Google this week published the results of its annual DevOps Research and Assessment (DORA) survey that finds 90% of the IT professionals surveyed are now using artificial intelligence (AI) tools, with 80% reporting they are more productive as a result.

The survey of nearly 5,000 IT professionals also finds that respondents now have a median average of 16.22 months of experience using AI tools, with respondents spending a median of two hours of their most recent workday interacting with AI, representing about one-quarter of an eight-hour workday.

Among AI users, a full 60% of AI users said they employ it about half the time or more when encountering a problem or completing a task. Only 7% report “always” using AI when faced with a problem to solve or a task to complete, while 39% said only sometimes do they seek AI help.

Overall, 65% report relying on AI a moderate amount (37%), a lot (20%), or a great deal (8%). The number one use case for AI is writing new code (71%), followed by analyzing requirements (49%), internal communications (48%) and calendar management (25%). A large number of respondents also use AI for literature reviews (68%), modifying existing code (66%), proofreading (66%), and creating or editing images (66%).

The survey also, however, uncovered a significant amount of AI skepticism, with 30% of the technology professionals surveyed reporting little or no trust in the code generated by AI.

Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, said the results make it clear that while AI is an accelerator it is not a cure-all for software development. Teams with strong internal platforms, disciplined engineering practices, and clear workflows are seeing AI multiply their speed and impact more than others, he added.

For example, the survey finds that 90% of organizations have adopted at least one platform and there is a direct correlation between a high quality internal platform and an organization’s ability to unlock the value of AI, making it an essential foundation for success.

In this latest edition of the DORA report, Google is also now identifying seven common team profiles or archetypes, based on a cluster analysis of the survey data it collected, with the lowest ranking having foundational challenges (10%) while at the top end are harmonious high achievers (20%). The more mature the DevOps team is in terms of its ability the greater the impact AI is having, according to the DORA report. Assessments of where teams fall on that spectrum is based on lead times for changes, change failure rates, deployment frequency, rework rates and recovery times for failed deployments.

The takeaway for technology leaders is that they should invest first in the quality of the system of work across platform engineering, automated testing, and DevOps feedback loops, said Ashley. AI can then deliver sustainable gains in software delivery and product performance, he noted.

Overall, the DORA report finds that the top two clusters of the archetypes represent nearly 40% of the total sample, which the report claims provides proof there doesn’t need to tradeoff between speed and stability because top performers are excelling at both.

It’s not exactly clear how much AI is accelerating the pace at which applications are deployed but there is evidence to suggest DevOps teams are becoming more efficient. However, reliance on AI can vary widely depending on the nature of the task. Writing a script to automate a DevOps workflow doesn’t typically require the same level of depth and expertise as business logic. At this point, however, it’s no longer a question of whether development teams will use AI, but to what degree.