Source URL: https://cloud.google.com/blog/products/ai-machine-learning/introducing-doras-inaugural-ai-capabilities-model/
Source: Cloud Blog
Title: Introducing the DORA AI Capabilities Model: 7 keys to succeeding in AI-assisted software development
Feedly Summary: Artificial intelligence is rapidly transforming software development. But simply adopting AI tools isn’t a guarantee of success. Across the industry, tech leaders and developers are asking the same critical questions: How do we move from just using AI to truly succeeding with it? How do we ensure our investment in AI delivers better, faster, and more reliable software?
The DORA research team has developed the inaugural DORA AI Capabilities Model to provide data-backed guidance for organizations grappling with these questions. This is not just another report on AI adoption trends; it is a guide to the specific technical and cultural practices that amplify the benefits of AI.
The DORA AI Capabilities Model: 7 levers of success
We developed the DORA AI Capabilities Model through a three-phase process. First, we identified and prioritized a wide-range of candidate capabilities based on 78 in-depth interviews, existing literature, and perspectives from leading subject-matter experts. Second, we developed and validated survey questions to ensure they were clear, reliable, and measured each capability accurately. Lastly, we evaluated the impact of a subset of these candidates using the rigorous methodology of designing and analyzing our annual survey—which reached almost 5,000 respondents. The analysis identified seven capabilities that substantially either amplify or unlock the benefits of AI:
Clear and communicated AI stance: Your organization’s position on AI-assisted tools must be clear and well-communicated.This includes clarity on expectations for AI use, support for experimentation, and which tools are permitted. Our research indicates that a clear AI stance amplifies AI’s positive impact on individual effectiveness and organizational performance, and can reduce friction for employees. Importantly, this capability does not measure the specific content of AI use policies, meaning organizations can achieve this capability regardless of their unique stance—as long as that stance is clear and communicated.
Healthy data ecosystems: The quality of your internal data is critical to AI success. A healthy data ecosystem, characterized by high-quality, easily accessible, and unified internal data, substantially amplifies the positive influence of AI adoption on organizational performance.
AI-accessible internal data: Connecting AI tools to internal data sources boosts their impact on individual effectiveness and code quality.Providing AI with company-specific context allows it to move beyond a general-purpose assistant into a highly specialized and valuable tool for your developers.
Strong version control practices: With the increased volume and velocity of code generation from AI, strong version control practices are more crucial than ever. Our research shows a powerful connection between mature version control habits and AI adoption. Specifically, frequent commits amplify AI’s positive influence on individual effectiveness, while the frequent use of rollback features boosts the performance of AI-assisted teams.
Working in small batches: Working in small batches, a long-standing DORA principle, is especially powerful in an AI-assisted environment.This practice amplifies the positive influence of AI on product performance and reduces friction for development teams.
User-centric focus: A deep focus on the end-user’s experience is paramount for teams utilizing AI. Our findings show that a user-centric focus amplifies the positive influence of AI on team performance. Importantly, we also found that in the absence of a user-centric focus, AI adoption can have a negative impact on team performance. When users are at the center of strategy, AI can help propel teams in the right direction. But, when users aren’t the focus, AI-assisted development teams may just be moving quickly in the wrong direction.
Quality internal platforms: Quality internal platforms provide the shared capabilities needed to scale the benefits of AI across an organization.In organizations with quality internal platforms, AI’s positive influence on organizational performance is amplified.
Putting the DORA AI Capabilities Model into practice
To successfully leverage AI in software development, it’s not enough to simply adopt new tools. Organizations must foster the right technical and cultural environment for AI-assisted developers to thrive. Based on our seven inaugural DORA AI Capabilities we recommend that organizations seeking to maximize the benefits of their AI adoption:
Clarify and socialize your AI policies: Ambiguity about what is acceptable stifles adoption and creates risk. Establish and clearly communicate your policy on permitted AI tools and usage to build developer trust and provide the psychological safety needed for effective experimentation.
Treat your data as a strategic asset: The benefits of AI on organizational performance are significantly amplified by a healthy data ecosystem. Invest in the quality, accessibility, and unification of your internal data sources.
Connect AI to your internal context: Move beyond generic AI assistance by investing the engineering effort to give your AI tools secure access to internal documentation, codebases, and other data sources. This provides the necessary company-specific context for maximal effectiveness.
Double-down on known best practices, like working in manageable increments: Enforce the discipline of working in small batches to improve product performance and reduce friction for AI-assisted teams.
Prioritize user-centricity: AI-assisted development tools can help developers produce, debug, and review code more quickly. But, if the core product strategy doesn’t center the needs of the end-user, then more code won’t mean more value to the organization. Explicitly centering user needs is a North Star for orienting AI-assisted teams toward the realization of a shared goal.
Embrace and fortify your safety nets: As AI increases the velocity of changes, your version control system becomes a critical safety net. Encourage teams to become highly proficient in using rollback and revert features.
Invest in your internal platform: A quality internal platform provides the necessary guardrails and shared capabilities that allow the benefits of AI to scale effectively and securely across your organization.
DORA’s research has long held that even the best tools and teams can’t succeed without the right organizational conditions. The findings of our inaugural DORA AI Capabilities Model are a reminder of this fact and suggest that successful AI-assisted development isn’t just a purchasing decision; it’s a decision to cultivate the conditions where AI-assisted developers thrive. Investing in these seven capabilities is an important step toward creating an environment where AI-assisted software development succeeds, leading to enhanced outcomes for your developers, your products, and your entire organization.
To explore the DORA AI Capabilities Model in more detail and to access our full 2025 DORA State of AI-Assisted Software Development, please visit the DORA website.
AI Summary and Description: Yes
**Summary:** The text discusses the DORA AI Capabilities Model, a framework designed to help organizations effectively integrate AI into software development. This model highlights seven key capabilities that organizations should develop to unlock the full potential of AI, emphasizing the need for clear policies, healthy data ecosystems, and a user-centric focus among other components.
**Detailed Description:** The DORA AI Capabilities Model provides a data-driven guide for organizations looking to enhance their use of AI in software development. By identifying seven essential capabilities, it aims to enable companies to successfully leverage AI tools and practices.
Key points include:
– **Clear and Communicated AI Stance:** Organizations must have a clear stance on the use of AI tools, which can help reduce employee friction and enhance organizational performance.
– **Healthy Data Ecosystems:** A robust internal data environment is crucial for achieving higher outcomes from AI adoption, emphasizing quality and accessibility.
– **AI-Accessible Internal Data:** Effective connection between AI tools and internal data sources increases their impact. This allows AI to deliver highly contextual assistance rather than generalized support.
– **Strong Version Control Practices:** Mature version control habits are essential, especially in the context of augmented code generation from AI. Regular commits and rollback capabilities enhance team performance.
– **Working in Small Batches:** This principle aligns with traditional DORA practices, facilitating smoother AI-assisted development by reducing friction and optimizing product performance.
– **User-Centric Focus:** Prioritizing the end-user experience is key; neglecting it may lead to suboptimal AI adoption and potential negative impacts on team performance.
– **Quality Internal Platforms:** Providing robust internal platforms supports the scalability of AI benefits across organizations.
The model emphasizes the importance of cultivating an organizational environment conducive to AI through strategic policy communication, investment in data quality, and prioritizing user needs. It concludes by asserting that success with AI in software development is less about the tools themselves and more about the organizational conditions supporting their usage.
Organizations are encouraged to invest in these capabilities strategically to ensure the successful integration of AI, ultimately leading to improvements in development outcomes, product quality, and overall organizational success. For additional insights, readers are directed to the full DORA report on AI-Assisted Software Development.