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Survey Sees AI Driving DevOps Productivity Gains Despite Challenges

A global survey of 636 software development professionals published today finds nearly two-thirds (64%) believe they are achieving at least a 25% increase in developer velocity and productivity using artificial intelligence (AI).

Conducted by Jellyfish, a provider of a software engineering intelligence platform, just under a quarter (24%) report there has been a 50% to 100% increase in developer velocity and productivity, while another 6% have seen an increase of 100% or more.

The top use cases for AI are code writing (53%), code review (49%) and code explanation (43%), with Claude Code (39%), Gemini Code Assist (35%) and GitHub Copilot (31%) being the top three tools adopted.

However, only slightly more than half (53%) said AI is improving the quality of the code being developed. Other challenges include increasing cost of AI tools (42%), reluctance in adoption from senior engineers (36%) and a proliferation of tools making it difficult to select the best one (31%).

Despite these issues and concerns, a full 80% said AI is a positive on productivity, with three-quarters of respondents (75%) said AI has made more time available to focus on high-value activities. For example, 46% said they expect to be able to spend more time on creating roadmaps for projects. Well over half (57%) said AI increases their job satisfaction.

Adam Ferrari, a software engineer who serves as a Jellyfish advisor, said it’s apparent that AI is automating many of the tasks that previously conspired to burn out many software engineering teams. In general, there is now more joy being experienced because teams are able to spend more time solving problems for the business versus having to manually complete rote tasks, he added.

While it’s clear AI tools are having an impact on software engineering, the level of skills and expertise that teams have is uneven. In fact, only 43% characterized their adoption of AI tools as high (33%) or very high (10%), compared to another third (33%) that described their adoption as being medium.

As such, gaps are starting to emerge between organizations that have mastered the level of prompt and context engineering required to succeed when using AI coding tools and those that are still experimenting, noted Ferrari.

There also may be other factors having an impact on productivity. For example, the percentage of respondents (91%) that work for organizations that have seen an increase in the last 12 months is far higher than the percentage of organizations seeing an increase attributed to AI. In fact, 84% noted that productivity is a top management concern, with more than three quarters (78%) reporting accountability has increased in the past 12 months.

Additionally, nearly two-thirds (64%) said they still need more or different data to diagnose issues around engineering productivity. Overall, 55% of the companies surveyed use a software engineering intelligence tool. A total of 39% noted there is a dedicated team for platform capabilities and only 12% have fully adopted an internal development portal (IDP).

AI, undoubtedly, is changing the relationship between software engineering and the rest of the organization. The challenge and the opportunity now is how best to move forward as the number of AI agents that are embedded into DevOps workflows continues to multiply.



from DevOps.com https://ift.tt/KscSivg
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