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In this episode of "How Many CTOs Does It Take?" podcast, hosts Scott Porad and Brad Hefta-Gaub dive into the 2025 DORA Metrics Report on the state of AI-assisted software development. They discuss key findings such as the near-universal adoption of AI, its impact on productivity, and its role in software delivery instability. Brad and Scott also explore the attributes of high-performance teams and the importance of organizational maturity in leveraging AI effectively. They conclude with reflections on AI's probabilistic nature and its implications for real-world applications.
00:00 Introduction and Welcome 01:08 Dora Metrics and AI: An Overview 03:28 Key Findings from the Dora Report 05:24 AI's Impact on Software Delivery 07:08 Organizational Clusters and Performance 11:00 Foundational Practices for AI Success 13:50 Challenges and Anecdotes 19:32 Challenges in Version Control for Machine Learning Models 23:15 The Importance of Small Batches in Development 25:01 User-Centric Focus in AI Adoption 26:36 Organizational Maturity and AI Success 31:15 AI Usage Statistics and Insights 35:30 Cautionary AI Tales and Lessons 37:22 Conclusion and Podcast Wrap-UpResources:
By Brad Hefta-Gaub & Scott PoradIn this episode of "How Many CTOs Does It Take?" podcast, hosts Scott Porad and Brad Hefta-Gaub dive into the 2025 DORA Metrics Report on the state of AI-assisted software development. They discuss key findings such as the near-universal adoption of AI, its impact on productivity, and its role in software delivery instability. Brad and Scott also explore the attributes of high-performance teams and the importance of organizational maturity in leveraging AI effectively. They conclude with reflections on AI's probabilistic nature and its implications for real-world applications.
00:00 Introduction and Welcome 01:08 Dora Metrics and AI: An Overview 03:28 Key Findings from the Dora Report 05:24 AI's Impact on Software Delivery 07:08 Organizational Clusters and Performance 11:00 Foundational Practices for AI Success 13:50 Challenges and Anecdotes 19:32 Challenges in Version Control for Machine Learning Models 23:15 The Importance of Small Batches in Development 25:01 User-Centric Focus in AI Adoption 26:36 Organizational Maturity and AI Success 31:15 AI Usage Statistics and Insights 35:30 Cautionary AI Tales and Lessons 37:22 Conclusion and Podcast Wrap-UpResources: