Work and AI 2030

Artificial intelligence (AI) is one of the most powerful future technologies of our time. Many AI discussions about ethics and innovation speed are based on the assumption that the fear of overpowering AI in general and of mass unemployment in particular paralyzes AI development. In this article, we argue that this “German Angst” is rather a fictional construct that has its home in the fictional representations of AI, but does not occur in social reality. People do not fear AI, but the power of the people who could abuse it. Therefore, organizations and politics should take into account the fundamental openness of the workers and create framework conditions that bring advanced digitization and human into an innovation-friendly, productive interaction.

This article shows common problems in the development of AI systems (artificial intelligence), such as fundamental misunderstandings between the user side and the AI development side. These arise from deficits in interdisciplinary collaboration and communication and can lead to undesirable consequences for employees or society as a whole in the long term. As a strategy to avoid these and other misunderstandings and to implement AI in the sense of a responsible innovation process, a process model is proposed, which can serve as a practical guide for using AI in the world of work.

By 2030, almost all humans and machines will work in teams. These teams have different new aspects, for example, what roles AI and humans can play in the team structure. The objective for using AI in teams will be team success and maintaining the team’s performance. Furthermore, typically employees will have a personal AI for their well-being and performance.

AI innovations that stem from scientific research can have a high economic potential. By supporting the exploitation of innovations with great socio-economic and -ecological impact by start-ups through policy, Germany has the opportunity to transfer the well-positioned scientific research into value creation. Even if only a small fraction of the projects will be successful in the market, the support is essential, as the effect radiates to the entire ecosystem. Because together with the research institutions, deep tech start-ups are drivers of innovation ecosystems. They not only attract established companies, investors and top talents, but are also a source of new start-ups and promote the diffusion of AI in the breadth.

The use of artificial intelligence creates risks for equality, fairness, dignity, personal protection and privacy for employees and companies, which contradict today’s corporate values. Corporate Digital Responsibility (CDR) offers solutions to enable trust in corporate action when using AI in the workplace. The article argues that establishing an AI ethics or AI governance is not enough, but rather a framework is needed that leads to competitive advantages. To realign the “moral compass” in the “algorithmic new territory”, companies are advised to implement CDR in the organization.

Based on mathematical models of biological learning processes, computer-based computational algorithms form the basis for ‘Machine Learning’ or ‘Artificial Intelligence’ (AI). Their technological translation offers a variety of applications and promises immense transformative potential for various sectors such as economy, technology and society. Approaches of AI ethics discuss the influence and desirability of such changes, for example, for work processes in affected industries; however, a discourse on the social side effects of technology that is driven purely from a technological perspective neglects the life and human science aspects of its origin as well as its complex impact on psychological, social and cultural systems. An embodiment approach of neuroethics can strengthen these reflexive elements in the AI debate and improve social discourse and agency regarding technology-induced transformations in the world of work.

Intelligent products are becoming more networked and autonomous. Product liability and safety are of central importance for such products, as new technologies bring new risks, but also new possibilities of hazard control. The article deals with the question to what extent a digital product monitoring obligation for smart products can be expected in 2030 or to what extent such an obligation can already be derived from existing regulations or regulatory trends.

One of the most common areas of application of AI in the world of work is likely to be the recruitment process. The use of AI-based language analysis can facilitate the tedious screening and filtering of suitable applications by staff (Wherever the grammatically masculine form is used for personal designations, persons of any gender identity are meant.) of the human resources department. This article shows the data protection and anti-discrimination law risks, but also possible solutions for a legally secure use and highlights why the use of AI can even offer an opportunity to reduce discrimination in the application process.

AI systems will increasingly be used in the employment relationship in the foreseeable future and will take over the employer’s selection and consideration decisions. German labour law generally allows such use. It is up to the legislator and the judiciary to ensure compatibility with European data protection law. Employers must ensure that the respective AI system takes into account existing legal requirements and can transparently reconstruct the criteria of its decision in the event of a legal dispute. For employees, the use of AI does not only entail a danger, but also the opportunity for more objective and qualitatively better decisions.

When using artificial intelligence (AI) in companies, there is no room for the construct of the electronic person (e-Person) as a liable legal entity. To avoid their liability, employers must exercise the utmost care when selecting and using AI. If the currently weak AI is replaced by a strong AI in the coming years, it is necessary to discuss new legal concepts early on.

The scope of the co-determination right pursuant to § 87 para. 1 no. 6 BetrVG (Works Constitution Act) has been interpreted very broadly by the courts in the past. The decisions of the courts from the past make the introduction of modern AI systems in companies considerably more difficult. This article critically examines this issue and presents possible solutions for a practical and up-to-date co-determination right.

This article deals with predictive policing as a possibility to create a forecast regarding the probability of committing a crime or breaching duties by a certain employee. Based on the knowledge gained in this way, the employer can take measures that are suitable to prevent or at least minimise the realisation of the predicted risk. However, it is particularly necessary to examine the legal basis on which the employer can rely when using predictive policing. This article examines the general clause of § 26 para. 1 sentence 1 BDSG (German Data Protection Act) and consent pursuant to § 26 para. 2 BDSG as possible legal bases.

In view of increasing social complexity and the associated, necessary modernisation and digitalisation of administration and justice, an increased use of artificial intelligence (AI) systems is being considered. Especially in this area, there are distinct limiting requirements or even hurdles for the implementation of AI systems. The article identifies these, describes further challenges and solutions, and ventures a cautious look into future developments of the use of AI by the state.

Intelligent IT systems can handle a growing spectrum of cognitive and physical tasks as well or better than humans. This enables these IT systems to act increasingly autonomously. However, the commercial automation potential associated with this development can only be exploited if people are willing to delegate tasks to intelligent IT systems. However, this willingness is not generally present, but requires the building of trust. This chapter addresses the question of how to promote trust-building in intelligent IT systems, and identifies control and transparency as two important trust antecedents. These insights have important implications for the successful collaboration between humans and intelligent IT systems in work environments of the future.

The introduction of AI in companies and other organisations offers opportunities and potentials both for employees, such as relief by AI systems, and for companies, such as process improvements or new business models. The introduction of AI has peculiarities that affect both the change management and the participation of the employees, including the processes of co-determination. The following chapter pursues the goal of raising awareness for the requirements of change management in AI and supporting the implementation of the introduction of AI systems with different phases of the change process. An important success factor is the involvement of the employees and the mobilisation for the use of the new technologies. If we apply a goal-oriented and human-oriented change management already today, this helps to achieve a successful use of AI technology in the working world 2030.

Artificial intelligence (AI) is spreading rapidly and permeating almost all areas of our lives—whether in the form of chatbots, newsfeeds, voice assistants or self-driving cars. Also in the economic work environment, AI is finding more and more fields of application. In addition to exploiting the advantages of AI technology to improve business performance, companies should also deal intensively with the associated risks in the future. Against the background of the application of AI at Deutsche Telekom, the authors explain in this article why trust in AI is so important and which approaches will be crucial for companies in the future to minimise risks and use AI in a trustworthy way.

The use of AI-driven technology is no longer a thing of the future: Artificial intelligence is transforming the labour market and will pave the way for new forms of employment. Because the decisions made today will shape tomorrow. How can the work environment of the future be made more bureaucratically flexible to create optimal conditions for hybrid work arrangements? And how do intelligent algorithms counteract the shortage of skilled workers in Germany?

Although digital finance is high on the agenda of many CFOs, there are still only a few companies that have already successfully transformed their finance function with AI. In this article, we show how AI revolutionises the financial planning and forecasting of companies and leads to a fundamental transformation of the finance function.

Artificial intelligence (AI) is an increasingly integral part of the world we live in. The future success of banks requires a holistic transformation that encompasses multiple levels of the organisation. This article addresses the individual business areas of the banking sector and highlights their transformation potential with regard to the use of artificial intelligence. The aim is to identify which areas are likely to change due to artificial intelligence and which areas can continue to adhere to their traditional work environment.

AI is attributed with a lot of potential for the production environment and is supposed to help make manufacturing more productive, human-friendly and sustainable. Despite these promising possibilities, AI is rarely used in production. In addition, AI and humans are far from a symbiotic collaboration. The reasons for this are numerous and it will be crucial for the German economy to resolve them to an even greater extent. With a view to the year 2030, the article presents six possible scenarios around AI in production, which show a gradually increasing influence of AI. In order for AI to be increasingly used, a systemic approach will be needed, which takes into account not only the production itself, but also framework conditions and social structures. It would be desirable even today to consider production as an experimental learning environment, in order to overcome technical, social and organisational hurdles together.

The digital transformation and the use of artificial intelligence (AI) pose great challenges for traditional industries such as the energy sector. The value creation along process chains characterised by manual work to operate large physical infrastructures increases the implementation complexity of valuable digital solutions and requires at the same time a high degree of maturity of digitally mapped information flows (data quality) and technologies (sensor technology, robotics). In order to overcome these high entry barriers and move from the theoretical “digital potentials” to the operationalisation of the “digital transformation”, the energy sector is more than other industries dependent on preparing its business from the grassroots upwards, along processes and information flows (Data Governance), and with the help of the resulting digital automation and service potentials (Servitisation) for the use of AI.

The study by Frey and Osborne (2013) and similar ones have determined large substitution potentials for certain activities and occupations due to digitalisation. However, deriving labour market effects from this is not directly possible, because the specific design of new technologies is shaped by ethical, legal, social, cultural, institutional and economic factors. To avoid negative impacts, employees take advantage of the opportunities for further education offered to them with the support of companies. In addition, AI must also be understood as a technology potential, whose specific social effectiveness in companies depends on different evaluations and forms of implementation in work organisation (see, among others, Hirsch-Kreinsen et al., 2018). In the second part, organisational design alternatives will be presented, which in turn have an impact on employment development and opportunities for further education.

Digitalisation and artificial intelligence (AI) are increasingly being applied and offer versatile opportunities for shaping the world of work. In this context, both mental and physical activities can be supported by appropriate assistance systems. This is associated with great potentials for the German industry—especially with regard to securing competitiveness, jobs and prosperity. The article describes stages of digitalisation and draws on results from two survey studies in the German metal and electrical industry from the years 2015 and 2019 to outline the current state of development and existing challenges of digitalisation and AI as well as the expectations associated with them. Requirements, importance and implementation activities are addressed as well as qualification needs, shift work and flexibility.

The increasing use of artificial intelligence for cost- and sustainability-oriented optimization of manufacturing processes leads to a restructuring of the working world within the producing industry. Employees on site transform into the orchestrator of a multitude of autonomously acting plants based on data analysis. However, the necessary expertise and underlying data basis are missing and prevent a full exploitation of the potentials of AI. A data exchange across company boundaries within a sovereign data space is required to solve the current and future challenges of the AI-shaped working world 2030. This contribution therefore deals with the tasks and functions of platform-based data marketplaces, their architecture to be developed and the necessary legal framework to enable people in the working world 2030 for advanced AI analyses.

The development of artificial intelligence will also influence the working world of crafts, even if AI currently plays a subordinate role in crafts. The use of AI opens up the opportunity for crafts to focus on the core of their activities, to increase productivity and thus to meet the challenges of the skills shortage with increasing demands. AI methods will continue to automate craft activities and shift the balance in favour of fewer large companies.

AI can positively improve the future world of work by taking over repetitive tasks from human workers and showing new connections. This is made possible by intelligent algorithms, fast computing and large storage capacities. Using three examples from the field of mobility, we want to show how AI can enable more creativity and holistic decisions and achieve higher reliability.

Already today, AI applications are in use in urban mobility for traffic management systems, repair and maintenance, autonomous driving and mobility as a service—accompanied by a permanent discourse on legal and ethical issues. By the year 2030, constant employment numbers are expected, although the nature of the activities will change significantly.

The increasing complexity in industrial production requires new solutions and approaches to ensure the competitiveness of the European industry. This article therefore focuses on the question of how artificial intelligence can be used in industry to cope with the increasing cost pressure and what consequences this has for the world of work. In particular, smart factories and team robotics are examined in detail to look at the opportunities and risks of AI applications in a neutral way. The theoretical classification is enriched by practical examples to establish the connection to the current and future developments. Finally, an outlook is formulated, which system dynamics methodology can be applied to develop promising solutions for the competitiveness of the European industry, which have positive effects on both the working worlds and the overall economic welfare.

The transition to alternative powertrain systems and the increasing complexity of automotive software poses great challenges for the automotive industry. This is especially true when novel development paradigms profoundly change the previous tradition in terms of product development. AI is such a driver of change, which we will illuminate in this chapter with regard to its influence on the technical development of future vehicle platforms and mobility products. We will address both the product and the development process as well as the company side.

AI applications will emerge in all areas of the rail sector. From network and train operations to maintenance and customer interface. First AI applications are already live—yet the development in the rail sector is still at the beginning. The effects of AI applications on employment are unclear. The reasoned assumption of the authors is that employment will become more qualified and more employment will be created. The central driver is the goals of the transport transition and the urgent need to use AI to increase capacity and performance in the rail sector—and thus generate more employment.

AI in aviation is already used in very different areas and also offers multiple application possibilities in the future. Thus, AI transforms the entire aviation business and with it the work environments of the aviation staff.

Tasks of intralogistics are traditionally optimised with the help of algorithms. In contrast to methods of operations research, AI can overcome rigid objective functions and react flexibly to occurring events. The availability of real-time data and its evaluation also enables the prediction of events based on pattern recognition and thus a greater customer orientation. In addition, by 2030, real time simulations in the digital twin will become the norm and intralogistics will merge with overarching logistics chains. Furthermore, the use of drones will make the solution space for distances three-dimensional, which will lead to efficiency increases that were not possible before. Nevertheless, human beings remain the key factor in logistics. Wearables and exoskeletons enable the free collaboration with co-robots in a confined space, the human being becomes an integral part of a networked logistics system.

Artificial intelligence has the potential to fundamentally transform medicine. Even as of today, AI programs show that they can outperform doctors in the evaluation of medical imaging data. Sensor-based monitoring in combination with self-learning algorithms shifts the focus increasingly from the clinic to the home environment, from therapy to prevention. The systematic analysis of structured information using data mining methods provides new insights into the causes of diseases and the success of medical interventions and therapies. The key will be how information is integrated in the future and how the individual retains sovereignty over his or her data.

The use of artificial intelligence (AI) in medicine is associated with great hopes for more targeted and improved diagnoses and treatments on the one hand and increased efficiency and quality of health care on the other. The realisation of these potentials requires changes. Digitisation and the widespread use of AI will fundamentally change where and how medical services will be available, how and by whom decisions will be made along the entire medical treatment path and which treatments will be performed with regard to the outcomes achieved. The great potentials are accompanied by legitimate concerns, for which safe and pragmatic solutions need to be found. Used correctly, AI will improve health care overall and contribute to solving the challenges of today’s health systems.

“The use of artificial intelligence is indispensable for the development of new therapies. AI approaches are applied along the entire value chain—from the discovery and optimisation of new active ingredients, to the conduct of clinical trials, to pharmacovigilance, to name just a few examples.” The search for new active substances has already been influenced by learning algorithms decades ago. Statistical methods—AI of the second generation—have also been in use for a long time in drug development, in the evaluation of studies and in production. Recently, however, new fields of application have been opened up by the emergence of deep learning, which go beyond the medical-chemical domain and enable new possibilities of cross-functional use for large companies. This article shows the way from isolated applications to a new, holistic vision of value creation at Bayer AG.

Originally developed to enable broad access to medical care in an equitable manner, health insurance companies in Germany are now attributed a very different role by their insured. Accordingly, health insurance companies redefine their purpose and business model and there is no way around artificial intelligence when attempting to better meet evolving customer needs. Potential lies in applications related to both the internal processes and products and services for customers. A desirable future vision is that health insurance companies are allowed to evaluate the data that has long been converging with them in such a way that they can give their insured even more individual recommendations thereby enabling them to achieve their optimal personal health level. But currently they are still stuck in a rather rigid corset, while globally operating corporations can already deal with health data in a very different way.

For the renowned big IT tech giants, it is equally true that they have been economically very successful so far mainly in the data and big data business, so that it is now only logical for them to use their core competencies to also “fish” in adjacent or new markets. On the one hand, health systems that are partly dilapidated and under pressure to perform, with IT deficits in their entire value creation (e.g. demanded increase in quality of care while reducing costs), and on the other hand, seemingly unlimited resources with the highest focus on customer satisfaction and partly aggressive marketing and customer loyalty strategies, result in a very promising environment for newcomers and their innovations in the health sector. The aim of this article is to present the most important strengths and innovative approaches of the “big IT tech” (e.g. Google, Amazon, Microsoft, Apple) for new work environments in the health sector.

How do I manage as a small or medium-sized enterprise (SME) to successfully use artificial intelligence (AI)? Existing consulting services that support SMEs in this regard usually focus on technical aspects of AI. The solution approach presented here is to qualify one’s own managers and employees as AI experts, so that they can identify AI applications for the value creation of their own company and design them productively and humanely. This gap could be closed by a complementary qualification offer. Through an AI qualification tailored to the needs of the medium-sized sector, managers and employees in SMEs could be specifically enabled to realistically assess AI applications and enable a profitable AI use. This AI qualification would supplement the existing skills and the existing professional knowledge of the employees with AI competencies.

Although numerous studies and research on the topic of AI in education have been conducted in the last 60 years, the actual relevance for application has only gained momentum in recent times. In particular, the development of data-driven and AI-supported Educational Technology (EdTech) is considered as an emerging field in the education sector, which promises a change in the teaching and learning culture. This article provides insights into sub-discourses on AI in EdTech and identifies needs for action that are essential for a sustainable design of AI in EdTech. One aim is to understand the changes that come with AI in the EdTech area, in order to create an awareness of one's own power of participation and thus a self-determined handling in the context of datafication and digitalisation in education.

The need for further education is growing in the digital transformation. New concepts and technologies are required to keep up with the speed of development needs. The success of further education will be determined in the future by the solution of three further education gaps: motivation, competence and transfer. Artificial intelligence provides an important building block for this. Through learning analytics, personalised learning, task automation and smart content, the current challenges of further education can be overcome. Important debates of artificial intelligence in further education will also be determined in the future by ethical questions (e.g. discrimination) as well as questions about the objectifiability of human development.

The use and long-term utilisation of AI-supported assistive systems in vocational rehabilitation can offer great potential to improve the participation of persons with disabilities in working life. A prerequisite is the availability of market-ready intelligent assistive technologies that generate personal benefits for persons with diverse disabilities and can be used by companies with low effort. The diffusion of these technologies can succeed if the assistance potential of AI technologies is explained and AI-supported assistive technologies are developed and introduced in a demand-oriented and participatory way. It is also important that access for persons with disabilities to these technologies is ensured, competencies for their use are promoted, and continuous further development and support for their use are ensured.