The challenges arise from the characteristics of AI-based methods: The performance of technical systems that use machine learning (ML) methods can often only be poorly estimated in advance. This makes it difficult to make reliable statements about safety and reliability. This is offset by a large potential benefit: Used successfully, data-driven methods can often make decisions faster and better than would be possible with classically developed methods. In this way, they support humans, relieve them and complement them. In industrial production, ML processes lead to higher-quality and thus longer-lasting products, increase resource efficiency or enable predictive maintenance. In the field of mobility, ML processes can increase driving safety, e.g. by emergency braking in dangerous situations, and thus save lives.
To integrate AI-based components effectively and efficiently into existing or new applications, a systematic approach is essential. Established systems engineering process models are intended for complex technical systems. However, the use of AI and ML brings new challenges that a dedicated process model should explicitly address.
Systematically Developing and Operating AI Solutions with AI Systems Engineering
PAISE® , the Process Model for AI Systems Engineering, is specifically designed for the development and operation of AI-based systems. It combines approaches from computer science and data-driven modeling with those of classical engineering disciplines to overcome the challenges. AI Systems Engineering, translated as AI engineering, is what the scientists* call the interdisciplinary approach they have been working on since mid-2020. "With AI Systems Engineering, we want to systematize the development and operation of AI-based solutions. Only if AI methods can be used reliably from an engineering perspective will there be an opportunity to leverage the high value creation potential," says Prof. Dr.-Ing. habil. Jürgen Beyerer, head of Fraunhofer IOSB and the scientific board of directors in CC-KING. "With PAISE®, we have created a set of tools that also provides small and medium-sized companies in particular with a practical guide for achieving this goal."
In development, it can be difficult to estimate the performance of an overall cyber-physical system with AI components in advance. "This means that changes to the high-level design of the overall system may still be necessary at a late stage," says Constanze Hasterok, a scientist at Fraunhofer IOSB and editor of the PAISE® model. "Among other things, this effect occurs when the final ML models are trained with data from real operations. For new developments, however, high-quality data from operation is typically not available until late." For operations, he said, monitoring and, ideally, automatically adjusting ML models is necessary when systems and their environmental conditions can change over time.
In addition, there are difficulties related to staff: As a rule, companies - especially medium-sized ones - do not have their own AI experts. At the same time, managers need to know which AI expertise should be available in the long term for the operation of AI-based systems and how the development process and its interim results are to be evaluated.