Consider a world where metrics and dashboards do not exist, where your work is free from constraints and you have the freedom to explore your imagination, creativity, and innovative ideas without being tethered to anything.
It may sound like a utopian vision that anyone would crave, right? But, it is not a sentiment shared by business owners and managers. They operate in a world where OKRs, KPIs, and accountability define performance. In this environment, dreaming and fairy tales have no place.
Given that distributed teams are becoming more prevalent and the demand for rapid development is skyrocketing, managers seek ways to maintain control. Managers have started favoring “DORA metrics” to achieve this goal in development teams. By tracking and trying to enhance these metrics, managers feel as though they have some degree of authority over their engineering team’s performance and culture.
But, here’s a message for all the managers out there on behalf of developers - DORA DevOps metrics alone are insufficient and won’t provide you with the help you require.
Before we understand, why DORA is insufficient today, let’s understand what are they!
The widely used reference book for engineering leaders called Accelerate introduced the DevOps Research and Assessment (DORA) group's four metrics, known as the DORA 4 metrics.
These metrics were developed to assist engineering teams in determining two things: A) The characteristics of a top-performing team, and B) How their performance compares to the rest of the industry.
The four key DORA metrics are as follows:
Deployment Frequency measures the frequency of code deployment to production or releases to end-users in a given time frame. It may include the code review consideration as it assesses code changes before they are integrated into a production environment.
The DORA report is an assessment framework developed by Google Cloud’s DevOps Research and Assessment team, serving as the industry standard for evaluating software development and delivery practices.
It is a powerful driver of agility and efficiency that makes it an essential component of software development. DORA metrics are widely used to benchmark internal processes within organizations, encouraging a culture of continuous improvement. High deployment frequency results in rapid releases without compromising the software’s robustness, hence, enhancing customer satisfaction.
The 2025 DORA Report introduced five key metrics: deployment frequency, lead time for changes, change failure rate, mean time to recovery (MTTR), and deployment rework rate.
This metric measures the time between a commit being made and that commit making it to production. It helps to understand the effectiveness of the development process once coding has been initiated.
A shorter lead time signifies the DevOps teams are efficient in deploying code while a longer lead time means the testing process is obstructing the CI/CD pipeline. Hence, differentiating elite performers from low performers.
This metric is also known as the mean time to restore. It measures the time required to solve the incident i.e. service incident or defect impacting end-users. To lower it, the team must improve their observation skills so that failures can be detected and resolved quickly.
Minimizing MTTR enhances user satisfaction and mitigates the negative impacts of downtime on business operations.
Change failure rate measures the proportion of deployment to production that results in degraded services. It should be kept as low as possible as it will signify successful debugging practices and thorough testing and problem-solving.
Lowering CFR is a crucial goal for any organization that wants to maintain a dependable and efficient deployment pipeline. A high change failure rate can have serious consequences, such as delays, rework, customer dissatisfaction, revenue loss, or even security breaches.
In their words:
“Deployment Frequency and Lead Time for Changes measure velocity, while Change Failure Rate and Time to Restore Service measure stability. And by measuring these values, and continuously iterating to improve on them, a team can achieve significantly better business outcomes.”
Below are the performance metrics categorized in
for 4 metrics –
DORA metrics are a useful tool for tracking and comparing DevOps team performance. However, they do not capture the full state of AI-assisted software development, as they may miss important details and nuances that influence code quality and team outcomes. Unfortunately, DORA metrics don’t take into account all the factors for a successful software development process. For example, assessing coding skills across teams can be challenging due to varying levels of expertise. These metrics also overlook the actual efforts behind the scenes, such as debugging, feature development, and more. Ultimately, DORA metrics only highlight what is already present in team processes and performance, rather than revealing new issues.
While DORA metrics tell us which metric is low or high, it doesn’t reveal the reason behind it. For example, DORA metrics do not provide context for AI outputs—while 90% of developers report using AI, there is a significant trust paradox, with 30% expressing little or no trust in AI-generated code. Suppose there is an increase in lead time for changes, it could be due to various reasons. For example, DORA metrics might not reflect the effectiveness of feedback provided during code review. Hence, overlooking the true impact and value of the code review process.
The software development landscape is changing rapidly, with modern software development practices now emphasizing intentional AI adoption as a core driver of productivity and workflow improvement. Adoption rates have surged to near-universal levels, as organizations integrate AI tools into their engineering processes. According to DORA annual reports, AI adoption among software development professionals has reached 90%—a 14% increase from last year—with many dedicating a median of two hours daily to working with AI. DORA annual reports provide data-driven insights into these industry-wide trends, including the impact of AI on software engineering.
Hence, the DORA metrics may not be able to quickly adapt to emerging programming practices, coding standards, and other software trends. For instance, code review has evolved to include not only traditional peer reviews but also practices like automated code analysis. DORA metrics may not be able to capture the new approaches fully. Hence, it may not be able to assess the effectiveness of these reviews properly.
DORA metrics are a great tool for analyzing DevOps performance. However, they are most effective in organizations with strong internal platforms and well-structured systems that support frequent deployment and rapid iteration. These key metrics work best when the underlying system and processes enable teams to deploy often, quickly iterate on changes, and improve accordingly. In organizations with tightly coupled systems and slow processes, the benefits of AI adoption are minimal, highlighting the importance of robust internal platforms to fully realize the value of DORA metrics. For example, if your team adheres to certain coding standards or ships software monthly, it will result in low deployment frequency almost every time and helps to deliver high-quality software.
Relying solely on DORA metrics to evaluate software teams’ performance has limited value. Leaders must now move beyond these metrics, identify patterns, and obtain a comprehensive understanding of all factors that impact the software development life cycle (SDLC). The DORA AI Capabilities Model provides a research-backed framework that identifies seven essential capabilities organizations should develop to maximize AI's impact. These capabilities include clear AI strategies, high-quality internal platforms, and a culture of learning.
For example, if a team’s cycle time varies and exceeds three days, while all other metrics remain constant, managers must investigate deployment issues, the time it takes for pull requests to be approved, the review process, or a decrease in a developer’s productivity.
If a developer is not coding as many days, what is the reason behind this? Is it due to technical debt, frequent switching between tasks, or some other factor that hasn’t yet been identified? Therefore, leaders need to look beyond the DORA metrics and understand the underlying reasons behind any deviations or trends in performance. Organizations achieve the greatest return from AI by maintaining a strategic focus on team quality, workflows, and platform performance, rather than relying on AI tools alone. Research shows that successful organizations adopt AI gradually, focusing on feedback loops and continuous improvement to amplify positive outcomes.
For DORA to produce reliable results, software development teams must have a clear understanding of the metrics they are using and why they are using them. DORA can provide similar results for teams with similar deployment patterns. However, stream management—specifically Value Stream Management (VSM)—is essential for identifying and fixing bottlenecks in software delivery. Investing in platform engineering, quality engineering, and value stream management is crucial for AI-enabled teams to ensure stability and quality throughout the delivery process. It is also essential to use the data to advance the team’s performance rather than simply relying on the numbers. Combining DORA with other engineering analytics is a great way to gain a complete picture of the development process, including identifying bottlenecks and areas for improvement.
However, poor interpretation of DORA data can occur due to the lack of uniformity in defining failure, which is a challenge for metrics like CFR and MTTR. Using custom information to interpret the results is often ineffective. Additionally, DORA metrics only focus on velocity and stability. Throughput is another important measure of software delivery, as AI adoption can increase throughput but may also reduce stability if not managed with robust engineering practices. The AI productivity paradox, highlighted in recent DORA reports, shows that while individual developer output rises with AI tools, organizational delivery can remain flat—underscoring the need for better integration of AI tools into existing workflows. DORA metrics do not consider other factors such as the quality of work, productivity of developers, and the impact on the end-user. So, it is important to use other indexes for a proactive response, qualitative analysis of workflows, and SDLC predictability. It will help to gain a 360-degree profiling of the team’s workflow.
To achieve business goals, it is essential to correlate DORA data with other critical indicators like review time, code churn, maker time, PR size, and more. Organizations must provide the necessary support—through evolving culture, processes, and systems—to help teams realize the full benefit of AI adoption. The greatest benefit is achieved when AI is integrated within a robust model of software development, supported by strong control systems such as automated testing and mature version control. The 2025 DORA Report indicates that while AI can accelerate software development, it can also lead to instability if robust control systems are not in place. Using DORA in combination with more context, customization, and traceability can offer valuable insights and a true picture of the team’s performance and identify the steps needed to resolve bottlenecks and hidden fault lines at all levels. Ultimately, DORA should be used as a tool for continuous improvement, product management, and enhancing value delivery.
DORA metrics can also provide insights into coding skills by revealing patterns related to code quality, review effectiveness, and debugging cycles. This can help to identify the blind spots where additional training is required.
Typo is a powerful engineering analytics tool for tracking and analyzing DORA metrics, drawing on insights from the latest assisted software development report, such as the DORA 2025 Report, which provides data-driven analysis of industry-wide trends and the impact of AI on software engineering. It provides an efficient solution for software development teams seeking precision in their DevOps performance measurement and delivers high-quality software to end users.
While DORA serves its purpose well, it is only the beginning of improving engineering excellence. The 2025 DORA Report and its key findings reveal that 90% of organizations have adopted platform engineering, which is crucial for unlocking the value of AI and improving DORA metrics. These trends, as the report reveals, are expected to shape organizational performance and software development over the next decade. To effectively measure DORA metrics, it is essential to focus on key DORA metrics and the business value they provide. Looking at numbers alone is not enough. Engineering managers should also focus on the practices and people behind the numbers and the barriers they face to achieve their best and ensure customer satisfaction. It is a known fact that engineering excellence is related to a team’s productivity and well-being. So, an effective way is to consider all factors that impact a team’s performance and take appropriate steps to address them.
Implement engineering metrics, gather SDLC insights & set continuous improvement goals in your dev teams.
Try Live Demo
We're on a mission to build engaged, productive tech teams. Try it out for free!
Follow us on:
