{"id":963,"date":"2019-04-25T10:20:33","date_gmt":"2019-04-25T08:20:33","guid":{"rendered":"https:\/\/lam.unisg.ch\/blog\/?p=963"},"modified":"2019-04-25T16:56:43","modified_gmt":"2019-04-25T14:56:43","slug":"are-you-already-investing-in-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/lam.unisg.ch\/blog\/en\/are-you-already-investing-in-artificial-intelligence","title":{"rendered":"Are you already investing in Artificial Intelligence?"},"content":{"rendered":"

Do you belong to those managers ready to invest in AI-projects during the next one to three years?\u00a0 Or do you prefer postponing this decision by three to five years? According to a new survey<\/a>, 93% of executives from high-growth enterprises agree that AI investments should be considered urgent. In low growth companies, 64% of managers postpone this decision. Especially in low-investment settings, where competition is rather small, one likes to emphasize that, as competitors are inactive, the urgency of being active itself decreases. This is particularly dangerous when it comes to AI, because technology scales enormously. If a competitor – such as a new start-up, you’ve never heard of, or Amazon – has suddenly captured a considerable market share, it is difficult or even impossible to win it back. (This is particularly true in the service sector and slightly less in the industry). That is why you don’t want to fall behind on this issue; and if you do, this has to be a conscious choice rather than an action taken out of convenience.<\/p>\n \n

\n
\n
Executive School Programmes:<\/h5>\n
\n Open Programmes
<\/div>\n\n

\n Managing AI (Arificial Intelligence) effectively<\/a> <\/h4>\n\n
\n From data to value-added automated pattern recognition <\/div>\n \n <\/div>\n <\/div>\n \n

Furthermore, studies proof that on management levels, superficial technical terms often characterize the discussion of artificial intelligence, while a profound understanding of the various algorithms and necessary data is usually missing. Even though this fact is rather disappointing, it is generally regarded as sufficient, since expensive specialists can always be considered to resolve further questions. However, we are convinced that this assumption can be an obstacle to the successful implementation of AI-based solutions. As a manager, you need to know more about AI. This does not mean that you should be familiar to programming, but the ability to grasp basic concepts of AI technology needs to be existent.<\/p>\n

What is artificial intelligence (AI) anyway? What does machine learning (ML) mean?<\/strong><\/p>\n

Algorithms of machine learning predict target values on an individual level. This prediction can be for single customer as well as for machines. These target values are crucial to commercial success and efficient process design. Take a bank for instance that wants to predict whether someone is credible. The databases is filled with information about customers who have either paid their loans without any problems, or had to be depreciated. All defaulting borrowers will then be marked 1 and all others will be marked 0. This variable is the target variable, usually called Y variable. Data on other variables such as place of residence, gender, age, education and income are simply called X variables. These X variables are important, as they influence the Y variable. An algorithm learns from the data that results of a combination of X-variables and repayment results (Y) and calculates a repayment probability. Since the algorithm learns these patterns independently from the data, this particular process is called machine learning.<\/p>\n

\"Mustererkennung\"<\/a>
Example for machine learning (click on the picture)<\/figcaption><\/figure>\n

Another application example would be to predict whether a machine would fail and\/or require maintenance. In this case, the X-variables are temperature, running time, product type etc. and the algorithm can predict when the machine needs maintenance before it really stops. Other examples of X and Y variables are:<\/p>\n