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Agile Gurugram & Delhi National Capital Region 2026 | AISDLC - Why, What and How by Rahul Kumar Gupta.pdf

This document outlines how AI is transforming the Software Development Life Cycle (SDLC) and what it takes for organizations to transition from traditional development practices to an AI-powered, AI-native SDLC.

Technology◦
www.agilegurugram.com AI Software Development Life Cycle RAHUL KUMAR GUPTA • Why • What • How
www.agilegurugram.com 2 27 Years TEAD X Leader - Technology, Engineering, AI & Architecture, Digital Transformation VP  Digital Engineering & GenAI CoE | Incedo Leading Enterprise AI, AISDLC & Agentic AI transformation Former Leadership Publicis Sapient • HCL Tech. Global Delivery Americas • Europe • APAC • Australia Clients: AT&T  Symantec • LEGO  L'Oréal • Goodyear • Coca-Cola , Endeavour, NTT Data, NEC, ATG, Elasticpath, Hakuhodo, IHG, Elsevier, MelbourneIT RAHUL KUMAR GUPTA RAHUL KUMAR GUPTA
www.agilegurugram.com
www.agilegurugram.com 3 WHy ? Reimagining Software Engineering for the AI Era To Transform engineering lifecycle by Embedding AI into every phase to accelerate delivery, improve quality, and reduce cost. RAHUL KUMAR GUPTA
www.agilegurugram.com Reality of Software Engineering PROJECT ARCHITYPE CHALLENGES REASONS Legacy Migration & Modernization 6070% of time goes understanding and doing • Legacy code analysis • Documentation gaps • Dependency mapping • Impact analysis • Aging technology stack • Undocumented business logic • Loss of SME knowledge • Tightly coupled architectures • Missing QA TC and TS New Development 5060% of time goes non dev activities • Req. Mgmt. • Planning & Coordination • Quality Assurance • Release Coordination • Continuous evolving business needs • Multiple stakeholder • Changing priorities • Fragmented design decision kill • Delayed feedback loops between Dev,QA,Ops Operations Support 5060% of time spent on • Diagnosis RCA • Incident detection & Triaging • Prod. support activities • Knowledge Retrieval • Fragmented Monitoring • Lack of incomplete operational Knowledge • Lot of manual support processes • Distributed knowledge Repo • Repetitive troubleshooting Existing Product Enhancement 3550% of time spent on • Analyzing existing Functionality • Validating • Impact Analysis & Dependency Manage • Release • Distributed knowledge Repo • Technical debt • Complex Integrations • Shared codebase • Frequent Releases RAHUL KUMAR GUPTA Learn how AI transforms the retail value chain and unlocks insights from retail data
www.agilegurugram.com Legacy Migration & Modernization Reality of Software Engineering PROJECT ARCHITYPE CHALLENGES REASONS New Development Operations Support Existing Product Enhancement 6070% of time goes understanding and doing • Legacy code analysis • Documentation gaps • Dependency mapping • Impact analysis 5060% of time goes non dev activities • Req. Mgmt. • Planning & Coordination • Quality Assurance • Release Coordination 5060% of time spent on • Diagnosis RCA • Incident detection & Triaging • Prod. support activities • Knowledge Retrieval 3550% of time spent on • Analyzing existing Functionality • Validating changes • Impact Analysis & Dependency Manage • Release coordination • Aging technology stack • Undocumented business logic • Loss of SME knowledge • Tightly coupled architectures • Missing QA TC and TS • Continuous evolving business needs • Multiple stakeholder • Changing priorities • Fragmented design decision kill • Delayed feedback loops between Dev,QA,Ops • Fragmented Monitoring • Lack of incomplete operational Knowledge • Lot of manual support processes • Distributed knowledge Repo • Repetitive troubleshooting • Distributed knowledge Repo • Technical debt • Complex Integrations • Shared codebase • Frequent Releases Scope For Saving RAHUL KUMAR GUPTA
www.agilegurugram.com 3 WHat ? An AINative Approach to Software Delivery framework that combines Human , AI agents, enterprise knowledge, automation, and governance to augment and orchestrate the SDLC RAHUL KUMAR GUPTA
www.agilegurugram.com AISDLC AISDLC AILed SDLC is a approach where artificial intelligence drives every phase of the lifecycle—from ideation and requirements to design, coding, testing, deployment, and operations. Shifting software development from people-dependent execution to intelligence-driven value creation. Human provides Intent/ BRD Intent Agents do analysis, generates User Stories, Acceptance criteria, Generates Plan , Strategy, backlog , User Scenarios, Sprint plan, release plan Req. & Planning Agents Design & Strategy Agents Agents generates Prototypes, UX design , Strategy , System Architecture, Technical Designs , Tech stack Coding Agent AI Generates Code, Documentation, UT & API scripts Infrastructure & Deployments scripts, DB Scripts, , Mock Release & Support Agents Agents generates runbook, release notes, do deployments, release mgmt. Monitors, Systems, EWD , RCA , Self healing Quality Agents Agents generates Functional and NFR TC and Automation Test scripts for Sanity, Regression, Impact, Perf, Test data,etc FEEDBACK / REVIEW LOOP Human In Loop RAHUL KUMAR GUPTA
www.agilegurugram.com Traditional Agile led SDLC TIME COST SCOPE QUALITY AILED SDLC VELOCITY CONTROL INTELLIGENCE •Lead time/cycle time •Release frequency •Team cost per sprint •Cost per feature •Budget variance •Production •Improvement •reduction in cycle time •Policy - driven execution with human veto •Reduced blast radius for failures •Full traceability of agent decision •%scope completed vs planned •Story points delivered •Feature completion rate •Agent orchestration •Prompt optimisation •RAG effectiveness Agile optimizes human effort using scope,time and cost Agentic SDLC optimizes autonomous execution using intelligence ,velocity and control Traditional SDLC vs AISDLC QUALITY
www.agilegurugram.com Traditional SDLC vs AISDLC DIMENSION TRADITIONAL SDLC AI AUGEMENTED SDLC AILED SLDC Development / Engineering Human Oriented + Minimal Automation Human Oriented Supported By GenAI Driven By GenAI , Supported By Human Execution Manual and Stagewise AI Assisted, Individual Task AI Orchestrated , E2E Decision Making Human Expertise Human Expertise with AI Recommendations AI Decisions with Defined Policies Quality & Governance Manual Reviews and Testing AI generated Test and Review + Human Approval Autonomous testing, Review and self-healing, Human Responsibility Executes Every Tasks Supervise and validate AI output Define goals, policies, and exceptions Business Outcome Longer TTM , Higher Efforts Improved Productivity Much Faster delivery, lower operational cost RAHUL KUMAR GUPTA
www.agilegurugram.com Traditional SDLC vs AISDLC DIMENSION TRADITIONAL SDLC AI AUGEMENTED SDLC AILED SLDC Development / Engineering Human Oriented + Minimal Automation Human Oriented Supported By GenAI Driven By GenAI , Supported By Human Execution Manual and Stagewise AI Assisted, Individual Task AI Orchestrated , E2E Decision Making Human Expertise Human Expertise with AI Recommendations AI Decisions with Defined Policies Quality & Governance Manual Reviews and Testing AI generated Test and Review + Human Approval Autonomous testing, Review and self-healing, Human Responsibility Executes Every Tasks Supervise and validate AI output Define goals, policies, and exceptions Business Outcome Longer TTM , Higher Efforts Improved Productivity Much Faster delivery, lower operational cost Traditional SDLC Human based Engineering AI Augmented SDLC AI Assisted Engineering AI Led SDLC AI Driven Engineering HITL RAHUL KUMAR GUPTA Learn how AI transforms the retail value chain and unlocks insights from retail data
www.agilegurugram.com AISDLC  Sample flow Generates BRD using Req agents and Tools Use stories and Acceptance cases Using Reg agents and Tools Generate Solution Architecture Documents Rapid Prototyping Using AI agents ans tools Refine wireframe Using AI Agents and tools Generate Frontend code using AI agents and Tools Feature Coding, UT , API and Doc Using Code & docs Agent Create QA Strategy Using Strategy Agents Functional & NFR script and Suites using Quality agents Rel & Deployments DevOps Scripts Using AI Human-In-Th e-Loop QA Lead Engineer Designer Prod Mgr. Architect AI Agents REST API CONNECTOR MCP WEBHOOKS RAHUL KUMAR GUPTA
www.agilegurugram.com AISDLC  Sample flow Generates BRD using AI Tools Use stories and Acceptance cases Using AI Tools Generate Solution Architecture Documents Rapid Prototyping Using AI tools Like Loveable Refine wireframe Using AI tools Like Figma Generate Frontend code using AI Tools like Figma, Locofi Feature Coding Using Code Agent Create QA Strategy Using AI UT , API and Functional script User Docs using Doc and Test agents Rel & Deployments DevOps Scripts Using AI Human-In-Th e-Loop QA Lead Engineer Designer Prod Mgr. Architect AI Agents REST API CONNECTOR MCP WEBHOOKS AISDLC transforms how software is built, tested, deployed, and operated by Automating Repetitive Tasks, and Accelerates End-to-End Software Delivery AI should not replace SDLC. RAHUL KUMAR GUPTA
www.agilegurugram.com 3 HOw ? Transform Through People, Platform, and Governance By building AI-ready teams, establishing a scalable AI foundation, and implementing trusted governance RAHUL KUMAR GUPTA
www.agilegurugram.com AISDLC Adoption Framework AI Foundation BUILD digital backbone for AISDLC Setup • Enterprise Knowledge Repository • AI Agents, Prompt & Skill Libraries • AI Platform & LLM Ecosystem • SDLC Tool Integrations & APIs • AI Tool Evaluation & Proof of Value PoV • Workflow Automation People & Skills AI Foundation Enterprise AI Governance AI Lead Software Engineering People and Skill EMPOWER people to work intelligently with AI Prepare teams for • AI Readiness • AI Skills & Literacy • AI Tool and Framework Adoption • Role Evolution • Human-AI Collaboration • Change Management Enterprise AI Governance ENSURE AI is trusted, secure, and compliant • AI Council • AISDLC Operating Model • AI Talent & Workforce • Responsible AI • Security & Compliance • Policies & Standards RAHUL KUMAR GUPTA
www.agilegurugram.com AISDLC Adoption Framework AI Foundation BUILD digital backbone for AISDLC Setup • Enterprise Knowledge Repository • AI Agents, Prompt & Skill Libraries • AI Platform & LLM Ecosystem • SDLC Tool Integrations & APIs • AI Tool Evaluation & Proof of Value PoV • Workflow Automation People & Skills AI Foundation Enterprise AI Governance AI Lead Software Engineering People and Skill EMPOWER people to work intelligently with AI Prepare teams for • AI Readiness • AI Skills & Literacy • AI Tool and Framework Adoption • Role Evolution • Human-AI Collaboration • Change Management Enterprise AI Governance ENSURE AI is trusted, secure, and compliant • AI Council • AISDLC Operating Model • AI Talent & Workforce • Responsible AI • Security & Compliance • Policies & Standards Objective is to build AIFirst Mindset & AI Native Capabilities RAHUL KUMAR GUPTA
www.agilegurugram.com AISDLC POD Structure and Role Transitions Aspects POD Size POD Combination POD Structure POD Structure Transition Shared / SME Traditional SDLC 810 People 5 Engineers 3 QA 1 QA Automation Lead 1 BA 1 TL / Architect 1 Scrum Master 1 Design Lead QA lead , Performance Engineers, Solution Architect, DevOps 45 People 3 Engineers 1 QA /BA 1 TL/ Arch SDLC Agents QA lead , Performance Engineers, Solution Architect, DevOps lead, Automation Lead, Scrum / Program Mgr, Design Lead AISDLC AI Tech Lead AI Product Manager AI Prompt Engineer Full Scale engineer) AI QE Validator AISDLC AI Architect Tech + QE Tech Lead Role Transition Traditional SDLC Project. Mg FS QE / BA Tech Architec t QE DevOp s QE Lead Product Mgr QE Architec t RAHUL KUMAR GUPTA
www.agilegurugram.com RAHUL KUMAR GUPTA
www.agilegurugram.com AISDLC Ecosystem Users Prompt Library New Dev Migration Modernizatio n Mobile Security & Compliance Proj mgmt Design Agent Library Reqs & Planning Agent QE Agent Analysis Agents Strategy Agents Coding Agent Design Agents AI  Soln, Utilities & Plugin DB Tools LLM Model Claude Codex Claude Gemini IDE VSCode Antigravity KIRO Cursor Web Tool Agentic Platform Claude Coworker Langflow / Crew.ai Amazon BedRock M365 Studio Cline Github Co-pilot Knowledge Repository Benchmark Datasets Best practices Guidelines PoVʼs Chat GPT Gemini Pro AI Engineer AI QE Validator AI Proj Mgr AI Architect. Designer Gen AI Engineer. Gen AI Architect. AI Support Ext Systems Confluence JIRA Git Git Actions Figma Kafka Database M365 copilot Local LLM Training Material Blueprints Code Review MCP Library DB JIRA FIGMA Confluenc e SDLC Workflow Template Library New Dev New Feature Dev Ops Modernization Skill Library DB Tech Domain Generic Tech Ops SaaS Deployment s Custom Innovation AI Sandbox PoC Env. RAHUL KUMAR GUPTA
www.agilegurugram.com 5 RAHUL KUMAR GUPTA AISDLC Adoption • Prepare for AI First mindset and AI Native Capabilities •Setup AI Council •Create Libraries •Setup Sandbox •Create Training Program •Standardize AI Tools •Train people on AI Process and Tools •Release Playbook • X - Maturity • Y – Timeline •Identify Pilot Project & Project Architype. •Setup AI Council for Project •Identify AI Native POD •Setup workflow tools / agents prompts for project. •Create Domain or project specific Skill or Agents •Identify Project for E2E. •Setup workflow tools / agents prompts for project. •Baseline Metrices and Outcome •Rollout AISDLC Workflow for most of projects. •Conduct Workshop learnings. •Release refine Libraries and best practices •Optimize Libraries •Re-baseline Practices and Metrices •Plan to Adopt Selg Optimizing AISDLC • Pilot with real, small-scale project requirements in parallel • Adopt for End-to-end implementation in a live but limited-scope project • Rollout AISDLC across different delivery projects / Archetypes • Enhance and optimize AI SDLC 1. 2 . 3 . Setup Pilo t Adopt 4 . Scal e 5 . Optimiz e
www.agilegurugram.com 3 OUTcome From AI Adoption to Business Impact AISDLC transforms software engineering by accelerating time-to-market, improving productivity and quality, reducing costs, and creating sustainable business value RAHUL KUMAR GUPTA
www.agilegurugram.com PROJECT ARCHITYPE AISDLC Outcome METRICES PRODUCTIVITY COST QUALITY TTM Legacy Migration & Modernization 4060% 3040% 3040% 3050% New Development 4050% 3040% 3040% 4050% Production Support 5070% 4050% 40-50% (fewer incidents) 5070% Existing Product Enhancement 5060% 3050% 3040% 5060% RAHUL KUMAR GUPTA
www.agilegurugram.com Requirements Planning Design Quality Assurance Development Deployment Support & Maintenance Hours Days Weeks Month s 510X Faster Delivery 24X Faster Delivery Shift LEFT Traditional SDLC VS AISDLC AISLDC AI Augmented SDLC Traditional SDLC Years RAHUL KUMAR GUPTA
www.agilegurugram.com 3 Q&A The Future Starts with a Conversation Every AI transformation begins with curiosity. Let's discuss your questions, challenges, and ideas for adopting AISDLC. RAHUL KUMAR GUPTA
www.agilegurugram.com 3 “ To improve is to change; to perfect is to often change. RAHUL KUMAR GUPTA
www.agilegurugram.com Letʼs build the next era of Intelligent Software Engineering RAHUL KUMAR GUPTA • AI powered • People driven • Future ready Thanky ou Building the future • Together