13 Impacts of Generative AI That Changed Software Development
Naveen Kumar Singh
Naveen is a professional agile coach and has been working independently for a long time in the Asia... Read more
GenAI is changing the way people work by giving them new ideas and automating the boring parts that used to slow things down. But what does that mean for your job every day? GenAI is already at your door, offering a smarter, faster way to do everything from coming up with new features to reviewing code to making sure a release is perfect.
This post will talk about the impact of AI on software development lifecycle. It helps teams work together better, ship better products faster, and focus more on what really matters: making great products. You're not the only one who is curious (or even a little skeptical). Let's talk about the real effects, the chances, and how you can make the most of this exciting change.
What Is Generative AI?
Generative AI (GenAI) is a type of artificial intelligence that can create new content, such as text, images, code, or audio, based on patterns it has learned from large datasets. Unlike traditional AI models that focus on recognition or classification, GenAI models generate output: they write code, generate documentation, create UI designs, compose emails, and more.
It’s powered by large language models (LLMs) like OpenAI’s GPT, Google’s Gemini, Meta’s LLaMA, and Anthropic’s Claude, trained on massive amounts of internet text and/or domain-specific data.
How Does GenAI Enhance Software Development Process
Generative AI for software development isn't just a cool new technology; it's quickly becoming a trusted partner for Agile development teams. GenAI can help you with smart support while you're writing tests, planning a sprint, or pushing out code. This is how it's changing the whole process of making software:
1. Code generation that happens on its own
Imagine being able to explain a feature in simple English and getting a working code snippet in a matter of seconds. That's what GenAI can do for you.
It speeds up development by writing boilerplate code, repeating logic, or even whole functions. This lets developers focus on architecture, performance, and the user experience. Many Agile teams are using tools like GitHub Copilot and OpenAI Codex to make this happen every day.
GenAI is like an intern who never sleeps and is always ready to do the boring work so your team can add real value.
2. Finding and fixing problems
Do you remember how much it hurt to spend hours looking for a bug only to find out it was just a missing semicolon or a small logic error?
GenAI looks at your codebase, compares patterns, predicts common mistakes, and lets you know about problems before they become big ones. It can even suggest fixes or make tests that focus on the parts of the code that are most likely to cause problems.
This kind of proactive debugging fits right into Agile's cycle of checking and changing things, which gives teams more confidence when they release.
3. Better Code Documentation
Let's be honest: not all developers like writing documentation. GenAI is really good at it, which is good.
It automatically makes comments, usage instructions, function summaries, and even diagrams of the architecture. There are no more outdated Confluence pages, and new team members can get up to speed much faster.
And since Agile values working software and being able to keep it up, GenAI's help here is worth its weight in gold. It keeps your product team on the same page and makes your codebase easy to understand.
4. Improved performance and the ability to grow
GenAI is like having a performance architect ready to help. It looks at logs, checks how the system is working, finds performance problems, and even suggests changes to the infrastructure or ways to refactor code.
GenAI makes sure that your code is clean and that your systems can grow easily, whether you're making a mobile app for a lot of people or a SaaS product that can grow.
You won't have to guess anymore during retrospectives; instead, you'll get data-based suggestions to help you do better each sprint.
5. More creative ideas and thoughts
GenAI does the boring work, so teams can be more creative. Want to try out a new algorithm? Make a model of a different way to build microservices? Look into other UX flows? GenAI can help you find places to start and look at your options more quickly.
It's like having a partner who has read millions of lines of code and knows what will work and what won't. This makes teams more willing to try new things, which leads to a culture of innovation.
6. Making test automation go faster
AI and agility go hand in hand. Because quality testing is a big part of Agile delivery, and GenAI is very good at it. It can automatically write unit tests, make test data, suggest edge cases, and even check how well the tests cover everything. Also, because Agile teams often deploy several times a week (or even every day), AI-generated regression tests make sure that nothing breaks when features are released quickly.
The best part is? It lets QA engineers spend less time on writing repetitive scripts and more time on exploratory testing and UX validation.
7. Better DevOps and CI/CD pipelines
GenAI can look at Continuous Integration & Continuous Delivery (CI/CD) workflows and suggest ways to improve performance, fix errors, or change the way the pipeline is set up. It can also help you guess what might go wrong with a deployment or a rollback based on what has happened in the past.
Think of it as a DevOps consultant who is always there to help you make sure that releases go more smoothly, quickly, and reliably across teams.
This is very helpful for Agile teams that want to meet deadlines without having to worry about build failures or deployment delays all the time.
8. Better collaboration among departments
Agile works best when people from both business and tech work together. But sometimes, the product owner and the developers don't speak the same language.
GenAI helps fill this gap by creating user stories, acceptance criteria, or business rules into code snippets, flow diagrams, or test cases. This means that there will be fewer misunderstandings, quicker feedback loops, and better coordination between stakeholders and delivery teams.
It works like a built-in translator for Agile ceremonies, which makes it easier for people in tech and business to talk to each other.
9. Learning and improving your skills all the time
One of the best things about GenAI is that it helps developers get better. GenAI encourages developers to try out new patterns and methods by giving them real-time suggestions, alternatives, and best practices. It's like having a teacher built into your IDE.
This raises the team's overall skill level over time and creates a workforce that is more flexible, adaptable, and ready for the future. This is great for Agile environments where change is always happening.
10. Automated UI/UX Design Suggestions
GenAI is increasingly being used to generate design ideas, component libraries, and layout suggestions based on user behavior, design system guidelines, or product themes. Developers and designers can input basic requirements, and the AI outputs multiple UI wireframes or component mockups—sometimes even production-ready code. This accelerates the handoff between design and development, reduces time spent on revisions, and helps teams adhere to consistency across digital experiences.
11. Faster Onboarding for New Developers
For Agile teams where people shift in and out frequently, onboarding is a challenge. GenAI tools can now act as on-demand mentors, answering questions like “What does this function do?” or “Where is the API implemented?”
Instead of digging through legacy code or Confluence pages, new developers can use GenAI to navigate unfamiliar codebases, understand dependencies, and ramp up much faster—reducing knowledge silos.
12. Streamlined Security Review and Vulnerability Detection
Security is no longer left to the final stages. Developers are using GenAI to identify vulnerabilities in real time, from SQL injections to insecure authentication flows.
GenAI not only flags issues but also recommends secure alternatives and best practices. This fits perfectly with Agile’s "shift-left" mindset by integrating security checks early into the SDLC—saving costs and ensuring safer releases.
13. Custom AI Agents for Developer Productivity
Beyond Copilot-style assistants, some teams are creating tailored GenAI agents trained on their own codebase, internal documentation, and domain-specific language. These agents help answer company-specific dev questions, automate routine Git tasks, and even prepare release notes or sprint demos.
It’s like building a virtual team member who speaks your product’s language and evolves with every iteration, boosting overall team velocity and alignment.
Companies like Shopify, Bloomberg, and Salesforce have openly shared that they are building or have built custom LLM-powered assistants tailored to their internal tooling, repositories, and documentation.
Shopify’s “Dev Degree” AI Assistant: Built to help internal developers answer questions based on Shopify’s internal systems and practices.
Salesforce's Einstein Copilot for Developers: Extends GPT-style models with domain-specific knowledge for code generation, documentation, and task automation within Salesforce environments.
BloombergGPT: A large language model trained on finance-specific data and internal datasets, used to answer questions, generate insights, and assist developers in the fintech domain.
Become a certified expert in Scrum development and harness the power of GenAI like never before. Acquire new skills, boost your career prospects, and stand out from the competition.
Generative AI software development is undeniably transforming the way we build applications. For Agile teams, it’s essential to stay mindful of the potential risks that come with integrating this powerful technology into their workflows. Whether you're planning sprints, writing, prioritizing user stories, coding, or testing, it's crucial to evaluate both the benefits and challenges. Here are some key risks to watch out for:
1. Dependency on AI Tools
Let's be honest: AI can really help when you're busy. It writes boilerplate code, makes test cases automatically, and even helps with cleaning up the backlog. But here's the catch: if a team relies too much on GenAI tools, they might not be able to think of new ways to solve problems.
When developers stop thinking critically and start copying and pasting AI suggestions without understanding them, the software becomes weak and the team can't deal with problems that come up out of the blue as well. Agile teams do best when they are always learning, working together, and getting better at their jobs. GenAI should help that way of thinking, not take its place.
Use GenAI as a co-pilot, not as an autopilot. To keep people's minds sharp, encourage pair programming, code reviews, and retrospectives.
2. Job Displacement Concerns
GenAI automates things like writing code, testing units, making documentation, and more. This can help people get more done, but it also makes people worry about job security, especially for people who do the same thing over and over or tasks that aren't very complicated.
Now, the real value is in the skills that machines can't easily copy, like being able to solve problems creatively, design systems, understand users, and work with people from different fields. That's where Agile team members can really shine.
Put money into growth. Cross-skill into jobs that involve AI oversight, system thinking, and designing things with the customer in mind. GenAI is not here to take your place; it is here to help you grow.
3. Security Risks
AI learns from data. A lot of it. And sometimes that data has private or sensitive information in it. If you don't properly sandbox or monitor GenAI models, they could leak information, make your codebase less secure, or even let threats from outside your system get in.
Also, some AI-generated outputs might have code patterns that aren't safe, which can go unnoticed during fast-paced sprints.
Include security in the Definition of Done. Add security checks to your CI/CD pipelines and GenAI audits to your planning or grooming sessions for your backlog. AI moves quickly, so your security measures need to keep up.
4. Ethical Concerns
Ethics in software development is a big deal already, and AI has made it even more important. GenAI can unintentionally replicate biases inherent in its training data. This could lead to features or functions that are unfair, exclusive, or even harmful to users.
Agile teams often stress understanding users and getting feedback all the time. But if the models you use are biased or hard to understand, your product might not be user-centered.
So, it’s a responsibility of a developer using GenAI to make sure to include ethical review points in their workflow. Ask yourself, "Is this fair?" during sprint planning or reviews. Is this open to everyone? Could it hurt someone by accident? If you're not sure, get feedback from a variety of people.
5. Lack of Transparency
Some GenAI tools are black boxes. You put in a prompt, and a solution comes out. But how did it get there? That lack of clarity can be dangerous in Agile settings where working together, having a shared understanding, and going through cycles of inspection and adaptation are all very important.
It's hard to trust the solution or make it better if the team doesn't know how AI came up with its suggestions.
Use explainable AI tools whenever you can, and always ask "why" before "what." During daily stand-ups or retrospectives, encourage teams to check the outputs of AI and share what they know.
6. New developers will have to wait longer
GenAI can help experienced developers get things done faster, but it can make it harder for new developers to learn. If junior developers get AI tools that "just work" right away, they might not learn the basics or how to solve problems.
This can create a skills gap in the team because only the most experienced members really know how the architecture or design choices work.
Use AI and learn by doing at the same time. To help new developers build their confidence, give them small but important tasks to do without AI. Encourage people to mentor each other, work together, and make stories better together.
7. Misalignment with Agile Values
Agile is mostly about people and how they work together, not about processes and tools. If GenAI is used wrong, it can turn that equation upside down by putting too much focus on speed and automation and not enough on collaboration, feedback, and craftsmanship.
Keep Agile values at the top of your mind. GenAI can help speed things up, but not at the expense of team unity, shared understanding, or customer value.
7 Generative AI Tools for Software Development
Generative AI in software development is changing the future of developers all over the world, who are using tools that make their work faster, cleaner, and more efficient. These smart tools are giving developers superpowers (without the cape) by speeding up coding tasks and getting rid of tech debt. These tools are meant to make your life easier, your code cleaner, and your workflows faster, whether you're deep into DevOps or just starting to plan your next sprint.
Let's look at the seven best tools that generative AI developers use and swear by.
1. GitHub's Copilot
GitHub Copilot is probably the most well-known name in AI-powered coding right now. GitHub made Copilot, and OpenAI's GPT-4 powers it. It works as a helpful coding partner right in your IDE. It can write comments, suggest full functions, and even write code snippets by knowing what you're working on right now.
This tool can help developers who have to do the same coding tasks over and over again or who have to write boilerplate code get a lot more done. Copilot is easy to use and great for quick iterations, but it's not perfect. It can have trouble with more complicated logic and sometimes suggests solutions that need to be improved. Many developers of generative AI still say that it speeds things up and makes things easier to think about, especially when things are really busy.
2. ChatGPT
A lot of people think of ChatGPT as just a writing tool, but it has quickly proven its value in the world of generative AI software development. Developers use it to come up with ideas, write code, fix bugs, and even make documentation. It feels like talking to a helpful, experienced teammate because of its conversational interface.
It's especially helpful when you need a new point of view and can't think of anything else. That being said, it works best when used with the developer's judgment, since some answers may be out of date or too simple. Still, ChatGPT is a great tool for developers, especially for teams that want to use generative AI in a wider range of ways in software development.
3. Tabnine
Tabnine gives AI-assisted coding a more personal touch. It learns your coding style and gives you code completions in real time that are aware of the context. Developers like how it fits right into their IDEs and helps keep the team's codebase consistent.
It works with a lot of different IDEs, like Android Studio, Eclipse, and VS Code, so it can be used in a lot of different ways. It may not be able to handle all of your refactoring needs, and it may take some time to get used to your habits, but it's a good choice for developers who want a coding assistant that grows with them. Tabnine is a good long-term partner for generative AI developers who want more customization.
4. AI for Stepsize
Stepsize AI doesn't write code, but it could help you keep your codebase from getting messy. Stepsize connects to your IDE and helps you keep track of and prioritize problems as they come up. Its main goal is to cut down on technical debt.
Agile teams that want to take tech debt seriously during sprint planning and retrospectives will find it to be a good fit. It makes the messy parts of development easier to deal with by pointing out problems and helping you deal with them right away. It might take some work to get the whole team on board, but the long-term benefits of adding issue tracking to your daily routine are hard to ignore.
5. Mintlify
Mintlify changes the way code documentation is done. It makes clean, organized docs from your code automatically, which saves a lot of time, especially when you're onboarding new people or handing off work. Developers like how it makes complicated codebases clearer and keeps documentation up to date with code changes.
It also lets you change things up with templates and formatting styles, which keeps everything the same and easy to follow. The quality of the output depends on how well the code is written, and some manual changes may be needed. However, Mintlify still cuts down on documentation time by a lot and makes the whole process a lot more fun.
6. Bugasura
Bugasura changes the way teams keep track of and fix bugs. This tool was made to help developers work together. It combines issues, marks duplicates, and suggests impact levels, which helps teams stay focused on what's important.
It's especially helpful when there are a lot of releases going on, when finding and fixing bugs quickly can make or break a sprint. Developers like its easy-to-use interface and smart automation, even though it may take some time to get used to its speed or learn how to use the filtering options. Bugasura helps Agile teams keep better track of bugs by bringing order to the sometimes chaotic world of bug tracking.
7. WhatTheDiff
WhatTheDiff is like a helpful reviewer who is always ready to go over your pull requests. It looks at the changes you made to your code and automatically makes detailed, easy-to-understand descriptions.
This saves time and makes it easier for teams that move quickly to work together. Developers find it very useful for keeping team members who aren't technical up to date and for cutting down on the time they have to spend explaining every little change. It doesn't support every language and needs to be integrated with GitHub or GitLab, but teams that value clarity and communication love it because it speeds up the review process.
Wrapping Up
Using Generative AI software development teams can work smarter, not harder, by cutting down on the time they spend on boring tasks and encouraging new levels of creativity and teamwork. Of course, it has responsibilities, just like any other strong tool. AI should be used as a partner, not a crutch, and developers should keep being thoughtful, ethical, and involved.
The tools will change, and so will the chances. To do well in this new age, generative AI developers need to be curious, open-minded, and willing to learn new things. No matter how much experience you have with GenAI tools, now is the best time to look into, test, and improve your development skills. Humans and AI must work together to make software that is better, faster, and smarter.
Ready to maximize GenAI's potential?
Unlock the power of GenAI with Professional Scrum Developer training. Learn how to leverage this cutting-edge technology effectively to its fullest potential and stay ahead of the competition.
Yes, generative AI can facilitate cross-platform development by automating the adaptation of code and design elements to different platforms, thereby reducing development time and effort for multi-platform applications.
Yes, developers may need knowledge and skills in machine learning, data science, and AI technologies to effectively leverage generative AI tools and platforms in software development.
Generative AI can be beneficial for a wide range of software development projects, including web development, mobile app development, game development, and more. However, its suitability depends on factors such as project requirements, available resources, and the complexity of the problem domain.
Generative AI can help reduce development costs and timelines by automating repetitive tasks, reducing manual effort, and enabling rapid prototyping and iteration. However, the initial investment in AI infrastructure and training may be required.
By verifying results, preserving code quality, avoiding bias, and safeguarding user data, developers can guarantee the responsible use of generative AI. They ought to integrate ethical development methods, critical thinking, and AI support.
Generative AI increases output, speeds up coding, automates documentation, finds bugs early, and fosters creativity. It simplifies processes and frees developers from tedious work so they can concentrate more on solving complicated issues.
Sign up for Agilemania Newsletter
Stay updated with the latest Agile & Scrum trends.
Naveen Kumar Singh
Naveen is a professional agile coach and has been working independently for a long time in the Asia Pacific. He works with the software development team and product team to develop awesome products based on empirical processes.
Join PRISM, the leading Project Management Conference—exploring Human + AI for sustainable success.
