Greater numbers of individuals, businesses, and governments are embracing artificial intelligence. This has led to growth in certain sectors of the global economy. But there is a growing gap between those businesses and sectors who benefit from AI and those who don’t.
Work structures are changing
With the increasing integration of artificial intelligence (AI) in various sectors of the economy, work structures there are also changing. It is important to recognize the potential of AI, which can develop better quality, precision, and reliability for companies. This doesn’t mean humans should be replaced by machines. Rather, humans and machines should collaborate for high performance. This would also lead to improved working and living conditions. The goal is to create new forms of work organization with innovative methods.
The use of artificial intelligence can lead to higher quality, precision, and reliability for many areas in industry and utilities. But for this to happen, the use of the AI itself must first become reliable. Against this background, new approaches are needed in the work areas of AI development and AI use with close integration between the expertise of the developer and user domains.
The use of models
For example, the fields of work of data scientists with those of radiology. To achieve this, there should be a process model for changed work organizations and roles. Tasks should not be rationalized away in such a way that human work is taken over by tech. Rather technology should become better by interlocking with human expertise. This further develops the jobs and the potential of AI. Increased acceptance of AI also means that we can speak of human-centered work. It’s about a human-centric use of artificial intelligence. It is about the interaction of human work performance and artificial intelligence in order to find solutions. A good illustration would be a picture of AI spread out over people as a protective roof. As a result, a connection can be created that brings about new solutions.
The use of AI cuts across different sectors, including the financial sector, manufacturing, insurance, and healthcare. In the case of the companies, it is about new business models, new approaches to quality testing, new forms of work in areas where AI is used, related participation issues, etc. Despite the different professional orientations, there are synergies from an overarching common logic. This means that a process model that we develop can be applied to all areas. That’s what’s exciting
How Reliable is Artificial Intelligence?
AI is very useful, especially in aspects such as detecting anomalies. Such anomalies are difficult for the human eye to see and require a lot of professional excellence experience. However, AI needs to be trained well before it can be of help. This depends very much on the data AI is fed with beforehand. If the data is not reliably processed and contains errors it has a negative effect on the reliability of AI. This defeats the purpose of leveraging artificial intelligence. In the past, bad documentation may not have been so bad but now it is more and has more consequences. With AI, you introduce automation. And when you have errors that means you are automating errors, which have far-reaching effects.
The errors must be cleaned up, understood, and classified. Anyone who is supposed to work with an AI-supported diagnosis must of course be able to use it reliably and be able to take responsibility for the decision. The situation is different in a related area of work, medical-technological radiological assistance. There you first accept what is technologically delivered.
AI is built into machines. There is a question asked as to whether the machine displaces the workforce or devalues their inputs. Do I only have to press a button in my work? Is the expertise I have acquired still relevant? If these cannot be answered clearly and tangibly for those affected, then the questions of how work and job profiles are developing must be dealt with more intensively.