The hype about artificial intelligence is great. But what does reality look like in companies?
Photo above left: the discussion participants Bernd Bugelnig, Clemens Wasner, Harald Piringer and Martin Szelgrad (c) Sela Krobath; photo above right: the discussion round (c) Sela Krobath; photo below left: the audience (c) Sela Krobath, photo below right: Harald Piringer in an interview with Report Verlag (c) VRVis
On Thursday, March 7th, 2019, Harald Piringer, head of the Visual Analytics research group, was a podium guest at the discussion "Machine and Deep Learning for Business". This panel discussion was hosted by Report Verlag and moderated by editor-in-chief Martin Szelgrad. The use of artificial intelligence in business was discussed, specifically the benefits and practice.
Today, no company can ignore digitization any longer. Buzzwords like industry 4.0 and artificial intelligence are on everyone's lips. And yet only very few companies use the full potential of their data for process improvements and the development of new business fields. In practice, digitization projects involving the use of artificial intelligence are usually expensive and risky. According to many studies, this is mainly due to the gap between the world of technical experts on the one hand and the world of modern analytics on the other. Experts know their processes, but have little access to complex analytics. And Data Scientists master algorithms, but need up to 80% of their time to get to know and prepare the data for concrete projects. This makes digitization projects expensive. And it is not uncommon for project results to bypass business benefits due to the separation of technical expertise. Therefore such projects are risky.
Harald Piringer explains the goal of his work: To turn companies and their employees into digitization champions who use company data comprehensively and purposefully. By closing the gap between specialist knowledge and analytics in two directions: Firstly, with his software Visplore, which gives technical experts such as process engineers intuitive access to modern analytics. And secondly, it reduces the effort for data scientists to quickly understand data and prepare it for the use of artificial intelligence methods. The success of his approach is documented not only by numerous publications, but above all by the use of the software in more than 10 companies. For example, company partner RHI Magnesita was able to implement significant process improvements, but energy companies were also able to significantly increase the accuracy of forecasting models.