Technology & Innovation

Finding Europe’s AI edge

The Brussels edition of Economist Enterprise’s "Reinventing competitiveness" roundtable series, supported by Accenture, convened business and policy leaders to discuss Europe's strengths and vulnerabilities in the AI race. While America and China lead in frontier-model development, participants argued that Europe can still lead in applying AI to industry—if it embraces innovation, manages risk and builds on its existing strengths.

From pilot to scale: the data enabler

European companies are moving beyond AI experimentation to deploy the technology at scale. Accenture says this reflects what it sees among clients across Europe, the Middle East and Africa (EMEA). The share of AI projects focused on scaling rather than piloting AI has increased from roughly 25% last year to about half in 2026. While Europe trails America and China in developing frontier models, it is home to major players like Mistral and boasts some of the world’s leading universities and research institutes. “We have important players, and sometimes we forget what we have across the technology stack, from hardware to applications,” said one executive at the roundtable.

To capitalise on these strengths, Europe must overcome several obstacles. First, firms need trusted data systems, especially in regulated sectors. Building them requires technology teams to overcome incompatible data and ageing infrastructure. One executive argued for safeguards that enable customers and employees to use AI tools in a trusted setting while ensuring data is classified properly.

Without trustworthy and well-organised data, AI cannot deliver enduring value. “If we ask our members what they’re struggling with, the top ten priorities are about how to use data, where the data is, how to work with all employees to get the benefits of the data, and using AI to drive insights,” said one expert. Time is of the essence. While Europe’s top companies are adopting AI as quickly and effectively as their US peers, the continent’s small and mid-market firms are lagging.

The rate of improvement in large language models and the quality of enterprise AI tools are helping companies move quickly, but experts warn against viewing AI as a "magic wand". Senior leaders must be trained to set realistic expectations and align AI with risk management frameworks. While customer service and software can see quantifiable gains in weeks or months, capital-intensive sectors face different realities. For example, in the energy sector, AI might be viewed through a 20- or 30-year investment lens.

Managing organisational change

Chief technology and information officers may lead AI transformation, but success depends on people across the organisation. “Most companies are really struggling with implementation of AI, whether it's about the involvement of the boards, ICT, HR. It impacts all domains at the same time, and that's what makes it so difficult,” said one executive.

Success requires incentives to adopt AI, including for middle managers who are essential to implementation, and avoiding unrealistic expectations. The management challenge is to balance decentralisation and encourage experimentation and ownership on the ground with a unified view from above.

“The true value of AI lies not in technology alone, but in an organisation's ability to empower its people to capitalise on it. Humans must be placed in the lead — not merely in the loop.”

^{– Kristof Lambert, Chief Executive Officer for Belgium and Luxembourg, Accenture}

Starting small helps organisations better understand their data, but leads to complexity and sprawl, making oversight harder. Participants noted that AI affects every part of an organisation, making it difficult to develop a coherent strategy without broad involvement.

“AI only delivers when people trust it, adopt it, and use it to amplify what they do best. Companies should invest as much in process reinvention and change management as in technology”

^{– Mauro Macchi, Chief Executive Officer for Europe, Middle East, and Africa (EMEA), Accenture}

Europe’s challenge and opportunity

While Europe has world-leading universities and promising AI startups, achieving technology sovereignty will require a long-term industrial strategy. Its digital economy is fragmented, making technology businesses harder to scale. While Europe has forged rule-based foundations for today’s digital economy, including GDPR, the AI Act and the Digital Markets Act, experts worry about a cultural tendency to ‘avoid chaos’ at the cost of limiting innovation.

To compete against the US and China, Europe urgently needs to act as a unified bloc. “Europe can do many things if we pool our capabilities – infrastructure, data centres, factories, and so on,” said one expert. “We don't have chips at the same scale or hyperscaler capabilities [as the US and China], but we are extremely good in different niches, like data centres, power management, and data management.”

Europe is home to a growing community of AI contenders. “We have 8,000 AI startups, so there are probably partners for companies that you don't even think about, but that you need to find,” noted one expert. To thrive and grow, entrepreneurs and companies need access to more risk capital. One participant argued that companies would benefit from closer ties to Europe's universities. “We have many forms of knowledge and perspectives.” Looking ahead, experts called for long-term regulatory consistency, fiscal incentives for workforce training, a focus on European comparative advantage, and building trusted data governance environments.

Positively, the European Technological Sovereignty Package, published by the European Commission in the summer of 2026, will strengthen the bloc’s influence over the technologies that will define the next decade, from semiconductors to cloud, and reduce Europe's dependence on foreign suppliers. Through multiple investment programmes, acts and proposals now on the table, the EU is building the industrial capacity and soft power needed to shape its AI destiny.

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