The promising academic results in the field of machine learning have already had an impact in practice. Organizations of different size and domain are aiming at facilitating the seemingly limitless possibilities of this “new” technology. The goals are always the same: increased productivity, quality and the engineering of new product and service offerings to differentiate themselves in the market. However, this is still a hard task to achieve. In this talk, experiences made in different industrial and consulting settings are presented in a distilled form, to motivate the speaker’s hypothesis: In order to successfully develop the intended products, engineering certainly plays a key role. Yet, people and process are complementing aspects of success, especially when it comes to technology transfer in an environment with lots of uncertainty.The lessons learned can hopefully be discussed and integrated into best practices as well as into the (academic) training of software engineers, thus benefiting the community.

Mahdi Manesh heads the Software Engineering & Deep Tech Chapters at Porsche Digital. His current research and consulting activities span various aspects of advanced software technologies and CIO advisory with a focus on building digital products and services in the automotive industry. Mahdi received Ph.D. and M.Sc. degrees in Computer Science from the University of Koblenz-Landau as well as a B.Sc. in Computer Science from the Johannes Gutenberg University Mainz