AI Tools in DevOps Survey Results
Enterprise adoption of AI development tools is happening more quickly than expected, changing how developers work and impacting existing processes for change and release management. The rapid adoption has forced I&O leaders to catch up, driving changes to automation, security, risk management…
EAE Episode 26
Pete Goldin (DEVOPSdigest) joins Dan Twing and I to discuss how AI development tools are impacting developer workflows and the automation systems that run those processes. Pete and Dan share insights from their recent AI in DevOps report, highlighting what's working right now and where governance gaps are creating risks.
Enterprise adoption of AI development tools is happening more quickly than expected, changing how developers work and impacting existing processes for change and release management. While much of the focus has been on AI code generation, these new development tools will also change application architectures, connectivity, performance and reliability. The rapid adoption has forced other Infrastructure and Operations leaders to catch up, driving changes to automation, security, risk management, change control and other functions.
Automation leaders should identify the interaction points between these new AI development tools and DevOps processes and their automation systems, considering the following areas:
- Change Detection: Update logging and alerting to recognize the new execution patterns, modified dependencies, and configuration changes at the points of integration.
- “Before” Baseline: Document current performance metrics (e.g., job success rates, execution times, resource utilization, ...) in order to measure impact as these new AI tools are deployed.
- Process Guardrails: Implement checkpoints to identify where the AI tools have introduced changes that can result in a failure to meet service levels for critical processes.
Episode Links
AI in DevOps Webinar (On-Demand)
Pete Goldin, DEVOPSdigest Editor and Publisher