The alumni organization for the University of Zurich’s Executive MBA program presents an annual award for the most innovative final thesis from the current Executive MBA course. The prize honors theses which stand out on account of their creativity and whose results promise successful implementation of creative ideas in an organization. The evaluation may focus both on the impact of innovation on the internal processes and characteristics of an organization – such as positive changes to the corporate culture, image, etc. – and on external effects in the sense of positive social, economic and environmental changes. The award is handed out at the graduation ceremony.
Alumni Award 2022
The award goes to Simon Amrein and Simon Inderbitzin for their thesis „Unlock verbal communication skills driven by an AI application“.
Computer learning and artificial intelligence are keywords that have already passed the peak of inflated expectations according to the Gartner hype cycle. They already seem to be on the slope of enlightenment. The today awarded thesis shows a very hands-on and yet very cross-linked application of different findings, models and algorithms that connect in order to measure successful communication.
Simon&Simon started off their thesis with the assumption that there was a lack of research in the area of management communication in the digital age. They initially asked managers what their current communication challenges were, researched existing speech analysis tools and put together their findings in order to create a Minimal Viable Product to capture, transcribe and analyze speech.
Even though the MVP was rather used out of curiosity and from an exploration point of view by managers, it represents a very inspiring example of how machine learning and existing technology can be used in order to answer day-to-day business challenges.
Our congratulations to Simon Amrein and Simon Inderbitzin for winning the award of the most innovative master thesis within the Executive MBA program Digital Transformation 2022 and their contribution to the everyday use of machine learning.