The EIT (European Institute of Innovation and Technology) Climate-KIC (Knowledge and Innovation Community) presents itself as “Europe’s largest public-private innovation partnership focused on climate innovation to mitigate and adapt to climate change”. It is composed of academic institutions, the public sector and businesses – and, so far, it has incubated more than 1800 “climate-positive companies”. Focusing on a range of development objectives such as healthy, clean cities, landscapes as carbon sinks, just transformations etc., the EIT Climate-KIC also has an interest in understanding how AI will impact different efforts to achieve and maintain sustainable development.
Most recently, it might hold itself busy with the start of newly acquired projects, which were selected after launching the ‘Innovation Call 2022’ in September 2021. Especially, because technologies play an important role in addressing, forecasting and adapting issues related to climate change, the EIT Climate-KIC also invited applications from the domain of AI, which it recently published more research on. This article serves to provide an overview of the EIT Climate-KIC’s report “Emerging AI and Data Driven Business Models in Europe”, which has, among others, gathered information on regulatory frameworks across the European Union (EU) and carried out an AI survey to understand the impact of AI in its own network.
AI Business Model Survey
As the report reveals, the goal of the AI Business Model Survey was “[t]o explore how AI technology firms (including both providers and adopters) within the EIT XKIC partnership programme currently execute their business models”. The EIT XKIC partnership programme thereby referred to the partnership between EIT Climate-KIC, EIT Manufacturing, EIT Urban Mobility and EIT Health, which illustrates that findings span across different sectors. Whereas this approach might make the survey also more representative – as in mirroring a tiny bit of the EU’s (start-up) economy, it might be worth mentioning that more than two thirds of the survey participants were AI solution providers with AI technology adopters making up for almost one third of survey participants and with AI technology investors constituting a minority.
Some interesting findings were that:
- More than half of the “AI developers rel[ied] on external partners to develop AI solutions”;
- Image processing, diagnosis and computer vision were among the most frequently developed AI solutions;
- Asia, North America and Europe were estimated to be the best markets for AI development in future years with Europe offering the most growth potential;
- There is still more scepticism (60%) than optimism (40%) about creating revenue through AI, but positive impacts were especially noticed with regard to deploying AI for supply-chain management followed by manufacturing;
- Similarly, a bit more than half of the respondents stated that AI deployment did not reduce costs except in the latter two domains and product and service development;
- AI benefits were mostly perceived to be related to data analysis and management, the reduction of waste of resources, manufacturing output and efficiency, support of R&D activities, logistics, employee security etc.;
- Barriers with regard to AI deployment were associated with lacks of technical feasibility (60%), lack of data availability (50%) and data quality/integrity (50%), whereas risks were associated with cybersecurity (72,73%) and regulatory compliance (54,55%) issues and personal privacy (54,55%);
- Whereas security and analysis/reporting were identified as the most important functions of data platforms, costs and usability came next;
- Whereas AI developers were certain that their software would enable data security, they were sceptical towards their customers’ usage of their platforms;
- Driving AI value when there is still scepticism involved could relate to aligning business goals with a particular AI strategy and investing in AI talent.
EU Regulatory Frameworks
As stated in the report, all Member States of the EU and Norway have joined the ‘Declaration of Cooperation on AI’ with their signature. But, since the adoption of the declaration, much time has passed and much work has been done. For instance, in October 2020, the Second EU AI Alliance Assembly took place with participants such as Christiane Wendehorst addressing “the difference between the ‘physical dimension’ and the ‘social dimension’ of AI, and the implications of that divide for AI regulation”. Then, in April 2021, an ‘Impact Assessment of the Regulation on AI’ was published by the European Commission (EC) alongside the ‘2021 Coordinated Plan on AI’ and the ‘Proposal for a Regulation Laying Down Harmonised Rules On AI’. However, while the EU’s AI Strategy might play an important role in regulating AI deployment across Europe, the effectiveness of national strategies also needs to be assessed.
Whereas one might assume that the aims of national AI strategies across the EU must somehow all overlap, EIT Climate-KIC shows that Member States actually emphasize different aspects of AI deployment in society. Germany’s AI Strategy, which was launched by the end of 2018, for instance circles around the goal “[t]o become a leading centre for AI by pursuing [the] speedy and comprehensive transfer of research findings into applications”. Meanwhile, France’s AI strategy from March 2018, is said to aim “allocat[ing] €1.5 billion of public funding to AI by 2022 to help France become an AI research and innovation leader” and Finland’s AI strategy from October 2019 is said to aim “develop[ing] a safe and democratic society with AI; [using] AI to provide the best public services in the world; and for AI to bring new prosperity, growth and productivity to citizens”.
In other words, some EU Member States emphasized more directly that they regard the effective deployment of AI as essential in the process of maintaining economically successful, whereas other countries more directly expressed that the adequate and effective deployment of AI in society will reinforce some of the founding values of the EU such as democracy. Whereas the latter does not contradict, it illustrates why AI strategies across the EU highlight different aspects and list different measures. Whereas EIT Climate-KIC explains that Germany puts an emphasis on putting findings from research into practise – which has recently been highlighted by the adoption of the model project on AI in Darmstadt in cooperation with the Hessian Centre for Artificial Intelligence (hessian.ai), France additionally puts an emphasis on using AI excellence and innovation to “renew existing industries” and Finland’s AI strategy emphasizes, among others, international links for research purposes alongside the encouragement of (private) investments. Especially, because EU Member States are not all at the same starting point and will also face different challenges as they adopt AI at large scale, and because AI strategies develop over time, it will remain crucial to stay updated on recent developments.
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