to deliver both.\n\n> Want to learn more about GitLab Duo Agent Platform? [Get a demo or start a free trial today](https://about.gitlab.com/gitlab-duo-agent-platform/).",[22,23,281],{"featured":29,"template":14,"slug":729},"gitlab-and-anthropic-governed-ai-for-enterprise-development",{"content":731,"config":740},{"title":732,"description":733,"authors":734,"heroImage":736,"date":737,"body":738,"category":11,"tags":739},"Give your AI agent direct, structured GitLab access with glab CLI","The GitLab CLI (glab) provides AI agents structured, reliable access to projects via the MCP, eliminating friction. This tutorial shows how you can speed up code review and issue triage.",[735],"Kai Armstrong","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776347152/unw3mzatkd5xyfbzcnni.png","2026-04-27","\nWhen teams use GitLab Duo, Claude, Cursor, and other AI assistants, more of the development workflow runs through an AI agent acting on your behalf — reading issues, reviewing merge requests, running pipelines, and helping you ship faster. Most developers are already using the GitLab CLI (`glab`) from the terminal to interact with GitLab. Combining the two is a natural next step.\n\n\nThe problem is that without the right tools, AI agents are essentially guessing when it comes to your GitLab projects. They might hallucinate the details of an issue they've never seen, summarize a merge request based on stale training data rather than its actual state, or require you to manually copy context from a browser tab and paste it into a chat window just to get started. Every one of those workarounds is friction: it slows you down, introduces the possibility of error, and puts a hard ceiling on what your agent can actually do on your behalf. `glab` changes that by giving agents a direct, reliable interface to your projects.\n\n\nWith `glab`, your agent fetches what it needs directly from GitLab, acts on it, and reports back — so you spend less time relaying information and more time on the work that matters.\n\n\nIn this tutorial, you'll learn how to use `glab` to give AI agents structured, reliable access to your GitLab projects. You'll also discover how that unlocks a faster, more capable development workflow.\n\n\n## How to connect your AI agent to GitLab through MCP\n\n\nThe most direct way to supercharge your AI workflow is to give your AI agent native access to `glab` through Model Context Protocol ([MCP](https://about.gitlab.com/topics/ai/model-context-protocol/)).\n\n\n MCP is an open standard that lets AI tools discover and use external capabilities at runtime. Once connected, your AI assistant can read issues, comment on merge requests, check pipeline status, and write back to GitLab, all without copying anything from the UI or writing a single API call yourself.\n\n\n To get started, run:\n\n\n ```shell\n # Start the glab MCP server\n glab mcp serve\n ```\n\n\n Once your MCP client is configured, your AI can answer questions like *\"What's the status of my open MRs?\"* or *\"Are there any failing pipelines on main?\"* by querying GitLab directly, not scraping the web UI, not relying on stale training data. See the [full setup docs](https://docs.gitlab.com/cli/) for configuration steps for Claude Code, Cursor, and other editors.\n\n\n One detail worth knowing: `glab` automatically adds `--output json` when invoked through MCP, for any command that supports it. Your agent gets clean, structured data without you needing to think about output formats. And because `glab` uses the official MCP SDK, it stays compatible as the\n protocol evolves.\n\n\n We've also been deliberate about *which* commands are exposed through MCP. Commands that require interactive terminal input are intentionally\n excluded, so your agent never gets stuck waiting for input that will never come. What's exposed is what actually works reliably in an agent context.\n\n\n ## Let your AI participate in code review\n\n\n Most developers have a backlog of MRs waiting for review. It's one of the most time-consuming parts of the job and one of the best places to put\n AI to work. With `glab`, your agent doesn't just observe your review queue, it can work through it with you.\n\n\n ### See exactly what still needs addressing\n\n\n Start with this:\n\n\n ```shell\n glab mr view 2677 --comments --unresolved --output json\n ```\n\n\n This input returns the full MR: metadata, description, and every\n unresolved discussion, as a single structured JSON payload. Hand that to\n your AI and it has everything it needs: which threads are open, what the\n reviewer asked for, and in what context. No tab-switching, no copy-pasting\n individual comments.\n\n\n \n ```json\n {\n \"id\": 2677,\n \"title\": \"feat: add OAuth2 support\",\n \"state\": \"opened\",\n \"author\": { \"username\": \"jdwick\" },\n \"labels\": [\"backend\", \"needs-review\"],\n \"blocking_discussions_resolved\": false,\n \"discussions\": [\n {\n \"id\": \"3107030349\",\n \"resolved\": false,\n \"notes\": [\n {\n \"author\": { \"username\": \"dmurphy\" },\n \"body\": \"This error handling will swallow panics — consider wrapping with recover()\",\n \"created_at\": \"2026-03-14T09:23:11.000Z\"\n }\n ]\n },\n {\n \"id\": \"3107030412\",\n \"resolved\": false,\n \"notes\": [\n {\n \"author\": { \"username\": \"sreeves\" },\n \"body\": \"Token refresh logic needs a test for the expired token case\",\n \"created_at\": \"2026-03-14T10:05:44.000Z\"\n }\n ]\n }\n ]\n }\n ```\n\n\n Instead of reading through every thread yourself, you ask your agent *\"what do I still need to fix in MR 2677?\"* and get back a prioritized summary with suggested changes. This all happens from a single command.\n\n\n ### Close the loop programmatically\n\n\n Once your AI has helped you address the feedback, it can resolve\n discussions:\n\n\n ```shell\n # List all discussions — structured, ready for the agent to process\n glab mr note list 456 --output json\n\n # Resolve a discussion once the feedback is addressed\n glab mr note resolve 456 3107030349\n\n # Reopen if something needs another look\n glab mr note reopen 456 3107030349\n ```\n\n\n\n ```json\n [\n {\n \"id\": 3107030349,\n \"body\": \"This error handling will swallow panics — consider wrapping with recover()\",\n \"author\": { \"username\": \"dmurphy\" },\n \"resolved\": false,\n \"resolvable\": true\n },\n {\n \"id\": 3107030412,\n \"body\": \"Token refresh logic needs a test for the expired token case\",\n \"author\": { \"username\": \"sreeves\" },\n \"resolved\": false,\n \"resolvable\": true\n }\n ]\n ```\n\n\n\n Note IDs are visible directly in the GitLab UI and API, no extra lookup needed. Your agent can work through the full list, verify each fix, and\n resolve as it goes.\n\n\n ## Talk to your AI about your code more effectively\n\n\n Even if you're not running an MCP server, there's a simpler shift that makes a huge difference: using `glab` to feed your AI better information.\n\n\n Think about the last time you asked an AI assistant to help triage issues or debug a failing pipeline. You probably copied some text from the GitLab UI and pasted it into the chat. Here's what your agent is actually\n working with when you do that:\n\n\n ```text\n open issues: 12 • milestone: 17.10 • label: bug, needs-triage ...\n ```\n\n\n Compare that to what it gets with `glab`:\n\n\n \n ```json\n [\n {\n \"iid\": 902,\n \"title\": \"Pipeline fails on merge to main\",\n \"labels\": [\"bug\", \"needs-triage\"],\n \"milestone\": { \"title\": \"17.10\" },\n \"assignees\": []\n },\n ...\n ]\n ```\n\n\n Structured, typed, complete; no ambiguity, no parsing guesswork. That's the difference between an agent that can act and one that has to ask\n follow-up questions.\n\n\n If you're using the MCP server, you get this automatically: `glab` adds `--output json` for any command that supports it. If you're working directly\n from the terminal, just add the flag yourself:\n\n\n ```shell\n # Pull open issues for triage\n glab issue list --label \"needs-triage\" --output json\n\n # Check pipeline status\n glab ci status --output json\n\n # Get full MR details\n glab mr view 456 --output json\n ```\n\n\n We've significantly expanded JSON output support in recent releases. It now covers CI status, milestones, labels, releases, schedules, cluster agents, work items, MR approvers, repo contributors, and more. If `glab` can\n retrieve it, your AI can consume it cleanly.\n\n\n ### A real workflow\n\n\n ```shell\n $ glab issue list --label \"needs-triage\" --milestone \"17.10\"\n --output json\n ```\n\n\n ```text\n Agent: I found 2 unassigned bugs in the 17.10 milestone that need triage:\n 1. #902 — Pipeline fails on merge to main (opened 5 days ago)\n 2. #903 — Auth token not refreshing on expiry (opened 4 days ago)\n Both are unassigned. Want me to draft triage notes and suggest assignees based on recent commit history?\n ```\n\n\n ## Your agent is never limited to built-in commands\n\n\n `glab`'s first-class commands cover the most common workflows, but your agent is never limited to them. Through `glab api`, it has authenticated access to the full GitLab REST and GraphQL API surface, using the same session, with no extra credentials or configuration required.\n\n\n This is a meaningful differentiator. Most CLI tools stop at what their commands expose. With `glab`, if GitLab's API supports it, your agent can do it. It's always working from a trusted, authenticated context.\n\n\n A practical example: fetching just the list of changed files in an MR before deciding which diffs to pull in full:\n\n\n ```shell\n # Get changed file paths — lightweight, no diff content yet\n glab api \"/projects/$CI_PROJECT_ID/merge_requests/$CI_MERGE_REQUEST_IID/diffs?per_page=100\" \\\n | jq '.[].new_path'\n\n# Then fetch only the specific file your agent needs\nglab api \"/projects/$CI_PROJECT_ID/merge_requests/$CI_MERGE_REQUEST_IID/diffs?per_page=100\" \\\n| jq '.[] | select(.new_path == \"path/to/file.go\")'\n ```\n\n\n ```text\n \"internal/auth/token.go\"\n \"internal/auth/token_test.go\"\n \"internal/oauth/refresh.go\"\n ```\n\n\n For anything the REST API doesn't cover (epics, certain work item queries, complex cross-project data), `glab api graphql` gives you the full\n GraphQL interface:\n\n\n ```shell\n glab api graphql -f query='\n {\n project(fullPath: \"gitlab-org/gitlab\") {\n mergeRequest(iid: \"12345\") {\n title\n reviewers { nodes { username } }\n }\n }\n }'\n ```\n\n ```json\n{\n \"data\": {\n \"project\": {\n \"mergeRequest\": {\n \"title\": \"feat: add OAuth2 support\",\n \"reviewers\": {\n \"nodes\": [\n { \"username\": \"dmurphy\" },\n { \"username\": \"sreeves\" }\n ]\n }\n }\n }\n }\n }\n\n ```\n\n\n Your agent has a single, authenticated entry point to everything GitLab exposes without the token juggling, separate API clients, or configuration\n overhead.\n\n\n ## What's coming and your feedback\n\n\n Two improvements we're actively working on will make `glab` even more useful for agent workflows:\n\n\n **Agent-aware help text.** Today, `--help` output is written for humansvat a terminal. We're updating it to surface the non-interactive alternative\n for every interactive command, flag which commands support `--output json`, and generally make help a useful resource for agents discovering\n capabilities at runtime — not just humans.\n\n\n **Better machine-readable errors.** When something goes wrong today, agents get the same human-readable error messages as terminal users. We're\n changing that so errors in JSON mode return structured output, giving your agent the information it needs to handle failures gracefully, retry intelligently, or surface the right context back to you.\n\n\n Both of these are in active development. If you're already using `glab` with an AI tool, you're exactly the audience we want feedback from.\n\n\n * **What friction are you hitting?** Commands that don't behave well in agent contexts, error messages that aren't actionable, gaps in JSON output\n coverage. We want to know.\n\n * **What workflows have you unlocked?** Real usage patterns help us prioritize what to build next.\n\n\n Join the discussion in [our feedback issue](https://gitlab.com/gitlab-org/cli/-/issues/8177) — that's where we're shaping the roadmap for agent-friendliness, and where your input will have the most direct impact. If you've found a specific gap, [open an issue](https://gitlab.com/gitlab-org/cli/-/issues/new). If you've got a fix in mind, contributions are welcome. Visit [CONTRIBUTING.md](https://gitlab.com/gitlab-org/cli/-/blob/main/CONTRIBUTING.md) to get started.\n\n\n The GitLab CLI has always been about giving developers more control over their workflow. As AI becomes a bigger part of how we all work, that means making `glab` the best possible interface between your AI tools and your GitLab projects. We're just getting started and we'd love to build the next part with you.\n",[22,23,24],{"featured":29,"template":14,"slug":741},"give-your-ai-agent-direct-structured-gitlab-access-with-glab-cli",{"content":743,"config":751},{"title":744,"description":745,"authors":746,"heroImage":736,"date":748,"body":749,"category":11,"tags":750},"GitHub Copilot's new policy for AI training is a governance wake-up call","Learn what GitHub's Copilot policy change means for regulated industries, and why GitLab's commitment to customer data privacy matters.",[747],"Allie Holland","2026-04-20","GitHub recently [announced](https://github.blog/news-insights/company-news/updates-to-github-copilot-interaction-data-usage-policy/) a significant change to how it handles data from Copilot users. Starting April 24, 2026, interaction data from Copilot Free, Pro, and Pro+ users, including inputs, outputs, code snippets, and associated context, will be used to train AI models by default, unless users actively opt out. Copilot Business and Enterprise customers are exempt under existing contract terms.\n\nFor organizations in regulated industries, including finance, healthcare, defense, and public sector, the policy shift raises questions that go beyond individual developer preferences. It forces a harder look at a question that engineering and security leaders should be asking every AI vendor in their stack: Do you train on our code? \n\nGitLab's answer is no. GitLab does not train AI models on customer code at any tier, and AI vendors are contractually prohibited from using customer inputs or outputs for their own purposes. The [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/) makes that commitment auditable: a single location documenting which models power which features, how data is handled, subprocessor relationships, and data retention periods. The GitLab AI Transparency Center also lists the compliance status of each feature, including confirmation that GitLab's current AI features do not qualify as high-risk systems under the EU AI Act. It's a standard GitLab CEO Bill Staples has consistently [reiterated](https://www.linkedin.com/posts/williamstaples_gitlab-1810-agentic-ai-now-open-to-even-activity-7443280763715985408-aHxf?utm_source=share&utm_medium=member_desktop&rcm=ACoAABsu7EUBcb_a1-JHKS9RC0B5rf8Ye-5XM60) and one reflected in GitLab's mission and [Trust Center](https://trust.gitlab.com/).\n\n## What the policy change actually means\n\nGitHub's announcement also specifies that the data may be shared with GitHub affiliates, including Microsoft, for AI development purposes.\n\nA policy change of this nature forces organizations to re-examine their AI governance posture, audit their Copilot license tiers, and confirm that the right controls are configured across their teams.\n\n## Why AI governance matters in regulated environments\n\nSource code is often among an organization's most sensitive intellectual property. It may contain references to internal systems, reflect proprietary business logic, or touch data flows governed by strict retention and access policies. When that code passes through an AI assistant, questions about training data usage, model vendor relationships, and data residency become compliance concerns.\n\nThe exposure is particularly acute for financial services firms that have invested in proprietary algorithms, fraud detection logic, credit risk models, underwriting rules, trading strategies. When AI tooling processes that code and uses it to train models serving competitors, vendor data practices become an IP concern.\n\nFinancial institutions operating under [the Federal Reserve's Supervisory Guidance on Model Risk Management (SR 11-7) and the](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm) [Digital Operational Resilience Act (DORA)](https://eur-lex.europa.eu/eli/reg/2022/2554/oj/eng) are required to maintain documented, auditable oversight of third-party technology providers, including understanding how those providers handle data. Third-party AI tools used in development workflows increasingly fall within the scope of model risk oversight, and material changes to vendor data practices require updated documentation. \n\nIn the public sector, [the National Institute of Standards and Technology Special Publication 800-53 (NIST 800-53)](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) and the [Federal Information Security Modernization Act (FISMA)](https://www.cisa.gov/topics/cyber-threats-and-advisories/federal-information-security-modernization-act) establish that sensitive or classified code must never leave a controlled boundary. For U.S. Department of Defense and intelligence community environments in particular, a vendor's default data posture is an operational concern. In healthcare, [the Health Insurance Portability and Accountability Act (HIPAA)](https://www.hhs.gov/hipaa/index.html) governs how patient-adjacent data is handled by third parties, and development environments that touch clinical systems increasingly fall within that scope.\n\nAcross all of these contexts, the common thread is the same: A vendor policy that changes data usage defaults, requires individual opt-out, and offers different protections depending on account tier introduces exactly the kind of uncontrolled variable that compliance teams cannot afford.\n\n## What regulated industries actually need from AI vendors\n\nRegulated organizations have largely moved past debating whether to adopt AI in development workflows. The focus now is on doing so in a way they can defend to regulators, boards, and customers. That shift has surfaced a consistent set of requirements regardless of sector.\n\n**Contractual certainty.** Regulated firms need to know, with specificity, what happens to their data. A clear, documented, unconditional commitment is what's required, not something that varies by plan or requires action before a deadline.\n\n**Auditability.** Model risk management frameworks require organizations to understand and validate the AI systems they deploy, including the training data behind those models and the third parties involved in their development. Vendors who cannot answer these questions create documentation risk for the organizations relying on them.\n\n**Separation from vendor incentives.** When an AI vendor trains models on customer usage data, code and workflows become inputs to a system that also serves competitors. For institutions with proprietary trading logic, underwriting models, or fraud detection systems, that's a genuine IP exposure.\n\n## GitLab's position on AI data governance\n\nGitLab does not use customer code to train AI models. This commitment applies at every tier, and AI vendors are contractually prohibited from using inputs or outputs associated with GitLab customers for their own purposes.\n\nThis is a deliberate architectural and policy choice, not a feature of a particular pricing tier. As GitLab's [post on enterprise independence](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) notes, data governance has become \"an increasingly critical factor in enterprise technology decisions, driven by a complex web of national and regional data protection laws and growing concern about control over sensitive intellectual property.\"\n\nGitLab is also cloud-neutral and model-neutral while supporting self-hosted deployments, not commercially tied to any single cloud provider or large language model (LLM). That i[ndependence matters](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) for regulated organizations evaluating vendor concentration risk. The [AI Continuity Plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/) documents how vendor changes are managed, including material changes to how AI vendors treat customer data, a direct response to the governance requirements under frameworks like [DORA](https://handbook.gitlab.com/handbook/legal/dora/). \n\n## The governance gap AI teams need to close\n\nGitHub's policy update is a reminder that for organizations in regulated industries, understanding exactly how an AI tool handles data is a prerequisite for using it at all. That means asking vendors for clear, documented answers: Is our data used for model training? Who are your AI model subprocessors? What happens if a vendor changes its data practices? Can we deploy in a way that keeps all AI processing within our own infrastructure? What indemnification do you offer for AI-generated output?\n\nVendors who can answer those questions clearly, and document those answers in an auditable form, are vendors you can build on. **Those who cannot will create compliance debt every time they ship a policy update.** And when a vendor can change its data practices with 30 days notice, that's not a partnership built for regulated industries. That's a liability.\n\n> Learn more about GitLab's approach to AI governance at the [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/).",[22,23],{"featured":13,"template":14,"slug":752},"github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call",{"promotions":754},[755,768,779,791],{"id":756,"categories":757,"header":758,"text":759,"button":760,"image":765},"ai-modernization",[11],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":761,"config":762},"Get your AI maturity score",{"href":763,"dataGaName":764,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":769,"categories":770,"header":771,"text":759,"button":772,"image":776},"devops-modernization",[23,570],"Are you just managing tools or shipping innovation?",{"text":773,"config":774},"Get your DevOps maturity score",{"href":775,"dataGaName":764,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":780,"categories":781,"header":783,"text":759,"button":784,"image":788},"security-modernization",[782],"security","Are you trading speed for security?",{"text":785,"config":786},"Get your security maturity score",{"href":787,"dataGaName":764,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":789},{"src":790},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":792,"paths":793,"header":796,"text":797,"button":798,"image":803},"github-azure-migration",[794,795],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":799,"config":800},"See how GitLab compares to GitHub",{"href":801,"dataGaName":802,"dataGaLocation":243},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":804},{"src":778},{"header":806,"blurb":807,"button":808,"secondaryButton":813},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":809,"config":810},"Get your free trial",{"href":811,"dataGaName":50,"dataGaLocation":812},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":814},{"href":54,"dataGaName":55,"dataGaLocation":812},1777493620188]