The exponentially growing amount of available data, together with the increasing complexity of analytical algorithms, is changing the requirements of data analysts for IT infrastructure. A powerful desktop or dedicated server has two basic disadvantages: the inability to be easily scalable and the long update cycle. Modern analytical platforms, such as SAS Viya, address these shortcomings by using modern container technology and developing a CI / CD approach. Thanks to these principles, SAS Viya supports the analytical process from start to finish, from raw data processing to the final decision based on advanced AI. At the same time, users can choose whether to choose to use a private cloud, or rather relieve themselves of the need to administer the hardware and use one of the extensive offer of public cloud services.

Today, most large industrial companies create analytical teams whose goal is to apply modern methods of artificial intelligence to increase the efficiency of their production. However, experience has shown that the introduction of similar methods in manufacturing companies often brings completely different challenges than for example in the case of their use in financial companies. In addition to the necessary investments in infrastructure and software, it is often necessary to change the company's "philosophy". Despite the complexity of this process, many manufacturing companies are already successfully using artificial intelligence for more efficient management and are thus better prepared for the so-called fourth industrial revolution. In this paper, we will show examples of such companies and look at the common factors that helped the successful implementation of AI into real production.

Discussion of invited speakers:
Peter Blaškovitš, SIEA 
Lukáš Demovič, SAV
Emil Fitoš, ITAS  
Radovan Furmann, CEIT
Ivan Kasanický, SAS Slovakia
Peter Semančík, Resco
Branislav Valach, Resco