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Driving Business Value Through Cloud-Native Architectures: Insights for CTOs 

Cloud-native architectures empower CTOs with agility, scalability and innovation. Learn best practices for leveraging containers, Kubernetes, DevOps & AI ...

Bhaskar Dhawan | September 4, 2025 | AI and DevOps integration, cloud cost optimization, cloud modernization leadership, cloud scalability and resilience, cloud transformation, cloud-native architectures, cloud-native best practices, cloud-native security compliance, containers and microservices, CTO strategy, DevOps and cloud native, enterprise digital transformation, future-proof cloud strategy, Kubernetes orchestration, serverless architecture

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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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