Over the last couple of years, AI coding assistants have changed the day-to-day reality of software engineers more than any single tool in the past decade. AI has not only accelerated how quickly developers write code, but it has also fundamentally changed how we build software. Tasks that once consumed hours of engineering time, such as writing unit tests, scaffolding APIs, generating boilerplate, validating integrations, and even debugging, can now be completed in minutes using AI coding assistants. how This efficiency has reshaped expectations across organisations: leaders assume delivery speed should increase, project timelines should shrink, and teams should “do more with less”. Program managers expect developers to deliver more features in the same amount of time, and executives assume engineering velocity should increase proportionally with AI adoption. Yet despite this massive shift in capability, the underlying software development lifecycle (SDLC) used by most teams hasn’t evolved in decades. Developers are operating with new tools but old processes. As a result, teams often rush into implementation without enough design clarity, accumulating technical debt faster than ever. AI has changed the pace of software delivery, but the process has not caught up—and this mismatch is causing friction for developers, engineering managers, and product teams alike. pace process This article explores where the traditional SDLC falls short in the age of AI and proposes a more adaptive, realistic lifecycle that reflects how modern teams actually work. Traditional Software Development Lifecycle Traditional Software Development Lifecycle For years, the classic SDLC has been the industry standard—structured, predictable, and intended to reduce risk in complex software projects. The traditional cycle includes: Planning & Requirement Analysis
Stakeholders collect business needs, identify constraints, define scope, and estimate effort.


Defining Requirements
Requirements become detailed functional and non-functional specifications. Everything is documented before design begins.


Design
Architects and senior engineers map out system components, data models, workflows, and integration points. Historically, this phase determined long-term system quality.


Development
Engineers implement the design, write code, build modules, and integrate components. This phase used to consume the majority of engineering time.


Testing
QA and developers validate functionality, performance, and reliability. Automated tests, manual testing, integration testing and user testing all happen here.


Deployment
Software is released to production environments, accompanied by monitoring, rollback strategies, and operational readiness.


Maintenance
Teams fix bugs, monitor system health, reduce technical debt, and refine the system over time. Planning & Requirement Analysis
Stakeholders collect business needs, identify constraints, define scope, and estimate effort. Planning & Requirement Analysis Planning & Requirement Analysis Stakeholders collect business needs, identify constraints, define scope, and estimate effort. Defining Requirements
Requirements become detailed functional and non-functional specifications. Everything is documented before design begins. Defining Requirements Defining Requirements Requirements become detailed functional and non-functional specifications. Everything is documented before design begins. Design
Architects and senior engineers map out system components, data models, workflows, and integration points. Historically, this phase determined long-term system quality. Design Design Architects and senior engineers map out system components, data models, workflows, and integration points. Historically, this phase determined long-term system quality. Development
Engineers implement the design, write code, build modules, and integrate components. This phase used to consume the majority of engineering time. Development Development Engineers implement the design, write code, build modules, and integrate components. This phase used to consume the majority of engineering time. Testing
QA and developers validate functionality, performance, and reliability. Automated tests, manual testing, integration testing and user testing all happen here. Testing Testing QA and developers validate functionality, performance, and reliability. Automated tests, manual testing, integration testing and user testing all happen here. Deployment
Software is released to production environments, accompanied by monitoring, rollback strategies, and operational readiness. Deployment Deployment Software is released to production environments, accompanied by monitoring, rollback strategies, and operational readiness. Maintenance
Teams fix bugs, monitor system health, reduce technical debt, and refine the system over time. Maintenance Maintenance Teams fix bugs, monitor system health, reduce technical debt, and refine the system over time. Where This Model Breaks in the Age of AI Where This Model Breaks in the Age of AI While this lifecycle worked for years, AI has fundamentally disrupted two core assumptions: Implementation is no longer the bottleneck: AI can generate large portions of code, tests, and documentation in minutes.
Design and requirements now lag behind execution: Developers jump into implementation faster than teams can refine requirements or create thoughtful designs. Implementation is no longer the bottleneck: AI can generate large portions of code, tests, and documentation in minutes. Implementation is no longer the bottleneck Design and requirements now lag behind execution: Developers jump into implementation faster than teams can refine requirements or create thoughtful designs. Design and requirements now lag behind execution Because the classic SDLC treats development as the slowest and most expensive phase, its structure fails when coding becomes fast. The result: Rushed design decisions
Intentional shortcuts to meet deadlines
Increased technical debt
More rework during iteration cycles Rushed design decisions Intentional shortcuts to meet deadlines Increased technical debt More rework during iteration cycles Teams are building faster than they can plan. Problems in Today’s AI-Accelerated SDLC Problems in Today’s AI-Accelerated SDLC 1. Increased Leadership Expectations 1. Increased Leadership Expectations AI has created a perception that engineering should deliver dramatically more with fewer people. Goals have become more aggressive as organisations underestimate the cost of planning, design, and long-term system thinking. 2. Reduced Long-Term Vision 2. Reduced Long-Term Vision With pressure to deliver quickly, teams focus on the best achievable milestone rather than the long-term product vision. Systems become optimised for next month, not next year. best achievable milestone 3. Less Emphasis on Design 3. Less Emphasis on Design Developers jump into implementation before deep design discussions. AI accelerates execution but does not replace architectural thinking. Updating design mid-development is now common and chaotic. 4. Accelerated Technical Debt 4. Accelerated Technical Debt Short-term solutions pile up quickly. As teams implement features for immediate milestones, long-term stability becomes an afterthought. Solutions to Rebalance Development in the AI Era Solutions to Rebalance Development in the AI Era 1. Set Proper Expectations Early 1. Set Proper Expectations Early Engineering teams must push back where needed and provide realistic input during planning. Strong leaders factor engineering insights into timelines rather than assuming AI will solve all bottlenecks. 2. Align Ideas With Execution Speed 2. Align Ideas With Execution Speed Execution is faster than ever, but ideation and requirement clarity are not. Product teams must adopt long-term thinking and avoid constantly pivoting mid-cycle, which leads to rework and wasted effort. 3. Update the SDLC Itself 3. Update the SDLC Itself The biggest change needed is structural: the SDLC should reflect how software is actually being built today. AI has made Proof-of-Concept (POC) development an essential pre-design step. POCs are now necessary to test AI capabilities, validate feasibility, and explore user interactions before committing to long-term architecture. However, POCs should inform feasibility and not dictate architecture. The New AI-Era Software Development Lifecycle The New AI-Era Software Development Lifecycle AI has introduced two major additions to the SDLC:POC Development and Continuous Iteration. POC Development Continuous Iteration New SDLC Flow New SDLC Flow Planning → Requirement Definition → Minimal Design → POC Development → User Testing → Feedback Review → Iterate Planning → Requirement Definition → Minimal Design → POC Development → User Testing → Feedback Review → Iterate Two Parallel Workstreams Now Exist Two Parallel Workstreams Now Exist 1. Productionizing the Working Prototype: 1. Productionizing the Working Prototype: Take minimal but functional features
Deploy to production
Monitor, maintain, stabilise, and optimise Take minimal but functional features Deploy to production Monitor, maintain, stabilise, and optimise 2. Iterating Based on User Feedback: 2. Iterating Based on User Feedback: Update requirements
Improve design
Rebuild or refine implementation
Conduct more user testing Update requirements Improve design Rebuild or refine implementation Conduct more user testing This loop continues until the product reaches maturity. The Real Challenge The Real Challenge The biggest puzzle today is minimising churn, and how to iterate quickly without causing back-and-forth chaos across design, development, and product teams. We're all still figuring out the right balance between speed and stability. Conclusion Conclusion AI has dramatically accelerated software development, but our processes haven’t evolved at the same pace. The traditional SDLC assumes slow implementation and steady planning - an assumption that no longer holds true. By embracing POCs, iterative cycles, and realistic expectations, teams can turn AI from a source of chaos into a catalyst for better engineering. The industry is still learning how to adapt, and so am I.The pursuit of a smooth, AI-aligned development lifecycle continues.

This story contains new, firsthand information uncovered by the writer.

How AI Is Quietly Reshaping the Software Development Lifecycle

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