The impact of artificial intelligence on software development? Still unclear

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Although artificial intelligence (AI) is seen as the ultimate productivity tool for software development, the impact of AI development tools on teams is still in its infancy.

These are the conclusions from a recent report on DevOps trends, published by Google Cloud’s DevOps Research and Assessment (DORA) team, and based on data from 36,000 technology professionals worldwide.

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It is common today for experts to suggest that AI will provide a significant boost to software development and implementation productivity, along with developers’ job satisfaction.

“To date, our research does not support this,” said the report’s authors, Derek DeBellis and Nathen Harvey, both at Google.

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“Our evidence suggests that AI slightly improves individual well-being measures – such as burnout and job satisfaction – but has a neutral or perhaps negative effect on group-level outcomes such as team performance and software delivery performance.”

These flat findings are likely due to the fact that we are still in the early stages of AI adoption, they suspect: “There is a lot of enthusiasm about the potential of AI development tools, as evidenced by the majority of people posting on at least We will add a bit of AI to the tasks we asked about. But we expect it will take some time before AI-powered tools see widespread and coordinated use across the industry.”

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Despite AI’s limited impact to date, the research does identify the factors driving development stores forward. In their research, DeBellis and Harvey isolated a segment of the “elite” professionals who are at the top. These professionals require only a one-day turnaround time to implement changes to applications, compared to a week to a month for underperforming shops. They can deploy software multiple times a day. They also report a change failure rate for software with errors of 5% or less. In contrast, companies in underperforming software stores have figures above 60%.

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While AI can help IT professionals in the future, there are best practices that the elite group is pursuing that are making a difference today. The co-authors identify these practices:

  • Build with users in mind: Google’s research shows “that a user-centric approach to building applications and services is one of the strongest predictors of overall organizational performance. Teams that focus on the user perform 40% better than teams that don’t .”
  • Create a healthy culture: “Teams with a generative culture, made up of people who felt involved and belonged to their team, perform 30% better than organizations without a generative culture.”
  • Strive for high quality documentation: “High-quality documentation amplifies the impact that DevOps technical capabilities – for example, continuous integration and trunk-based development – ​​have on organizational performance. Overall, high-quality documentation leads to 25% higher team performance compared to low-quality documentation quality.”
  • Divide the work fairly: “We find that respondents who take on more repetitive work are more likely to experience higher levels of burnout, and that women and members of underrepresented groups are more likely to take on more repetitive work. Women or those who have indicated their own gender do so in 40% of cases. more repetitive work than men.”
  • Take advantage of cloud flexibility: “For example, using a public cloud leads to a 22% increase in infrastructure flexibility compared to not using the cloud. This flexibility in turn leads to teams with 30% higher organizational performance than teams with an inflexible infrastructure.”

Contrary to widespread and deeply held beliefs, software developers do not work in isolation. Instead, they work in teams and strive to focus on their business. The research helps shed light on what matters to top-performing developers – and AI is still more of a shiny object than a differentiator.

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