AI in application development needs more than speed to succeed

The acceleration of AI in application development has reached a new peak, but many organizations are learning that tooling alone won’t solve deep-rooted inefficiencies. While AI agents and generative platforms promise to help developers move faster, the conversation is shifting from experimentation to operational maturity.

The Reality of AI Integration

AI can enhance productivity, but it can’t replace the foundational capabilities required to build, secure, and operate software at scale. Developers face pressure to deliver new applications faster—often on an hourly basis—but the success of AI integration hinges on automation, infrastructure discipline, and clear accountability.

In a recent episode of theCUBE Research’s AppDevANGLE podcast, Richard Seroter, chief evangelist at Google Cloud, emphasized that AI should work for developers, not the other way around. "The AI works for me," he said. "I don’t work for the AI. That’s the way we’ve got to have our mindset, and there’s still ownership that we have to take."

Operational Maturity is Key

Adopting AI to accelerate application development is becoming a default expectation, but without solid pipelines and DevOps discipline, AI can amplify inefficiencies. Seroter highlighted the need for infrastructure automation, a mature API strategy, and up-to-date data management. "Are you sourcing AI agent advice based on support information, but you’re only updating that support material once every six months? That’s not going to work," he said.

Teams with secure, automated CI/CD pipelines, testing, and observability are best positioned to benefit from AI. Without these guardrails, organizations risk injecting unvetted AI-generated code into production, introducing security vulnerabilities and tech debt. "Now we can do AI-generated code and trust it because we have a good code review process, security scan process, gated releases [and] rollback techniques… but you’ve got to finish some things first before this is going to be a big benefit to you," Seroter added.

Platform Engineering and Specialization

Platform engineering is critical to abstract infrastructure complexity for developers. Expecting generalists to be experts in backend development, security, infrastructure as code, and machine learning is unrealistic. Mature organizations are building internal platforms to deliver scalable services and reusable patterns, allowing developers to focus on delivering business value through AI.

"There’s still something you’re going to have to have depth in… but, saying ‘I’m going to hire a general-purpose dev who writes front-end, back-end, secures it, talks to databases [and] does [machine learning]…’ you are dreaming," Seroter said.

Conclusion

Successful AI adoption in software development isn’t about skipping steps—it’s about enabling velocity through maturity. Organizations that view AI as an augmentation layer, not a replacement for good engineering practices, will thrive in this era of accelerated change.

Watch the full conversation on YouTube.

Image: SiliconANGLE