8 ways developers are using AI in 2025

The market size of generative AI is likely to reach about USD 365.05 billion by the year 2030. Generative AI isn’t just a buzzword anymore—it’s reshaping how code is written, optimized, and deployed.

Developers are collaborating with AI tools that automate tedious tasks, generate entire codebases, and even suggest innovative solutions.

So, what does this mean for the future of software development? Are we on the brink of a new era, or is there still a long way to go? Let’s explore how generative AI is redefining the industry in 2025 and beyond.

Benefits of AI in software development

If you are still dependent on the traditional approaches for developing software, there are many benefits to using generative AI. Continue reading to learn the significant advantages of deploying this technology in software development.

1. Increased productivity

Around 65% of executives believe that generative AI will change the way people work, while 72% of individuals are optimistic about its potential for increased productivity. Writing codes, generating tests, and creating software are indeed time-consuming and monotonous tasks. It takes several lines of code to create a single feature on the website.

What if some of the tasks could be automated? That is exactly where generative technology comes into the picture. This technology can easily automate tasks like documentation and test generation. Consequently, developers’ overall productivity increases drastically as they have more time to focus on the complex and creative aspects of the software development process.

2. Accessibility

Generative AI plays an important role in making development more accessible. Wondering how? Well, it allows the developers to benefit from low-code and no-code development. 

As a result, even non-technical users with limited knowledge of AI programming languages can build applications. AI makes software development much easier for those lacking the technical know-how. 

3. Improved code quality

In this competitive era, code quality matters. However, with manual efforts, achieving the desired level of perfection can be tough at times. Making use of generative AI can enable developers to enhance the quality of code with ease.

For example, various AI models can figure out common mistakes in the code and recommend the best algorithms to make it more efficient. That means developers will have more efficient and cleaner codes with fewer chances of bugs slipping through. All these can help improve the overall performance of the applications.

4. Enhanced creativity

Software development is creative in itself. However, when you have loads of tasks to manage and deadlines to meet, creativity often gets compromised. That is where generative AI proves to be helpful.

This technology enhances the creative ability of the developers and allows them to find different ways of addressing a problem. As it is capable of analyzing huge data sets and identifying patterns, generative AI can offer ideas that are hard to perceive by human developers. Besides increasing creativity, the technology also improves the overall productivity of developers. 

Uses of AI in software development

1. Smart debugging

A single error in the code can disrupt the overall performance as well as functionality of the software. Hence, the debugging process remains essential and cannot be overlooked. Generative AI proves to be useful during this phase of software development.

You no longer have to face the hassle of waiting for issues to occur at runtime and then fixing them. Instead, generative AI helps indicate potential bugs in real time. Therefore, bugs can be easily fixed by developers ahead of time, before they hit the end users, and cause any dissatisfaction.

Additionally, this technology can add value to the process of bug remediation. At times code may compile and run correctly. However, they can still fail to provide the desired results or perform the intended actions. Generative AI uses prompts to proactively analyze codes and ensure whether they are likely to perform as desired. 

Moreover, this technology can provide developers with a summary report of potential defects. This report allows the software development and testing teams to find erroneous code patterns, understand subtle errors, and recommend fixes.

2. Code generation

When developers write codes manually, it may take several months to build feature-rich web applications. Hence, developers seeking to reduce development time need approaches that allow faster application deployment. Here, generative AI can help. 

Though generative AI may not be able to generate complicated business applications at present, it allows the creation of boiler code that developers can complete. This technology can automate the task of writing code based on specifications or prompts.

For example, most developers are well aware of using SQL to write, read, as well as update database information. But when it comes to writing SQL procedures for handling complicated database tables, they find it challenging. 

In such a situation, counting on generative AI is an excellent idea. The generative AI tools make use of prompts to enable developers their specific requirements. Moreover, they can recommend SQL procedures or calls that can be simply copied and pasted by the developers. 

3. Code review

The significance of code reviews in software development cannot be undermined. But verifying the quality of codes manually can be challenging and stressful for developers. Generative AI helps review code quality and security with ease. Moreover, it can even evaluate the adherence of codes to the best practices in the industry. 

With generative AI, spotting errors and fixing them becomes faster. As a result, companies can conduct the code review sessions in much less time and prepare for the launch of applications.  This technology can serve as an active member of the code review team. By highlighting the areas that require more attention and offering quality guidance, it lets developers make the necessary improvements with ease. 

4. Testing

Testing is yet another crucial phase of software development where generative AI proves to be beneficial. When it comes to the testing process, different methodologies have different views. For example, test-driven development prioritizes the need for creating test cases even before a single line of code is written. 

On the other hand, there are methodologies where the development teams engage in testing at the end of sprints. One thing that remains common in all the methodologies is the involvement of the stakeholders. 

However, with generative AI, you do not have to worry about such hassles. This technology has the ability to create test cases and automate the testing process. As a result, speeding up the entire process becomes easy. Moreover, your teams get more time to focus on other critical tasks.

5. Learning and training

The field of software development keeps changing over time. From new language features to new design patterns and new tools, there is always something coming up online. Therefore, developers must be willing to learn continuously and adapt to the evolving needs of the industry. 

No doubt, there are several courses available for the upskilling of the developers. However, ongoing help always proves to be valuable. Generative AI has the potential to identify the specific areas where the developers are struggling. 

For example, whenever a developer makes an attempt to use an outdated API, this technology can offer instant support and training. Through prompts, it can provide developers with guidance as to how to perform different new tasks without much struggle. 

6. Documentation

In software development, documentation is undoubtedly a vital step. However, this step can take up a lot of valuable time for the developers that could have been used in other productive tasks. Want to save more time and speed up the software documentation process? Generative AI is the perfect solution.

This technology can automate or improve documentation for codebases. For example, generative AI can ask developers about their decisions relating to the choice of API and libraries while coding is still in progress. Then it can expand the explanations and document them in easy-to-understand formats.

7. Refactoring

Refactoring is another crucial element of the software development lifecycle wherein developers take a section of code without changing its functionality and make it run better. There are several motives behind refactoring. It may include lowering memory consumption, improving scalability and speed, leveraging a new language instruction, or updating an algorithm. 

Refactoring becomes easier and hassle-free with generative AI. It helps developers restructure and optimize existing codes for enhanced performance. By observing how a code works and what it does, the predictive capability of generative AI allows developers to find better approaches effectively. 

8. Troubleshooting

Troubleshooting an issue and identifying the root cause is truly time-consuming and tough for developers. That is where generative AI comes to help. This technology has the potential to effectively compare the actual and intended outputs for efficient root cause analysis. 

As a result, developers can identify the specific problems in much less time and repair them at the earliest. All these can have a positive impact on the overall experience of the users. 

Generative AI doesn’t stop with that. It is also capable of automatically upgrading the different versions of the software library and platform software. 

Challenges and risks

Generative AI undoubtedly benefits the software development sector in several ways. However, it does come with a set of challenges that cannot be overlooked. Take a look at the key challenges!

Security

Generative AI may be quite helpful, but it isn’t perfect. Security issues can stem from its use. As the technology relies on machine learning, it is vulnerable to various data manipulations and malicious attacks if not secured properly. Therefore, it becomes important for software development companies to take appropriate measures to avoid such attacks and ensure optimum protection of their applications. 

Intellectual property

There is no way you can find out why generative AI has made a certain decision. As a result, this presents a challenge of ensuring accountability and transparency. Moreover, the growing incidents of identity theft, hacking, and intellectual property stealing have become a matter of concern relating to generative AI use for software development.

Overreliance on AI

At times, developers may be so used to generative AI that they would want to do everything with the help of the technology. However, that isn’t a smart move. Make sure to maintain the right balance of using the generative AI and coding skills of developers for software development projects. 

Mitigating risks

Generative AI indeed introduces a lot of exciting possibilities in the world of software development. But it also has a set of risks and challenges that need to be addressed. 

Contextual understanding of generative AI is a major limitation. So, developers are required to possess the necessary expertise to frame accurate prompts that will allow the technology to deliver the desired output or generate code. 

Moreover, the significance of code review must not be overlooked at any cost. Even if you decide to take the help of AI, make sure to go through the code produced carefully. The main goal is to identify potential errors or bugs. 

A point to note here is that generative AI isn’t a foolproof solution. Therefore, human intervention is vital to ensure the reliability and quality of the code.

Bugs can still slip through, which is why having a solid bug tracking system in place is a lifesaver. Tools like Backlog can help teams catch and resolve issues efficiently, keeping development smooth and reliable.

Final thoughts

Generative AI is shaking up the world of software development, offering new ways to speed up coding, automate tasks, and push innovation forward. But as powerful as it is, it’s not a perfect solution. Developers still need to step in—whether it’s crafting the right prompts, reviewing AI-generated code, or catching bugs before they become bigger issues.

The key to success? Finding the right balance between AI automation and human expertise. With the right tools and a thoughtful approach, teams can harness the power of generative AI while maintaining high-quality, reliable code.

Author bio

Harikrishna Kundariya, is a marketer, developer, IoT, Cloud & AWS savvy, co-founder, and Director of eSparkBiz, a Software Development Company. His 14+ years of experience enables him to provide digital solutions to new start-ups based on IoT and SaaS applications.