We all know AI is the buzzworthy topic right now. Since your brain is likely buzzing with potential use cases, allow us to suggest one: AI-powered software development. On this episode of Catalyst, Clinton is joined by Nate Berent-Spillson, Launch by NTT DATA’s VP of Engineering and author of the Frictionless Enterprise book, to discuss all the ins and outs of this new dev superpower.
AI’s impact on software development can be limitless, but it also comes with the challenge of sifting through the hype to identify genuine value. Nate’s POV? Prioritize pragmatic AI applications that deliver tangible benefits. Tools like GitHub Copilot and AI-enhanced IDEs accelerate the developer journey from ideation to deployment, reducing friction and increasing productivity. Initially limited to simple code suggestions, these tools now support entire blocks of code, test writing, and complex logic, making once-tedious tasks faster and easier.
Shifting from autopilot to intentional development
One of the most significant mindset shifts AI introduces is “intention-driven development” — where developers’ focus is on defining the problem, leaving AI to handle the solution’s details. This approach transforms the coding process from methodical, line-by-line development to a prompt-based, outcome-driven mindset. By providing context and desired outcomes, developers can use AI to generate and refine code more effectively. At the same time, though, this shift requires unlearning old habits and relearning within an AI-powered environment.
Measuring developer productivity in an AI-powered world
Developer productivity metrics have always been notoriously difficult to measure and can be even more challenging to analyze in AI-powered development. Nate’s solution? Skip the individual productivity metrics (and the unhealthy competition and stress that typically accompany them) and focus on team-based metrics like DORA (DevOps Research and Assessment). Measuring release frequency, lead time, failure rate, and recovery time, gives the enterprise a more holistic analysis of AI-powered development’s efficiency.
It’s not all about numbers, though. Nate also recommends measuring qualitative metrics such as the SPACE framework (Satisfaction, Performance, Activity, Communication, Efficiency), which prioritize developer experience and flow.
Creating a learning-conducive environment
Part of the shift to AI-accelerated development is properly preparing and empowering your team. Nate emphasizes the importance of a “safe space” for learning, where developers can experiment with new tools without fear of judgment or unrealistic pressure. Other ways to make your team feel comfortable include communicating a clear vision, supporting everyone from early adopters to skeptics, setting realistic timelines, and encouraging a collaborative team atmosphere.
Looking ahead: The future of AI-powered development
AI will soon automate code reviews, security scans, and test generation, moving bottlenecks down the development pipeline and enabling faster iterations. Nate envisions a future where the entire process, from idea to deployed code, is frictionless, allowing developers to focus on innovation rather than repetitive tasks.
AI-powered development is not about replacing developers, but rather enhancing their ability to innovate and deliver value faster. With the right mindset, supportive environment, and balanced approach to metrics, organizations can unlock significant value from AI, transforming the developer experience and the entire software development lifecycle. And with Launch by NTT DATA’s proven AI-Powered Development Acceleration process, enterprises can enjoy these benefits faster than they ever thought possible.
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