AI in Software Development: From Chasing Productivity to Gaining Time

Developers are at the heart of your software development, so improving their experience is a natural priority. But while much of the current conversation focuses on increasing developer productivity with AI, we believe it’s time to think bigger. To move from helping a few teams do better to a full lifecycle transformation. Why settle for a single win when AI can drive improvements across the board? Let's rethink what's possible. 

Don’t Just Go Deep, Go Wide 

Artificial intelligence is making its mark on software development. Tools like GitHub Copilot are changing how code is written by offering real-time assistance, learning opportunities, and smart suggestions. Automation of repetitive tasks further amplifies the productivity gains. However, while these developments are extremely valuable, we believe they are barely scratching the surface of what AI can do. That's why, at Xebia, we set out to explore its full potential across the entire lifecycle. From identifying features and user stories, to tracking bug reports and telemetry.   

Software Development Today 

The Software Development Lifecycle (SDLC) forms the foundation of any successful software project, encompassing key phases such as feature management, planning, development, testing, deployment, release, and maintenance. Although DevOps aims to ease the flow between these stages, the SDLC often remains fragmented. Communication breakdowns, misaligned priorities, and slow feedback loops lead to bottlenecks, delays, and increased costs. This is precisely where AI can step in. To bridge gaps, accelerate workflows, and enhance decision-making. 

Real-World Challenges 

Bridging the Gap  

To better understand AI’s potential in software development, consider a common scenario: the introduction of a new feature. As a product owner working within the problem space, you’ve envisioned the feature and now need to convey its requirements to developers in the solution space. Bridging the gap between these spaces often presents challenges: 

• Misalignment in communication: You add a feature to the backlog, and instantly, developers flood you with questions or push back. Why? Because you’re speaking different languages to express your needs, causing a communication bottleneck. But what if AI could step in as the translator? Enabling clear, direct communication between everyone involved?  

• Underestimating complexity: You might think the feature is simple to create, but the developer knows it’s a much larger task. As a result, you will likely end up in discussions if it is worth all the effort. What if AI could step in here to offer real-time assessments of complexity, making sure decisions are based on accurate insights?  

• Overestimating time: The biggest risk? Assuming a feature will take forever to build and never even suggesting it. In reality, it might be a quick win. Valuable opportunities are lost this way, simply because the right information wasn’t available at the right time. But what if AI could instantly clarify the true scope, ensuring no opportunity slips through the cracks? 

Staying in the Flow 

As we move through the SDLC, the challenges only intensify. Once development is underway, staying focused becomes harder. For example, when a user submits a bug report, developers need to interpret the information provided. This often leads to unnecessary back-and-forth communication, consuming time better spent on innovation or resolving critical issues. What if AI could step in and provide that clarity before the developer even lays eyes on the report?  

In addition, each new ticket or issue requires developers to switch contexts. This is disruptive because they constantly need to shift from one task to another. On average, it takes 23 minutes and 15 seconds to refocus on the original task after being interrupted. So, with every distraction, a lot of valuable coding time is lost. And that’s not all. Studies show that frequent interruptions lead to higher levels of stress, pushing developers to work faster and, consequently, make more mistakes. Which again adds stress. If your developers are spending a large part of their day switching between tasks, this could be affecting job satisfaction and code quality. (1) 

These are just a few examples that illustrate the current challenges in the SDLC. And while tools like GitHub Copilot help with specific developer tasks, we can get so much more out of them than we are today!  

A Broader Perspective  

At Xebia, we see a real opportunity for a broader, more integrated use of AI across the SDLC. Instead of focusing on one phase or one set of tasks, we aim to infuse AI end-to-end by leveraging both existing and purpose-built tools and agents.  

Benefits: 

• Gain time: Beyond coding faster, free up actual time so teams can focus on high-value work, not just more output. 
• Empower teams: AI supports everyone involved in building and delivering software and services. 
• Drive the development of Agentic AI: AI that can be orchestrated to operate across the entire SDLC. 
• Improve alignment: Create a shared sense of ownership across teams to boost both job satisfaction and output quality.  

AI For Everybody 

Making AI an integral part of your SDLC means introducing multiple AI tools or agents to the development process that each have their own role but can also work towards a common goal. Applying the principal of ‘AI orchestration’ throughout the lifecycle, Xebia ensures the right tool is applied in the right place. How we do it? First, we identify your specific needs. Then we map out the tools your developers are currently using. Combining that information, we either select AI tools that complement your existing ecosystem or build custom solutions tailored to your workflows. By orchestrating this ‘suite of AI agents’ across your entire SDLC, we help extend the benefits of AI beyond developers to all teams involved.  

A Win for Your Software, a Giant Leap for Your Business 

Introducing AI to your SDLC is just the beginning. Naturally, the first focus is on IT. But once you see the value AI brings here, it becomes easier to expand its role across the organization. The lessons and tools from software development can inform broader adoption. From operations to customer support. Finally, when these teams grow confident using AI, the next step is to infuse that intelligence directly into your products and services, delivering smarter, more personalized experiences to your customers. 

“When AI is woven into the fabric of your SDLC and your business, it doesn’t just solve one problem. It creates a ripple effect, unlocking efficiency, innovation, and better outcomes across the board.” 

Your AI Journey — With Xebia 

“The question isn’t whether AI can boost your business, it's how far you are willing to take it." 

Are you exploring the role of AI in your organization? We would love to guide you on that journey, and make sure you have what you need to be successful. Partnering with Xebia, we make sure everything is in place for AI enablement, from use cases, a solid cloud foundation and an AI platform. We bring a proven approach, including:  

Enablement: We enable your teams and your organization, prioritizing value realization, enterprise skilling, and responsible AI use and governance.  

Partnership: We work with you or for you. The choice is yours. From embedding AI Champions into teams, to the roll-out of our train-the-trainer model and scaled delivery through engineering hubs.  

Support at all stages: We meet you where you are. We offer Quickstarts to help you get up to speed, Scaled Enablement to scale adoption, and remediation whenever you face a challenge.  

End-to-end AI services: From identifying business value and enhancing your IT landscape, to building a strong AI platform (with data readiness), we provide full AI enablement. Ensuring maximum impact and responsible adoption. 
  

Our goal? To help you become an AI-enabled organization with the foundation, skills, and knowledge to succeed, long after we’re gone. 

1) Incredibuild. "How Much Does Context-Switching Cost Your Dev Team?" - https://www.incredibuild.com/blog/how-much-does-context-switching-cost-your-dev-team