Colleague KI: How We Get Ready To Work With Self-Learning Systems

Artificial intelligence (AI) is changing the division of labor between humans and technology. In the factory and the office, AI systems, self-learning software and robots can relieve employees of monotonous, repetitive tasks and make work processes more efficient. The vision of the AI ​​age is a working world in which humans and the AI ​​system work together productively, and the strengths of humans and technology are optimally used – for the benefit of the employees. It is now essential to prepare people for their new everyday work and AI systems through further training and qualification offers.

AI systems can support employees in their day-to-day work. The systems are trainable and can make independent conclusions and decisions. More and more employees are working with AI: They program, develop and train AI systems and apply them in different contexts. For this, the employees need adapted or completely new types of skills. The requirements depend on how people and AI systems perceive their role in a unique collaboration.

Humans did the full scope of the respective task; today, some errands can be solved by an AI system alone. There are a multitude of conceivable forms of cooperation in the spectrum between these extremes. If a job can be planned and structured to a high degree, an AI system can take over large parts of the processing, while employees are primarily responsible for control and monitoring functions. The less plannable and structured a task and the more complex a situation that must be grasped and mastered, the more critical human experience becomes and the better the employees are suited to processing. You can then receive appropriate support from AI.

Three Fields Of Competence For Employees In Dealing With AI

The required competencies can be roughly classified into three fields, each of which depends on the role of the employee:

  • Technological specialists and essential knowledge to cope with the content-related and technical requirements of a task and the digital requirements that result from the use of AI – for example, in the area of ​​machine learning. If employees come into contact with AI, they are aware of the AI ​​systems used in the company and their basic performance capabilities.
  • They are dealing with AI systems to understand and shape the changing division of labor between people and technology and act accordingly. This includes personal meta-skills (such as reflection or problem-solving), skills in human-machine interaction, and IT and data skills. When employees program, develop and train AI systems, they mainly need a deeper understanding of AI as well as basic programming knowledge, ample data competencies from data science or data analysis as well as an in-depth understanding of the type, differences and possible uses of various machine learning processes, including the tools for training the algorithms. Competencies in ​​human-machine interaction are then required when employees use AI systems or work with them in unique ways – for example when working with chatbots or robots. Cognitive skills (such as the ability to reflect or judge) are critical to assess, evaluate, contextualize, understand and check decisions or conclusions made by an AI system.
  • Design the context of the AI ​​systems to understand them as a standard element in daily work and further develop and control work and change processes based on them. This includes personal and social skills required when using AI shifts one’s focus on tasks. These competencies are critical if the activities are less routine and carried out decent rally independently or in a team. Meta Competencies such as working in a group, communication, willingness to learn, personal responsibility and self-organization are required. Furthermore, strategy and solution-oriented skills can be used when employees are relieved of standardized and plannable tasks in the changed collaboration with AI and are increasingly concerned with creative problem solutions. These include, in particular, creativity, the joy of experimentation and transdisciplinary thinking.

It should not be forgotten that some skills are essential, even if AI systems have them to a greater extent. Problems arise, for example, when employees lose skills due to the introduction of AI that enables quick human intervention. This aspect should also be considered when asked which skills employees will need in the future to work with AI.

Six Steps To Successful Competence Management

Artificial intelligence will shape the working world of the future. AI systems offer great potential for new business models, higher productivity, and more prosperous work. Early and structured qualification of people is necessary so that companies and employees can benefit from the potential of AI.

Specifically, developing employees’ skills can take place in six steps. In the first step, responsibilities are defined before the tasks are assigned in the second step. Then it is considered which specific skills are necessary for dealing with AI to solve the task AI-based so that in the fourth step, these skills can be bundled into competence profiles. A competence analysis can then be carried out. The employees are assigned to the respective profiles and then evaluated individually about their existing competencies in dealing with AI.

The recent whitepaper Competence Development for Artificial Intelligence of the working group “Work / Qualification, Human-Machine Interaction” of the platform learning systems offers an orientation on how companies succeed in the step into the AI ​​age. It shows how competency needs to be developed in different role profiles and how the necessary development of AI competencies can be achieved through task-oriented competency management.

Also Read: AI Contains Opportunities And Risks

 

 

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