Our society is changing incessantly and at increasing speed, and the corona pandemic does not change that. Our life is difficult to imagine without technology and artificial intelligence, and those who refuse to do so may miss the boat. But where are the technical achievements really taking us, and with what purpose are they being expanded?

The time “between the years” I was among other things busy with Precht’s new book “Artificial Intelligence and the Meaning of Life”, an essay! This stimulates thought, but also a contradiction. So here are just a few passages that I have marked:

  • The goal of almost all artificial intelligence is to gain more control and generate greater profits; be it through medical or military technology, more efficient production, lower costs and even better knowledge of the citizen or customer.
  • Technology can make our lives more comfortable, but human beings are much more than that, they have developed a culture and morality that no computer in the world will ever understand, let alone reproduce.
  • Human intelligence is pervaded by emotion and intuition, spontaneity and association. Common sense is not a synonym for rationality, but to the same extent empathy with the situation under the influence of values.
  • There are too few people who care about both the environmental issue and the technical progress brought by AI; that is why the gulf of our time runs between transhumanism and posthumanism.
  • Artificial intelligence can be beneficial or turn into a nightmare. The limit is relatively easy to name: Wherever artificial intelligence decides the fate of human life, i.e. it is directed at people, it is better to stay away from it.

How do you see things? Which statement would you agree with and which would you disagree? Please write me a comment.

Selected AI application fields

Of the four categories of AI processes (speech understanding, image recognition, machine learning, knowledge-based systems), image recognition processes make the greatest positive contributions to the innovative performance of companies using AI. At the same time, AI applications in the area of ​​process automation increase the probability of introducing innovations and thus generating higher direct economic returns. The use of machine learning processes shows positive contributions in the area of ​​product innovations for individual types of innovation. In the area of ​​process innovation, only a relatively weak positive effect on the introduction of logistics processes can be observed. The use of knowledge-based systems, e.g. via cognitive modeling or semantic technologies, shows only isolated positive contributions to innovation performance. Methods of speech understanding have almost no influence on the innovation performance of companies using AI.

There are already numerous fields of application by financial service providers, energy supply companies and industrial companies.

In addition, there are already new coalitions and a number of positive examples in the HR departments:

  • Recruiting: In previous posts – e.g. Trainee recruiting in times of corona: what is changing? what remains? – I have already explained a lot about it. The STRIMservices determines i.a. for some large companies as part of the RPO recruiting metrics and indices; therefore I know how difficult it is to have enough high quality data! Only on this basis can algorithms take effect and be linked to bots, for example. Bots can i.a. evaluate candidate answers to specific questions and “recommend” who will go to the next round. Bots continuously optimize their recommendations using machine learning.
  • Personnel development: When I carry out projects on strategic workforce planning and competence management and we come to talk about action plans with a view to PD, then usually lengthy processes start despite the integration of tools. Companies such as Deutsche Telekom (AI-supported courses to close skill gaps) or IBM (Watson Talent Framework) are already progressive – albeit still an exception today.
    As in recruiting, bots could also be used here – from competency comparison, through suitable further training measures, to booking and evaluation.
    In addition, AI-supported PD should in my opinion be “coupled” more closely with personnel planning and organizational development (OD). We should put OD and PD “in one hand” not only in terms of content, but also organizationally.
  • Personnel deployment planning (PDP): In this area, AI was the first to establish itself in the form of evolutionary algorithms; above all in hospitals and chemical companies. Usually findings about the expected order volume, production figures, planned sales or customer frequency flow into a forecast of the workload.
    In the future, AI could also be used as part of the PDP when putting together project teams, especially in large international projects.
    PDP must be clearly distinguished from strategic workforce planning, which is mainly based on data from strategic corporate planning.

Effects of the use of AI

A study published in December 2020 by the Federal Ministry for Economic Affairs and Energy (BMWi) examined the contribution of the AI application fields outlined above to the innovation performance and performance of the German economy. Here are just a few excerpts:

  • Companies that use AI are better able to produce sophisticated innovations with a high degree of novelty.
  • The use of AI has a clearly positive impact on company returns.
  • There is no increase in sales as a result of the use of AI, i.e. the turnover with AI-based innovations replaces turnover that was previously achieved with products without the use of AI.
  • AI leads to a noticeable increase in employment; above all hiring new specialists.
  • The use of AI does not lead to higher productivity in companies using AI.

Conclusion

The digital networking of products and services, production and sales systems as well as processes within companies and between companies creates a wide range of opportunities to increase the performance and benefit of offers and processes and to develop new solutions.

In addition to ethical and cultural issues, the use of AI also comes with numerous challenges and hurdles. In addition to the creation of the technical and data requirements, it is necessary to integrate the AI ​​approaches into existing systems and structures in the company and to partially reorganize responsibilities and processes. This often requires new skills that require the hiring of new employees or entering into new collaborations.

Back to the opening question: blessing or curse? My conclusion: If AI is not used monocausally with a view to finances, but also includes ethical, cultural and organizational issues, then the pendulum can swing for the better. But – to say it with the greek poet Hesiod: The gods sweat before success (with AI). The use of AI is by no means a guarantee of success in the innovation process. High development costs often go hand in hand with a not inconsiderable technological risk (feasibility of the AI ​​application) and uncertain market acceptance.