Everyone is talking about Artificial Intelligence. Recent market reports have revealed what is actually behind it. Better, those claim that the market and recent techniques are still strongly underutilized.
What we believe
As claimed above, everyone is talking about Artificial Intelligence (AI) or related terms like Smart Services, the Internet of Things (IoT), etc. AI is on everyone’s lips – in research, in industry, in politics, as well as in society.
In research, Deep Learning (DL) is one of the most famous buzzwords in AI. In contrast to older Machine Learning attempts, DL Neural Networks train themselves to always produce better computations. Politicians claim to support the AI market through grants for AI start-ups for example, or the government in Germany publishes a specific plan . And, of course, the industry provides us with an overload of digital assistants. For example, our heating in our house is controlled – at least if one can afford it, the streaming provider knows better what kind of movies we want to watch than we do. Some of us maybe wonder how long it will take until we are completely controlled by machines.
As it really is
Of course, many of all these great new products help us. In simple words, the providers claim to make our work easier and to sweeten our everyday life. However, is that really the case? For example, when buying products online. The product suggestions are made by so-called Recommendation Engines. Those work pretty well if we remain true to our preferences. But if we change our preferences and tastes, they are quickly overwhelmed. Or, when looking at our email mailbox at work or in private life. We are often more concerned with deleting automatically generated emails than concentrating on reading. Instead of consuming, we filter – and that in almost all situations in life that are now supported by digital assistants. So, several market research reports have been published in the last month about what is really behind AI. Some interesting institutions that evaluate the AI market are:
- The strategy consulting house McKinsey & Company.
- The network PricewaterhouseCoopers International.
- The consulting house Roland Berger Holding GmbH.
- The management consultant house Accenture Plc.
- The network Ernst & Young Ltd.
- The strategy consulting house Boston Consulting Group.
- The strategy consulting house Deloite.
A management perspective
From a management perspective, we want to know which industries have the greatest potential for using AI technologies, what is the most important criterion and immediate requirement for the implementation of AI technologies, and which technologies we can expect in near future.
According to several recent studies, the focus in AI to support our professional life is on a technology named Robotic Process Automation (RPA) [2-4]. RPA is the umbrella term for attempts trying to automatize human work . In the focus of RPA are mainly work processes that have : a high rate of repetition, and a low rate of complexity.
So in other words, the current main focus in AI is not on rocket science and very complex techniques like we could maybe have expected. No, it is on digital agents that work based on pre-defined rules, user interfaces, and or more or less simple automation techniques. But why is RPA in the forefront? This has different reasons:
- Most firms see a sufficient application of AI not in autonomous AI, but in applications that focus on supporting the human worker. In Germany for example, 71 %, whereby 63 % see the highest potential of automation using RPA . Same for the European Union, where 54 % of firms surveyed, concretely plan to automate at least 10 processes using RPA until 2020 .
- RPA is not changing the business logic of a process. RPA is solely focusing on the Presentation Layer. So in the case of inaccuracy, the damage is still manageable.
- Most work processes in firms are not very complex . Instead of focusing on the deep-level processes that occur very in-frequent, RPA is focusing on the range of processes having the highest frequency.
- RPA is considered as the basis for later applying complex AI. Even if we already produce a big amount of data every day, the data we produce is often not very well structured. In addition, firms have a lack of infrastructure. Humans produce errors, the errors are part of the data. RPA instead is capable not only to automatize but also to standardize in parallel. So the data that is produced by RPA is less error-prone. And, in the meantime firms create appropriate data. Through this, the firms can prepare the infrastructure for real AI.
A worker’s perspective
From a worker’s perspective, we want to know how RPA can help, or if RPA maybe will replace my job. This is also part of the above-mentioned studies and can be summarized as follows [2-4]:
- The human remains center and managers stay responsible.
- RPA is focusing on supporting work, not on replacing humans.
- When using RPA, humans shall concentrate on creative work.
- Humans are required to control the RPA and evaluate results.
- RPA is not able to think strategically as humans can do.
In other words, RPA will not dominate our working life. It will change our working world bit by bit. However, we will only notice this gradually. The reason is that most of us already use digital assistants – in our working life or in our private life.
The term RPA is little used when talking about AI. Everyone claims to work on, benefit from, or at least utilize complex AI. Of course, there exist firms that make use of techniques like Reinforcement Learning. However, the majority of firms do not use AI, or the firms are still preparing to use AI. So in the near future, we can expect that the majority of AI techniques we will use does not replace any concrete job. No, it is just used to simplify our jobs, to make those more joyful, and will ensure us to focus on complex tasks.
 Die Bundesregierung (2018). Strategie Künstliche Intelligenz der Bundesregierung.
 Roland Berger Holding GmbH (2018). 10 theses about AI – A companies eye view of the future of AI.
 PricewaterhouseCoopers International (2019). Eine Befragung von 500 Entscheidern deutscher Unternehmen zum Status Quo – mit Bewertungen und Handlungsoptionen von PWC.
 McKinsey & Company (2019). Smartening up with Artificial Intelligence (AI) – what’s in it for Germany and its industrial sector?
 van der Aalst, W. M. P., Bichler, M., and Heinzl, A. (2018). Robotic process automation. Business & Information Systems Engineering, 60(4):269–272.
 Aguirre, S. and Rodriguez, A. (2017). Automation of a business process using robotic process automation (rpa): A case study. In Figueroa-García, J. C., López-Santana, E. R., Villa-Ramírez, J. L., and Ferro-Escobar, R., editors, Applied Computer Sciences in Engineering, pages 65–71, Cham. Springer International Publishing.
 Hofmann, P., Samp, C., Urbach, N. (2019). Robotic process automation. In Electronic Markets.