Slow, large-batch change approval processes are major bottleneck. Working in small batches can unlock the true potential of your AI investments.

The New Stack is a publication covering trends and technologies in cloud-native development, DevOps, and software delivery. Developers can learn about containerization, Kubernetes, and cloud computing, as well as explore topics such as microservices architecture, serverless computing, and continuous integration/continuous delivery (CI/CD) pipelines.

The New Stack

Slow, committee-based change approval processes create bottlenecks that waste AI productivity investments. Working in small batches is the fundamental principle for improving software delivery throughput, quality, and compliance. Organizations should streamline change approvals through peer review, automation, and eliminating cross-team committees. When AI generates more code, existing code review bottlenecks worsen unless addressed systematically using constraint management principles: identify the bottleneck, exploit current capacity, subordinate other processes to match its pace, then elevate through smaller batches and automation. Research from DORA and decades of practitioner experience confirm that small batches and lightweight approvals deliver better outcomes than large-batch, committee-driven processes.

Stop Wasting AI Investment on a Broken Change Approval Process

A Simple, Pivotal Idea: Working in Small Batches