Where AI Works Well in Development

AI is particularly effective at low-complexity, clearly defined tasks.

Creating a simple responsive single-page website, writing a small data-processing script, building an individual frontend component, or modifying an existing piece of code are good examples. In these cases, the task can usually be described precisely and the result can be checked relatively quickly.

AI is also useful in the hands of an experienced developer when a larger project is properly broken down into smaller, clearly separated components. It can help create individual functions or modules, generate repetitive code, write tests, build interface elements, or refactor existing code.

There is one important condition:

In my experience, AI is particularly useful for:

• low-complexity, clearly defined development tasks;
• simple applications and responsive single-page websites;
• well-separated subtasks performed under professional supervision;
• repetitive work that follows clear and predictable rules.

Its greatest advantage is speed. Once the work has been properly defined, AI can often execute it much faster.

What it does not replace is the thinking and planning that define the work in the first place.

Artificial intelligence has quickly become part of the everyday toolkit in web and software development. With a few well-written instructions, it can generate code, find bugs, modify existing solutions, and even put together simple websites or applications.
This can easily create the impression that anyone can now program, but the reality is more complicated.
AI can significantly speed up development, but it isn’t suitable for everything. The more complex a task, website, application, or software system becomes, the more important professional experience, planning, and proper review of the finished solution become.

Problems tend to appear as complexity increases.

In a larger website, web application, or software system, individual components don't operate independently. They exchange data, share business logic, connect to databases and external services, and may depend on each other in ways that aren't always immediately obvious. A seemingly minor change in one area can introduce problems somewhere completely different.

AI may be capable of building the individual pieces, but that doesn't mean it understands the system as a whole, its business objectives, or the long-term consequences of its decisions. It can produce a solution that appears to work while hiding poor data handling, incomplete access control, inappropriate architecture, or code that becomes increasingly difficult to maintain. Experience shows that even with smaller tasks, AI often generates code that works on its own but contains minor hidden errors. These errors may not cause any problems within the individual piece of code itself, but once the modules are combined, they can cause the system as a whole to return incorrect data or produce incorrect logic.

AI should not be trusted independently with:

• the complete implementation of highly complex, interconnected requirements;
• the architecture and development of complex websites, web applications, backend systems, and software;
• interpreting and prioritising constantly changing requirements;
• systems where failure could create significant security or business risks.

This doesn't mean AI has no place in these projects.

Within a complex project, it can still take a considerable amount of work off a developer's shoulders when applied to clearly separated tasks. The overall architecture, relationships between components, data handling, integrations, and final technical decisions, however, still need human expertise.

The same applies to agile development, where requirements may change continuously. AI can help implement and document changes, but it cannot independently decide which business requirement should take priority or reliably assess how a change will affect the entire solution.

Just Because It Works Doesn't Mean It's Good

Someone without development experience can easily feel that AI has enabled them to build a website or application, which in most cases does actually work. After a few prompts, there is a working interface. Data can be saved. Buttons respond. Pages load. The application appears to do what it was supposed to do. But that's only the visible part of development.

Building a professional website or software solution isn't simply about making something that runs. Code quality, error handling, performance, security, accessibility, testability, and long-term maintainability all need to be considered. AI can confidently produce incorrect or incomplete solutions in any of these areas. The user may notice nothing at first because the problem only appears with larger datasets, multiple concurrent users, unexpected input, different devices and browsers, or a future change to the project. A non-developer may therefore be able to create a working website or application with AI. That doesn't make them a developer.

The difference is no longer simply about who can produce code. The real difference is who can determine whether the code and the solution behind it are correct, secure, maintainable, and suitable for long-term use. Professional experience becomes particularly important during review. An experienced developer is much more likely to recognise poor architecture, security weaknesses, hidden dependencies, performance issues, and problems that aren't visible during normal use.

Security Risks and the Limits of AI-Generated Code

The more data a website or application processes and the greater its responsibility, the riskier it becomes to use AI-generated code without professional review. For a simple static website or a small utility running locally, the risks may be relatively limited. Once authentication, user permissions, payments, databases, APIs, external integrations, or personal data enter the picture, thorough technical review becomes essential. At that point, it isn't enough to check whether the website or application appears to work.

You also need to know whether users can access only the data they are authorized to see. Passwords need to be handled correctly. API keys and other credentials need to be protected. User input needs to be handled safely. The system must prevent unauthorized users from retrieving or modifying data. AI won't necessarily account for all of this on its own, often even when we specifically draw its attention to these issues. If security requirements aren't explicitly included in the task, an AI coding tool may primarily focus on delivering the requested functionality. The result can look perfectly usable while still containing serious vulnerabilities.

We have tested several AI systems, and our general experience is that when we pointed out an error in the logic of the code they had generated, they were often unable to correct it properly. It is common for AI to fix the specific error by regenerating the relevant section of code, only to introduce a different error in the process. In many cases, it took more time to explain to the AI what it had done wrong, what it needed to pay attention to, and to get a correct result than it would have taken a developer to fix the code several times over or write it from scratch. There is more room for experimentation with prototypes, simple websites, demonstrations, and internal tools with limited access. When sensitive data, authentication, financial transactions, or significant business risk are involved, strict human oversight is essential.

The Bottom Line: Expertise Still Matters

The real value of AI in web and software development isn't its ability to build complete solutions without human involvement. Its strength lies in its ability to accelerate tasks that are precisely defined, clearly scoped, and relatively easy to verify. For simple websites, smaller applications, individual development tasks, and well-separated components, AI can save a considerable amount of time. As complexity increases, however, architecture, security, technical review, and development experience become increasingly important.