AI adoption is accelerating internationally. Turning capability into execution remains the challenge.
However, a closer look at businesses in the US, UK, Germany, and France reveals a more nuanced reality. New research from Infor™ found that while confidence in AI adoption is high, decision-makers are still navigating the operational challenges required to make AI work at scale. This reflects a broader shift in how organisations are thinking about AI, from isolated experimentation toward embedding intelligence into core business processes.
The result is a defining tension in today’s AI landscape: businesses believe they are ready, but many are still working through what it takes to execute.
Key Findings
- An international AI execution gap is emerging: While roughly 70% of businesses in markets like the US (70%) and UK (74%) report having the capability to manage AI implementation, a meaningful share of organisations still face structural barriers, highlighting a disconnect between readiness and real-world execution.
- Data security is a concern across markets: Nearly a third of businesses in the US (34%), Germany (34%) and France (32%), and 45% in the UK report data security concerns as a barrier, reinforcing that trust and governance are foundational challenges to fully scaling AI.
- Talent gaps are becoming a pressure point: 20% of organisations in the UK, 24% in France, 27% in the US, and 28% in Germany cite a lack of internal AI expertise, indicating that the challenge is no longer isolated to specific regions, but shared across markets. As AI continues to evolve at a rapid pace, keeping skills current has become a moving target, making it increasingly difficult for organisations to find talent that can keep up.
- Infrastructure is the underlying bottleneck: Across regions, businesses consistently point to data, integration, and system limitations as core barriers, suggesting that the ability to operationalise AI is constrained less by access to technology and more by the environments in which it is deployed. At the same time, the pace of AI innovation is increasing investment complexity, as organisations must continuously invest in new tools and employee training while ensuring those efforts translate into measurable value.
The confidence gap: Adoption is outpacing readiness
At a high level, businesses are confident in their ability to adopt AI, with roughly 70% to 75% reporting they have the capability to manage implementation. But that confidence does not fully translate into execution.With 32% to 45% citing data security as a barrier and 20% to 28% pointing to talent gaps, organisations appear to understand both their potential and the structural hurdles still standing in the way.
Across all four markets, a meaningful share of organisations continue to report barriers that complicate adoption. Across the four markets surveyed, data security concerns impact 32% of businesses in France, 34% in both the US and Germany, and 45% in the UK, while talent constraints range from 20% to 28% across markets.
Trust and risk are universal considerations
One of the most consistent findings across markets is the role of data security and governance - as well as confidence in the accuracy of AI outputs.While the level of concern varies—with 45% of UK businesses citing data security as a barrier compared to 34% in the US, 34% in Germany, and 32% in France—the broader pattern is consistent: trust remains foundational to AI adoption.
Across regions, organisations are not just evaluating what AI can do, but whether it can be deployed securely, responsibly, and in alignment with regulatory expectations.
As AI becomes more embedded in core operations, trust is no longer a secondary consideration. Governance, visibility, and control must be built into how AI systems operate, not added after the fact.
Talent is becoming a shared constraint
As organisations move beyond early experimentation, talent is emerging as a critical factor in scaling AI.20% of businesses in the UK, 24% in France, 27% in the US, and 28% in Germany report a lack of internal AI expertise as a barrier. The relatively narrow range across markets indicates that this is not a localised issue, but a shared global challenge.
Importantly, this reflects a shift in where organisations are encountering friction. Early in the AI lifecycle, the focus is often on access to tools and use cases. As adoption progresses, the challenge becomes execution, and execution requires specialised skills. In this context, talent is both a resource constraint and a scaling constraint. AI adoption ultimately depends on the people responsible for using it day to day, making it critical that solutions are intuitive, actionable, and aligned with how work actually gets done.
Infrastructure is the real bottleneck
Perhaps the most important takeaway across all four markets is what organisations are not struggling with.
Access to AI tools is no longer the primary barrier. Instead, challenges related to data, systems, and integration are consistently cited as limiting factors.
With 32% to 45% of businesses reporting data-related concerns, it is clear that many organisations are attempting to layer AI onto environments that were not designed to support it.
This creates a structural limitation:
- Fragmented data reduces AI effectiveness
- Legacy systems slow integration
- Inconsistent governance introduces risk
When systems are fragmented, even well-designed AI struggles to deliver consistent, real-world outcomes at scale. As a result, the ability to modernise data infrastructure may be a stronger predictor of AI success than investment in AI alone.
This is where architecture becomes critical. AI is most effective when it is built on data models, workflows, and systems that reflect the realities of a specific industry, rather than layered onto generic platforms. Without that foundation, even advanced AI capabilities struggle to deliver consistent, scalable outcomes.
From adoption to execution
Globally, businesses are entering a new phase of AI maturity.
The initial wave of adoption was defined by experimentation: testing tools, piloting use cases, and exploring potential. The next phase is defined by execution: integrating AI into core operations in a way that delivers consistent, measurable value.
Across the US, UK, Germany, and France, organisations are navigating this transition in parallel, often encountering similar challenges regardless of geography.
The execution gap does not look the same in every industry. In manufacturing, for example, the bottleneck is often legacy infrastructure. In healthcare, it is governance and compliance. In distribution, it is data fragmentation across supply chains.
The organisations closing the gap fastest are those working with tools designed for their specific operational realities. In practise, that means AI that is embedded directly into workflows, supports real decisions, and delivers value in the context of everyday work, not as a separate layer of analysis.
Final thought
Even as confidence levels approach 70% or higher in some markets, between 32% and 45% of businesses still cite data security concerns, and roughly 20% to 28% report talent-related barriers. The challenge is whether they can overcome the structural constraints that limit their impact.
AI adoption is no longer the differentiator. Execution is.
What the data ultimately makes clear is that solving this challenge will require more than incremental investment in AI tools. It will require a shift in how AI is delivered and operationalised across the enterprise.
That means moving beyond one-size-fits-all AI toward solutions that are industry-specific, embedded into core workflows, and built on a strong data and governance foundation.
AI delivers the most value when it is grounded in the context of how a business actually operates and has the ability to act across systems and not just generate insights, but rather, enable coordinated action across systems and workflows In this model, intelligence is directly connected to execution, enabling organisations to move faster while maintaining control.
The next phase of AI adoption will be defined by organisations that can turn capability into consistent, scalable performance. That shift will depend on AI experiences that are grounded in real work, designed for real users, and capable of delivering precise, measurable value from day one. Ultimately, widespread success will hinge on getting three things right: scalability, accuracy, and cost-effective implementation. As these elements continue to mature, it becomes less a question of if AI will deliver on its promise, and more a question of when.
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- Our Latest News: Key Insights for the Enterprise AI Adoption Impact Index
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About the research
This analysis is based on survey data collected from business decision-makers across the United States, United Kingdom, Germany, and France in March–April 2026. The research was conducted by YouGov on behalf of Infor.
The survey included:
- 251 respondents in the United States
- 257 respondents in the United Kingdom
- 266 respondents in Germany
- 250 respondents in France
The study explored how organisations are approaching artificial intelligence, including current adoption levels, perceived readiness, and key barriers such as data security, talent, and infrastructure.
Respondents represent a cross-section of industries and organisation sizes, providing a global view of how businesses are navigating the shift from AI experimentation to execution.
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