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TAB
CNCF Formally Convenes End User Technical Advisory Board
The End User Technical Advisory Board (TAB) promises to serve as the voice of the end user community ...
Mike Vizard
|
March 22, 2024
|
cloud native
,
end user
,
kubernetes
,
TAB
,
technical advisory board
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AI in CI/CD: Where Are You Really?
Step
1
of
7
14%
How would you describe your organization’s current level of AI adoption within your CI/CD pipeline?
(Required)
No active use of AI in CI/CD
Experimenting with AI in isolated areas (e.g., code suggestions, testing)
Limited production use in specific pipeline stages
Broad adoption across multiple stages of the pipeline
AI is fully integrated and operational across the CI/CD lifecycle
Not sure / don’t know
How would you describe your current CI/CD environment?
(Required)
Primarily legacy / Jenkins-based with custom integrations
Jenkins-centered with some modern tooling layered in
Hybrid environment with multiple CI/CD platforms and toolchains
Mostly modern, managed CI/CD platforms
Fully integrated, platform-driven architecture
Not sure / don’t know
In which areas of your software delivery pipeline are you currently using AI? (Select all that apply)
(Required)
Code generation / developer assistance
Automated testing / test generation
Build and pipeline optimization
Deployment automation
Monitoring and incident response
Security / compliance automation
We are not currently using AI in our pipeline today
In which areas of your software delivery pipeline are you considering using AI? (Select all that apply)
(Required)
Code generation / developer assistance
Automated testing / test generation
Build and pipeline optimization
Deployment automation
Monitoring and incident response
Security / compliance automation
Which AI use cases are delivering, or do you expect to deliver, measurable value in your CI/CD pipeline? (Select up to three)
(Required)
Faster development cycles
Improved code quality
Reduced testing effort
Improved reliability / fewer incidents
Reduced operational costs
None of these
Not sure / cannot assess
What, if anything, is limiting your organization’s progress with AI in CI/CD? (Select up to three)
(Required)
Fragmented toolchains
Lack of integration
Lack or unavailability of high-quality data
Data silos across pipeline stages
Security or governance concerns
Lack of internal expertise or skills
Unclear ROI or business case
Difficulty integrating AI into existing workflows
Performance or reliability concerns
Organizational resistance to change
Nothing is limiting my organization’s progress with AI in CI/CD
Not sure / don’t know
How do you expect your use of AI in CI/CD pipelines to change over the next 12–18 months?
(Required)
No plans to adopt AI
Continuing experimentation only
Expanding use in selected areas
Scaling across multiple pipeline stages
Making AI core to delivery strategy
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