Software development has evolved from monolithic systems to microservices, from waterfall to agile, and from manual releases to DevOps. Today, we are seeing another major shift — AI is becoming embedded across the entire lifecycle. AI supports engineering teams by improving productivity, quality, and speed. For data-driven organizations, where accuracy and governance directly influence outcomes, AI-driven development creates a competitive advantage. In this article, I share how AI is shaping each phase of the SDLC based on practical experience and what teams should consider to use it effectively.

1. Requirements and Planning

The requirements and planning phase sets the foundation for everything that follows. Most delivery issues do not begin in code. They start with unclear goals, missing context, or stakeholder misalignment.

AI helps accelerate discovery and bring structure to unstructured inputs.

How AI is used

• Extracting requirements from emails, documents, and meeting notes
• Structuring inputs into PRDs and user stories
• Generating acceptance criteria and identifying dependencies
• Assisting with effort estimation based on historical patterns

Product and engineering teams use tools like ChatGPT, Claude, Notion AI, and Confluence AI to clarify requirements, draft user stories, and convert notes into structured documentation.

Things to remember

• Never paste raw client data blindly. Always sanitize sensitive information.
• AI output is a draft, not a decision. It may look polished but miss edge cases.
• Your experience matters more than the prompt.
• Use AI to prepare faster, not to skip stakeholder conversations.

Outputs from this phase directly feed into UI prototyping, sprint planning, and backlog creation, making early clarity extremely valuable.

2. UI Design and Prototyping

Once requirements are clear, the next step is turning ideas into visual and clickable experiences. AI speeds up this phase by enabling quick prototypes.

How AI is used

• Generating wireframes and UI layouts from text prompts
• Creating clickable prototypes for early validation
• Exploring multiple design directions

Teams use tools like Bolt, Figma AI, and Uizard to rapidly design and validate user experiences.

Things to remember

• AI does not know your design system unless you specify branding and rules.
• It works well for first drafts, not final flows.
• AI can draw screens but cannot experience user friction — usability requires human judgment.

3. Implementation

This is where AI often provides the fastest return when used correctly. It reduces boilerplate and cognitive load so engineers can focus on problem solving.

How AI is used

• Generating repetitive or boilerplate code
• Explaining legacy or unfamiliar code
• Assisting with refactoring and performance improvements
• Accelerating onboarding in large codebases

Teams use GitHub Copilot, Cursor, Windsurf, Antigravity, and VS Code AI extensions to improve developer productivity.

Things to remember

• Never accept generated code without reviewing it carefully.
• Define coding rules and conventions early.
• Start with limited permissions when using agents.

AI works best as a pair programmer and reviewer, not a replacement for engineering judgment.

4. Testing

Testing has traditionally been time-consuming and expensive. AI reduces manual effort while improving coverage.

How AI is used

• Generating unit, integration, API, and UI tests
• Expanding test coverage quickly
• Assisting regression and exploratory testing

Teams use ChatGPT, Claude, GitHub Copilot, Playwright, and Testim to automate and strengthen testing.

Things to remember

• AI defaults to happy-path tests — ask for edge cases explicitly.
• Never use real production data.
• Treat generated tests as drafts and review them carefully.

5. Deployment and DevOps

In deployment, AI adds value by making releases safer and more predictable.

How AI is used

• Predicting build or deployment failures
• Scoring deployment risk
• Detecting post-deployment anomalies
• Recommending or triggering rollbacks

Organizations use GitHub Actions, GitLab CI, Harness, Datadog, New Relic, Azure Monitor, and AWS DevOps Guru for intelligent monitoring and reliable releases.

Things to remember

• Keep humans in the approval loop initially
• Start with low-risk services
• Increase automation gradually

6. Monitoring and Maintenance

AI helps teams move from reactive firefighting to proactive operations.

How AI is used

• Correlating logs, metrics, and traces
• Detecting anomalies and performance degradation
• Predicting incidents using historical patterns
• Suggesting remediation steps and runbooks

Teams use Datadog, New Relic, Dynatrace, Elastic Stack, PagerDuty, Azure Monitor, and AWS CloudWatch for intelligent monitoring and faster incident response.

Things to remember

• Clean telemetry matters more than advanced AI
• Start with insights before auto-remediation
• Always mask sensitive data in logs and traces

AI in the SDLC isn’t just a tooling upgrade it’s a paradigm shift.

From practical experience, AI delivers real value when treated as a multiplier of good engineering practices, not a shortcut around them. We explore this emerging human-AI development model in greater depth in our upcoming eBook on Vibe Coding.

If you are exploring how AI can strengthen your SDLC, let’s start the conversation.