The Software Development Life Cycle (SDLC) provides a framework for structured, predictable, and efficient software delivery. But in 2025, “best practices” for the SDLC look a little different, especially when AI is embedded into every stage of the lifecycle.
At Intelligenic, we work at the intersection of software engineering and artificial intelligence. We’ve seen firsthand how AI reshapes the rules—and the opportunities—across the SDLC.
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Here’s what best practices look like when AI is utilized to build software:
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1. Let AI Handle the Repetitive Work
From generating boilerplate code to writing unit tests or detecting issues with code, AI tools are most powerful when they free your team from low-leverage tasks.
Best practice: Automate what slows you down, so your engineers can focus on design, logic, and innovation. Today, we easily spend 80% of our time on repetitive and mundane tasks and only 20% of our time innovating. Flip the 80/20 so that your team spends 80% of its time on innovation.
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2. Use Data-Driven Planning, Not Just Gut Feel
AI can analyze historical sprint data, issue patterns, and team velocity to improve sprint planning and estimation accuracy.
Best practice: Incorporate predictive analytics into planning rituals to reduce under/over-estimation. This will lead to assigning the right people for the right tasks, improving delivery quality, and increasing outcome predictability.
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3. Make Code Reviews Smarter (Not Just Faster)
AI-powered code review tools can surface subtle issues—performance concerns, security flaws, or even anti-patterns—before they hit production.
Best practice: Combine human expertise with AI insights for more consistent and scalable quality control. This also significantly reduces the time required for these reviews accelerating overall velocity in delivery software solutions.
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4. Test Early, Test Often—with AI Support
AI can help generate meaningful test cases, identify risky code paths, and even suggest what to test next.
Best practice:Modern SDLC best practices involve "shifting testing left" to identify and resolve issues early, reducing time and cost. Integrating AI further enhances testing by analyzing data to identify risks and automatically generate relevant test scenarios, improving test coverage, efficiency, and the quality of the final software product before you start coding. Starting early allows you to plan for better testing and to ensure the software you build more accurately meets the needs of your stakeholders.
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5. Monitor and Learn Continuously
Post-deployment, AI can analyze logs, detect anomalies, and flag root causes faster than any traditional APM tool.
Best practice: Treat deployment and early usage of the software as the start of the learning cycle—AI helps you react in real time. Analyzing the data from deployment and the usage of the system will help your teams react more quickly and address problems more accurately.
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6. Integrate AI Seamlessly, Not Just Strategically
Tools that interrupt developer flow won’t get used. The best AI in the SDLC lives where your team already works: in the IDE, GitHub, Jira, and Slack.
Best practice: Embed AI where it fits your culture and workflows. Integrate and orchestrate the operations of the tools that you already use. The goal is to make your team more efficient and productive at what they do by reducing effort, time, and cost.
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Final Thought: SDLC Is Still Human-Centered
AI isn’t replacing your engineers. It’s augmenting their capabilities and helping teams focus on what humans do best: creativity, collaboration, and complex reasoning.
Building better software faster requires your organization to more efficiently execute all aspects of the SDLC. Utilizing AI can help accomplish this.
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We’d love to hear from you: How is your team using AI in your software lifecycle? What challenges or wins have you seen?
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#AIinSDLC #DevTools #SoftwareEngineering #Agile #MLOps #DeveloperExperience #DevEx #SDLCbestpractices #Intelligenic
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