As the AI Platform Lead, you are auditing the reliability of your production systems.

The scenario clearly describes a transition from manual, ad-hoc processes to automated, standardized pipelines that manage the full AI lifecycle—deployment, monitoring, retraining, and rollback. This is a hallmark of Mature MLOps practices .

In the "Managed" maturity stage, organizations establish repeatable, reliable, and automated processes for operating AI systems at scale. Mature MLOps enables:

Continuous integration and deployment of models

Automated monitoring and performance tracking

Controlled retraining and version management

Rapid rollback in case of issues

Reduced dependency on manual intervention

These capabilities significantly improve operational reliability, scalability, and consistency , which are all explicitly highlighted in the scenario.

Other options do not align:

AI-First Culture relates to organizational mindset, not operational automation.

Formal Governance Framework focuses on policies and controls, not pipeline automation.

Centralized CoE relates to organizational structure, not lifecycle execution.

CAIPM emphasizes that achieving the "Managed" stage requires industrialized AI operations , where MLOps practices ensure stable, scalable, and efficient model management.

Therefore, the correct answer is Mature MLOps practices , as it best represents the described transformation.