Linear B

Understanding the true impact of AI tools on productivity is a key challenge for engineering leaders. It's essential to know if these tools are improving performance or creating downstream issues, such as increased code rework. In this quick demo, Andrew Zigler from LinearB shows you how to use the AI Insights Dashboard to get a comprehensive view of how AI tools affect your software delivery lifecycle. You'll see how to measure AI adoption and its impact on your engineering organization's performance. In this video, you'll learn how to: • Compare AI-generated code vs. human-driven activity (commits, comments, PRs) to see a clear distribution of AI's footprint. • View trends on AI tool adoption and code acceptance rates over time. • Correlate AI usage with key quality metrics like Rework Rate, Refactor Rate, and PR Size to understand its downstream impact. • Combine these quantitative metrics with qualitative developer feedback from Surveys to get a complete, holistic view of AI's effectiveness. To get these actionable insights for your own team, start a free trial: ➡️ https://linearb.io/start-free-trial Questions? 💻 Learn more about LinearB: https://linearb.io/ 📩 Book a demo: https://linearb.io/book-a-demo?utm_source=YouTube&utm_medium=social&utm_campaign=202504-ai-product-announcement&utm_content=Workshop Chapters: 00:00 Introduction 00:18 Compare AI vs. Human Activity (Commits, Comments & PRs) 01:32 Track Developer Adoption & Acceptance Rates 02:26 Combine with Qualitative Survey Feedback 03:36 Correlate AI Impact on Rework & Refactor Rates 04:38 Summary

In this webinar, Andrew Zigler chats with experts Adnan Ijaz (AWS), Birgitta Böckeler (Thoughtworks) and Suzie Prince (Atlassian), about AI's evolving role in the software development lifecycle. Drawing insights from LinearB's DevEx Guide to AI Driven Software Development report, the panel explores opportunities beyond code generation, strategies for measuring AI's impact, and how to foster responsible AI experimentation. Discover how leading tech companies are thinking about AI from requirements and architecture through to deployment and beyond. In this webinar, you'll learn about: • Key findings from the DevEx Guide to AI Driven Software Development • Opportunities for AI in non-coding SDLC stages like requirements, architecture, and PR reviews. • How leaders from Atlassian, AWS, and ThoughtWorks approach AI adoption and agentic AI. • Strategies for measuring the true impact of AI tools, moving beyond individual productivity metrics. • The importance of qualitative feedback, code quality, and risk monitoring with AI. • Fostering a culture of responsible AI experimentation, balancing curiosity with safety and ethics. • Understanding and addressing bottlenecks in the SDLC created or impacted by AI. • LinearB's approach to AI-driven automation and observability in development pipelines. Questions? 💻 Learn more about LinearB: https://linearb.io/ 📩 Book a demo: https://linearb.io/book-a-demo?utm_source=YouTube&utm_medium=social&utm_campaign=202504-ai-product-announcement&utm_content=Workshop Chapters: 00:00 Welcome and Introduction 01:52 Overview of Today's Webinar 02:29 Introduction to the DevEx Guide 03:25 Meet the Expert Panelists 05:54 AI's Impact on the SDLC 17:31 Measuring the Impact of AI Rollouts 28:37 Risk Monitoring and Debugging with AI 30:03 Experimenting with AI in Development 31:50 Responsible AI Usage 36:25 Balancing Enthusiasm and Skepticism 42:58 Automation and AI in Code Review 46:51 Closing Thoughts and Future Directions