European companies must strengthen the capabilities needed to scale AI faster if they are to address the growing productivity gap and enhance the region’s overall competitiveness, according to a new report released by Accenture.

The study highlights that the average European worker now produces only 76% as much as their US counterparts, a significant decline from being on par 30 years ago, with persistent underinvestment in technology identified as a major cause.

Despite the recent Draghi report into European competitiveness pointing to AI as a potential solution to Europe’s productivity problems, Accenture’s research found that European companies are yet to take full advantage of the AI opportunity.

Currently more than half (56%) of the 800 large European companies surveyed have yet to scale a major AI investment. Yet if all large (€1 billion+) European companies enhanced their AI capabilities to match those of leading industries, Accenture found that almost €200 billion could be added to annual business revenues.

Mauro Macchi, CEO of Accenture in EMEA, said: “At a time when geopolitical uncertainties are on the rise, finding a solution to Europe’s productivity gap has never been more crucial.  AI provides a unique opportunity for Europe to reinvent its economy and significantly boost its competitiveness.

“European firms are making progress but need to further leverage cloud, modernize data architecture and focus on skilling in order to scale AI faster and unleash its full potential. A coordinated industrial strategy, including shared AI infrastructure and investments will also avoid dispersion of initiatives and help businesses across all European countries access powerful computing, R&D, and training. Europe has all it needs to take advantage of the AI revolution.

“Now is the time to execute on it.”

The study found that large European companies are adopting AI more quickly than smaller companies, which could further impact Europe’s productivity and competitiveness.

While nearly half (48%) of Europe’s largest companies have scaled at least one transformational generative AI initiative, less than a third (31%) of smaller companies have. Europe has a higher concentration of smaller companies compared to the US, making this size of company a significant opportunity.

AI adoption also varies across industries. Some sectors such as automotive, aerospace and defence are leading the way, while others, including telecommunications and utilities, lag behind.

The report emphasises that since these sectors provide critical regional infrastructure such as energy systems and digital networks, their relatively low AI maturity poses a competitiveness and sovereignty concern for the region.

The industrial sector, which accounts for more than a quarter of Europe’s GDP is also yet to tap into the full potential of AI. These disparities underscore the need for a more uniform approach to AI adoption across all industries.

The report also identifies key capabilities, from data to talent, that companies need to drive value from AI investments. These AI capabilities are well spread across countries, with Switzerland, Germany, the UK and France, on average, home to slightly more AI-ready companies, than Italy and Spain.

To fully realize the benefits of AI, the report calls for the development of a robust, competitive AI ecosystem in Europe. This includes helping smaller companies level up on AI, nurturing a sovereign European AI ecosystem, and developing a coordinated industrial strategy.

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Additionally, AI literacy and workforce development are critical, as many European workers (60%) are concerned about job displacement, and a third (36%) do not feel suitably trained to use AI efficiently.

The report identifies several key barriers to scaling AI, including setting up a robust data foundation, building and maintaining multi-disciplinary teams, managing security and privacy risks, and demonstrating business value. To overcome these challenges, the report recommends:

• Data Foundation: Invest in breaking down data silos and building an end-to-end data foundation with quality data.

• Multi-Disciplinary Teams: Focus on talent development, including training and continuous learning opportunities, to build and maintain the necessary expertise.

• Security Risks: Adopt a secure digital core to reduce vulnerabilities, redundancy, and technical debt.

• Business Value: Identify and implement concrete use cases with proven ROI.

• AI Literacy: Provide more training and support to employees and ensure that AI tools are accessible and user-friendly.