A 2023 Accelerate State of DevOps Report published today by the DevOps Research and Assessment (DORA) team at Google Cloud found teams with generative cultures composed of people who felt included on their team have 30% higher organizational performance than organizations without a generative culture.
The report was based on a global survey of 36,000 IT professionals and tracked deployment frequency, change lead time, culture, change failure rates and failed deployment recovery times.
Overall, the survey found that while just under a third of respondents were successful in terms of their ability to deploy on demand (31%) and keep remediation efforts after deployments to 5% (33%), only 18% had lead times for changes of less than one day, while only 17% had a failed deployment recovery time of less than an hour.
This is an area where DevOps teams appear to need the most amount of help; nearly two-thirds (64%) of software releases or updates required some level of remediation after deployment, according to the report.
Nathen Harvey, developer advocate for DORA and Google Cloud, said the one area most underappreciated when it comes to improving performance is documentation. High-quality documentation leads to 25% higher team performance, the survey found.
DevOps teams that rely on the public cloud also benefited from a 22% increase in infrastructure flexibility, resulting in 30% higher organizational performance, noted Harvey.
The survey also noted that, as far as reliance on artificial intelligence (AI) to automate DevOps workflows is concerned, it’s still early. But most respondents are, at the very least, experimenting with one or more use cases.
In general, the management of DevOps will soon be very different as more AI capabilities are leveraged to eliminate bottlenecks and reduce toil. The immediate challenge is developers are benefiting more from AI than software engineers, which is resulting in more code starting to simultaneously move through DevOps pipelines. There is little doubt that in the months ahead there will be increased reliance on AI to manage pipelines at greater scale. In the meantime, it will be increasingly difficult to manage codebases that are steadily getting larger as developers become more productive.
At the same time, more organizations are embracing platform engineering as a methodology for centralizing the management of DevOps workflows at levels of scale that previously were difficult to achieve.
Hopefully, the combination of platform engineering and AI will result in less overall burnout among DevOps teams. In the short term, the burnout issue may become more challenging to address as the amount of code being created increases.
In the longer term, the results of the DORA survey may be substantially different next year as AI and platform engineering are employed more widely. The current survey results are not substantially different than in previous years, in the sense that DevOps teams are still challenged by many of the same issues.
Of course, as important as it might be to improve those metrics, the most important thing any DevOps team should focus on is developer productivity which, despite the existence of DevOps pipelines, remains relatively low.