- On this page
- What is AI-augmented software development?
- How is AI used in software development and what are the top business use cases?
- What AI tools do software developers use?
- What are the benefits of AI in software development for businesses?
- What are the challenges and risks of using AI in software development?
- AI in software development: Where does Switzerland stand?
What is AI-augmented software development and what are its uses?
AI-augmented software development integrates Artificial Intelligence tools into the engineering process. Developers may leverage AI tools to generate and analyze code, detect and fix bugs, optimize workflows, and create documentation. The use of AI-augmented tools is not limited to software engineering. For example, it also supports project management.
Why will AI-augmented software development shape business in the years to come?
Companies using AI to improve their products and services will need to keep up with the latest innovations. In addition, AI-driven software development will disrupt the IT industry by enabling businesses to perform specific tasks without expert help, which saves time and money. Transformation towards AI can already be seen, for example, in digital commerce.
How does AI-augmented software development differ from traditional application development?
By automating multiple steps of the software development life cycle, it improves the experience of everyone involved in the process – from programmer and administrator to security analyst and tester.
Where do Swiss companies stand regarding AI in software development?
While Swiss companies are open to the benefits AI-driven software development undoubtedly has to offer, they still harbor reservations towards products from smaller providers. Many companies in Switzerland are already experimenting with AI tools but are waiting for the offerings of more established and hence more trustworthy providers before rolling out these tools on a broad scale.
How will AI-augmented software development impact software and digital product engineering?
AI-augmented software and digital product engineering (DPE) are a perfect combination: While DPE adds new technology to traditional software development stacks, it depends on AI to drive innovation and reduce costs.
How will the integration of AI into software development impact privacy and compliance?
At present, this remains a gray area, particularly with respect to code and the automated suggestions provided for existing code. The use of AI to recommend changes in existing code may give rise to legal disputes if suggested code resembles open-source code that is already governed by licenses that prohibit reuse. In the next few years, the AI-augmented offering is expected to improve, which should clarify the legal implications.
Artificial Intelligence (AI)
AI is the emulation of processes and capabilities of human consciousness, most commonly by computers. With various technologies and techniques ranging from machine learning (ML) to natural language processing (NLP), AI systems can analyze data, make decisions, generate natural language, and perform other tasks that previously required and were exclusively associated with human intelligence.
Digital product engineering (DPE)
DPE is the application of engineering practices and principles in the design, development, and optimization of predominantly software-based digital products and solutions.
Generative AI is a category of artificial intelligence systems that autonomously generate content, predominantly text and images. With advanced algorithms, they can understand patterns and relationships within data and based on these conclusions create new and original content.
Integrated development environment (IDE)
An IDE is a software application that streamlines the processes of creating, editing, testing, and debugging software code. This is achieved by providing integrated tools and features and thus a comprehensive framework that boosts productivity and simplifies the development workflow.
Large language model (LLM)
LLMs are a variety of AI models that specialize in tasks related to human language. By means of enormous bodies of text and deep learning techniques such as neural networks, LLMs have achieved astonishing capabilities in the processing, i.e., understanding, and generation of natural language.
Machine learning (ML)
As a subset of AI, ML focuses on developing algorithms and models that can be trained and improved by large amounts of new data. In this manner, computers can adapt to new information, learn, recognize patterns, make inferences, predictions, decisions, and perform other tasks they have not been explicitly programmed for.
Natural language processing (NLP)
NLP is a field of AI concentrating on the interaction between computers and humans via language. Algorithms and models are designed to make use of large language databases to not only enable computers to understand and interpret human language, but also generate texts that are natural, meaningful, and contextually relevant.