At Meta’s LlamaCon conference, Microsoft CEO Satya Nadella shared that 20–30% of the code in Microsoft’s repositories is now generated by AI tools (SiliconANGLE, 2025).
Satya’s statement is not surprising when we consider that almost every software developer, QA expert, project manager, business analyst, and almost all the stakeholder now uses AI in some form to get their work done. The results are clear: teams are delivering faster, the quality is getting better, and businesses are able to yield more within the shorter timelines.
AI has come far from being an experimental tool and now it’s considered a central part of the software development life cycle (SDLC).
But does this mean we have fully given AI the driver’s seat, allowing it to do everything from analyzing ideas to producing launch-ready software? The reality is more complex.
In this blog, we will explore how AI is revolutionizing software development life cycle and examine its impact across requirements, design, coding, testing, deployment, and maintenance.
Custom AI Solutions for Modern Businesses
Amplify team performance & maximize ROI without burnouts.
“The Software Development Life Cycle (SDLC) is a structured methodology used by development teams to design, build, test, deploy, and maintain software systems.”
The main purpose of using SDLC is to minimize project risks and ensure high-quality outcomes. To do so the SDLC breaks the process into distinct phrases, where each phase is different in nature but plays a crucial role in completion of software.
As every business is unique in their operations, the exact approach can vary across organizations but the core idea of SDLC consistently revolves around six fundamental phases: requirements & planning, design & prototyping, development, testing & QA, deployment and ongoing maintenance.
Let’s explore how AI is changing the SDLC and whether it is redefining the process entirely or acting as a powerful accelerator for existing operations.
Evolution of SDLC Landscape With AI
Back in 1970, a Waterfall methodology was introduced where each phase had to be completed before the next one began. While it brought structure for the SDLC but it often failed to keep pace as the software development required a more refined approach for faster executions.
About 30 years later (in 2001), a group of software developers introduced the Agile methodology. This newly introduced allowed teams to revisit and rework specific parts of a project instead of waiting for the entire cycle to finish. The flexibility within the agile methodology improved software quality and delivery speed. Maybe this is one of the reasons Agile is still used by many organizations across the world even in 2025.
Now, with AI in software development, teams are entering a new era. Developers are using generative AI in software development as a reliable assistant that can remove bugs at the earliest stages, write complete code from simple prompts, and accelerate the entire coding process. Even design teams use AI to test different variations, QA teams leverage it for full regression testing and anomaly detection, and operations teams use AI to create smarter maintenance plans.
The impact is clear. AI productivity in software engineering is evident through shorter development cycles and higher quality outputs.
With AI evolution, we are now in an age where failing to integrate AI into the SDLC means reduced productivity, competitive disadvantage, and missed opportunities.
Let’s deep dive to figure out “How AI is Revolutionizing The Software Development Life Cycle.”
Use of AI Across SDLC Stages
Let us bust the myth that AI is now part of every stage of the software development life cycle (SDLC), not just coding. The modern organizations are providing their teams with adaptive custom AI software that are specifically designed for their business. Results?! Each stage is becoming faster, smarter, and more efficient with AI integration.
Use of AI Across the SDLC
Here’s how AI is being used at every stage of the software development life cycle.
1. AI in Requirements & Planning
Nowadays even all the meeting software are powered with AI that automatically records conversations as transcripts and generative AI can auto generate summaries. From these transcripts, teams can draft requirement documents with less manual effort and share with clients, making the process faster and more accurate.
In the case of the customized AI solutions, automation goes one step ahead and also helps identify missing information by comparing the newly provided requirements with data from previous projects that were of the same nature.
Once the requirements are finalized, AI-assisted planning tools can generate different project roadmaps, estimate effort, and flag potential risks. However, at this stage human involvement remains essential, as AI works best as a support system rather than a complete replacement.
2. AI in Design & Prototyping
Product Design reflects how the final product will look and function, that’s why most of the creative work is still very human-dependent. At the same time, designers are using AI to generate variations of their designs, from trying out different color palettes to turning wireframes into alternative layouts. This gives them inspiration for areas they might not have considered on their own.
With AI support, designers can test more variations and prototypes to see which ones work better. AI can also learn from previous projects and apply the organization’s signature design philosophy, helping bring more consistency and quality to the final designs.
3. AI in Software Development
Development is the phase where AI has completely revolutionized how engineers write and manage code.
“In a large-scale survey conducted by Atlassian in 2025, 99% of developers reported saving time using AI tools, with 68% stating they save more than 10 hours per week.”
The clear stats show the importance of generative AI in software development. Here’s how developers are using AI:
Code generation – creating functions, classes, or entire modules from natural language instructions.
Debugging and error detection – identifying bugs, security gaps, and performance issues with instant fix suggestions.
API and library recommendations – suggesting the best tools for specific use cases.
Collaborative coding – maintaining consistency and quality across large codebases.
Development has never been this easy before. These capabilities show how AI productivity in software engineering is no longer optional but a competitive advantage in modern development.
4. Testing & QA
As developers now check their code with AI, anomalies are becoming less frequent. For QA teams, AI in software testing brings major improvements through AI-powered QA automation. It helps with test case optimization by automatically identifying redundant or missing cases, streamlines automated regression testing to catch issues faster, and enables visual testing to spot subtle UI changes across devices. By catching defects earlier and reducing repetitive manual effort, AI is also reducing technical debt with AI, ensuring higher quality releases in shorter cycles.
5. AI in Deployment & Ops
Traditionally, operations teams had to manually monitor logs, handle incidents, and scale infrastructure, which often led to slow response times and unplanned downtime.
Today, AI automates tasks like predicting failures and managing rollouts with fewer errors. Through AI-driven DevOps, systems detect anomalies in real time, analyze logs, and trigger self-healing actions instantly.
Studies show that AIOps platforms can reduce incident resolution time by up to 60% by proactively addressing minor issues using predictive analytics (Quinnox, 2024).
6. Maintenance & Legacy Modernization
“Morgan Stanley used AI to modernize its massive COBOL systems, saving more than 280,000 developer hours (WSJ, 2025).”
Ongoing support has always been one of the costliest parts of the software development life cycle (SDLC). It could have cost Morgan Stanley millions, but legacy modernization with AI turned it into a success story.
By automating code translation, streamlining documentation, and reducing repetitive fixes, AI is now making long-term maintenance faster, cheaper, and more reliable. This shift shows how AI in software maintenance is not only cutting costs but also reducing technical debt with AI, ensuring systems stay future-ready instead of being weighed down by outdated code.
Benefits of AI in SDLC
AI integration in software development life cycle (SDLC) is delivering clear advantages across every stage of development. From planning to maintenance, AI is helping teams work faster, reduce errors, and improve overall software quality. Beyond efficiency, it is also changing how decisions are made, ensuring projects align better with business goals and long-term sustainability.
Challenges & Risks
While AI brings clear benefits to the software development life cycle (SDLC), it also introduces new challenges. Here are the key challenges organizations face with the fast adoption of AI:
AI mistakes– Automation can introduce errors that slip past review.
Decline in human expertise – Less reliance on critical thinking may weaken developer skills.
Creativity concerns – AI suggestions can narrow innovation instead of inspiring fresh ideas.
Project Delivery Demands – As AI speeds delivery, clients may push for unrealistic timelines.
Data Privacy Risks – Sensitive project data fed into AI tools may raise compliance and security issues.
Bias in AI Outputs – Models trained on flawed data can embed hidden biases in code or design.
AI is quite beneficial but it’s the user’s responsibility to keep the charge in their own hands. The best way to address these challenges is for teams to balance efficiency with human intelligence and creativity.
AI hasn’t replaced humans in SDLC (at least not yet), but AI is no longer optional. It’s essential for competitive advantage because organizations that embrace AI in software development are moving faster, delivering higher-quality products, and reducing costs.
At TEKHQS, we help teams unlock the full potential of AI integration in software development with custom solutions tailored to your SDLC process. From intelligent requirements gathering to generative AI in coding, our AI-driven systems empower your teams to deliver more with less effort.
Partner with TEKHQS to build AI solutions that transform your SDLC end to end.
Hopefully, this blog gave you a clear view of how AI is being applied across the entire SDLC in real-world scenarios. Stay connected with us and keep visiting for more insightful blogs.
FAQS
How is AI changing the way software is developed?
AI in software development is speeding up the entire process by automating tasks such as code generation, debugging, and refactoring. Developers can now rely on intelligent tools to handle repetitive work while focusing on creative problem-solving. Beyond coding, AI integration in software development supports design, testing, deployment, and maintenance, making projects faster and more cost-efficient.
What impact does artificial intelligence have on the software development life cycle?
Artificial intelligence is reshaping the software development life cycle (SDLC) by improving productivity at every stage. From auto-generating requirement documents to optimizing test cases and predicting deployment issues, AI in software development ensures better quality and faster delivery. Generative AI in software development also helps teams align outcomes more closely with business objectives.
In what ways can generative AI transform the software development lifecycle?
Generative AI in software development is redefining how teams approach projects. It can draft requirement documents, design prototypes, and even write full modules of working code. With AI integration in software development, teams can reduce technical debt, speed up testing, and improve overall efficiency, making the SDLC more adaptive and future-ready.
How can AI support teams throughout the SDLC?
AI in software development provides support across the entire SDLC. It captures meeting transcripts, generates design variations, assists in writing and debugging code, automates QA through AI-powered testing, and even handles legacy modernization. This broad AI integration in software development helps teams deliver faster, smarter, and higher-quality results while reducing manual effort.
Table of ContentToggle Table of Content
At Meta’s LlamaCon conference, Microsoft CEO Satya Nadella shared that 20–30% of the code in Microsoft’s repositories is now generated by AI tools (SiliconANGLE, 2025).
Satya’s statement is not surprising when we consider that almost every software developer, QA expert, project manager, business analyst, and almost all the stakeholder now uses AI in some form to get their work done. The results are clear: teams are delivering faster, the quality is getting better, and businesses are able to yield more within the shorter timelines.
AI has come far from being an experimental tool and now it’s considered a central part of the software development life cycle (SDLC).
But does this mean we have fully given AI the driver’s seat, allowing it to do everything from analyzing ideas to producing launch-ready software? The reality is more complex.
In this blog, we will explore how AI is revolutionizing software development life cycle and examine its impact across requirements, design, coding, testing, deployment, and maintenance.
Custom AI Solutions for Modern Businesses
Amplify team performance & maximize ROI without burnouts.
What is SDLC
“The Software Development Life Cycle (SDLC) is a structured methodology used by development teams to design, build, test, deploy, and maintain software systems.”
The main purpose of using SDLC is to minimize project risks and ensure high-quality outcomes. To do so the SDLC breaks the process into distinct phrases, where each phase is different in nature but plays a crucial role in completion of software.
As every business is unique in their operations, the exact approach can vary across organizations but the core idea of SDLC consistently revolves around six fundamental phases: requirements & planning, design & prototyping, development, testing & QA, deployment and ongoing maintenance.
Let’s explore how AI is changing the SDLC and whether it is redefining the process entirely or acting as a powerful accelerator for existing operations.
Evolution of SDLC Landscape With AI
Back in 1970, a Waterfall methodology was introduced where each phase had to be completed before the next one began. While it brought structure for the SDLC but it often failed to keep pace as the software development required a more refined approach for faster executions.
About 30 years later (in 2001), a group of software developers introduced the Agile methodology. This newly introduced allowed teams to revisit and rework specific parts of a project instead of waiting for the entire cycle to finish. The flexibility within the agile methodology improved software quality and delivery speed. Maybe this is one of the reasons Agile is still used by many organizations across the world even in 2025.
Now, with AI in software development, teams are entering a new era. Developers are using generative AI in software development as a reliable assistant that can remove bugs at the earliest stages, write complete code from simple prompts, and accelerate the entire coding process. Even design teams use AI to test different variations, QA teams leverage it for full regression testing and anomaly detection, and operations teams use AI to create smarter maintenance plans.
The impact is clear. AI productivity in software engineering is evident through shorter development cycles and higher quality outputs.
With AI evolution, we are now in an age where failing to integrate AI into the SDLC means reduced productivity, competitive disadvantage, and missed opportunities.
Let’s deep dive to figure out “How AI is Revolutionizing The Software Development Life Cycle.”
Use of AI Across SDLC Stages
Let us bust the myth that AI is now part of every stage of the software development life cycle (SDLC), not just coding. The modern organizations are providing their teams with adaptive custom AI software that are specifically designed for their business. Results?! Each stage is becoming faster, smarter, and more efficient with AI integration.
Use of AI Across the SDLC
Here’s how AI is being used at every stage of the software development life cycle.
1. AI in Requirements & Planning
Nowadays even all the meeting software are powered with AI that automatically records conversations as transcripts and generative AI can auto generate summaries. From these transcripts, teams can draft requirement documents with less manual effort and share with clients, making the process faster and more accurate.
In the case of the customized AI solutions, automation goes one step ahead and also helps identify missing information by comparing the newly provided requirements with data from previous projects that were of the same nature.
Once the requirements are finalized, AI-assisted planning tools can generate different project roadmaps, estimate effort, and flag potential risks. However, at this stage human involvement remains essential, as AI works best as a support system rather than a complete replacement.
2. AI in Design & Prototyping
Product Design reflects how the final product will look and function, that’s why most of the creative work is still very human-dependent. At the same time, designers are using AI to generate variations of their designs, from trying out different color palettes to turning wireframes into alternative layouts. This gives them inspiration for areas they might not have considered on their own.
With AI support, designers can test more variations and prototypes to see which ones work better. AI can also learn from previous projects and apply the organization’s signature design philosophy, helping bring more consistency and quality to the final designs.
3. AI in Software Development
Development is the phase where AI has completely revolutionized how engineers write and manage code.
“In a large-scale survey conducted by Atlassian in 2025, 99% of developers reported saving time using AI tools, with 68% stating they save more than 10 hours per week.”
The clear stats show the importance of generative AI in software development. Here’s how developers are using AI:
Development has never been this easy before. These capabilities show how AI productivity in software engineering is no longer optional but a competitive advantage in modern development.
4. Testing & QA
As developers now check their code with AI, anomalies are becoming less frequent. For QA teams, AI in software testing brings major improvements through AI-powered QA automation. It helps with test case optimization by automatically identifying redundant or missing cases, streamlines automated regression testing to catch issues faster, and enables visual testing to spot subtle UI changes across devices. By catching defects earlier and reducing repetitive manual effort, AI is also reducing technical debt with AI, ensuring higher quality releases in shorter cycles.
5. AI in Deployment & Ops
Traditionally, operations teams had to manually monitor logs, handle incidents, and scale infrastructure, which often led to slow response times and unplanned downtime.
Today, AI automates tasks like predicting failures and managing rollouts with fewer errors. Through AI-driven DevOps, systems detect anomalies in real time, analyze logs, and trigger self-healing actions instantly.
Studies show that AIOps platforms can reduce incident resolution time by up to 60% by proactively addressing minor issues using predictive analytics (Quinnox, 2024).
6. Maintenance & Legacy Modernization
“Morgan Stanley used AI to modernize its massive COBOL systems, saving more than 280,000 developer hours (WSJ, 2025).”
Ongoing support has always been one of the costliest parts of the software development life cycle (SDLC). It could have cost Morgan Stanley millions, but legacy modernization with AI turned it into a success story.
By automating code translation, streamlining documentation, and reducing repetitive fixes, AI is now making long-term maintenance faster, cheaper, and more reliable. This shift shows how AI in software maintenance is not only cutting costs but also reducing technical debt with AI, ensuring systems stay future-ready instead of being weighed down by outdated code.
Benefits of AI in SDLC
AI integration in software development life cycle (SDLC) is delivering clear advantages across every stage of development. From planning to maintenance, AI is helping teams work faster, reduce errors, and improve overall software quality. Beyond efficiency, it is also changing how decisions are made, ensuring projects align better with business goals and long-term sustainability.
Challenges & Risks
While AI brings clear benefits to the software development life cycle (SDLC), it also introduces new challenges.
Here are the key challenges organizations face with the fast adoption of AI:
AI is quite beneficial but it’s the user’s responsibility to keep the charge in their own hands. The best way to address these challenges is for teams to balance efficiency with human intelligence and creativity.
Also Read: How AI Automation Is Transforming Businesses Across The Globe
Supercharge Your SDLC with AI
Unlock the full potential of AI in every stage of software development.
Crux
AI hasn’t replaced humans in SDLC (at least not yet), but AI is no longer optional. It’s essential for competitive advantage because organizations that embrace AI in software development are moving faster, delivering higher-quality products, and reducing costs.
At TEKHQS, we help teams unlock the full potential of AI integration in software development with custom solutions tailored to your SDLC process. From intelligent requirements gathering to generative AI in coding, our AI-driven systems empower your teams to deliver more with less effort.
Partner with TEKHQS to build AI solutions that transform your SDLC end to end.
Hopefully, this blog gave you a clear view of how AI is being applied across the entire SDLC in real-world scenarios. Stay connected with us and keep visiting for more insightful blogs.
FAQS
How is AI changing the way software is developed?
AI in software development is speeding up the entire process by automating tasks such as code generation, debugging, and refactoring. Developers can now rely on intelligent tools to handle repetitive work while focusing on creative problem-solving. Beyond coding, AI integration in software development supports design, testing, deployment, and maintenance, making projects faster and more cost-efficient.
What impact does artificial intelligence have on the software development life cycle?
Artificial intelligence is reshaping the software development life cycle (SDLC) by improving productivity at every stage. From auto-generating requirement documents to optimizing test cases and predicting deployment issues, AI in software development ensures better quality and faster delivery. Generative AI in software development also helps teams align outcomes more closely with business objectives.
In what ways can generative AI transform the software development lifecycle?
Generative AI in software development is redefining how teams approach projects. It can draft requirement documents, design prototypes, and even write full modules of working code. With AI integration in software development, teams can reduce technical debt, speed up testing, and improve overall efficiency, making the SDLC more adaptive and future-ready.
How can AI support teams throughout the SDLC?
AI in software development provides support across the entire SDLC. It captures meeting transcripts, generates design variations, assists in writing and debugging code, automates QA through AI-powered testing, and even handles legacy modernization. This broad AI integration in software development helps teams deliver faster, smarter, and higher-quality results while reducing manual effort.
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