From Coding Speed to Systemic Productivity: How Amazon Web Services Helps Organizations Measure the True Impact of AI in Software Development

Software development has always been about more than just writing code. It’s a complex, interconnected system involving developers, product managers, platform engineers, and operations teams. True efficiency depends not on how fast one person can code, but on how smoothly the entire system delivers value.

As AI coding assistants become mainstream, many organizations initially focus on how these tools help developers write code faster. Yet, as AWS experts note, improving one stage of development doesn’t automatically increase overall speed—bottlenecks simply shift elsewhere.

If your developers generate code 30% faster, but reviews, testing, or requirement clarifications still lag behind, customers won’t see faster delivery. Optimizing the full software delivery lifecycle—not just the coding stage—is where real business value emerges.

As AWS leaders Phil Le-Brun and Joe Cudby summarize:

“We’re seeing a shift from focusing on individual productivity to understanding team productivity across the entire organization and software delivery lifecycle.”

Thinking in Systems: The AWS Approach

At Amazon Web Services (AWS), software development is viewed through a systems thinking lens. Traditional productivity metrics—such as lines of code or commits—fail to capture how teams work together to deliver reliable, secure, and scalable software.

That’s why AWS developed the Cost to Serve Software (CTS-SW) framework, a comprehensive metric that evaluates the total cost of delivering software, including development, infrastructure, and operational factors.

CTS-SW = Total cost of software delivery / Number of delivery units

Delivery units can be defined as microservice deployments or pull request completions, depending on the architecture. This holistic approach gives organizations a measurable view of the performance and efficiency of their entire software delivery system—not just developer output. Using this model, AWS teams have achieved measurable efficiency gains, reducing delivery costs by 15.9% year-over-year in 2024.

Beyond Code: AI as a System Accelerator

As AI evolves, its influence extends across the entire software development value stream. AWS’s Kiro, a specification-driven integrated development environment (IDE), exemplifies this shift.

Kiro helps teams:

• Generate and validate code faster
• Automate documentation
• Create unit tests automatically
• Support product managers in requirements analysis
• Assist UX designers in rapid prototyping
• Enable operations teams to enhance observability

By integrating AI across roles—not just developers—organizations can align productivity improvements across every stage of software creation.

However, as parts of the system accelerate unevenly, new bottlenecks may appear. The CTS-SW framework helps detect and address these constraints early, using “tension metrics” to balance speed with quality, security, and resilience.

Measuring What Matters: Key AI Impact Metrics

To understand how AI truly affects your organization, AWS recommends measuring beyond development velocity. Consider tracking these business-aligned metrics:

Continuous tracking across teams helps identify how AI adoption influences the overall flow of value.

Using A/B Testing to Measure AI Impact

To separate perception from performance, AWS advocates A/B testing as a method to measure the real-world effects of AI coding assistants.

1. Select comparable teams — working on similar products and technologies.
2. Introduce AI assistants to one team, while the other continues with standard practices.
3. Measure business metrics such as value delivery, cycle time, quality, and team engagement.
4. Run for several release cycles to gather statistically meaningful results.

This data-driven approach helps organizations determine whether AI integration leads to measurable improvements in productivity, quality, and customer outcomes—beyond anecdotal evidence.

Start Measuring Now

Every software organization is unique, and so is its response to AI integration. Relying on general claims about “X% AI-generated code” can be misleading—it measures activity, not outcomes.

To truly understand AI’s impact, leaders must start measuring how AI tools reshape their software delivery systems as a whole.

The winners in this new era of AI-driven development will be those who measure, learn, and continuously optimize—not just those who code faster.

Conclusion

AI assistants are transforming the way teams develop and deliver software—but their true power lies in how they enhance collaboration, quality, and business outcomes across the entire value chain.

With frameworks like AWS Cost to Serve Software (CTS-SW) and intelligent tools like Kiro, organizations can quantify improvements, balance innovation with reliability, and optimize for long-term value.

As an official AWS Partner, Softprom helps enterprises adopt AI-driven development practices, integrate AWS tools, and build measurable strategies for productivity and efficiency.

Contact Softprom to learn how AWS AI services and frameworks can help your organization measure and maximize the impact of AI in software development.