The entire working world is changing and is already showing the effects of advancing automation in all areas. Opinions differ as to whether euphoria or caution is appropriate in view of the consequences of this restructuring. While a new OECD study sees almost one in five jobs in Germany threatened by process automation, the Centre for European Economic Research (ZEW), for example, predicts a job increase of 560,000 jobs by 2021.


How deeply rooted the fear of a takeover of the machines is in the minds of German employees is even reflected in online generators that are supposed to show the risk potential for certain job sectors in percent. In the case of the Süddeutsche, for example, the user should be able to answer the question “How likely is it that I will be replaced by a computer? ” – ironically from a computer. In order to protect your company from such fears of the future and at the same time to profit powerfully from a new automation strategy, you should take the following 5 points to heart:

1. Rules & Data: Stay informed!


Rule-based and data-driven action models are often differentiated in online business. In general, rule-based here means static, because a fixed model and rules that can be defined by the business user or based on predefined user-persona are used. Accordingly, each digital touchpoint of a user profile is assigned a specific follow-up action at certain points along the customer journey. Thus, there is no flexible process. A system is rule-based when data is organized exclusively via hypotheses in the form of predetermined rules. As an example, the following can be given: “The third call of an advertisement shows clear buying interest”.

Data-driven models, on the other hand, are dynamic and recalculate the follow-up actions of each click along the user’s customer journey in a completely individual way. Evaluation and implementation take place permanently and in real time. In background processes, several click paths are compared, whereby both conversion-strong and aborted paths are taken into account. Data-driven is defined here, therefore, by the use of underlying data in order to recognize certain patterns of causality within seconds and to spontaneously transfer them into a dynamic model. In an online shop, for example, it is possible to show previously completely unknown users without any visitor history situational product suggestions based on the access device or even the current weather.

2. Infrastructure is needed!


German companies have already recognized the high importance of process mining, robotic process automation and analytics for digitization. Thus resulted the “Process Mining & RPA 2019” study by IDG Research Services, that almost 80 percent of companies are now convinced of digitization and process automation.

Before you can start a new project, the necessary conditions must of course be created. For the implementation of automation processes, a prior inventory of all business processes is helpful, which can be reconstructed and evaluated by process mining. From then on, the motto is: Do not leave any resources unused, dissolve all data silos, create a uniform data basis! For example, it should be avoided that different work areas use different data systems. If a company lacks important skills, it makes sense to cooperate with external service providers to build a digital infrastructure.

3. Use your own data!


Automation is a big topic in many companies, but is often not yet implemented efficiently and profitably enough. The internal data stocks sometimes grow over decades in a company and are – correctly used – pure gold for a data scientist. If these data treasures are in turn neglected, a great potential is quickly lost. Using intelligent tools, such as Machine Learning, previous data on customers, users or products can be integrated and evaluated in a flash. Data-driven, a number of findings can be extracted from existing silos that were previously lost in disorderly masses of data. A well-planned automation strategy is therefore not a new start for a company, but a comprehensive optimization of common working methods and functions. So be smart, master the data chaos and turn your Big Data into Smart Data!

4. Must-have Machine Learning!


Machine Learning (ML) is not witchcraft, even though some companies have not yet embraced algorithms and artificial intelligence as an integral part of their work processes. If, on the other hand, ML tools are regarded as aids for accumulating and organizing essential knowledge from huge amounts of data and then immediately putting the knowledge gained from this into practice, the whole thing sounds more tempting.

It is also easier to ensure data quality and security by setting up automatic control processes. In the course of digitization and automation processes, the increasing mass of data can no longer be successfully controlled by rule-based systems. As purchasing processes become more and more dynamic and a number of touchpoints can be created on various channels before the product is purchased, online retailers must follow suit in order to meet increasing customer expectations. For targeted marketing campaigns and customer acquisition measures, you should therefore rely on the implementation of operational intelligence and ML systems instead of just empirical values. According to a BCG study, such a rethink can increase a company’s turnover by as much as 20% while saving 30% in costs.

5. Get employees on board!


Some companies are already increasingly exploiting their automation potential and profiting greatly from it. This is also confirmed by the Forbes study Intelligent Automation, which examines how automation can be used sensibly to create efficiency and productivity gains. Intelligent Automation Platforms are designed to help companies automate work processes that are considered too undemanding, repetitive and tedious by human colleagues. These colleagues, on the other hand, are in many ways a key element in operational automation solutions. Thus, the aim should not be to replace employees, but to create a healthy balance between people and machines/computers in everyday working life. It is also important to create transparency, acceptance and trust in the new technologies among colleagues.

Due to the major changes in everyday working life when implementing automation solutions, change management is an important keyword here. If employees from non-analytical company areas are introduced to automation processes in small steps and trained in their basic understanding of data and data analysis, any reservations usually disappear into thin air. According to the Forbes study, as many as 92% of all companies surveyed reported higher employee satisfaction as a result of their automation initiative. One of the reasons for this is that existing teams were replaced by their monotonous areas of responsibility and deployed in higher-value activities such as individual customer service. True to the motto ‘happy people are more productive’, this has resulted in a clear increase in efficiency for many companies.