Europe’s AI strategy: Smart caution or missed opportunity?
Europe is banking on AI to help solve its economic problems. Productivity is stalling, and tech adoption is slow. Global competitors, especially the U.S., are pulling ahead. A new report from Accenture says AI could help reverse that trend, but only if European companies move faster and invest more boldly.
Right now, most are not. The report shows that only 8 percent of large AI investments aimed at transforming business operations have been scaled in Europe. Many organizations in Europe are stuck in pilot mode, with AI adoption often limited to short-term tools rather than long-term change. Smaller companies in particular are struggling to keep up, both in resources and in technical capability.
There is also a deeper question looming. As geopolitical tensions rise and U.S. control over cloud and AI infrastructure grows, can Europe build a more independent, secure AI ecosystem of its own?
Top five barriers European companies face when implementing GenAI at scale (Source: Accenture)
Growing productivity gap
The report begins with a warning about productivity. In the 1990s, Europe and the United States were nearly equal in worker output. Today, the average European worker produces only 76 percent as much as a worker in the U.S. One major reason, according to Accenture, is slower investment in technology.
Companies in Europe are spending less on AI, cloud platforms, and data infrastructure. In high-tech sectors, productivity growth in the U.S. has far outpaced Europe. The report argues that AI could help close the gap, but only if it is used to redesign how businesses operate. Using AI to automate old processes is not enough.
According to Leonid Feinberg, CEO of Verax AI, the slow pace of adoption has deeper roots. “In my opinion, there are several main reasons why EU companies are adopting AI slowly. Some of these are shared with similarly sized organizations in the rest of the world, but others are more pronounced in Europe.” He points to the complexity of building enterprise-grade AI systems, a shortage of skilled software talent, a culture of risk aversion, and general conservatism when it comes to changing tools, processes, and workforce models.
AI adoption in Europe remains cautious and uneven
Many companies in Europe are still in the early stages of AI adoption, often focusing on small, low-risk tools instead of scaling transformative initiatives.
Accenture splits AI investment into two types. The first is basic adoption, things like chatbots or AI assistants that help employees with simple tasks. These are useful but don’t change the business itself. The second type is what the report calls strategic bets. These are larger, long-term investments meant to overhaul key operations or open up new ways of working.
Only 8 percent of these strategic bets have been scaled so far across Europe. In contrast, industries like automotive and aerospace are further along. In the automotive sector, 70 percent of companies have scaled at least one of these larger AI initiatives. Other sectors, such as telecoms and utilities, are far behind.
Size also matters. Nearly half of businesses with revenues over $10 billion have scaled at least one strategic AI investment, while only 31 percent of those earning between $1 billion and $9.9 billion have done the same.
Lack of capabilities and high failure rates
Feinberg also notes that many European companies assumed AI apps would be easier to build than traditional software, only to discover they are just as complex, if not more so. This mismatch between expectations and reality has slowed down internal projects. And the problem isn’t unique to Europe.
As Oliver Rochford, CEO of Aunoo AI, points out, “AI project failure rates are generally high across the board.” He cites surveys from IBM, Gartner, and others showing that anywhere from 30 to 84 percent of AI projects fail or fall short of expectations. “The most common root causes for AI project failures are also not purely technical, but organizational, misaligned objectives, poor data governance, lack of workforce engagement, and underdeveloped change management processes. Apparently Europe has no monopoly on those.”
Still, Rochford believes Europe’s slower pace of AI adoption is a deliberate choice, not just a failure to keep up. “Europe is deliberately positioning itself as a global leader in responsible, human-centric AI. You can see this just by looking at the EU AI Act and related frameworks, with their focus on trust, transparency, and safety.”
The five imperatives for scaling AI
To help companies move from experimentation to real results, Accenture outlines five imperatives that organizations should focus on. These are not just technical tasks. They involve strategy, leadership, culture, and long-term thinking.
1. Lead with value. Companies need to focus on business outcomes, not just playing with new tools. High-impact AI projects should be tied to core processes, not treated as side experiments.
2. Reinvent talent and ways of working. Adopting AI is a workforce shift. Companies need to prepare their teams, build new skill sets, and rethink roles. This includes investing in training and creating a culture that supports change.
3. Build an AI-enabled, secure digital core. Scaling AI requires strong data systems, infrastructure, and secure platforms. Without this digital foundation, AI efforts are more likely to stall or fail. Companies also need to modernize legacy systems so AI can integrate across the business.
4. Close the gap on responsible AI. Responsible AI includes transparency, fairness, privacy, and accountability. As AI starts playing a bigger role in decisions, earning trust inside and outside the company becomes even more important.
5. Drive continuous reinvention. AI is an ongoing process. Businesses must stay agile, keep learning, and adapt their strategies as the technology and market evolve.
Sovereignty and strategic direction
The report also highlights concerns about Europe’s reliance on U.S.-based cloud and AI infrastructure. The idea of AI sovereignty is gaining traction, with leaders calling for stronger domestic infrastructure and a federated approach to innovation.
Mateo Rojas-Carulla, Chief Scientist of Lakera, doesn’t mince words about the stakes. “Leveraging AI is non-negotiable if Europe wants to remain competitive across industries. Right now, we’re falling behind, with only a few players like Mistral attempting to build foundational models, and a general lack of aggressiveness in investment and entrepreneurship. Europe’s AI talent is exceptional, but it’s not being matched with the scale or speed required.”
He supports a balanced approach: “While sovereignty matters, especially for resilience, we must avoid isolationism and focus on a hedged, pragmatic approach that combines access to the best global models with a bold European push to lead in AI innovation.”
Europe’s different bet on the future
Rochford pushes back on the narrative that Europe is simply lagging. “Europe ‘falling behind’ always makes for nice headlines, but it creates a false binary between responsible innovation or economic irrelevance. Europe is simply betting on a different future.”
He argues that Europe’s approach, which focuses on trusted, safe, and compliant AI, especially in regulated sectors, could become an advantage if global norms shift toward transparency and accountability. “The approach is a high-conviction, long-term strategy that may appear slow or risk-averse now, but could prove prescient if the global AI landscape ends up needing the sort of safeguards Europe is pioneering.”
Whether Europe’s strategy succeeds will depend not only on how the technology evolves, but on what the world comes to value most in AI: speed, power, or responsibility.