National Initiative for AI-based Transformation to the Data Economy
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About the National Initiative for AI-based Transformation to the Data Economy
The National Initiative for AI-based Transformation to the Data Economy (NITD) is a joint project of acatech – National Academy of Science and Engineering and the Federal Ministry for Digital and Transport (BMDV). The NITD is one of the flagship projects of the federal government’s digital strategy (in German) with EUR 32 million in funding from the Federal Ministry for Digital and Transport.
Why is the NITD necessary?
Artificial Intelligence (AI) is a key technology that is transforming our daily lives and global competition to an ever-greater extent. To ensure a smooth transition to an AI-based data economy, Germany and the EU need to have a strong ecosystem for data economics and AI. To this end, the National Initiative for AI-based Transformation to the Data Economy (NITD) was launched by the Federal Ministry for Digital and Transport (BMDV). Led by acatech – National Academy of Science and Engineering, the NITD will serve as a neutral actor and provide support for the data economy in Germany and Europe between now and the end of 2025.
I want to further the development of artificial intelligence in Germany. For that reason, I am campaigning at EU level for clear rules that provide scope for innovation and against technology bans. At the same time, we also have to create better conditions for digital innovations in our country. AI developers need easy access to data, clarity on future standards as well as tailored funding programmes. We have a strong research landscape when it comes to AI and data economies. With this new initiative, we want to speed up practical implementation.
Dr. Volker Wissing, Federal Minister for Digital and Transport
In launching the NITD the federal government has created a framework for such important initiatives as the setting up of data spaces, the assurance of quality and testability, and the scaling of AI innovation in the market. The preparation process and successful establishment of a Mobility Data Space has created a blueprint for further data spaces. Companies, research institutions, public bodies and non-profits throughout Germany and Europe are involved in establishing such data spaces. The NITD is paving the way for the next stage of the journey to digital sovereignty, which is characterised by innovative and successful applications that follow European values and rules in the AI-based data economy.
Germany and the European Union must achieve strategic sovereignty by equally strengthening security, resilience and sustainability . This in turn requires digital change that is economically successful and beneficial to society. Our continent has a strong research base, a diverse industrial landscape and highly educated people brimming with ideas. Data spaces are creating infrastructures for a fair exchange of data. We want to build on that. We want to create trust and foster cooperation so that we get quicker at the utilisation of data and at the scaling of new business models.
Jan Wörner, acatech President
The NITD in practice
The NITD connects research and initiatives in Germany and Europe and aims to give our AI-driven data economy the push it is lacking: the scope of its work extends from a comprehensive pool of data to trustworthy AI applications and on to pre-competitive support for data-driven business models. Taking a practical and cooperative approach, the focus of the NITD’s work is threefold:
1.) Facilitate data sharing between partners on an equal footing
Europe’s path towards the data economy is based on fair data sharing and equality of opportunity for competitors. It pursues decentrality – in contrast to growing monopolisation by the major software platforms. Partners who share and use data should not have to rely on intermediaries who themselves store data and use them in their own business models. This is where data spaces come in. They facilitate the exchange of data between various entities (companies, research institutions, authorities and public bodies) without a central platform provider. The participants themselves determine with whom they share what data on what conditions. Common agreements, standards and rules make this possible.
The strength of data spaces is their decentrality: the participants hold onto their own data, and access to the data is only given to defined partners as needed, for a certain period of time and for a certain purpose. Data spaces facilitate flexible cooperation between participants while protecting digital sovereignty. Data spaces thus provide a unique opportunity: the partners concerned can realise the potential of the data economy and, at the same time, eliminate dependencies and loss of control. A European model of the data economy such as this could create a real alternative to the traditional platform economy model that is currently prevalent.
The Mobility Data Space is already bringing many partners together and giving rise to applications. Large numbers of data spaces are cropping up in other domains. Linking up different data spaces holds the prospect of significant additional benefit. For example, supply chains could be digitally connected by integrating data from production, logistics and sales, which would improve flexibility, resilience, efficiency, economy and customer satisfaction. Within agriculture, weather data, yield reports and sustainability metrics could be compiled, which would increase both yield and sustainability.
Data spaces are much more than a technological development. They are a strategic approach for Germany and Europe to increase sovereignty in the course of the digital transformation. The NITD is working on this and, together with German and European partners, is doing the organisational and technical groundwork as well as developing use cases. The aim is the implementation and use of data spaces and thus to create a competitive, self-determined, internationally attractive model of a decentralised data economy.
2.) Create uniform quality and testing standards for trustworthy AI
When it comes into force, the EU’s AI Act will require artificial intelligence to undergo reliable tests that meet statutory requirements. The NITD will trial and develop scalable test methods; this process will run alongside comprehensive engagement with stakeholders from civil society, business and science. This will create more trust among AI users as well as increase planning security for AI developers and companies. One outcome will be guidelines for a transparent AI quality label that is consistent with German and European values.
In addition, the NITD is setting up an AI quality and innovation centre: it will be a point of contact and an experimental space for all stakeholders and will bring transparency to how artificial intelligence works and what its benefit is.
3.) Apply AI research to innovations
Despite the strength of AI research in Germany, AI applications with market success tend to be brought out by other countries: the most successful AI products come from the US, while German innovations find it hard to gain a foothold. For this reason, the federal government wants to support the market launch and growth of AI innovations that are important to the economy and to society. The NITD is developing a concept for identifying outstanding AI innovations and using tailored public-private funding instruments.
Fundamentals of the project
1. What is Artificial Intelligence?
Artificial Intelligence (AI) concerns systems and algorithms that are able to replicate the cognitive abilities of humans. These abilities include learning, the processing of information, decision-making and problem-solving. AI systems can be based on large volumes of data to identify patterns and contexts and make decisions. They can also be based on rules and logic to simulate human thinking and action. AI systems are generally trained using statistical methods and machine learning to improve their skills. AI technology is employed in various use cases, such as in the automation of processes, in image and speech recognition, in medical diagnostics and in robotics. The possibilities and use cases of artificial intelligence are almost endless and progress on these fronts will continue to be made in future. For example, the picture beneath the title of this page was generated using AI – such possibilities support creativity and work processes but also entail new risks such as the risk of deep fakes.
You can read more about this term and its origins here (in German).
2. What is a data space?
A data space is a federated (decentralised), open infrastructure for the sovereign exchange of data based on common arrangements, rules and standards. It enables companies and public institutions to share data safely and on an equal footing without having to relinquish sovereignty over their data. Thanks to the decentralised structure, consumers and companies themselves determine their contractual partners and terms of contract; that is, the extent, duration, purpose and partners involved in the sharing of data.
Control is thus gained and competence built up, which in turn enhances the sovereignty of market players. Ultimately, this also strengthens the European digital economy and, with that, the geopolitical sovereignty of Europe.
The EU and Germany want to reduce dependence on major digital platforms and cement Europe’s digital sovereignty. Thus, data spaces are currently being created in sectors such as healthcare, environment, energy, finance and agriculture. acatech is the lead on the pilot projects Mobility Data Space (MDS) (in German) and Culture Data Space (in German), which are already under way.
Data spaces promote innovation and fair competition:
Data spaces increase equal access to data for companies, public institutions and researchers
They support collaborations and synergy between various stakeholders, such as companies, research institutions and government agencies
They lower the barriers to market entry for small and medium-sized enterprises
They enhance trust between users and companies thanks to transparent data security
The analysis of information in data spaces facilitates personalised solutions
They improve the forecasting of and adaptation to industry trends and market changes
