It’s not the end of the human role in app development. The software industry is still far from being completely overtaken by AI-assisted development, removing the need for developers. On the contrary, AI in software development serves as a complementary technique that extends human capacity while opening space for strategies, creativity, and efficiency.
Katie Mikova
9min read
AI frees people from cognitive overhead and tasks that slow down development processes. What was once entirely driven by human creativity and manual effort is now possible with the help of innovative technologies and automation. Think of writing boilerplate code line by line, waiting on builds and tests, or crafting UIs pixel by pixel. These tasks were reliant on designer-developer capabilities and took ages. However, the plethora of AI tools is growing, intending to eliminate much of this time-consuming groundwork.
Over the past few years, we’ve seen a tremendous shift in how artificial intelligence integrates into various fields, including app development. The most recent survey by Reveal sums up that “the main priority in 2025 is AI adoption, with 73% of tech leaders planning to expand the use of AI within organizations in the next year.” Daunting and repetitive tasks like creating and reusing a single button, building out themes, or even generating code and databases are now being redefined and handled differently.
We have AI-assisted development with the goal of enhancing app-building processes. Things that were considered manual processes are now being streamlined and are carried out in a faster, less friction-heavy way. App Builder’s App Development Trends Report indicates that 76% of the survey respondents (300 tech innovators and C-level executives) expect AI to make low-code tools more efficient than ever.
However valuable and growth-propelling AI is, there’s always something to consider regarding it. That’s why I want to zoom in and explain what lies behind AI-assisted software development.
Let’s figure out the benefits and the challenges.
AI-assisted development (AIAD) leverages AI technologies to automate, improve, and speed up different stages of the app-building cycle. At its core, it is propelled by two underlying methods: machine learning (ML) and natural language processing (NLP). Because it is all about iterative approaches and reducing manual overheads, AI-assisted app development fits naturally within agile methodologies and low code with core principles like automation, speed, efficiency, continuous delivery, full low-code customization, and flexibility.
With machine learning algorithms, AI can understand existing practices to deliver corresponding results. For example, it can absorb the existing code, examine design patterns, or monitor user behavior. Doing this empowers it to emit relevant code for a given framework, detect and fix bugs, or recommend updates and enhancements.
Natural language processing, on the other hand, is a method that enables AI to interpret human language prompts and respond to them with the necessary design or code elements that users request for their application.
Imagine reducing 50 days of development to just a few hours and saving 4,500 years of developer work in a year. Sounds impossible? But this is precisely what Andy Jassy, Amazon’s CEO, claims to have achieved when they integrated AI into their internal systems and applied it to their Java upgrades.
So, yes, the potential of AI-assisted development is growing, helping startups, enterprises, and tech giants move faster and innovate better.
Much of the boilerplate code will be replaced by AI. The goal here is not to eliminate developers but to achieve a high-level abstraction and accelerate the design-to-code process by automating at least the most repetitive logic and mundane tasks. Experienced programmers and software engineers will still have to go into the machine code and inspect the logic and the boilerplate code that the dev environment generates.
But one thing is certain: AI-assisted development tools, like some of the most comprehensive and digitally driven low-code platforms we see today, reduce the need for time-consuming, error-prone hand-coding.
They do that by:
It’s no surprise that during one of the episodes of The Twenty Minute VC hosted by Harry Stebbings, Kevin Scott, Microsoft’s CTO, said that AI will write 95% of code in the next five years, but the code authorship will still be entirely human. Software development is being rewritten. And when teams can do more with fewer tools and resources, this results in measurable cost savings.
To illustrate this, let’s look at App Builder AI. It brings together the simplicity of low-code platforms and the intelligence of AI, enabling teams to:
Developers can generate sample datasets instantly, tailored to specific use cases like healthcare records or financial forecasts. They can also accelerate prototyping with AI-driven image generation capabilities. Whether you need placeholder graphics or final assets, the images are directly integrated into your app’s assets panel, saving time and eliminating bottlenecks.
AI-assisted development and AI-assisted development tools offer immense support for programmers, citizen developers, and QAs. There are quicker testing cycles and better collaboration between teams and departments.
In relation to this and the change in developer productivity and satisfaction, a GitHub survey indicated that “between 60–75% of users reported they feel more fulfilled with their job, feel less frustrated when coding, and can focus on more satisfying work when using GitHub Copilot.” Another 88% pointed out that they feel more productive, which is one of the goals of AI-assisted development.
Old systems pose various risks, security concerns, data breaches, vendor lock-in, proprietary functionalities that limit developers’ capabilities and the scope of their projects, outdated dependencies, no support or regular updates, and much more. In some cases, AI can easily translate old, inefficient, and hard-to-maintain code to modern frameworks.
While AI offers various advantages and has the potential to streamline processes and help businesses achieve digital innovation faster, it also has drawbacks. What should organizations, teams, and developers acknowledge then?
AI-driven development aims to simplify the entire development process from ideas to deployment. However, developers are still concerned about what happens to code ownership and control. Luckily, with low-code platforms like App Builder and its AI-driven functionalities, the end-user has complete control over the code.
Most of the AI-generated code today still doesn’t prioritize quality and maintainability. A developer shared a Copilot suggestion on Reddit that turned a simple sorting function into an unnecessarily complex code block. This means that teams must invest time and effort in inspecting, debugging, and troubleshooting potential issues.
AI-assisted software development has an advantage in terms of generic contexts. Tools can quickly generate code or suggest optimization, relying on existing code repositories, data, and patterns. Nevertheless, it’s more challenging with specific code suggestions, especially when AI doesn’t fully comprehend the business logic and user requirements.
AI-assisted development tools may require substantial training to get used to the platform’s features. In addition, understanding the suggestions and logic behind the generated code may also become a struggle.
Code quality assurance is a must if organizations want to ensure that apps built with AI models comply with industry standards and requirements. Some automated processes may not be able to provide this, which could lead to noncompliance and vulnerabilities that expose the entire project to security risks and legal complications. Sometimes, recommendations violate best practices or security protocols.
To explore AI-assisted app development’s risks, challenges, and limitations, read What Are the 5 Limitations of AI in Low-Code App Development?
Let’s again point out that AI doesn’t want to replace developers completely. It boosts workflows and processes as much as possible while preserving valuable human input and know-how. Weaving into different stages, here’s where AI-assisted app development takes place:
Phase 1: Strategy, Ideation, and Planning
AI-assisted tools can help define technical requirements, visualize user stories, and facilitate teamwork, sprint planning, and knowledge sharing.
Phase 2: Design
For example, with technologies like our Figma UI kit, users can quickly integrate design files and convert them to code for Angular, React, Web Components, and Blazor. If your organization doesn’t have a design team or wants to generate images, it can leverage App Builder AI. The tool can quickly produce the required visuals based on plain language prompts.
Phase 3: Coding
Another way AI integrates within dev workflows is by automatically generating code, producing code suggestions based on pre-defined contexts and patterns, code snippets, and more. This allows teams to quickly create POCs, MVPs, prototypes, or enterprise-grade web apps for different use cases.
Phase 4: Automated Testing, Release, and Maintenance
Then, we have the testing and quality assurance phase. AI-assisted development speeds up the process by generating unit tests, analyzing test results, scanning the code for issues, flagging bugs, optimizing pipelines, and making suggestions for improvements to the infrastructure.
It’s not the end of the human role in app development. The software industry is still far from being completely overtaken by AI-assisted development, removing the need for developers. On the contrary, AI in software development serves as a complementary technique that extends human capacity while opening space for strategies, creativity, and efficiency. The app-building cycle happens faster without compromising quality, whereas the developer focuses on higher-level architectural thinking, complex business logic, innovation, and performance optimization.
Now that we know that low code is here to stay, the question becomes, how is your organization preparing for its next chapter? For the last few years, low-code tools like App Builder have targeted various pain points in app building, enabling C-level executives and Dev Team Leaders to accelerate time to market, automate app […]
Can AI promote faster software rollouts? Will it lead to fewer people who write code manually? And is this the end of software development as we know it? Let’s see what’s the impact of AI on development jobs and what’s the role of low code here.
Harnessing the power of low code and AI is a strategic move that drives business growth and innovation at faster rates. And now with App Builder AI, businesses have an even greater advantage. App Builder AI combines the simplicity of low-code platforms with the intelligence of AI, enabling teams to build and deploy applications faster and more efficiently than ever before.
