Are We Facing an Role Revolution?

The speed of technical development in the field of AI is having profound repercussions on roles in the software development process. These are some of the current trends in how important roles in software development are currently changing.

Developers are being forced to deal with the ethical dimensions of their systems – issues related to fairness, data distortions and potential negative repercussions, which demand a high sense of responsibility, as well as the ability to think beyond the purely technical aspects of programming.

Going forward, not only will business analysts and requirements engineers need to gather functional and non-functional requirements, but will also have to examine the quality and availability of training data, all while working closely with experts in data sciences and machine learning. They’ll need to develop a feel of what problems are actually appropriate for AI solutions, and how to properly define requirements for learning systems.

Project managers are being faced with the challenge of managing development processes that are less deterministic, and significantly more iterative, and now need to plan on timetables and resources for data collection, model training and continuous improvement. And agile methods are becoming more important, since AI projects are often of an experimental nature and require more frequent adjustment.

Testers‘ roles are also undergoing a fundamental change – instead of carrying out pre-defined test cases using test data that was worked out in detail, testers now evaluate the performance and robustness of AI models under a variety of conditions. This includes checking for model accuracy, identifying distortions and making sure systems react appropriately in edge cases. As a result, testers need to become familiar with concepts like model overfitting, underfitting and generalization.

Finally, software architects are being faced with the task of designing systems that can seamlessly integrate AI components. They need to develop architectures that make it possible for models to handle large data sets, and also to enable the subsequent real-time processing of information. At the same time, architects must now take into consideration aspects such as scalability and maintainability as well as integration with existing systems, In addition, the architectures they design must be flexible enough to keep up with the rapid development of AI technologies.

An important foundation stone for you to benefit from this change process in your role is a solid basic knowledge of AI and how it works. The AI courses offered by Spirit in Projects provide you with tried-and-tested methodological knowledge.