DORA Insights: AI

AI Adoption Culture Productivity

Balancing AI tensions: Moving from AI adoption to effective SDLC use

Mar 10, 2026
While AI accelerates initial code generation and reduces start-up friction, it introduces hidden taxes like heavy verification overhead, skill degradation, and integration challenges. Leaders must adapt holistic metrics, review processes, and team training to ensure sustainable success. Read more

AI Education Learning UC Berkeley

Managing AI dependency: How students are establishing guardrails with AI

Feb 17, 2026
While AI makes challenging projects feel more accessible, it also sparks anxiety about becoming too dependent. Our research with UC Berkeley students found that students are establishing guardrails for themselves, treating AI as a support tool rather than a replacement for deep understanding. Read more

AI Education Learning UC Berkeley

AI as a tutor

Dec 12, 2025
While critics worry students use AI to cheat, our research reveals something different: students are using AI to learn. At UC Berkeley, many students in technical fields are using AI to teach them rather than do for them: AI was a supplement to the role of professor, TA, or tutor. Read more

AI Metrics Culture Security

Understanding builder intent in the AI era

Oct 17, 2025
As AI decouples roles from tasks, traditional developer personas are becoming less relevant. This article introduces the “Builder Mindset” framework, identifying four core intents—Founder, Optimizer, Accelerator, and Learner—to help teams design better AI-assisted experiences. Read more

AI Education Learning Developer Experience UC Berkeley

How customization supports developer engagement

Sep 23, 2025
While AI coding assistants excel at mechanical tasks, research with UC Berkeley students reveals they can introduce friction and cognitive load during complex, interpretive work. By customizing AI tools to align with specific tasks and preferences, developers can reduce this friction and create a more efficient, satisfying development experience. Read more

Choosing measurement frameworks to fit your organizational goals

Aug 6, 2025
Measuring software development effectiveness requires choosing the right framework to match your organizational goals. This guide explores popular frameworks like SPACE, DevEx, and DORA, offering practical steps for selecting metrics and adapting your measurement strategy for the AI era. Read more

AI Metrics Trust Security Culture

Concerns beyond the accuracy of AI output

Jun 30, 2025
While accuracy remains a key challenge, developers have broader concerns about generative AI, including data privacy, deskilling, and job displacement. This post explores these anxieties and offers five strategies to address them, such as clarifying acceptable use policies and emphasizing AI as a learning opportunity. Read more

AI Metrics Culture Adoption

Helping developers adopt generative AI: Four practical strategies for organizations

Jan 31, 2025
DORA research identifies four practical strategies to help organizations scale generative AI adoption from isolated experiments to widespread use. Key recommendations include increasing transparency about AI plans, addressing developer concerns, providing dedicated learning time, and establishing clear usage policies. Read more

AI Metrics Productivity

How gen AI affects the value of development work

Dec 10, 2024
DORA research reveals a paradox where developers using generative AI report higher satisfaction despite spending less time on work they consider valuable. We explore five dimensions of value—utilitarian, reputational, economic, intrinsic, and hedonistic—to understand how AI is reshaping the meaning of development work. Read more

AI Metrics Trust Productivity Adoption Security

Fostering developers' trust in generative artificial intelligence

Sep 13, 2024
Research shows that developers who trust generative AI tools are more productive and use them more frequently. We outline five strategies to foster this trust, including establishing clear policies, reinforcing feedback loops like code review, and encouraging experimentation without mandates. Read more