It is no secret that the market for AI tools and services is rapidly growing. According to Statista, for example, the value of the AI ​​market in 2025 promises to reach a staggering $243.7 billion and grow at a rate of 27.67% per year, reaching $826.7 billion by 2030.
This astonishing expansion has been made possible largely by various AI applications, such as in marketing & sales, customer service, automotive, and, of course, software development.
Recognizing the potential of artificial intelligence for creating software solutions, many tech companies from startups to giants like Microsoft, IBM, and Google have invested heavily in AI assistants and AI-related research and development.
But is AI always good in software development and what should you pay attention to when choosing a contractor that uses artificial intelligence in development processes?
Why Clients Should Care About AI Integration in 2025
Lately, AI integration has become a kind of indicator of the rate of a tech company and how much it keeps up with the times. It is believed that providers using AI coding assistants like GitHub Copilot and Cursor IDE can visibly accelerate coding, reduce costs, and improve software quality.
Even more impressive is the rise of AI models like Claude Sonnet 3.7, which now leads the field with both its regular and thinking versions. These advanced models help developers generate, refine, and troubleshoot code more effectively than ever before.
Two years ago, Statista calculated that software developers using an AI assistant finished their work almost twice as fast as those working without one. This means that one developer with AI systems could get as much done as two human developers without it.
Difference in software development tasks with and without AI capabilities, Statista
Nonetheless, although there are many benefits of implementing AI assistants into software development workflows, there are also some data protection concerns for software companies.
Because many AI models learn from public code repositories like GitHub, they can generate code that accidentally violates open-source licenses. For example, tools like Copilot don’t provide credit to the original authors, which is often required.
Even worse, AI can sometimes copy existing code word-for-word, meaning software development teams might unknowingly use copyrighted code, putting their company in legal complications.
Beyond liability concerns, AI-generated code can introduce security risks. AI developers may unintentionally leak proprietary algorithms or confidential data, which could break a company’s competitive edge.
More harmful, AI-generated code might expose sensitive information like hardcoded credentials, database connections, or personal customer data, putting both the business and its users at risk.
In 2023, for example, Samsung Electronics banned its employees from using ChatGPT and other AI-powered chatbots, becoming one of several companies to tighten measures on the use of generative AI for software development.
The actions were taken after the accidental leak of confidential internal source code was discovered by an engineer who uploaded it to ChatGPT.
The Role of AI Within Development Cycles
AI tools have become a regular part of daily work for many IT professionals. More than 50% of all machine learning engineers use AI every day, and nearly as many data scientists rely on it just as often.
Back-end and full-stack developers also make good use of AI in software development, though a bit less frequently, with about one in three using these tools daily. But what exactly can AI do?
Code Writing
As we already mentioned, AI coding assistants like GitHub Copilot, Cursor IDE with the help of Claude Sonnet 3.7 and other LLM’s have significantly improved coding speed and accuracy. They don’t just suggest snippets—they generate entire functions, refactor code, and even translate it between programming languages.
Modern IDEs like VSCode IDE now integrate AI-powered development modes that proactively edit files, fix errors, and generate new code.
- In GitHub Copilot, this is known as Copilot Edit.
- In Cursor, a similar feature is called Composer.
These AI-driven modes allow for hands-free code improvements, where the assistant not only suggests but actively modifies the codebase in real-time.
Most used artificial intelligence tools among developers as of 2024, Statista
Code Testing: Manual and Automated
Beyond speeding up development, AI technologies are also playing a significant role in the testing phase.
For instance, instead of having a human tester click through every button, form, and menu, AI-powered tools can simulate user actions to instantly spot any gaps, making testing way more thorough.
AI can also create test cases on its own by inspecting the code and foretelling where problems might pop up.
This is especially useful for checking if individual functions work correctly and demonstrating different parts of the application function together as demanded.
With tools like Selenium and Cypress that leverage AI, developers can also test websites by simulating how users interact with them in a browser. Some other AI-powered frameworks can even create test cases by themselves, saving developers time.
Code Security and Control
One of the main concerns in software development is protecting code from security risks, such as unauthorized access or data leaks. AI solutions, in turn, can autonomously scan the code for any potential security flaws and even suggest fixes before they become disastrous.
How SCAND Uses AI in Software Development: With Your Full Consent
At SCAND, we’ve fully embraced AI assistants to make software development faster, smarter, and more protected.
We, like many of our colleagues, use AI coding tools like GitHub Copilot to speed up development, helping our software engineers write code several times faster.
But we don’t just blindly rely on AI-generated code—our developers carefully examine and refine everything to guarantee high-quality software applications. The result? Faster delivery times without sacrificing reliability.
But AI doesn’t just help us write code—it also makes testing more reasonable. We use AI-powered tools for both manual and automated testing to catch issues before they reach production.
AI can also generate automated test cases straight from the code, making sure nothing slips through the cracks. This means fewer bugs, better performance, and a trouble-free app interaction for end users.
But the most important point in our use of artificial intelligence is that we embed AI into software development only when clients explicitly agree to it, which means no surprises and no hidden data sharing.
For those who question data security when integrating AI, we can locally run AI models like LLama (3B/8B parameters), StarCoder and DeepSeek-R1. This way, clients get all the benefits of AI while keeping their code completely private.
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