Europe leads in some aspects of AI research, but new business models and investment in scaling up is needed to bring the technology to market
EU companies are falling behind counterparts in China and the US in the global race to move artificial intelligence out of the research labs and into real life deployments, due to lack of technical feasibility studies, poor data quality and lack of investment in scaling up.
These are the headline findings of a new report by EIT Urban Mobility, the EU-supported innovation network set up to promote adoption of new technologies in transport.
The most pressing issue is technical readiness, which 60% of experts surveyed for the report singled out as a barrier to AI adoption. That is followed by poor data quality and availability, while finance is cited as an issue by 30% of AI experts in Europe.
All this is slowing down EU companies as they strive to keep pace with AI leaders in China and the US, including use of AI in urban mobility applications such as traffic forecasting, on-demand bus services, vehicle tracking and autonomous vehicles.
The EU has taken a lead in certain aspects of AI research and has set out its stall in relation to ensuring the ethical use of the technology.
The report focuses on how this can be translated through to the business models needed to support AI deployment and expansion of its applications in various sectors. It presents findings ranging from barriers and risks for AI adoption, algorithm development methodology, and revenue and cost models across different industries, with a particular focus on urban mobility. The report maps the AI landscape in Europe, comparing national policies, regulatory frameworks, and ethical guidelines, identifying priorities for strengthening the EU AI ecosystem. It also illustrates how AI applications have driven business transformation in urban mobility and other sectors.
Dragoș Tudorache MEP, chair of the European Parliament’s AI committee, agrees there are genuine barriers to AI adoption by industry, and stresses the importance of education and training in overcoming these hurdles.
“From this point, I trust that technology will continue to evolve, that funding for AI adoption will become increasingly available, and that data, the fuel of the new digital economy, will become increasingly available and cleaner than it is today. But I believe that in order to truly unleash the potential of AI over the long term, we also need to go back to the basics. And that is education and digital literacy,” he told Science|Business.
Once their customers understand and trust the technology, Tudorache said, companies can leverage the benefits of AI. “That is, in essence, what the EU is trying to do with setting the rules for European AI: striving for human-centric, ethical, and trustworthy AI is not just about values; it is also about long term economic preparedness for large-scale AI adoption,” he said.
EIT Urban Mobility found one in five experts think their companies should invest more in AI talent and training to equip themselves with necessary knowledge and skills.
The Commission has set up a €150 million AI fund to support early and growth companies and has proposed rules for AI, which are now being considered by EU policymakers.
Among EU member states, France plans to spend €1.51 billion supporting AI ecosystems in the next five years, while Germany has ambitions to become a leader in AI applications and is investing in translating research to market.
Maria Tsavachidis, CEO of EIT Urban Mobility, hopes the AI industry can benefit from the EU’s €750 billion pandemic recovery fund. “The recovery plans are a unique opportunity to strengthen EU´s efforts to leverage the vast opportunities of artificial intelligence, and to upskill the workforce,” she said.