#software-delivery

#software-delivery

My office is two kilometers away, and on foot the trip takes about twenty-five minutes, sometimes a bit more, sometimes less. Like most locals, I eventually switched to a bike, and the commute dropped to around 6.5 minutes. Later, I found a shorter route without bridges or traffic lights; my commute dropped down to about 4.8 minutes. If you plot these trips over time, you get a picture like this:

As AI transitions from proof of concept to production, teams are discovering that the challenge extends beyond model performance to include architecture, process, and accountability. Developers are learning to integrate AI into their delivery pipelines responsibly, designing systems where part of the workflow learns, adapts, and interacts with human judgment. From agentic MLOps and context-aware automation to evaluation pipelines and team culture, this transition is redefining what constitutes good software engineering.

In the never-ending quest for developer productivity gains, a new default setting has been applied to engineering leadership teams: buy an AI coding tool. It's an understandable instinct. AI can now produce code in seconds, and vendors promise gains measured in hours saved per engineer, each week. But for most teams, the results are underwhelming. Delivery timelines barely budge, and the sense of "we're moving faster" fades.

Organizations struggle to deliver software due to various factors such as late surprises and interruptions during final tests, leading to production issues and customer dissatisfaction.

Amazon Q Developer Pro allows organizations to customize the AI assistant with proprietary code and development practices, which leads to more efficient development processes and higher quality software delivery.