The digitization of nearly all processes within companies is continuing to advance. Emerging technical concepts like artificial intelligence (AI) and machine learning (ML) present new possibilities while also posing challenges for companies and their workforces. How can we ensure that the implementation and operation of these systems remain manageable, expanding the capabilities of individuals and companies rather than limiting them?
Both companies and their workforces face challenges arising from new data-driven algorithms, systems, and business models. Consequently, companies are grappling with questions such as: Do we possess sufficient skills within our organization to truly comprehend and master these technologies? What dependencies do we establish when we entrust external platforms with the collection, analysis, and evaluation of our data, including machine operating data? Conversely, what steps must we take to assume control over as much of this data processing and analysis as possible?
Employees ponder over their ability to master these new technologies with their current skills and competencies. Furthermore, they question the value of these skills and competencies in this context. How can they feel confident in dealing with technology whose inner workings they do not fully understand?
There is no magical formula that can comprehensively address all these questions. However, numerous individual building blocks already exist, which can be combined using suitable solution patterns tailored to specific operational situations. The most crucial among these building blocks, so to speak, is a systemic perspective: the technical solutions, operational organizational forms and processes, and the competencies of individual employees must align and be optimized together for practical success.
What is important in this optimization process? Three key criteria emerge: transparency and explainability, both in technical systems and organizational structures. Achieving this is not straightforward with AI-based systems, given their inherent opacity, which extends even to their developers. Nevertheless, existing technical solutions can help restore explainability, for instance, approximating algorithmic behavior through flowcharts. The other two criteria are certainty of action, ensuring that interactions with these systems yield intended outcomes with a high probability, and freedom of action, enabling users to choose from multiple possible actions instead of being constrained by the system.
To ensure that the design solutions within a company meet these criteria, the Institute for Innovation and Technology (iit) in Berlin has developed a step-by-step procedure that assists companies in finding the appropriate solution. This procedure involves guiding questions and expert advice. Specifically, for complex technical systems, Annelie Pentenrieder and colleagues at the iit have devised a method that empowers users to generate concrete, visually represented ideas about human-technology interfaces.

Questions regarding organization and qualification are often interconnected. For user companies seeking to confidently introduce and operate algorithmic or AI-based systems, a role concept assumes significant importance. This concept determines who will orchestrate the entire introduction, possess comprehensive IT and data science expertise, possess domain knowledge to decide on suitable IT approaches, and oversee the initiative from a management level. Each role entails specific tasks, responsibilities, and competence requirements. René Wöstmann, in collaboration with colleagues from TU Dortmund University and the RIF Institute for Research and Transfer, has presented a role concept encompassing all the necessary qualification modules, specifically tailored to SMEs.

All of these concepts and methods can be found in a freely available online book titled "New Digital Work - Digital Sovereignty at the Workplace," published by Springer-Verlag. Additionally, the book covers other topics, including:
 - How will AI impact jobs and the labor market?
 - What new opportunities for education and training arise from digitization?
 - How can we quantify prediction uncertainty in algorithms to facilitate accurate classification?
 - What do immersive human-machine interfaces entail, and how can they enhance work quality?
An event titled "New Digital Work and Digital Sovereignty in the Workplace" will also take place at EMO Hannover 2023 on September 20 and 21, 2023. This event, organized by the Institute for Innovation and Technology (iit), will explore the aforementioned content.
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Prof. Dr.

Ernst Andreas Hartmann

Digital lifecycle file for the circular economy

Networking machines is the key to efficiency, traceability, condition monitoring or machine learning.

World language of production: Key to efficiency

Digitalization is a driver and enabler of progress and competitive advantage in industry. Combining data with specialized algorithms creates AI.

Harnessing the power of AI compensate for the shortage of skilled workers

How can we ensure that the implementation of AI and ML remain manageable, expanding the capabilities of individuals and companies rather than limiting them?

Confidently Shaping Digitization in Manufacturing Companie

Confidently Shaping Digitization in Manufacturing Companies

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