A recent survey of 3,000 employees from Google’s 2024 Accelerate State of DevOps Report showed that over 75% of working professionals around the globe use AI daily, but trust in AI-generated code is significantly lower. According to the report published on October 22, 76% of professionals leverage AI for tasks like writing, summarizing, explaining, optimizing, and documenting code. The advantages of adopting generative AI include enhanced focus, increased productivity, improved job satisfaction, and better code quality.
However, the report also warns about the potential drawbacks. Generative AI can hinder software delivery performance, product quality, and the time spent on meaningful work. The authors emphasize that while AI positively affects various individual and organizational factors that support high software delivery performance, it’s not a cure-all.
This year marked the 10th iteration of the DORA study, which examined AI’s influence on burnout, job satisfaction, productivity, and organizational performance. The research measures success in software stability through key metrics, including change lead time and deployment frequency.
In terms of daily interactions with AI, professionals reported using:
– Chatbots (78.2%)
– External web interfaces (73.9%)
– AI tools in their integrated development environments (72.9%)
Some respondents adopted AI due to competitive pressures, with remarks about organizations that resist AI risking obsolescence. Less than 10% indicated that AI negatively impacted their productivity.
The findings showed that 81% of respondents believe their organizations are increasingly prioritizing the use of AI in applications. About 67% of developers feel more productive with AI assistance, yet nearly 40% reported having little to no trust in AI.
Most respondents expressed only moderate confidence in the quality of AI-generated code, leading to expectations that they would first use AI-generated content as a baseline to refine. Many voiced concerns about AI’s overall impact, with over 30% fearing it could harm the environment.
AI’s influence on software delivery and stability raises questions. Large volumes of AI-generated code might lead to slower, less stable changes. The principle of small batch sizes in software development remains crucial for maintaining quality.
When it comes to internal developer platforms, DORA found that 89% of respondents use them. This practice aims to enhance collaboration and self-service capabilities among teams.
The survey revealed:
– Initial performance gains with platform engineering are common, but often followed by a dip before stabilizing, mirroring trends seen in other transformations.
– Users of internal platforms reported 8% more productivity, while organizational performance improved by 6%.
– However, throughput and change stability fell by 8% and 14%, respectively, possibly due to increased rework or underlying challenges like team burnout.
Stable priorities are essential to avoid employee burnout. Leadership styles that endorse constant, rapid shifts can create confusion and overwhelm employees. Prioritizing clear expectations and celebrating successes can enhance team morale.
The report wraps up discussions on meaningful work and quality documentation. Developers thrive on understanding user needs, which in turn fosters a sense of purpose and satisfaction. Ultimately, the focus should remain on improvement and fostering a supportive work environment.
