Workforce Augmentation: Will Humans Become Obsolete Due to AI?
Automation plays a crucial role in digital transformation. Especially in areas such as administrative tasks, accounting, or customer service, companies rely on high efficiency in their processes to maintain a long-term presence in the market. Processes are supported by modern ERP systems or tasks are delegated to software robots (RPA). For problems that cannot be automated with simple rules, the use of artificial intelligence (AI) is increasingly being established. This raises the question of whether all processes will truly be automatable by AI in the future, rendering humans obsolete.
The answer to that is "no" because artificial intelligence still struggles in practice with complex and individual processes. However, the collaboration between self-learning machines and humans presents a potential that offers significant value, even for medium-sized companies, and can be leveraged through new technologies.
Humans have the ability to see the bigger picture. To understand the limitations of artificial intelligence, we can look at decision-making processes that involve "commonsense reasoning." Take a simple example from the insurance industry: If a customer calls the insurance company to report water damage caused by flooding from a nearby river, a human can quickly assess the truthfulness of this statement by checking if the house is located near a river and if there has been heavy rainfall during the reporting period. This process is very intuitive for humans and can be done using common sense, whereas it presents a significant challenge for even the most advanced AI systems.
The often indiscernible boundary between what artificial intelligence can and cannot accomplish can pose a major challenge for companies when automating complex processes. In cases where processes are highly individual, humans have the advantage because their decision-making is based on a wealth of life experience, requiring minimal information to make a judgment. On the other hand, most AI approaches require extensive data or training models with additional data.
Complementary instead of substitutive
In the past, the focus of automation was often on transferring individual tasks to machines so that humans no longer had to perform them. However, in practice, the aforementioned challenges mean that many tasks cannot be fully automated, and humans must still intervene in case of problems or inaccuracies. Therefore, it can be said that humans have not been replaced but rather work alongside machines, with humans stepping in when machines reach their limits.
A new trend is to involve humans as decision-makers from the outset and delegate only the automatable parts to machines. Humans and machines work closely intertwined instead of side by side. This concept is known as "Workforce Augmentation" or "Hyperautomation." Companies benefit from the advantages of both worlds. Humans continue to be involved as capable decision-makers, while machines are generally more efficient in repetitive tasks. The close integration of humans and machines unlocks the full potential and also establishes a kind of "four-eyes principle," enabling faster error detection and improving process compliance.
Challenges in integrating humans and machines
In practice, it is a significant challenge to effectively integrate humans and machines, and this can only be achieved through the use of modern technologies. It starts with the initial preparations for an automation project because initially, it is often unclear which steps are repetitive and which require human decision-making. Therefore, novel technologies like Process Mining are used to reveal processes based on data (Process Discovery) and automate the repetitive parts.
Once repetitive steps in the process have been identified, it must be ensured that they can be economically automated. The shorter the process segment to be automated, the lower the costs for automation should be. If requirements need to be gathered and automation developed by programmers, the practicality is often compromised, especially since IT capacities are limited in medium-sized companies.
Humans have the ability to see the big picture. To understand the limits of artificial intelligence (AI) deployment, one can examine decision-making processes that involve "common-sense reasoning."
Take a simple example from the insurance industry: When a customer calls an insurance company and reports water damage due to a nearby river flooding, a human can quickly assess the truthfulness of this statement by checking if the house is located near a river and if there has been heavy rainfall during the reported period. Humans can intuitively process this information using common sense, while this process poses a significant challenge for even the most advanced AI systems.
This often indiscernible boundary between what AI can accomplish and what it cannot presents a major challenge for companies seeking to automate complex processes. When processes are highly individualized, humans have the advantage because their life experience allows them to make decisions with minimal information. Most AI approaches, on the other hand, require extensive data or training models with large datasets.
Complementary Rather Than Substitutive
In the past, the focus of automation was often on transferring individual tasks to machines, relieving humans of those responsibilities. However, the aforementioned challenges in practice mean that many tasks cannot be fully automated, and humans still need to intervene when problems or inaccuracies arise. Thus, it can be said that humans have not been replaced, but rather humans and machines work alongside each other, with humans stepping in when the machine reaches its limitations.
A new trend is to involve humans as decision-makers from the beginning of the process and only assign the automatable parts to machines. Humans and machines work closely and in collaboration rather than separately. This is known as "workforce augmentation" or "hyperautomation." Companies benefit from the advantages of both worlds: humans remain involved as capable decision-makers, while machines are more efficient in repetitive tasks. The close integration of humans and machines unlocks the full potential. Additionally, it establishes a sort of double-check system, enabling faster error detection and improving process compliance
Linking Humans and Machines as a Challenge
However, in practice, it is a significant challenge to effectively integrate humans and machines, and this can only be achieved through the use of modern technologies. It starts with the initial preparations for an automation project because initially, it is often unclear which steps are repetitive and which require human decision-making. Therefore, novel technologies such as Process Mining are used to data-drivenly uncover processes (Process Discovery) and identify repetitive parts for automated processing.
Once identified, where repetitive steps exist in the process, it must be ensured that they can be economically automated. The shorter the process segment to be automated, the lower the costs should be for implementing automation. If requirements need to be gathered and automation developed by programmers, the practicality is often compromised, especially since IT capacities are limited, particularly in medium-sized companies.
Democratization in Automation
Therefore, modern solutions must offer the possibility to create automations without the need for IT specialists and without the need for extensive analysis. This allows for the exploitation of automation potential, even down to micro-automations. Additionally, the solution must be characterized by user-friendly usability to avoid creating new obstacles. The strategy of enabling all employees to utilize complex technology is also known as "democratization of technology." Furthermore, the transition between humans and machines should be as intuitive as possible, without friction. It is crucial to ensure that as many repetitive parts as possible are automated by the system. This not only increases the efficiency of companies but also improves the working environment for employees.
Conclusion
Although humans remain indispensable for many processes, there is an opportunity to enhance process efficiency through the use of modern technologies. These solutions actively involve humans in the process, significantly increasing productivity and the quality of the work environment. Considering such solutions is essential in a contemporary IT strategy.
