Artificial Intelligence in Germany

AI - How will Germany position itself in the economy?

Artificial intelligence is becoming a key technology for the whole economy. It will place the whole value chain on a new footing, not only for industry, but also for the crafts sector, trade, services and even agriculture.

Introduction

Artificial Intelligence (A.I) will transform companies in a pioneering way. It brings a fundamental change to the operation, positioning in the market and business success. As part of a holistic approach, AI technologies help increase productivity and reduce costs, creating more freedom for challenging tasks and unlocking opportunities for growth.

Federal Minister for Economic Affairs and Energy Peter Altmaier said in November 2018: “Artificial intelligence is becoming a key technology for the whole economy. It will place the whole value chain on a new footing, not only for industry, but also for the crafts sector, trade, services and even agriculture. The Federal Government’s strategy sends out a clear signal of our desire to boost the future competitiveness of Germany and Europe as a whole. We want to become a global leader on the development and use of AI technologies. For this purpose, we will make available €3 billion in the coming years. We want to support enterprises, especially when it comes to using AI – inter alia by sending out AI instructors to SMEs via our Mittelstand 4.0 centers of excellence and in the context of our agency to promote breakthrough innovations. In addition, we want to ensure a powerful data infrastructure so that we can keep pace with the large platforms.” (ENERGY, 2019)

Analysis of AI in Germany today

Germany has a high pool of skilled labor and infrastructure, making the emergence of AI in the market a swift growth. As a result, there have been various sponsored initiatives that aim in boosting Al intelligence and research. Al levels implemented in companies include assisted intelligence (These are installed hardwired systems that help in decision-making), Automation (either manual or automated and help with cognitive skills), Arguments Intel (are more involved in decision making because of human interactions), Autonomous intelligence (capable of adapting into different situations without human assistance). Studies in recent years have proved that the use of AI in the economy has led to increased productivity of more affordable goods as well as increases incomes. (McKinsey 2017)

Across universities, there are about 90 Chairs for AI, including at the Saarland Informatics Campus and the Karlsruhe Institute of Technology. (COMMISSION, 2018)

Germany hosts a variety of research centers and institutes:

  1. German Research Centre for AI (DFKI)
  2. Max-Planck-Institutes for Intelligent Systems, and Informatics
  3. Fraunhofer Institute for Intelligent Analysis and Information Systems, including the Fraunhofer Big Data Alliance
  4. AI research center Cyber Valley funded by the federal state Baden-Württemberg.

Germany has also activities in the industrial ecosystem. Several large corporations such as Siemens, BMW, Bosch, SAP, and Telekom are investing in the use of AI for various applications. In addition, a variety of spinoffs like Agro Links and SemVox employ around 100 people each. There are more than 80 AI-focused start-ups currently established in Germany. With an annual budget of more than EUR 3 billion, the German Research Foundation (DFG) is the main source of funding for basic research in AI in Germany. (COMMISSION, 2018)

The good news is that AI adopters from Germany indicate a strong focus on AI training (see figure 1). They tend to have a more tempered view of AI’s ability to enhance job performance and enable new human/machine partnerships, but they report stronger efforts around employee education. Respondents from Germany are more likely than their counterparts from other countries to train employees to use AI in their jobs, train developers to create new solutions with AI, and train IT staff to deploy these solutions.

This holistic approach to filling the AI talent gap is a source of advantage, and one from which other countries can learn. Organizations should not worry only about acquiring and educating a new generation of AI experts—they also should develop and retrain existing workforces. Leaders may also wish to explore how ensuring ethical AI can become a source of competitive advantage. For example, SAP has set up an advisory panel drawn from government, industry, and academia to help drive a set of principles for the company’s AI work. (Jeff Loucks, 2019)

Figure 1 Germany outpaces other countries when it comes to AI training Source Deloitte State of AI in the Enterprise survey, 2nd Edition, 2018.png
Figure 1 Germany outpaces other countries when it comes to AI training Source Deloitte State of AI in the Enterprise survey, 2nd Edition, 2018.png

Analysis in Europe and the rest of the world

Al has revolutionized production processes by enabling quality services for its customers while transforming companies’ day-to-day activities. Al has affected the competitive position of investors across the globe.

However, some European countries have not embraced the use of AI fully though countries like the United States and China have invested widely in digital industries, research to shape the European as well as global economic status.

Al has enabled a wide range of uses ranging from automating, personalization, prescribing and creation of insights. Most companies expect to benefit by optimizing operation, engaging with customers (through feedbacks), transforming goods and services as well as enabling employees.

However, many companies are unable to quickly integrate innovative trends and cutting edge technologies due to the burden of legacy systems and complex government processes. Research gathered indicates uneven development of Al across countries with different industrial and technological status. Most businesses are not able to access the required technology that would place them on a higher rank in a competitive world of entrepreneurship.

Sectors that are more mature in using AI are those that report higher competency in Advanced Analytics – particularly TMT (Telecom, Media/Entertainment & Technology), as well as Finance (including Banking, Investment & Insurance), and Life Sciences (including Healthcare & Pharma) all report lower competency in AI Leadership. A possibility is that in the pharmaceutical industry, AI chiefly resides in R&D, and has yet to affect the broader organization on the wider strategic level. Companies intend to use various levers to obtain these AI capabilities. (Kirschniak, 2018)

The strongest AI regions within the European Union are the United Kingdom, Germany and France. Figure 2 shows the top AI startup hubs.

Figure 2 the top AI start-up hubs within the EU
Figure 2 the top AI start-up hubs within the EU

European AI start-ups raised EUR 3.6 billion in 2017, almost three times more than in 2016. The top five industries they operate in are FinTech, HealthTech, MadTech (marketing, advertising and technology), business intelligence, and automotive. They focus mostly on B2B (business-to-business), which represents 76% compared to 24% for B2C (business-to-consumer). (COMMISSION, 2018)

Compare Germany to Europe and to the rest of the world

The United States leads in Europe in terms of entrepreneurship and business. China comes second owning 11% of the start-up worldwide while Israel takes the third position. Most industries in the world are ragging behind examples, automotive industry, real estate, agricultural industry, and the public administration. With such companies, being left behind in entrepreneurship, use of technology thus slugs resulting in poor productivity and minimal or no innovation from employees. Countries like the United States benefits from big digital players like Google, Amazon, Apple, and Facebook. Through social medial and the internet as a whole, entrepreneurs can sell their products and services as well as receive the appropriate data through feedbacks thus enabling business decisions, marketing strategies and services delivery. A company like Amazon or Google contributes widely when utilized since they have access to data, recent technologies, sophisticated engines, appropriate IT infrastructure as well as the ability to attract innovative and skilled labor. (Institute, 2019)

Figure 3 shows the global AI hubs.

Figure 3 Global AI Hubs Source Infograph SYNCED
Figure 3 Global AI Hubs Source Infograph SYNCED

Germany’s current AI readiness is relative to North Europe and North America. The score indicates that Germany’s adoption rate is likely to be similar to most of Northern Europe’s economies (e.g. Switzerland, Netherlands, and the United Kingdom). However, it is marginally lower than that in many Northern American economies (e.g. USA, Mexico, and Canada). Germany performs particularly well on elements of the Index such as ICT access and use, R&D investment by companies and gross capital formation. However, it lags on new business density and the cost of redundancy dismiss. Germany’s businesses have the foundations and potential to successfully adopt AI in the near term.

Key takeaways

Businesses that are lead the way in Al and automation are not limiting themselves to individual use cases. Technology can be implemented right through a company from design and photocopying to scales and servicing. Most manufacturers are moving away from identical production lines and building the capability to produce bespoke products with the same efficiency as they can with mass production. Al has enabled them to cope with inherent logistical challenges. Automobiles production, on the other hand, has been predicted to be faster and more efficient thanks to the ability to collect and analyze data at every single step. (Bundy & Wilson, 2006)

Getting traditional companies involved in the digital economy remains a priority for the government. These companies need to become a source of investment in startups. One of the limits of traditional AI has been the inability to understand human traits such as emotional state, for instance, exhibited in writing, physical condition, or tone of voice. With AI’s cognitive intelligence capacities within reach, machines are increasingly able to sense, recognize, and decode human traits. This holds the potential to fundamentally change the way people interact with machines, making technology capable of handling more complex tasks and ultimately augmenting humans to an extent previously unachievable. (Philippe Aghion, 2017)

The development of AI and the delivery of related projects are most often done with a mix of internal and external stakeholders. The rationale is multifaceted some companies are struggling to obtain the specific talent, whereas others opt for a partnership approach to be a faster and more flexible solution. These external alliances typically come in two forms: being focused on technology and technical AI expertise, or focused on strategy and business development. To address one of the biggest prerequisites of working with AI, access to large amounts of data, companies state that they are increasingly looking to entering into data partnerships where they either buy or exchange data with other parties. (Ajay Agrawal, 2018)

German companies rate Data Management as the second most important of the eight capabilities necessary to succeed with AI (4.3 average on a scale of 1 to 5) – slightly below the European average. In terms of the level of competency, 80% of companies surveyed in Germany consider themselves moderately competent or above in Data Management. However, the average score is only slightly above moderately competent. This suggests that some companies have developed a Data Management foundation but are still midway before achieving the capability level that will fully back their AI systems. Finding the right quantity and quality of data is essential according to many of the companies interviewed. (Kirschniak, 2018)

Best Practice for the Future and Reccomendations

A start-up business needs to understand AI. Thus, companies need to analyze how AI affects their organization, industry and related areas. Through analysis, the management can be use the collected data to identify risks and recognize opportunities such as those arising through new business models. For example, suppliers of raw material can become parts of manufacturers more easily than before by leveraging. Customers can also take part through feedback on products or services. Companies must involve both suppliers and consumers for success. Companies should be able to identify their strengths. The company’s goals and vision should be ambitious but still attainable. Alongside a risk and opportunity analysis, it is important to analyze one’s options. For example, there is a need to check which data is available for training AI systems. Besides, there is the question of to what extent companies may use data especially in light of the EU General Data Protection Regulation (GDPR) enforcement.

As an entrepreneur, it is important to develop a consistent AI strategy. An honest analysis will often show that data only exists in sufficient quantity and quality for the core business. Thus, a company’s own data from customer management and service systems can be more quickly and efficiently linked than external information, such as data from social media. Developing a consistent AI strategy starts with addressing this issue by defining how the business’s core competencies should be strengthened with AI systems and which innovative products, services or even business models are possible.

A start-up business must guarantee transparency and ethical behavior. Innovative products and services will only succeed in the long term if customers trust them. This is why ethical guidelines and transparency provisions are fundamental parts of any AI strategy.

Lastly, for successfully implement a strategy, companies need to hire AI experts and train employees. Another decisive factor is a corporate culture that embraces innovation. It is important to cultivate a sense of team spirit, because of people’s mentalities, the holding on to knowledge for the sake of control, and the rigid hierarchies only serve to inhibit new ideas and quick responses. As a result, there will be a rigid cultural use of AI in a business.

Conclusion

Entrepreneurs need to research and make AI a cultural norm in making a business decision for probable success. It is essential to provide for what consumers demand for in quality and quantity as well as in more affordable terms (that is, through personalization of a variety of goods). AI in entrepreneurship has cultivated other major disciplines like computer science and information systems that have helped register major profits. Companies are encouraged to invest in technological infrastructure such as communication channels and social structures.

Appendix A: About the research

This report is a part of the Schalk Vermögens- und Verwaltungsgesellschaft mbH research initiative „AUX Research Institute“ launched 2019 by our founder to gain free access to knowledge an research material. The research was undertook to establish a clearer understanding of the transformative opportunities a new risks arising from predictive maintenance. Further objectives of the project included helping business envision the new world resulting and identifying potential implications to the workforce (e.g. employment, skill distribution and productivity).

About AUX Research Institute

Founded in 2018, Schalk Vermögens- und Verwaltungsgesellschaft mbH is a independent consulting and research firm for digital transformation and start-ups. In 2019 we launched our new research institute, AUX Research Institute.

With our new Institution, the goal is to publish research on large scale and give access to everyone. Spreading knowledge and helping to educate people around the world.

!Download the articel here!

References

Ajay Agrawal, J. G. (2018). Prediction Machines – The Simple Economics of Artificial Intelligence . Boston: Horvord Business Review Press .

Bundy, A., & Wilson, S. (2006). Rob Milne: A Tribute to a Pioneering AI Scientist, Entrepreneur and Mountaineer.

COMMISSION, E. (2018). The European AI Landscape. Brussels: Directorate‑General for Communications Networks, Content and Technology.

ENERGY, F. M. (2019, 09 19). BMWI. Retrieved from Joint Press Release – Federal Government adopts Artificial Intelligence Strategy : https://www.bmwi.de/Redaktion/EN/Pressemitteilungen/2018/20181116-federal-government-adopts-artificial-intelligence-strategy.html

Institute, M. G. (2019, 09 23). ARTIFICIAL INTELLIGENCETHE NEXT DIGITAL FRONTIER? Retrieved from ARTIFICIAL INTELLIGENCETHE NEXT DIGITAL FRONTIER?: https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx

Jeff Loucks, S. H. (2019, 09 23). Future in the balance? How countries are pursuing an AI advantage . Retrieved from Future in the balance? How countries are pursuing an AI advantage : https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/ai-investment-by-country.html

Kirschniak, C. (2018). Effects of using artificial intelligence in Germany. Berlin: PricewaterhouseCoopers GmbH Wirtschaftsprüfungsgesellschaft.

Philippe Aghion, B. F. (2017, 10 10). Artificial Intelligence and Economic Growth. Retrieved from Artificial Intelligence and Economic Growth: https://web.stanford.edu/~chadj/AI.pdf

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