Accenture has warned that Europe’s largest companies are narrowing the AI readiness gap with North America, while smaller businesses risk falling further behind on the capabilities needed to turn investment into measurable performance.
The company’s inaugural AI Progress Barometer tracks the AI readiness of around 3,000 of the world’s largest companies, assessing the conditions needed to extract value from AI. The measures include accessible high-quality data, workforce skills, strategic direction, technology foundations, and processes that allow AI to be used consistently across operations.
European companies improved their AI readiness scores by 1.6 points over the past six months, compared with a 1.1-point improvement in North America and a 0.2-point improvement in Asia-Pacific. North America still leads overall, with an average score of 48.9 out of 100, compared with 43.1 for European companies.
The gap is narrowing fastest among the largest companies. European businesses with annual revenues above $10 billion now sit 2.1 points behind North American peers, at 47.4 compared with 49.5. Smaller European companies lag comparable North American organisations by 7.6 points, at 40.5 compared with 48.1, creating a pronounced long tail across the region.
Mauro Macchi, CEO for Europe, Middle East, and Africa at Accenture, said: “The speed of execution will define Europe’s future competitiveness.”
Several European markets recorded notable gains. Companies in France posted the largest improvement, rising five points to 43.1. The UK improved by 4.8 points to 44.5, while Spain rose by 4.6 points to 39.9. Across sectors, 10 of the 18 industries tracked by Accenture improved their AI readiness, led by insurance, travel, and consumer goods.
Insurance recorded the strongest sector gain, rising eight points to 48.6. Travel increased by 5.7 points to 46.7, and consumer goods rose by 5.2 points to 43.7. Accenture said European organisations have accelerated on strategic direction, talent, and process reinvention, although technology foundations slipped slightly, underlining weaknesses in cloud, data, and integration infrastructure.
The data points to a more uneven AI economy than headline adoption figures often suggest. Large enterprises are more likely to have the data architecture, transformation budgets, governance capacity, and executive sponsorship required to redesign workflows, change decision-making, and embed AI into customer-facing and operational processes. Smaller companies often operate with narrower technology teams, older systems, tighter capital budgets, and less room to absorb implementation failure.
That gap can alter competitive dynamics across supply chains. If large companies improve productivity faster than smaller suppliers, distributors, service providers, and regional partners, the benefits of AI adoption will become concentrated. Mid-market companies may then face rising expectations from larger customers without the same access to infrastructure, skills, or investment capacity.
The workforce strain is already visible in the broader debate over how roles are being reshaped. Skills gaps are widening as work changes, with employers under pressure from technological disruption, retraining needs, and weak conflict-resolution capability. Accenture’s barometer reinforces that pattern at a European scale: AI readiness is not a technology measure alone, but a combined test of people, processes, data, and governance.
Leadership now has a greater bearing on whether AI investment produces durable gains. Early pilots can sit inside innovation teams, data-science functions, or digital units, but scaled adoption requires decisions about operating models, accountability, risk appetite, workforce design, and budget priorities. AI is therefore becoming a management discipline as much as a technology programme.
The strongest European gains among large companies suggest that national champions and major listed groups are starting to convert AI spending into capability. The harder question is whether those gains will diffuse into the business base beneath them. A region can show progress in aggregate while leaving a large number of companies underprepared for productivity, cost, and customer-service shifts driven by AI.
Sector performance adds another layer to that divide. In insurance, AI can accelerate claims handling, risk assessment, fraud detection, and customer service. In travel, it can influence pricing, capacity management, route planning, disruption handling, and personalisation. In consumer goods, it can affect demand forecasting, marketing, product development, inventory planning, and customer engagement. Companies that lack the foundations to use those tools well may find themselves competing against businesses with faster decision cycles and lower operating friction.
European adoption is also shaped by regulation and technology sovereignty. Data protection rules, the AI Act, cybersecurity expectations, and concern over dependence on non-European technology platforms all influence deployment decisions. Strong governance can help companies build trust with customers, regulators, and procurement teams, particularly where explainability, security, and responsible use are central to buying decisions. Weak governance can slow deployment and leave organisations exposed to tools they do not fully understand.
The decline in technology-foundation scores is therefore significant. Strategy and leadership support can improve before the underlying systems are ready. AI still depends on usable data, modern infrastructure, integration with existing workflows, and secure deployment environments. Without those foundations, companies risk fragmented tools, duplicated pilots, and limited productivity gains.
The research also raises a broader policy question for Europe. Governments are investing in digital infrastructure, AI regulation, sovereign compute, skills programmes, and innovation funding, but company-level readiness will determine how far those measures translate into productivity. If only the largest businesses can act quickly, the economic benefit will be narrow rather than broad-based.
Europe’s AI position is improving, although the progress remains uneven. The dividing line is execution capacity: trusted data, skilled teams, stronger technology foundations, and leadership able to govern change across the organisation. Without those conditions, AI investment will widen the gap between companies that can redesign how work gets done and those still struggling to put the basics in place.
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