AI in Software Development

Security depends less on the tool itself and more on governance, data policies, and the technical environment. With clear guidelines, role-based access models, and a secure development environment, AI can be deployed in a controlled and compliant manner.

The greatest leverage comes not just from writing code, but from architecture, testing, documentation, knowledge management, and the automation of repetitive tasks. A structured approach is key, rather than the isolated use of tools.

Yes—in clearly defined use cases. In particular, so-called guided agentic workflows, in which humans remain involved at defined quality gates, are already being used in production. Fully autonomous software development, on the other hand, is not yet practical in many business contexts.

The right choice of tools depends on security requirements, architecture, team structure, and use cases. Today, companies rely on tools such as GitHub Copilot, Claude, Cursor, and open-source platforms, among others. What matters most is not the individual tool, but the right overall strategy.

Success can be measured using technical and organizational metrics, such as development time, throughput, code quality, error rate, reusability, or developer satisfaction. It is important to measure productivity gains in a structured way rather than simply evaluating them subjectively

Most companies start with a clearly defined pilot project and specific KPIs. This allows them to assess productivity gains, team acceptance, and governance requirements early on, before scaling up.