Hyper-automation is the logical evolution of many companies’ ongoing process automation initiatives. Through hyper-automation, companies want to ensure sustainable growth, increase efficiency, mitigate the consequences of the shortage of skilled workers, be more attractive workplaces, and gain competitive advantages. Only: The way there is anything but trivial. A structured approach and a careful selection of the relevant processes are essential for the success of a hyper-automation strategy.
AI As A Central Component
Artificial intelligence (AI) plays a key role as an enabler for hyper-automation. With AI, tasks that would have been unthinkable a few years ago can already be automated today. For example, it is possible to automatically compare the signatures of a person, which allows this person to be authenticated with a high degree of probability. Similar new AI services enable automating various repetitive activities at all company levels.
In the past, AI was used in selecting applicants, among other things. Although caution is advised here. For example, an AI solution for pre-selecting applicants discriminated against the female gender due to “noise” in the training data.
In terms of ethical AI, it is therefore important in such application scenarios to monitor the results and adapt the AI model during systematic errors. Chatbots provide another example of AI-powered automation. It is not uncommon for them to be so well trained today that it is sometimes difficult to identify whether the partner in the digital chat is a real person or a robot.
The principle of hyper-automation is that it puts these technical features in a business context. This is how she succeeds in creating added value within the framework of customer-centric processes – for internal and external customers of a company. The topic is experiencing further dynamics through the meanwhile established approach of using low or
The Basic Business Case
The business case behind the automation of a business process is relatively easy to calculate: the investment in the process must be covered by the savings that result over time. It is, therefore, usually advisable to prioritize strategies with high throughput. This can be, for example, account openings in the financial industry, customer inquiries in the service area, or processes in fields such as data structuring and governance. Sometimes suitable software solutions – using self-services with AI-based document, email, telephony, and chat routing – can create an almost completely hyper-automated line of business by automatically placing advertising based on process throughputs or cloud Resources are added or reduced.
Hyper Automation In Practice
The value proposition of hyper-automation is high in many cases, but the road to successful adoption is anything but trivial. Marketing texts by the actors like to praise hyper-automation as a readily available panacea for inefficient processes and against overburdening employees. In practice, however, it quickly becomes clear that companies that do not have digital DNA would face an enormous transformation task before they could use hyper-automation across the board and economically.
It often makes sense to initially only develop certain processes with manageable effort. If you want to make a hyper-automation project a success, you should choose it carefully and proceed in a structured manner during implementation. Otherwise, the risk of failure is considerable.
For example, a study by McKinsey (Driving impact at scale from automation and AI | McKinsey Digital | McKinsey & Company) found that those responsible are not satisfied with the results in more than half of their automation projects. This has to do with the fact that the projects take significantly longer than initially planned. There are reasons for this: Either the hyper-automation strategy prioritizes the wrong business processes, or the requirements of the departments do not have the level of detail necessary to initiate automation projects. In addition, hyper-automation requires the organization to be willing to rebuild business processes completely. A company’s database must also have the level of maturity that is essential for hyper-automation based on AI analysis. Conventional reporting standards are inadequate here.
The Example Call Center Process
So, a good hyper-automation strategy starts with prioritizing which processes are relevant. As a rule, it is advisable to build the backbone of the business model first. Because automation is particularly worthwhile when it addresses functionalities reused in numerous company processes. This creates the necessary impact on the project goals. If we choose call center processes as an example, in their environment, in the area of telephony, telephone routing (IVR) and the stable connection to the telephone provider would be possible fields of application. Only in the next step will the hyper-automation strategy consider services with a higher degree of complexity, are more subject-specific, and
significantly impact the project goals. To stay with the call center example: A worthwhile next project would be the AI-supported, automatic and clear identification of a customer based on their voice. This automatic voice recognition makes it possible to implement further services: for example, the visualization of the contact history and the AI-supported determination of the next best action (NBA). The result of this automation: the call center agents could increase their first resolution rate. Hyper Automation in this context would be the complete avoidance of the call by proactively offering self-services using pattern recognition or a completely automated next best action with a corresponding conclusion.
Digital Maturity Requirements
Not only wrong prioritization can prove to be a showstopper in the hyper-automation of business processes, but also ignorance about the digital maturity levels of processes and requirements. It is therefore important to describe in detail which steps the automated software or robot must process sequentially, in parallel, or recursively.
Several other questions need to be asked when it comes to digital maturity: Which peripheral systems are involved in the processes, and what is the task of the systems in the process sections? In which part of the product life cycle is the system located, and will it possibly be replaced in the future? What influence do functional and technical adjustments have on previous and subsequent processes? Knowing which regulatory requirements exist for an approach may also be important.
Also Read: Machine Learning And Artificial Intelligence In Online Trading
The New Customer Process At The Health Insurer
An example from the field of health insurance: When it comes to changing customers
and customers from statutory health insurance to private ones, the operating unit of the private insurer could formulate automation requirements such as these: “We want to be able to create our target customers in the shortest possible time using an AI-supported, fully automatic check and provide the insurance cards to the appropriate shipping addresses .”
Experience shows: After the queries about the definition of a target customer and the requirements for the modifiability of the technical description of the customer, at least one other department of the health insurer is already involved – for example, sales. The sales department describes the new customers from a technical point of view by determining which characteristics target customers ideally have and which fluctuation ranges there are. Our example shows how important it is for hyper-automation projects to draw on holistic knowledge of the process. This means: The process to be automated, and the business objects processed in it must be well documented.
Minimal Viable Product And Business Case
Another obstacle to hyper-automation can be a company’s unwillingness to reengineer business processes. Let’s stay with the example of private health insurance. The fully automatic design of the process requires, among other things, adjustments in sales, risk management, central data management, and possibly new cuts in the organization. These areas must agree to adapt their – sometimes high and detailed – requirements to the project and company goals. According to the Minimal Viable Product (MVP) approach, it makes sense to initially only implement the necessary functional requirements. Here it has proven useful to evaluate the importance of conditions using a business case for the technical implementation.
Machine Learning And Deep Learning
In our example of selecting the desired customers, AI-supported hyper-automation opens up another way of identifying these customers. Companies using AI in machine learning or even deep understanding can enable their systems to perform automated classification. In our example, machine learning (ML) would be based on the health insurance inventory data. Based on the data on customers who have already been won and rejected, ML models can be trained, which in the future can give a positive or negative recommendation for the business relationship in each case.
As already mentioned, however, it is essential that the inventory data used to train the ML model not only reflects the past but also fits with the future corporate strategy. If, for example, you want to avoid distortions in the AI-supported pre-selection of applicants in the human resources area that can result from inventory data – for instance, in the form of prejudices – deep learning could also be used.
Because Deep Learning (DL) works with neural networks. As a result, business users can implement complex decision-making criteria over several levels, which can also serve future product specifications. However, the prerequisite for a DL approach is that the business requirements are already very mature and that there is already a deep understanding of the process regarding the rules and the product specifications in the business model.
If, for example, you want to avoid distortions in the AI-supported pre-selection of applicants in the human resources area that can result from inventory data – for instance, in the form of prejudices – deep learning could also be used. Because Deep Learning (DL) works with neural networks. As a result, business users can implement complex decision-making criteria over several levels, which can also serve future product specifications.
However, the prerequisite for a DL approach is that the business requirements are already very mature and that there is already a deep understanding of the process regarding the rules and the product specifications in the business model. If, for example, you want to avoid distortions in the AI-supported pre-selection of applicants in the human resources area that can result from inventory data – for instance, in the form of prejudices – deep learning could also be used.
Because Deep Learning (DL) works with neural networks. As a result, business users can implement complex decision-making criteria over several levels, which can also serve future product specifications. However, the prerequisite for a DL approach is that the business requirements are already very mature and that there is already a deep understanding of the process regarding the rules and the product specifications in the business model.
Conclusion
On the hyper-automation journey, organizations must do the right things with the right priority and start small. It is important first to determine the maturity of business requirements, data, IT architecture, and organization to decide which processes are already possible, sensible, and promising for hyper-automation. In most cases and industries, the transformation to the hyper-automated enterprise will only happen step by step. After all, hyper-automation is not just a tool but a strategic vision of the company’s future. It is the next stage in the progressive digital evolution of companies.
Also Read: Automation Is Not A Panacea For Destructive Processes
