Reducing Manual Deployment Work by 70%

Our deployments worked, but mostly because a few experienced people knew the exact sequence. That was the situation I encountered on systems at Softcraft where releasing software involved more manual effort than the team was comfortable with. The process worked, but it depended on several steps being performed in the correct order. Someone had to build the right version, apply the correct environment configuration, coordinate database changes, deploy the services, and verify that everything was healthy. Most releases went fine, but they still had some tension because too much of the process lived in people’s memory.

The Manual Process

A written checklist is better than an undocumented process, but it can still leave important decisions implicit. One instruction might say “validate the environment” while an experienced engineer knows the five things that validation actually includes. Another step might work differently in staging and production. A recovery procedure might be clear only to the person who handled the previous failed release. That made releases harder to schedule and meant a normal deployment often required the right people to be available. New engineers could follow the checklist, but they couldn’t automatically inherit the judgment behind it. We wanted more of that knowledge to be part of the process instead of somebody’s memory.

Automating It in Steps

We didn’t try to automate every possible scenario in one large project. We began with the steps that were repeated most often and created the most risk. Builds and automated tests moved into CI/CD pipelines. Containerization made application packaging and runtime behavior more consistent across environments. Deployment steps became repeatable, and environment-specific configuration was handled more deliberately. We also added verification around the process. A successful command wasn’t enough. The pipeline needed to show if the application was healthy after the change. We used Docker, Kubernetes, automated tests, GitHub Actions and Azure DevOps. The tools weren’t the hardest part. We had to agree on what the pipeline should enforce and what a healthy release looked like. Over time, these changes reduced manual deployment effort by approximately 70%.

What Changed After Automation

The time savings were easy to measure, but the operational effects were just as important. Deployments became more consistent. The same steps ran in the same order. Failures appeared earlier, with clearer feedback. Fewer actions depended on the state of an engineer’s local machine. The team could release with more confidence because the same process ran every time. We didn’t need to reconstruct it from memory on release day. Automation also made improvement easier. When a process exists as code, the team can review it, test it, and change it. When it exists mainly as individual experience, even identifying the current process can be difficult. It’s tempting to treat deployment automation as an infrastructure concern that happens after development. My experience was the opposite. The pipeline influenced how we structured changes, managed configuration, wrote tests, and responded when something failed. Developers needed to understand it because delivery was part of the software lifecycle, not a separate activity at the end. It also changed the conversation during reviews. We could discuss if the code worked, but also how we’d build, deploy, observe and roll it back if necessary.

By the end, the manual effort was around 70% lower. The difference I remember most was the release itself: fewer messages asking who knew the next step, fewer checks tied to one person’s machine and less concern about whether the person with the most context was online.