Axel Labruna, Solutions Architect, Nubiral, and DEVOPS INSTITUTE Ambassador

Axel Labruna has specialized in DevOps for over 10 years and has been an ambassador of the DEVOPS INSTITUTE since January 2025. In this article, he examines what truly defines a high-performing DevOps team. Drawing on real-world examples and DORA metrics, he highlights the practices, processes, and emerging trends – such as AI integration and platform engineering – that set elite DevOps organizations apart and enable them to deliver continuous value at speed and scale.

Whether in software development, testing or data operations, “high-performing” teams are defined by their ability to respond quickly and effectively to business requirements.

In DevOps specifically, this demands a streamlined, automated software development cycle in which testing happens as early and as often as possible.

Recently, AI has become integral to the concept of high-performing teams, with many organizations integrating AI tools into their automation tests and pipelines to further streamline their processes.

The DevOps advantage: automation and support for other roles

The primary advantage of DevOps is its ability to automate processes, which reduces friction, bottlenecks and downtime. However, while automation has always been a key component, DevOps teams can also add value by using their expertise to support other roles.

For example, quality engineers often encounter challenges in test automation, resulting in basic tests that provide limited value to the overall pipeline. DevOps teams can assist by creating automated tests and assisting QA and security teams. They can demonstrate how to use tools and AI to enhance automation in controls, testing, and security validation.

Monitoring the costs

One area currently shaping the DevOps landscape is financial governance.

From a financial perspective, the shift from on-premise systems to the cloud was initially driven by the need for speed rather than cost efficiency. As a result, organizations often faced higher expenses than anticipated with cloud adoption. With the advent of AI-driven services, companies are now facing similar challenges. High-performing DevOps teams are addressing this by automating the monitoring and optimization of cloud and AI expenditures, aiming to strike a balance between innovation and cost-effectiveness.

Promoting platform efficiency

Platform engineering also plays a crucial role in DevOps, primarily through internal developer platforms (IDPs). These platforms equip developers, testers, and engineers with the tools they need to work more efficiently while adhering to essential guidelines. DevOps teams often serve as custodians of these platforms, ensuring they function correctly and adhere to best practices. If the platform underperforms, it may negatively impact the performance of the wider organization.

Measuring success: real-world results through DORA metrics

In the financial sector, where precision and compliance are critical, the benefits of these practices are clear. A large bank in Latin America wanted to enhance its deployment frequency, a key metric within the DORA framework. The bank’s objective was to democratise access to financial services and reach millions of new customers. To achieve this, it needed to enhance its digital capabilities and accelerate its delivery cycles.

The DevOps team introduced AI-powered coding copilots and provided comprehensive training to help staff use these tools effectively. They also implemented model context protocols (MCPs) and expanded automation for both quality and security testing. Another key initiative was the introduction of ephemeral environments – temporary test environments that can be created and destroyed automatically. This allowed each developer, QA engineer, and security engineer to use these environments for testing, thereby improving agility and reducing costs by consuming resources only when needed.

By focusing on measurable outcomes, such as DORA metrics, the team can quantify performance improvements and validate their automation strategy.

For instance, these initiatives saw a marked improvement in the bank’s delivery performance. Deployment frequency increased from one release every two weeks to one every three days. With further adjustments, the team can now achieve daily deployments when necessary.

AI was also used in the business requirements process. A pre-trained model enabled business stakeholders to articulate their needs in natural language, automatically generating user stories and both functional and non-functional requirements. This streamlined approach reduced the time-consuming back-and-forth communication between business and development teams, minimizing friction and further accelerating delivery.

The broader perspective: culture first, tools second

Many people still associate DevOps primarily with CI/CD pipelines. However, as the DEVOPS INSTITUTE emphasizes, DevOps is much more than that. High-performing DevOps teams succeed through a culture of collaboration, not just the use of technical tools. Effective cooperation among development, operations, security, and business functions enables teams to adopt these tools effectively. In this context, tools, automation, AI and platforms support the culture, rather than the other way around.

Ultimately, what distinguishes high-performing DevOps teams is not just their mastery of automation and tools, but their ability to integrate culture, process, and innovation to deliver continuous value — at both speed and scale.