ANIn Chennai Jan 2025 | Idea to Solution: Revolutionizing Development in the Age of AI by Ramachandran K
- 2. ● The AI era is here, and it is reshaping software development. ● This session explores how AI enhances productivity, improves code quality, and transforms workflows. ● Together, we’ll discuss: 1. Opportunities and challenges of AI in development. 2. Its impact on roles like CEO, designer, developers, testers, and managers. 3. The path to embracing AI effectively. Welcome to the Future of Coding
- 3. ● AI in coding is no longer science fiction; it’s driving real-world transformations in software development. ● According to Forbes, the support of AI can increase developer workrates by 126%, and the Stack Overflow Developer Survey has found that 82% of developers now use AI tools for writing code. ● Research by McKinsey, highlights that AI adoption in software development can increase team productivity by 35%. ● Tools like Cursor, OpenAI Codex, GitHub Copilot, and Tabnine are revolutionizing how we write, debug and optimize code. The State of AI in Software Development
- 5. ● Developer - Tedious and complex - code suggestion, debugging - No code and Low code - Cursor, Github copilot ● Designers - Layout design and code conversion - Locofy, Codia, Adobe sensei and figma ai plugins ● QA - Test case , regression testing - Autify, Applitools, Mabl and so on ● PM and PO - Completion rate, Road map, task prioritization - Monday.com, Aha, … AI’s Impact Across the SDLC
- 6. ● Increase Efficiency: Automates routine tasks like code documentation free’s up time for complex work. ● Improve Accuracy: Detects bugs and vulnerabilities quickly, enhancing code quality. ● Boost Productivity: Speeds up code generation and completion for faster project delivery. ● Personalize User Experiences: Helps create more user-centric software applications. ● Time Savings: AI can save up to 50% of time on routine programming tasks. ● Amazon and Uber How AI Makes Coding More Efficient
- 7. ● Code Quality & Reliability ● Data Dependency - Quality of AI models depends on the training data; outdated or biased data can result in poor code quality ● Maintainability & Complexity ● Security Concerns ● Intellectual Property & Licensing ● Integration Challenges ● Testing & Verification Limitations in AI for Code Generation
- 9. ● Start with AI-assisted tools: Introduce tools for coding, debugging, and planning before adopting full AI-driven environments ● Upskilling & Training: Encourage teams to learn emerging AI skills, like prompt engineering and ethical AI use. What’s Next for Developers
- 10. Question ?
- 11. Thank You Ramachandran K +91 8122739858 https://www.linkedin.com/in/ramakavanan/
Editor's Notes
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