AI in the SDLC: Where It Saves Time and Where It Doesn’t

Introduction

Software development is being transformed by AI. Developers can write code from prompts, create tests, learn new codebases, and document applications. What used to take hours can be done in a fraction of that time.

As engineering executives, you may see this as a silver bullet. Implement AI coding tools and your developers will build more software faster.

There’s more to it than that.

While AI can help developers build things faster, it won’t solve all the tasks required to deliver software. Once developers can generate their first draft, there are other tasks that need to be performed. Understand the requirements, ensure your architecture is suitable for your needs, review the code, and make sure your application is secure. These are just a few examples.

Developers may produce code more quickly with AI, but that won’t decrease the other activities that happen during software delivery. Those activities will simply move further down your pipeline.

The DORA report has done research on this. They surveyed close to 5000 professionals who work with technology in 2025. They discovered that AI magnifies your strengths and weaknesses. Their further analysis goes deeper into these concepts. So when considering how AI will improve productivity with software delivery, we need to think about the entire process. Can AI help you reduce time to market for your software?

In this article, we will discuss the various points of your software development life cycle (SDLC) and how you can use AI to get more work done faster. We will also look at how to not create more issues for yourself when adopting AI tools.

What Does AI in the Software Development Life Cycle Look Like?

The SDLC consists of multiple tasks that help you plan and deliver software to your customers.

By adding AI into your SDLC, you are opening your development process up to using Generative AI, Machine Learning, and Agentic AI to help speed up or perform these tasks.

Your SDLC could look very different depending on what AI you are able to implement. You can use it to write your code, generate tests, or write documentation. Perhaps you can build agents that will complete these tasks in sequence.

Here are some areas that companies have implemented AI in their SDLC.

• Planning: Generate summaries of customer requests, analyze gaps in information, and create acceptance criteria.
• Design: Generate potential implementation strategies, explain application flows, and produce design documentation.
• Development: Write code, auto-complete functions, understand functions that may not be familiar, and assist with debugging.
• Testing: Write test cases, generate test data, and find potential issues with your application.
• Security: Highlight vulnerabilities within your application and help developers understand how to fix them.
• Release: Create release notes and help debug your application when something goes wrong.
• Operate: Help understand legacy applications and identify what you need to change to complete a task.

Keep in mind there will be different levels of benefit based on these tasks. Some will require more knowledge of your business domain or involve higher risks. These tasks may not see as much benefit from using AI compared to others.

How can AI Help Me Complete Tasks Faster in my SDLC

Requirements Analysis

Requirements analysis is useful for organizing information, but less reliable at resolving ambiguity.

When you receive a new requirement, there can be lots of information thrown at you. Documents, meetings, emails, etc. You want to understand the new requirement and know if there are any missing pieces of information that you need to gather from your business stakeholders.

By using AI, you can help summarize the new information and group together topics that might relate to each other. You can also use it to generate questions that will need answers from your Product Owner or Subject Matter Expert.

Using AI to help you understand your requirements will allow you to spend less time trying to figure out the information in front of you. However, AI cannot determine what your business was looking for when you have a vague requirement.

Time saved:  Creating summaries of your information, generating first drafts, and asking questions that you may not have thought of.

Time NOT saved:  Understanding what your customer wants when there is ambiguity in your requirement.

Recommendation:  Use AI to help build your requirement information out. Then have your Product Owner make sure your Acceptance Criteria align with their original business requirement.

Architecture and Design

Architecture and design enable faster exploration, not automatic architectural judgment.

Once you have an understanding of what your customers are asking for, you can dive into how to implement those features. Your team may not have knowledge of how to create certain functionality, or you may need to better understand your current systems.

Using AI, you can ask your LLM to give you information on a technology. It can explain to you how two components will talk to each other and generate diagrams or documents to help with your design.

This will help speed up your development when researching new technologies and thinking about how your application can accomplish these goals. You will not have all of your answers from your AI, so keep that in mind.

Time saved:  Researching new technologies and creating your application design documents.

Time NOT saved:  Making decisions on your application design and what is best for your environment.

Recommendation:  Allow your AI tools to understand what information is applicable to your environment. This will allow you to create better architecture decisions and ensure you’re documenting them. Have your senior engineers review those decisions to confirm.

Code and Implementation

Coding and implementation offer one of the clearest opportunities for time savings.

Many developers feel that using AI to write code is going to help them be more efficient. They can quickly generate functions and classes to help them reach their end goal. Developers can use AI to explain code that they don’t understand, convert code from one language to another, and find bugs.

An article published by IEEE showed that developers could reduce the amount of time it takes to complete tasks. There is a caveat to that article that states how accurate they believe the numbers are, but it’s still helpful to see that you can improve on task completion time.

As seen above, AI can help developers be more productive by reducing the amount of time spent completing their tasks. When your developer knows what they are trying to achieve, they can use AI to build the functionality out.

Time saved:  Generating new code and explaining what’s happening inside a function.

Time NOT saved:  Figuring out the intent behind certain pieces of code and ensuring your AI is building the functionality you want.

Recommendation:  Have your developers complete simpler tasks that they are familiar with. Make sure they understand what the AI is creating so that you can improve the productivity metric.

Testing

Testing enables faster test creation, with more attention needed for validation.

Similar to writing code, AI can help write tests to prove out your functionality. Your AI can write unit tests that will check basic scenarios or generate test data. It can even help debug why a test may be failing.

Using AI to generate more unit tests can help your team build more test coverage but may not catch all your scenarios. Just because you generated more tests doesn’t mean they are good quality or catch all of your scenarios. You may spend less time writing tests, but you may not be finding any issues.

Also, with the introduction of AI into your application, you may need to start testing your AI. Your system could return different results when asked similar questions. So you will need to test that it returns the appropriate responses.

Time saved:  Building more unit tests to cover your scenarios and generating test data.

Time NOT saved:  Knowing that your generated tests will catch all of your scenarios.

Recommendation:  Ensure your tests are linked back to your Acceptance Criteria, and you’re catching all scenarios that may break. You may want to use different types of testing to help you identify issues that your unit tests are missing.

Code Review and Security

Code review and security enable faster detection, which can create more review work.

After a developer completes their code, they will submit a pull request. Someone on their team or QA can review the code they’ve written to ensure no issues are present. You can use AI to help highlight what changed and explain the reasons why.

AI can also help you look for security vulnerabilities that may have been introduced. However, if you are using AI to generate more code, you will likely need to spend more time reviewing that code. Developers may submit large amounts of code that are insecure or not optimized for their needs.

This will introduce more issues that need to be highlighted to your developers and may cause them to spend more time fixing them. Having tools that highlight too many issues will also create more work for your reviewers.

Time saved:  Helping reviewers understand why certain code was added and highlighting security concerns.

Time NOT saved:  Reviewing large chunks of code that were generated by AI and may have issues that need to be fixed.

Recommendation:  Continue your normal code review process but allow AI to assist with identifying concerns. Create guardrails to make sure you don’t spend more time reviewing AI-generated code.

Documentation

Documentation is a strong candidate for reducing repetitive work.

Documentation is one area that can benefit from using AI. Oftentimes, documentation can fall behind on projects as your developers focus on delivering their features. By using AI, you can auto-generate documentation for your codebase.

You can also use AI to create release notes that will summarize what changed during that sprint. If you have someone come onto your team who isn’t familiar with your application, they can ask your AI for help understanding it.

There are many other ways you can leverage AI to help keep documentation current with your codebase. However, remember that you want to capture the intent behind why things are done and not just explain your code.

Time saved:  Creating documentation to explain your application and changes made to it.

Time NOT saved:  Documenting the business decisions that are not always captured within your code.

Recommendation:  Use AI to generate your documentation but have those who work on the codebase review it for accuracy. You want your documentation to be kept in the same place as your code so it will stay updated.

Deployment and Operations

Deployment and operations enable better assistance, with automation requiring safeguards.

When deploying your software, you can use AI to analyze log files and help pinpoint where a problem may be occurring. If something fails in production, you can generate steps that need to be completed to repair the issue.

In some situations, your AI could actually run those remediation steps. But there are some factors to consider before allowing that to happen. What if the AI generates bad steps that cause more damage to your production environment?

There are lots of unknowns when dealing with your production environment, so caution is key.

Time saved:  Gathering information to help understand a failure that occurred in production.

Time NOT saved:  Figuring out the root cause of a failed deployment.

Recommendation:  Use AI to gather information that can help you pinpoint an issue. If you’re confident that the remediation steps are correct, you could begin using it to complete those tasks.

Maintenance and Modernization

Maintenance and modernization ensure more accessible code understanding, but context remains critical.

As applications age and get larger, you will start running into issues with understanding them. Many teams use AI to explain parts of the application that have become lost over time. By using AI, you can gain insight into your system and what areas you need to pay attention to.

This will allow your team to understand what areas you will need to dig deeper into and build a roadmap for changes you will need to make.

Time saved:  Helping you understand your current application and where you can build changes out.

Time NOT saved:  Not having all the knowledge that is embedded into your legacy application.

Recommendation:  Make sure you feed your AI all of your current code and any information you can find. Then cross-reference it with your actual systems to see if there are any differences.

Where AI in SDLC Often Fails to Deliver Expected Savings

AI will allow you to complete your tasks quicker, but it will introduce other challenges you’ll have to face.

By speeding up development, you will need to increase your code review times. Developers can spend less time writing code, but more time getting feedback from others. Your code review queue will be longer and contain larger chunks of code to review. You will want to make sure you can improve on that task and decrease the lead time for a change.

Just because you generate more unit tests doesn’t mean you are catching all issues with your code. Try to think about what you can test to see if your tests are finding all the bugs in your application. There are many ways to measure if your tests are finding issues.

By generating code, you will spend less time writing it, but may have to spend time modifying what was generated. Track how much code was written by AI and how many changes were made to those lines of code. You can include that information in your calculation to see if you’re really being more productive.

Having agents perform multiple tasks can also help speed up your pipeline. But there are factors that need to be considered when using agents. How will it access other tools? Will it be able to complete that task? What if it makes the wrong decision? All of these questions can cause delays to your work.

Make sure to measure all of the tasks that take place within your SDLC when implementing AI. You can’t just say you developed code faster; you have to know if your software reached the customer quicker.

How to Measure Whether AI Actually Saves Time

You need to gather metrics on all of your SDLC tasks that could be affected by implementing AI. Some metrics to look at are listed below.

Use these metrics to understand how you can improve your SDLC when using AI. Don’t just look at how much faster you can develop your software, but how you will measure the other tasks in your pipeline.

As you can see, there are a lot of different areas that will be affected when using AI to improve your SDLC. Below is a calculation that you can use to see if you’re actually improving your efficiency.

(Baseline for task – Task with AI implemented) + (time to verify AI results) = Net amount of time saved

Gather information for each task you’re interested in improving. You can see what areas you can improve and where you may need to spend more time. Compare this to what you can accomplish without AI to see what works best for your team.

Remember that if you’re paying for AI, you’ll want to factor in how much that is costing your business as well. There may be a point where you’re spending more money to not gain any time back from your team.

A Practical Roadmap for Adopting AI Across the SDLC

As mentioned above, there are many ways you can implement AI into your software development process. Below are a few tips that will help your team be more successful when introducing AI.

Find where your biggest pain point is and try to improve that first. Are you spending too much time writing unit tests? Perhaps AI can help generate those for you and free up your developers’ time. Maybe you’re losing time understanding your codebase, and AI could help build that context out for you.

Once you find an area you can implement AI into, you will want to measure if you can gain time back from that. Build out your metrics around those tasks so you can compare before and after AI is introduced.

Make sure you create guardrails around how your developers can use AI. Do they need to review all of the information it’s giving them? Are there certain tools your team is allowed to use? You want to limit your team from using AI in a way that could cause harm to your business.

After you have these in place, you can start allowing your developers to use AI in those areas. Find out if it’s helping them be more efficient and gather information to back that up.

Once you know that you’re saving time, you can begin to deploy AI in other areas of your SDLC. Continue to improve on your development process by using the metrics you’ve collected to make more informed decisions.

How Hexaware Supports AI-Enabled Software Development

Enterprise AI adoption requires more than giving developers access to a coding assistant. It calls for an approach that connects AI capabilities with engineering practices, application context, quality controls, and delivery workflows.

Hexaware’s AI-powered SDLC solutions with AWS combine its RapidX® platform with AWS’s Kiro agentic development environment. The announced approach addresses activities across the lifecycle, including requirements, design, coding, testing, and documentation, with an emphasis on structured delivery, security, and governance.

For applications with complex dependencies or significant legacy constraints, Zerovity™ provides application intelligence and agentic engineering capabilities to support code understanding, maintenance, testing, and modernization.

Quality engineering is equally important. Hexaware’s Zero Defects approach emphasizes preventing defects earlier, connecting quality evidence across engineering and delivery, and maintaining accountability for AI-enabled systems.

These capabilities address different parts of the challenge: improving development flow, bringing context to application work, and maintaining the controls needed for dependable software.

The right starting point depends on where an enterprise loses the most time today. For some teams, that may be repetitive implementation. For others, the greater opportunity may lie in testing bottlenecks, legacy code understanding, or release delays.

Conclusion

AI can save meaningful time in software development, particularly when tasks are repetitive, clearly specified, and easy to validate. Code generation, test drafting, documentation, code exploration, and routine troubleshooting are promising places to start.

The limits become clear when work requires deep business context, complex architectural judgment, difficult integration decisions, or high-stakes production changes. AI may still assist with these activities, but human review and engineering controls remain essential.

The most important distinction is between making a task faster and improving software delivery as a whole. Enterprises need to account for verification, rework, security, quality, and operational readiness before claiming a productivity gain.

Start with a specific bottleneck, establish a baseline, measure net time saved, and expand based on evidence. That approach helps engineering teams use AI where it makes a genuine difference while preserving the reliability and accountability that enterprise software demands.