Building software takes time, usually months, and sometimes even years, depending on the project's complexity. But with AI and Generative AI (GenAI), teams can move faster, cut costs, and improve quality. AI tools are now helping across every step of the Software Development Lifecycle (SDLC)—from planning to deployment. By using AI, engineering teams can cut their software development time up to 50% or even more. Combining GenAI and automation of testing and deployment, teams can be highly productive.
So, how much time can you really save using AI? Depending on the complexity, business logic, and team expertise, you can easily save 40-50% of overall development time.
Here’s a breakdown of how much time is usually spent in each phase, who’s responsible, and how AI/GenAI can help save 20–40% of overall development time.
🔍 1. Requirement Analysis & Planning
Time Spent: 10–15% of the project timeline
Responsible: Product Managers, Business Analysts, Architects
This phase defines what is being built and why. Teams gather business needs, write user stories, and define the scope.
✅ How AI Helps
- Summarizes meeting notes or interviews into user stories
- Suggests feature lists or requirements based on industry/product
- Translates vague business ideas into structured specs
⏱️ Time Saved: 10–30%
AI can help reduce back-and-forth between product and tech teams and cut planning time from weeks to days.
🎨 2. UI/UX Design & Architecture
Time Spent: 10–15%
Responsible: UX Designers, UI Developers, Solution Architects
This phase includes designing the look and feel of the app (UX/UI) and the system architecture that powers it.
✅ How AI Helps
- Analyzes mockups for consistency and accessibility
- Converts Figma or sketches into HTML/React code
- Flags poor UX or performance issues
- Suggests architecture blueprints for scalability or security
⏱️ Time Saved: 25–40%
GenAI speeds up design-to-code handoff and reduces time spent in review cycles.
💻 3. Development (Coding & Implementation)
Time Spent: 30–40% (largest portion)
Responsible: Software Engineers, Frontend/Backend Developers
This is where developers write the actual code and integrate it into systems.
✅ How AI Helps
- Autocompletes code and generates functions or classes
- Translates pseudocode or requirements into real code
- Suggests better performance or cleaner syntax
Example
GitHub Copilot writes 40–60% of the code in many real-world use cases.
⏱️ Time Saved: 30–50%
Developers spend less time on boilerplate and more on problem-solving.
🧪 4. Testing & Quality Assurance (QA)
Time Spent: 20–25%
Responsible: QA Engineers, SDETs (Software Dev in Test), Developers
Testing ensures the product works correctly and doesn’t break anything else.
✅ How AI Helps
- Auto-generates unit, integration, and UI tests
- Predicts bugs based on past commits
- Runs intelligent regression tests to find broken areas faster
⏱️ Time Saved: 30–60%
QA teams can automate repetitive tests and shift focus to edge cases and user experience.
🧾 5. Documentation & Knowledge Sharing
Time Spent: 5–10%
Responsible: Developers, Technical Writers, DevRel Teams
Good documentation helps new devs onboard, customers use the product, and teams understand APIs or libraries.
✅ How AI Helps
- Creates inline comments and API docs automatically
- Summarizes large codebases or libraries
- Converts code into readable documentation for non-tech stakeholders
⏱️ Time Saved: 50–70%
What used to take hours now takes minutes, especially for internal and API documentation. I've seen where I saved up to 90% of my time in documentation using AI.
🚀 6. Deployment & DevOps
Time Spent: 5–10%
Responsible: DevOps Engineers, Platform Engineers, Developers
This involves packaging and pushing the product live, ensuring reliability and performance.
✅ How AI Helps
- Suggests CI/CD pipeline optimizations
- Flags risky code changes or flaky tests
- Predicts deployment failures or server issues
⏱️ Time Saved: 20–40%
AI ensures smoother deployments and reduces downtime during production pushes.
📈 Summary Table: Time and Impact on SDLC
| Phase |
Time Spent |
Time Saved with AI |
Key Gains |
| Requirement Analysis |
10–15% |
10–30% |
Faster planning, clearer requirements |
| UI/UX & Architecture |
10–15% |
25–40% |
Rapid mock-to-code, better design quality |
| Development |
30–40% |
30–50% |
Faster coding, fewer bugs, improved code quality |
| Testing & QA |
20–25% |
30–60% |
Automated tests, quicker bug detection |
| Documentation |
5–10% |
50–70% |
Auto-generated docs, easier onboarding |
| Deployment & DevOps |
5–10% |
20–40% |
More reliable and efficient release cycles |
👨💼 Final Thoughts for Engineering Leaders
- AI won’t replace developers, but it will supercharge them.
- Teams that adopt GenAI early can reduce delivery times by up to 40%.
- Focus on high-impact areas like coding, testing, documentation, and UX screening for the fastest ROI.
Think of GenAI as an AI-assistant or a co-pilot—not a replacement. It’s here to amplify your engineers’ abilities, not automate them away.