According to a survey of 3, 000 employees in Google’s 2024 Accelerate State of DevOps Report ( DORA ), more than 75 % of working professionals worldwide use AI at least once daily for work.
The study, published on Oct. 22, revealed that 76 % of professionals use AI to write code, summarize information, explain unfamiliar code, optimize code, and document code. It outlined the many advantages of relational AI implementation, including increased target, efficiency, job satisfaction, and script quality.
But, conceptual AI can also negatively influence software distribution efficiency, product quality, and the time staff spend on valuable work, the report indicated. It also found that using AI does not necessarily reduce time spent on” toilsome work”, or tasks that lack “meaningfulness”.
According to the report,” AI has positive effects on a number of crucial individual and organizational factors that create the conditions for high software delivery performance.” ” But, AI does not appear to be a panacea”.
Google survey identifies benefits and drawbacks of generative AI.
This year’s study, the 10th iteration, focused on how AI impacts burnout, focus, job satisfaction, productivity, and the performance of products, organizations, and teams. DORA measures stability success through four key metrics: change lead time, deployment frequency, change fail rate, and failed deployment recovery time.
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In the course of their daily work, AI frequently came in the form of:
- Chatbots ( 78.2 % ).
- External web interfaces ( 73.9 % ).
- ( 72.9 % ) AI tools are integrated into their integrated development environments.
Some respondents said they would use AI to combat competitive pressures, and one interviewee claimed that businesses who do n’t adopt AI run the risk of being “left behind.” Another mentioned how their company views AI as” a significant marketing point.” More than 10 % of respondents claimed that AI had had a negative impact on their productivity.
Additional findings show:
- 81 % of respondents responded that” their organizations have changed their priorities to improve the incorporation of AI into their applications.”
- 67 % of respondents report that AI makes their code better, and that using AI makes them feel more productive.
- Nearly 40 % of respondents said they had “little to no” faith in AI.
On the other hand, a majority of respondents said they only” somewhat” trust the quality of AI-generated code. Interviews, as well as the study’s authors’, indicate this may mean developers expect to use AI as a baseline from which to tweak and correct the results.
” However, respondents also reported expectations that AI will have net-negative impacts on their careers, the environment, and society, as a whole”, the report reads. Over 30 % of respondents think AI will be detrimental to the environment.
AI may also impact software delivery performance, stability, and throughput. This may be because AI-written code can be produced in such large quantities. These larger changes are” slower and more prone to instability”, according to the report. Small batch sizes are still a crucial tenet in software development that has a direct bearing on quality.
Nearly 9 in 10 professionals use internal developer platforms
Platform engineering is a branch of the field that creates workflows to promote self-service and collaboration. DORA describes it as the intersection of social interactions between teams and technical performance — such as automation, self-service, and repeatability of processes.
Despite the broad definition of the term being left out, DORA found that 89 % of respondents used internal developer platforms. The report also found:
- A platform engineering initiative typically sees performance gains at the start of a performance improvement process followed by a decline and leveling out. This pattern is in line with other DORA studies on transformation initiatives.
- When using an internal developer platform, employees were 8 % more productive.
- When using an internal developer platform, organizations performed 6 % better.
- Throughput and change stability fell by 8 % and 14 %, respectively, when using an internal developer platform.
Why such a large drop in change stability? DORA suggests that the platforms could shorten the time for rework. Or, this figure might be a sign of a different pattern: teams with high levels of burnout and change instability may adopt platforms to address those issues.
Other findings include the significance of stable priorities
The full report goes into more in-depth analysis of these subjects. Additional takeaways include:
- The level of product quality depends on how well the company comprehends the needs of its users. User-centered software development is advantageous because it benefits both employees and businesses by creating a sense of purpose that directly addresses user needs.
- Organizations should give developers the assurance that their projects are worthwhile because it requires user feedback.
- Focus on creating quality documentation. This is documentation that is not necessarily comprehensive but instead is relevant, findable, and reliable.
- Unstable priorities can lead to employee burnout. Namely, “move-fast-and-constantly-pivot” mentalities from leadership can hurt employees. This mindset creates unclear expectations, decreases employees ‘ sense of control, and increases their workloads.
- Leaders should be positive. While they can still challenge their workers to think innovatively, leaders should also recognize employees ‘ successes.
The report stated that “rolling up your sleeves and just getting to work is the key to success.” The organization and your teams should aim to be” just a little better than you were yesterday.”
