AI Adoption in IT: 7 Ways to Automate and Where They Get Hard
96% of IT specialists now actively use AI in their work, from code review to infrastructure monitoring. The study identified seven main uses of AI in operations, but adoption faces a sharp problem. The main blocker is the need for a new skill: checking AI results before they are deployed to production.
AI-processed from ZDNet AI; edited by Hamidun News
The study shows that 96% of IT specialists have already deployed AI agents in their operations, but the main difficulty companies face is validating the results of the agents' work.
Which operational tasks does AI automate first?
According to the study, AI agents are used to automate code review and static code analysis on pull requests, infrastructure monitoring and log analysis.
Which operational tasks do AI agents handle?
The study identified seven areas of use: automating code review and static code analysis on pull requests, infrastructure monitoring, log analysis and four more operational tasks.
Which automation methods are IT specialists adopting with AI?
The study identified seven areas where AI agents are used: automating code review and static code analysis on pull requests, infrastructure monitoring and log analysis.
Key Questions Answered
Which operational tasks does AI automate first?
According to the study, AI agents are used to automate code review and static code analysis on pull requests, infrastructure monitoring and log analysis.
Which operational tasks do AI agents handle?
The study identified seven areas of use: automating code review and static code analysis on pull requests, infrastructure monitoring, log analysis and four more operational tasks.
Which automation methods are IT specialists adopting with AI?
The study identified seven areas where AI agents are used: automating code review and static code analysis on pull requests, infrastructure monitoring and log analysis.
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