Many teams start with a single, long pipeline job: build, test, and package all in one step that can take 30–60 minutes. When it fails, you learn about it late.

Optimization is about turning that monolith into smaller, parallelized stages with caching so you get useful feedback in minutes instead of an hour.

Cache dependencies and build artifacts between runs (for example, language package caches, Docker layers, or compiled assets). This avoids re‑doing the same heavy work every time.

Split independent tests into parallel jobs so total time drops without losing coverage. For example, run unit tests, integration tests, and UI tests in separate jobs that can run at the same time.

Flaky tests erode trust in CI. When a pipeline is “red” half the time for no good reason, engineers start ignoring it.

Track flaky tests explicitly, quarantine them if necessary, and invest time in fixing root causes (test data, timeouts, ordering issues) instead of adding infinite retries.

Measure your current pipeline: total runtime, longest stage, and most frequent failure points. Treat these like bottlenecks in a value stream.

Add one cache (for example, dependencies) and one parallel job. Re‑measure and compare before/after. Iterate until the majority of commits get usable feedback in under 10 minutes.