Hello, everyone. My name is Karthik Medhavi Benagamori Sriram. I'm excited here today to discuss about a topic that is fundamentally reshaping software development, that is, AI assisted development. The way we write, review and maintain code has been transformed by artificial intelligence. enabling developers to work more efficiently, reduce errors, and innovate faster than ever before. AI coding assistance is no longer a futuristic concept. It is already an integral part of the modern development workflows. From intelligent coding suggestions to automated testing and even real time debugging, AI is accelerating every aspect of software engineering. Companies that adopt AI driven development practices are seeing drastic improvements in productivity. code quality and deployment efficiency. In this talk, I will walk you through the real world applications of the AI in software development, examining how leading organizations are leveraging AI and discuss the essential skills developers need to thrive in this AI augmented future. Whether you are a software engineer, an engineering manager, or a technology leader, understanding AI's role in development, Is crucial to stay ahead in the industry. Let's dive in the state of art today. AI is not just assisting developers It is fundamentally reshaping the way how software is built. Let's start with one of the biggest transformations AI coding assistants like GitHub Copilot study show that these tools can accelerate development cycles by up to 55 percent and 96 percent of AI suggested code is accepted by developers in production environments. That's a staggering level of adaption, but AI's impact goes beyond just generating the code. AI powered tools now understand complex code bases and architectural patterns. providing developers contextually relevant suggestions across 20 plus programming languages and hundreds of frameworks. This level of sophistication allows AI to assist in debugging, optimizing performance, and even automating mundane coding tasks. Additionally, AI is reducing bug rates by 40%. automating 80 percent of test writing and drastically improving code documentation quality. The result development time is effectively cut in half. These statistics highlight why companies that embrace AI driven development gain a significant competitive advantage in speed, quality, and innovation. For developers, this means A shift in focus from writing repetitive code to strategic thinking, problem solving, and leveraging AI to enhance their workflows. The more we understand and integrate these tools, the more productive and efficient we become. Smart package management. Managing dependencies in large scale applications has always been a challenging task. AI powered package managers are now revolutionizing this space by intelligently resolving complex dependency conflicts and versioning issues. These smart resolution systems reduce build errors by 60%, ensures that developers spend less time debugging compatibility problems. Security is another critical factor in software development. Signatures, real time vulnerability detection, and automated security patches ensure that applications remain secure from evolving threats. Beyond security and dependency management, AI is also enhancing developer productivity by offering context aware coding suggestions that align with specific frameworks and libraries. This means that developers receive hyper relevant suggestions based on their projects. exact requirements and their package dependencies cutting development time by 40 percentage. Ultimately, AI powered package management ensures seamless software integrations, reducing downtime, improving security, and making development smoother and more efficient. Quality assurance and deployment pipelines have traditionally been resource intensive, but AI is transforming these areas by automating testing, optimizing CICD workflows, and predicting software failures before they occur. One major breakthrough is Testing, which uses machine learning to detect complex edge cases and automatically generate test suits. This technology reduces testing cycles by 60 percent while significantly improving test coverage, leading to high performance. high reliability and fewer defects in the production. Similarly, AI driven CICD integration is reshaping how software is deployed by analyzing historical build patterns and systems performance metrics. AI can optimize deployment strategies and anticipate failures before they occur. Smart rollback systems use predictive modeling to prevent outages, ensuring 99. 9 percent system uptime. AI is also bringing predictive analytics into software development. By analyzing historical data, AI can provide precise timeline forecasting with 85 percent accuracy Proactively identifying bottlenecks and recommending optimized resource allocation strategies. These tools are helping teams to reduce project delays by 40 percent making AI an essential asset in modern software engineering. The full potential of AI in software development is realized. when it is seamlessly integrating to DevOps, automation, and infrastructure management. First, AI powered DevOps practices are cutting development and deployment times by 40%. Thanks to automated infrastructure orchestration and real time anomaly detection, AI driven monitoring tools continuously scan system logs and performance metrics, identifying potential issues before they escalate. Second, End to end automation of development workflows ensures consistency and efficiency. AI driven decision making in CI CD pipelines, security scans, and code reviews enhances software reliability and minimizes human errors. The results speak for themselves. Companies implementing AI in development workflows achieve 99. 9 percent system reliability through automated testing and validation. 75 percent faster deployment cycles and better team performance through data driven accountability metrics. AI is not just an add on to software development. It is now a core component of efficient, secure, and scalable software delivery. To fully harness AI's capabilities, developers must understand how AI models function and how to integrate them effectively into their workflows. The first step is AI literacy, understanding fundamental concepts like prompt engineering, retrieval augmented generation, called RAG, neural networks, training data, and AI model capabilities. Without this knowledge, developers may struggle to use AI tools optimally. Second, structured learning is essential for AI adoption. Companies that invest in hands on workshops, AI documentation review, and real world exercises enable their teams to quickly adapt to AI powered development environments. Finally, informed decision making is the key. With a deeper understanding of AI strengths and limitations, developers can strategically tools, sorry, choose the right tools, fine tune models, and optimize AI driven workflows. The result in up to 40 percent of faster deployment and development cycles. and a more seamless AI adoption process. AI powered code embeddings are revolutionized how developers interact with large scale code bases. Traditionally, searching for relevant code snippets or understanding architectural patterns required manual efforts. Relying on keyword based searches and static analysis tools. Now, AI driven vector embeddings transform source code into high dimensional numeric representations, enabling AI models to comprehend code semantics at near human level. This transformation provides three major benefits. Large codebase navigation. Developers working on million line codebases often struggle to locate relevant functions, dependencies, or patterns. AI powered semantic search. Allows us to retrieve code base on meaning rather than just keywords. Reducing search time by 60 percent compared to traditional graph based searches. Pattern recognition. AI embeddings detect recurring architectural patterns, identify redundant code, and even suggest refactoring opportunities by analyzing semantic similarities across multiple repositories and code versions. Advanced code comprehension. These embeddings help AI models understand syntax, functional relationships, and context, making AI assisted development more accurate and intuitive. By leveraging AI powered embeddings, developers can write, refactor, and maintain codebases more efficiently than ever before. Retrieval Augmented Generation, RAG, is an exciting AI development that bridges the gap between AI driven coding assistance and real world context integration. Unlike traditional AI models that generate code in isolation, RAG enhances AI outputs by dynamically incorporating relevant documentation, previous code history, and organizational best practices. Let's break down how RAG is transforming software development. Context integration. Traditional AI generated code suggestions often lack project specific relevance. RAG improves accuracy by integrating knowledge from existing documentation, past commits, and data. and team wide coding standards, ensuring generated code is highly tailored to the project at hand. Adaptability. RAC dynamically learns from a team's architecture, naming conventions, and design patterns. This ensures that AI driven generated code aligns with organizational standards and requires fewer manual revisions. Comprehensive capability. RAG is not just about writing new code. It can assist in refactoring legacy systems, scaffolding new features, and generating test cases, providing end to end AI support throughout the software development lifecycle. By leveraging RAG, teams can streamline code generation, improve consistency, and reduce errors, ultimately making AI powered development more robust and production ready. AI tools are powerful, but their efficiency depends on how strategically they are implemented within a development team. Here we will explore three key strategies to maximize AI tool efficiency. Organizations that adapt a systematic approach to AI tool optimization achieves faster development cycles and reduce code defects. This involves structured integration practices, feedback loops, and continuous learning to refine AI's role in development. Prompt engineering. The AI matters. Advanced prompt engineering techniques can increase first attempt accuracy by 75%, reducing the need for manual revisions. Developers who craft contextual domain specific prompts get more reliable and relevant AI generated code. AI tools offer features beyond basic code suggestions. Organizations that fully leverage debugging, refactoring, and testing capabilities report a significant decrease in technical debt and greater improvement in system performance. By focusing on these strategies, teams can unlock AI's full potential and transform their development workflows for maximum efficiency. The impact of AI driven development goes beyond just software engineering. It extends into global innovation and societal transformation. AI is accelerating breakthroughs in multiple industries, reducing time to market by 60 percent and enabling teams to solve complex, challenging tasks faster. Here are the three key areas where AI is making a significant impact. AI Power Tools enables developers to tackle large scale, intricate challenges with greater confidence. By automating repetitive tasks and enhancing problem solving capabilities, AI allows developers to focus on creative, innovative solutions. AI assisted development is shaping industries beyond software engineering. In healthcare, AI driven development is helping researchers to build predictive models for disease detection. In sustainable energy, AI is optimizing resource allocation for smart grids. These advancements show how AI powered development is transforming the world across different industries. With greater power comes responsibility. As AI takes on a larger role in software development and other industries, teams must prioritize fairness, transparency, and accountability. Regular bias audits Explainable AI models and inclusive design principles must be in place to ensure ethical AI usage. The future of AI in development is bright, but it requires a balanced approach that considers both innovation and responsible deployment. As AI continues to evolve, ethical considerations must remain at the forefront of AI assisted development. Without proper governance, AI systems can amplify biases, introduce security risks, or lack transparency. To mitigate these concerns, organizations must focus on three key areas. Implementing bias detection systems. AI models are trained on vast data sets and biases can unknowingly creep into the system. By deploying robust bias detection frameworks, and scan for gender, ethnicity, and programming paradigm biases. Organizations can ensure that AI driven development remains fair and equitable. AI models should not operate as black boxes. Developers need audit trails that tracks every AI decision. model training data and deployment details, maintaining comprehensive documentation and enhances trust, traceability, and accountability in AI driven development. Ethical development requires cross functional collaboration. Companies should establish AI ethics board, conduct quarterly audits, and train engineering teams on ethical AI practices. Clear governance structures ensures that AI driven decision making aligns with organizational values. And societal norms by embedding these ethical foundations into AI assisted development Organizations can maximize AI's benefits while ensuring fairness security and security As AI transforms software development, the role of developers is evolving. Mastery of traditional programming alone is no longer sufficient. Developers must now blend AI enhanced capabilities with fundamental software engineering skills. Here are three crucial skill areas for modern developers. Developers must become proficient in leveraging large language models, understanding prompt engineering techniques, and integrating AI, large language model APIs into development workflows. These skills allows teams to harness AI for automated coding, debugging, and testing, and integrating AI into their own apps. Managing AI models in production requires expertise. in data pipeline optimization, model versioning, and performance monitoring. Developers or researchers experienced in these areas can ensure that AI driven applications remain reliable and scalable. As AI takes on a bigger role in development, architects must first design AI first systems that balance scalability, security, and ethical considerations. AI driven software solutions must be resilient, interpretable, and reliable. and adaptable. As AI powered development tools continue to advance, they're fundamentally transforming the way how we build software. From automating repetitive coding tasks to enhancing debugging and optimizing software performance, AI is accelerating innovation and redefining developer roles. AI is helping developers write, review, and debug code faster than ever before. AI driven tools automate tasks like code generation, error detection, predictive maintenance, allowing developers to focus on high level problem solving them and architectural decisions. Rapid prototyping is now possible in days rather than weeks, cutting development cycles significantly. The role of developers is shifting from manual coders to AI orchestrators. Instead of focusing on low level syntax, developers are now responsible for integrating AI tools, managing AI automation workflows, and optimizing AI generated code. This shift means developers must adapt to new skills like AI integration, model tuning, and ethical AI implementation. AI is powerful, but it cannot replace human intuition, creativity, and critical thinking. The future of software development lies in collaboration between AI and developers, where AI automates repetitive tasks, while humans focus on innovation, strategy, and complex decision making. AI is no longer just an assistant. It's an integral part of software development. Developers and organizations that embrace AI driven workflows will gain a competitive edge, accelerating innovation while ensuring responsible and ethical AI adoption. The future is not about AI replacing developers. It's about AI enhancing and empowering human expertise in building the next generation of software. Thank you for the opportunity. I hope this talk was helpful and insightful.