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Artificial Intelligence

How is AI-Native Software Development Lifecycle Disrupting Traditional Software Development?

This viewpoint explores how artificial intelligence is revolutionizing software development by automating coding, enhancing testing accuracy, and accelerating delivery pipelines. It highlights the shift toward AI-native engineering, where developers collaborate with AI to build, test, and deploy software.

Insights

This viewpoint explores the transformative impact of AI on modern software engineering. Key topics include:

Opportunities Presented by AI
The AI-Augmented Development Workflow
AI-Powered Solutions
Key Influencing Factors
Risks, Concerns & Mitigation Strategies
The Future of AI Native Software Development Life Cycle

Introduction

In today’s world demand for high-quality software is ever increasing, which is putting pressure on companies to accelerate their Software Development Lifecycle (SDLC). To stay ahead of the competition and innovate, companies need to improve their time to market and make their development process more efficient.

Software development is undergoing a major transformation. The next-generation development approach integrates cloud-native technologies, AI-driven automation, collaborative tools, and DevSecOps practices. This has empowered development teams to deliver better software solutions more efficiently and innovate effectively.

Gartner Forecast: According to a Gartner research, 70% of developers will use AI tools by 2027. As per Gartner’s 2025 Emerging Tech Report, 60% of rollouts in Enterprise AI will have Agentic capabilities.
Developer Statistics: During 2023- 2025, 920% growth in usage was observed by Github for Agentic frameworks like AutoGPT, BabyAGI, Crew AI, and OpenDevin. As per GitHub and Hugging Face, 4.1 million developers have experimented with agentic AI frameworks.

How are Human-Centric processes holding us back?

In human-centric SDLC, there are extensive manual processes involved which tend to be inefficient and slow down development time. These processes are usually repetitive in nature and involve some low-level tasks. Relying solely on human output for tasks like code generation, testing and bug fixing can make the development process time consuming and prone to human errors. Leveraging AI can bring productivity improvements which developers can utilize for higher pursuits. Here is a look at how human centric SDLC is holding us back and how leveraging AI can bring improvements.

Figure 1: Challenges in Human-Centric SDLC and AI Solutions

Figure 1: Challenges in Human-Centric SDLC and AI Solutions

AI-native Software development life cycle is rapidly evolving where AI driven tools are giving improvements in developer productivity, improving code quality, reducing the development time, and bringing innovation and efficiency. Here is a look at AI impact in the software development life cycle.

What does the AI-Augmented Development Workflow look like?

Given below is the impact of AI on the stages of development versus the traditional manual process and the status of AI usage. This needs to be tailored to a specific tech stack or project type.

Phase	Manual Process	AI Impact	Deliverables	AI applicability	Status of AI usage
Requirement	Manual elicitation through stakeholder inputs and documentation.	Analyzes historical data and user behavior to suggest initial requirements.	Requirements Specification	Low	AI can assist but requires manual refinement for complex or evolving needs.
System Design	Manual creation of architecture and design diagrams.	Assists in generating architectural blueprints and UX designs using templates and suggestions.	Architecture, Design, Prototypes	Low	AI tools are helpful but not yet reliable for detailed or nuanced design decisions.
Coding and Implementation	Manual coding by developers.	AI-assisted code generation, suggestions, reviews, and refactoring.	Executable Software, Source Code	Medium	Effectiveness depends on clear prompt formulation and requires manual refinement for tasks that need precision.
Testing and debugging	Manual test case writing and bug identification.	Test case generation and bug detection	Test Cases, Bug Reports	Medium	Useful for UI testing; limited in API and complex logic testing. Can give false negative/positives
Deployment	Manual deployment using scripts and configuration files.	Streamlined deployments and automated scripts.	Deployed Software	Low	Automation is possible but still requires manual oversight and validation.
Maintenance and Monitoring	Manual monitoring and fixing issues	Predictive maintenance using anomaly detection and log analysis.	Updates, Error Reports	Low	AI is emerging in this space but lacks full coverage and reliability.
Code Documentation	Manual documentation of code structure, logic, and dependencies.	AI tools generate documentation by analyzing code structures, variables, functions, and dependencies.	Technical Documentation	Medium	Works best with clear prompts and structured processes; needs manual oversight.

The AI Tooling Landscape

Here is a look at how AI is integrated into different phases. Use cases for distinct phases and AI tooling landscape below:

Phase	AI Augmentation	Tools
Analysis	Extract key insights from client discussions, documents, and historical data. User story generation from high level definition. Reverse Engineering, Code Analysis Predict timelines and optimize resource allocation. Identify bottlenecks. Assist with backlog grooming and sprint planning. Can predict task completion timelines, prioritize tasks, and forecast risks. Link requirements to code, test, and documentation for traceability	IBM Watson NLP / Google Cloud NLP: Analyze and interpret business requirements using advanced natural language processing. Jira + Atlassian Intelligence: Enables AI-driven search, rule automation, content generation, comment summarization, and integration with Slack/Teams for work item creation. Copilot4DevOps: Extracts requirements from raw data, generates use cases, performs impact/risk analysis, and supports dynamic prompting. IBM Engineering Requirements Management: Uses Watson AI for traceability, automated quality checks, and intelligent requirement lifecycle management.
Design	UX Design, Wireframing assistance Prototyping assistance Sequence, flow diagram assistance Assistance in the generation of architectural blueprints and system designs Design to Code automation Sentiment Analysis AI powered personalization Image generation, content generation for UX designs User Testing, Cognitive Testing, Accessibility Enhance UI design with AI-driven recommendations.	*AI Design:* Tools best suited for rapid prototyping rather than detailed design refinement Figma + AI Plugin –Generate UI design by generating layouts, components, and content from text prompts, and automating repetitive design tasks. Others: Uizard, UX Pilot, Visily, Galileo AI, Motiff. *Flowcharts:* Generate flowcharts and diagrams from text prompts, summarize diagram content. Lucidchart AI Miro Assist *Desing-to-code:These tools are efficient for simple to medium-complexity designs, but need manual effort for nested or complex layouts Locofy – AI-Powered design for frontend code (HTML, CSS, React, etc.) Anima – Available as Figma plug-in. Design to code platform (React, Vue, HTML, Tailwind). Accessibility:* Stark – AI powered accessibility tool and integrates with platforms like Figma, Sketch, and GitHub. *Cognitive testing:Tools are ideal for pre-launch testing but less effective for post-launch behavioral analysis. Neurons - Predicts user attention and emotional response to designs using neuroscience and AI. Attention Insight - It helps designers optimize layouts, ads, and websites before publishing by using AI heatmaps to simulate where users likely focus Image generation* Adobe Firefly – Image generation from natural language prompts with integration to Adobe Creative Cloud
Coding	IDE Integration - Integrates with code editors like Visual Studio Code, IntelliJ etc. Conversational Code Interface - Use NLP text prompts to generate new code, Analyze and explain the existing code Automated Code Analysis, Bug Detection, Security Scan - Identify bugs, code smells, and security vulnerabilities via static code scan, Suggested fixes Code Debugging - Can detect syntax errors, logical flaws, Error prediction based on identifying recurring patterns Code Completion - Autocomplete code sections, methods, and functions, Code suggestions based on context Unit Test Generation/ Automated Code Coverage Reports - Generate unit tests Pull Request Summaries, automate check-ins gateways, perform code review Code Comments - Auto generation of code comments and docstrings. Code Review - Identify quality issues, security vulnerabilities, and performance inefficiencies and offer feedback Code Refactoring - Tech Debt Fixes. Offer actionable insights for code refactoring Code Migration - Refactor legacy systems, porting old codebases into modern architectures (e.g., monolith to microservices). Documentation - Documentation for APIs, libraries, and projects.	Coding (IDE plug-ins + chat interface) GitHub Copilot: Comes with Agentic AI mode, powered by OpenAI, GitHub Copilot provides real-time code suggestions, autocompletion, and debugging support across multiple languages and IDEs. Amazon Q: Offers agentic AI that can autonomously generate, test, refactor, and document code, with deep integration into AWS services and IDEs Google Gemini Code Assist: Provides contextual code completions, debugging, documentation, and unit test generation across IDEs, with enterprise-grade customization and source citation Tabnine: Delivers context-aware code suggestions, refactoring help, and chat-based assistance, and on-premises deployment Others: Codeium, Jetbrains AI Assistant, Sourcegraph Cody, Code Rabbit, AWS Code Guru Code quality/ security Snyk (Deep Code AI): Uses AI to detect and auto-fix security vulnerabilities in code, with context-aware suggestions and custom rule creation. Helps with Secure coding, static application security testing (SAST), and developer productivity in CI/CD pipelines. Sonarqube: Offers AI Code Assurance and AI-generated fix suggestions using models like GPT-4o or Azure OpenAI and enforcing Code quality and governance. Other AI SCA/SAST/DAST tools: Veracode, Github Advanced Security, Checkmarx One, Fortify Documentation Works best with clear prompts and structured processes; may require manual refinement for complex or highly customized workflows. Scribe: Process documentation. Mintlify: Documentation from code and provide insights.
Testing	Bug detection/ prediction. Identify vulnerabilities and suggest fixes. Scan code to identify security vulnerabilities and suggest mitigation. Identify performance bottlenecks Generate automated unit tests Test and Defect Analysis Test case creation and maintenance Automated visual tests Self-healing tests: Test scripts adapt and handle changes in UI. Test planning, optimization, and prioritization	AI test automations mostly for UI. Lacks advanced API testing features. Testim - Test automation platform that can create, execute, and maintain UI and functional testing across web and mobile apps. Applitools (powered by Visual AI) – Automated visual testing and UI verification, cross-browser, and device testing with pixel-perfect accuracy. Functionize – AI-powered functional testing: Self-healing tests, natural language test creation and predictive bug detection. Selenium with AI - Leverages third-party AI integrations (e.g., Applitools, Testim, Functionize). Katalan Studio – AI-powered test automation tool Others – CoTester by TestGrid, Browserstack, Lambda Test KaneAI (Gen AI powered QA agent), Mabl (cloud-based test automation)
Deployment	AI-optimized CI/CD automation - predictive automation and intelligent pipeline management IaC script writing, support for IaC workflows Automation script writing assistance Rollback and recovery Modify deployment strategies based on test results or performance metrics. AI chatbots help communicate and collaborate, automating routine tasks and providing insights. Assists with deployment impact analysis, deployment status updates, and incident reporting. Automated Workflows with streamlined CI/CD processes, with reduced manual intervention.	Jenkins x –Integrates with AI agents and AIOps. CircleCI – Offers AI-assisted pipeline editing, real-time debugging, and actionable insights to optimize CI/CD workflows. Github Actions + AI agents - Uses OpenAI-powered agents for auto-scaling, merge conflict resolution, and AI-driven code reviews. Harness AI – AI native platform Ansible AI, Terraform AI: Enhance IaC workflows with intelligent provisioning, configuration optimization, and proactive error detection. Others: Azure DevOps with AI, GitLab with AI, Bitbucket + Atlassian Intelligence, Spacelift
Maintenance	AI-powered observability, anomaly detection, root cause analysis, predictive insights, and remediation Analyze historic system data to predict potential system failures / downtimes and offer recommendations for preemptive action. Detect deviations from standard patterns. Performance Analytics: Real-time monitoring ensures optimal system performance. Resource Optimization: Identify heavily used resources and suggest optimal solutions. AI triage and incident management Self-healing automation, feedback loop to development, dependency mapping and change impact Auto-update recommendation	New Relic AI – Observability assistant Dynatrace (Davis AI) - Autonomous Observability, AI-powered insights. Datadog AI (Bita AI) - AI teammates for observability, debugging, and automated remediation workflows. Splunk AI Ops - Event correlation, anomaly detection, predictive analytics, and proactive incident response. PagerDuty, incident.io - AI incident management Others: Sysdig AI

What are the AI Platforms/ Services in wide use?

Here are some of the AI platforms and services that are widely used. These platforms offer end-to-end tools for building, training, deploying, and managing AI models, with enterprise-grade infrastructure and compliance.

Amazon Bedrock - Offers access to pre-trained foundation models (Claude, Llama, Titan, etc.) for building generative AI apps via API
Amazon Sagemaker AI - A full-fledged ML platform for training, tuning, and deploying custom models with deep control over infrastructure and workflows
Azure Open AI – Offers secure access to GPT-4o, DALL·E, Codex, and other models with enterprise-grade governance
Azure AI Foundry - A unified platform for building, customizing, and managing AI agents and applications with support for multi-agent orchestration and enterprise compliance
Google Vertex AI- End-to-end platform for building, training, and deploying models
Google Gemini - Offers AI tools integrated into Google Cloud and Workspace for NLP, code generation, and productivity improvements

Other platforms like Anthropic Claude, OpenAI, IBM Watsonx, and Cohere offer alternatives without full cloud infrastructure.

OpenAI Platform (Direct): Provides direct access to GPT-4o, DALL·E, Whisper, and Codex via API, with fine-tuning and embedding support.
IBM Watsonx: Combines foundation models, governance, and ML lifecycle tools for enterprise AI development focused on responsible AI and compliance.
Cohere: Multilingual LLMs for retrieval-augmented generation (RAG), embeddings, and classification suited for search, recommendation, and knowledge-based applications.
Anthropic Claude (via API or Bedrock): Capabilities include long-context reasoning and enterprise-grade NLP with safety and interpretability.

What are the considerations for evaluating models for projects?

Integrating Large Language Models (LLMs) into software development can be highly beneficial, but it requires careful planning and execution. Key Considerations are as follows:

Eco System and Integration

API Availability: Ease of integration into existing systems like IDEs, version control (Git), CI/CD pipelines, and project management systems
Tooling and SDKs: Support for development tools (IDE plug-ins) like GitHub Copilot, AWS Q, Google Gemini Code Assist, and Snyk.
Community and Support: Availability of documentation, forums, and vendor support.
Implementation
Type: Various project types including Greenfield, Brownfield, Transformation, Migration, and Steady State Maintenance.
Language: Support for multiple programming languages such as Java, .Net, Python, JavaScript, and SQL.
Customization and control
Whether the underlying model can be chosen or fine-tuned
Model – LLM / SLM
Model Size and Family: Consideration of model size and whether it is open-source or proprietary.
Use Case Fit: Adaptation of the model to specific project domains or data.
Latency and Throughput: Performance metrics.
Cost
Usage Cost: Pricing models for hosted LLMs, including pay-per-token subscription or flat-rate pricing.
Infrastructure Cost: Costs associated with on-prem LLMs.
Cost Effectiveness: Evaluation of cost efficiency at scale.
Privacy, Security, Compliance
Data Retention: Whether the model retains or logs data.
Legal Requirements: Compliance with GDPR, HIPAA, and other legal standards
Can be self-hosted, source code availability

What does the Implementation Plan to integrate AI in SDLC look like?

Here is an outline for a structured plan for integrating AI into Software Development.

What does the Implementation Plan to integrate AI in SDLC look like?

What are the Risks, Challenges and Mitigations in AI implementations?

Some challenges faced with AI implementation are given below:

Data Privacy and Security

Non enterprise LLMs include tradeoffs which will be unacceptable, like prompts and code in future updates to vendor products, potential breach of data privacy, and intellectual property leaks. Proprietary LLMs and tools like Github copilot, AWS Q, Google Codey etc. are suited for enterprise use.

Lack of Deep Understanding

AI tools have limitations with handling solutions which need Novelty and Complexity and Deep understanding of the architecture, business logic of the project, specific constraints etc.

Accuracy and Reliability

AI may give superficial suggestions which may not address the true root cause. It may not always produce optimized or error-free suggestions. It may suggest insecure coding practices without understanding the security implications of the code. Understanding large codebases can be challenging. Providing clear detailed and specific prompts, manual review of the output and breaking down large tasks into smaller, manageable pieces. The code needs to undergo code quality and security scans.
The code suggestions are not consistent.
False Positives/Negatives: AI testing tools can flag non-issues or miss actual bugs or vulnerabilities, requiring human validation.

Hidden cost of manual reviews

There is a Dependency on Human Expertise for reviewing the AI code output. Coverage Gaps in AI-generated tests can be addressed by human reviews. AI tests might not provide adequate coverage for complex scenarios or critical edge cases.

Ethical AI Implementation

Ethical AI relies on some Core Principles to address AI shortcomings:
Transparency: Open documentation of algorithms used to develop trust.
Bias Mitigation: Safety mechanisms like filtering training data, RLHF (Human Guided Learning), Guardrails, prompt moderation, output filtering, Ethical frameworks, feedback loops.
Regulatory Compliance: Global standards Adherence like the GDPR and AI Act.
Industry Initiatives: Google’s AI Principles, Microsoft’s Responsible AI Framework

Building the Agentic future

Agentic AI are autonomous systems, unlike the Gen AI tools which passively respond to queries and can independently perform multi-step tasks. Agentic AI is capable of:

Proactively identifying tasks and opportunities
Planning and executing sequence of actions
Adapt approaches, learn from experience
Human Collaboration, collaboration with other AI systems

In SDLC, AI systems can independently perform complex development tasks while being aware of the broader project context. Orchestrator Agent is used to coordinate activities across specialized agents.

Functions independently, making decisions and pursuing goals while seeking minimal human guidance
Evaluates situations to determine the optimal path forward
Develops, implements, and refines workflows to meet specific objectives

Example of Github Copilot Agentic mode:

Autonomous: Agent mode of GitHub Copilot can perform multistep coding tasks autonomously. It can accept natural language prompts. It can run suggest code edits, run tests, format, and refactor code and execute commands based on a single prompt.
Contextual understanding: It can understand the full context of the codebase rather than in pieces.
Performs complex operations like setting up project configurations and integrating libraries.
Problem solving iterations: An agentic loop is operated to refine its work.

Specialized AI agents embedded in distinct phases of development can achieve improved levels of productivity, quality, and innovation, contributing to a significant paradigm shift in the way software is developed.

Key Takeaways

The future of software development is being transformed by AI-native engineering. AI is gradually transforming SDLC by automating repetitive tasks and enhancing productivity, though its effectiveness varies across phases.

In the requirements and design stages, AI offers low impact, mainly assisting with data analysis and blueprint suggestions, but still requires manual refinement for complex needs. During coding and implementation, AI tools provide medium impact through code generation and suggestions, though precision tasks still need human oversight. Testing and debugging benefit from AI in UI test case generation and bug detection, but limitations exist in API testing and accuracy. Deployment and maintenance see low AI adoption, with automation possible but manual validation still essential. Documentation is moderately supported by AI, which can generate structured outputs but needs clear prompts and review.

Project planning and collaboration is enhanced by AI-powered project management tools. AI tools are transforming the way developers code and ensuring secure software. Not just the code, AI testing tools are reshaping QA to ensure reliable software. AI-driven tools automating dev-ops, reducing system downtime, and improving resource management. AI tools assist in the maintenance of applications as well. Ethical AI is constantly evolving to address Bias mitigation and transparency.

The AI adoption plan follows a phased approach. Explore foundational tools, Pilot AI in low-risk projects, Scale to broader use cases, and Evolve into autonomous systems.

Evolving roles: As developers transition from manual coders to AI orchestrators, their focus will shift toward designing intelligent systems, integrating automation, and upholding ethical and secure software practices.

The emergence of AI-assisted software development approaches like Vibe coding where developers use natural language to guide large language models (LLMs) to create code.

AI as assistant, where AI tools function as assistants to augment developer capabilities, is the most prevalent model in use. AI as collaborator where AI tools engage in dialogue with developers is emerging. AI as an autonomous agent is in the early stages and gaining traction.

It is important to see the future of software development as a human and AI collaboration rather than humans versus AI — with each contributing unique strength that could produce alone.

References

Throughout the preparation of this viewpoint, information and insights were drawn from a range of reputable sources, including research papers, articles, and resources. Some of the key references that informed us of the content of this viewpoint include: