10 Ways AI Will Revolutionize Software Development (And How You Can Prepare)5 min read·Just now--Artificial Intelligence (AI) is rapidly transforming how we build software. From accelerating requirements gathering to automating testing and deployment, AI is not only augmenting human capabilities but also reshaping the very processes that define the Software Development Lifecycle (SDLC). In this article, we’ll explore how AI is impacting each phase of the SDLC — and what it means for developers, engineering leaders, and the future of software delivery.This post is intended for software engineers, engineering leaders, and AI-curious tech professionals looking to understand how AI can practically integrate into modern SDLC workflows.The Software Development Lifecycle (SDLC) at a GlanceThe SDLC traditionally includes the following phases:Requirements analysisSystem designImplementation (coding)TestingDeploymentMaintenanceLet’s examine how AI influences each phase, with a focus on real-world tools, techniques, and implications.1. Requirements Analysis & PlanningNatural Language Processing for RequirementsOne of the most persistent challenges in software projects is capturing clear, unambiguous requirements. AI tools using Natural Language Processing (NLP) can analyze documents, emails, and meeting transcripts to extract relevant, actionable items.Technical Benefits:Key Phrase Extraction: Tools like spaCy, NLTK, or LLMs can identify core requirements and domain-specific terms.Semantic Relationship Mapping: AI can detect contradictions, overlaps, or missing requirements.Requirements Prioritization: Predictive models suggest which features deliver the most business value, based on project history.Predictive PlanningAI-enhanced forecasting can significantly improve planning accuracy:Effort Estimation…