AI in software development: from hype to practical implementation

The use of AI in software development has rapidly evolved from experimentation to a strategic tool. AI technologies such as LLMs, code assistants, and generative models are no longer a vision of the future, they are already part of the daily work of development teams today. Still, many technical leaders have questions: what is AI? How can AI be effectively applied within the software development process? What are the tangible benefits? And which risks require active management? This article addresses these questions, drawing on practical insights and the latest technological developments.

What is AI?

Artificial intelligence (AI) refers to systems that perform tasks typically requiring human cognitive abilities. In software development, this includes the use of Large Language Models (LLMs), natural language processing (NLP), and machine learning algorithms that can generate, analyze, or improve code. These technologies help developers automate repetitive tasks, thereby accelerating the development process without relinquishing control.

Why apply AI in software development?

Working with AI tools accelerates iterations, shortens time-to-market, and can contribute to higher code quality. Modern AI tools are integrated into IDEs, CI/CD pipelines, and testing environments. This enables development teams to respond more quickly to changing requirements, make use of automatically generated test cases, and analyze or refactor complex codebases – provided that proper oversight and integration are in place, without compromising on quality.

What does AI mean concretely for your development team?

Developers now use AI daily for tasks such as:

• LLM-driven code generation through tools like GitHub Copilot or Amazon CodeWhisperer
• Automatic test generation based on code context and user stories
• Refactoring and performance suggestions based on code analysis
• Rapid interpretation of legacy codebases using semantic search
• Onboarding support through automatic documentation and explanations

The deployment of AI is fundamentally changing the way of working: discovery, validation, and build phases are becoming less linear and increasingly integrate customer data in iterations, leading to higher quality and shorter lead times (Gartner). At the same time, the role of developers is evolving. They are not being replaced but are becoming collaborators with AI tools. AI enhances development capacity for repetitive or structural tasks, while human expertise remains essential for strategic decisions, validation, and architecture (Gartner).

How AI structurally increases productivity and quality

Research shows that AI tools can save developers up to 90% of the time when generating documentation, 38% in test generation, and 59% when writing accompanying explanations (IBM). In addition, real-time feedback and best-practice suggestions contribute to structural improvements in code quality.

Collaboration within teams also improves: suggestions from AI tools like GitHub Copilot promote consistent implementations, even in distributed environments. For junior developers, AI lowers the learning curve by providing clear prompts, contextual explanations, and semantic documentation.

Besides saving time, AI offers strategic added value on multiple levels:

• Quality assurance in CI/CD processes, with AI timely detecting regressions or performance issues.
• Code quality improves through real-time feedback, consistency in naming and structure, and detection of potential bugs before execution.
• Faster onboarding, as developers better understand complex systems through contextual explanations and semantically generated documentation.
• More efficient collaboration, with AI suggestions that standardize and facilitate alignment, especially in distributed teams.
• Improved test coverage and defect detection, as AI automatically identifies edge cases and systematically expands tests.

How to ensure security, control, and reliability in AI deployment

AI tools introduce risks that can only be managed with adequate technical governance:

• Traceability & accountability: Use formal frameworks such as the most relevant ISO/IEC standards to make AI decisions reproducible and auditable (IBM).
• Bias & ethics: AI models reflect the data they are trained on. Without corrections, existing biases may be reproduced. Regular audits, transparency, and diversity in training data are essential.
• Overreliance: Developers should view AI as an assistant, not an authority. Ongoing peer reviews, clear coding standards, and knowledge retention remain necessary.
• Security & compliance: AI generates syntactically correct code but lacks security context. Actively apply OWASP principles, secure coding guidelines, and threat modeling, supported by AI governance tools.
• Licenses & IP: The origin of generated code is not always transparent. Use commercial licenses and contractually establish IP guidelines.

Human + AI = better software (if managed correctly)

AI provides development teams with structural advantages, but only when developers and AI actively collaborate. The developer remains responsible for validation, design decisions, and contextual understanding; AI supports by accelerating repetitive and predictable tasks or tackling highly specific and complex challenges.

Strategic technical leadership therefore focuses on:

• Developing AI competence within teams (such as prompt engineering and model operations)
• Embedding AI in the PDLC through tooling, training, and governance
• Cultivating a working method where humans and machines operate complementarily

There are significant opportunities to gain value from AI. But are you integrating AI properly into your processes and teams? Paying close attention to the personnel policies of a nearshore partner, and specifically adopting a TaaS (Team as a Service) approach, increases the chances of building a high-quality and successful development team that demonstrably contributes to your business goals.

AI in action: practical examples from NetRom projects

At NetRom, we apply AI in various ways, including automatically generating test scripts based on NLP prompts, analyzing and refactoring legacy codebases during cloud migrations, and accelerating data structuring in data engineering workflows. In test automation, this delivers significant value: generated unit tests cover edge cases more quickly and thoroughly, leading to higher test coverage without additional manpower. These applications are always integrated into our existing development practices, with quality, scalability, and security as guiding principles.

Would you like to learn more? We’re happy to tell you all about it.

How to proceed? Collaborate effectively with AI

For many, applying AI practically within the software development process remains a challenge with few concrete steps. Start today!

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