Artificial intelligence is gradually being integrated into the processes of European businesses. Automation, data analysis, decision support: its applications are multiplying and opening up new possibilities to accelerate digital transformation.
But as AI takes on a more prominent role within businesses and digital transactions, another issue becomes central: namely trust. A company may use artificial intelligence without necessarily being ready to rely on it for sensitive or critical processes. Reliability, data privacy, human oversight, and technological dependence then become decisive factors.
This issue of trust in AI is clearly evident in the responses of the 700 decision-makers surveyed in France, Germany, Italy, and Spain as part of the 2026 European Digital Trust Barometer, conducted by Namirial in partnership with Xerfi. Adoption rates are rising, but they do not always reflect the level of trust placed in the technology.
Consequently, the question is no longer simply how far companies are willing to adopt AI, but under what conditions they will be willing to trust it.
AI is becoming embedded in European business processes
Artificial intelligence is no longer confined to isolated experiments. It is gradually finding its place in business processes, though adoption rates vary by country.
France and Spain have the highest adoption rates, at around 68 to 70%, while Germany and Italy stand at around 54 to 55%. These disparities show that the integration of AI is already well underway, though it is not progressing uniformly across Europe.
This adoption is already accompanied by concrete expectations regarding the value that AI can bring to businesses. Process automation tops the list in France (45%) and Spain (52%), while Germany ranks document analysis as its top priority (42%). Italy stands out by prioritizing fraud detection (64%). These differences illustrate that uses and expectations still vary significantly across markets.
But measuring adoption alone is not enough to assess companies’ maturity regarding AI. Using technology to automate certain tasks or support employees does not imply the same level of trust as entrusting it with a role in managing sensitive or critical processes.
Adoption and trust: a revealing discrepancy
Adopting artificial intelligence and trusting it are two very distinct steps. As AI becomes integrated into business processes, the question is no longer just about its ability to improve or automate certain tasks, but about the level of responsibility companies are willing to entrust to it.
The data clearly illustrate this gap. In France, where AI adoption stands at about 68%, only 42% of decision-makers consider it reliable for critical processes. Conversely, Germany has a lower adoption rate around 55%, but the highest level of trust among the four countries studied, at 52%.
Italy also shows a smaller gap, with approximately 54% adoption and 51% trust. In Spain, where AI usage stands at nearly 70%, 53% of decision-makers say they are willing to trust it with critical processes.
These gaps serve as a reminder that maturity regarding AI cannot be assessed solely through its adoption rate. The more critical its role becomes, the more other criteria come into play: system reliability, data quality, transparency, and the ability to maintain human oversight. These dimensions are, in fact, at the heart of the European approach to trustworthy AI, particularly with the AI Act.
The observed levels of trust thus show that several barriers still need to be overcome before AI can be more widely integrated into the most sensitive processes.
What is still holding back trust in AI?
As artificial intelligence becomes integrated into business processes, the questions are shifting. It is no longer just a matter of assessing what AI can offer, but also the risks associated with its use.
Data privacy is the primary concern in France. In Germany and Italy, decision-makers are more concerned about the dependence on non-European suppliers, while in Spain, the loss of human control is a major concern. These differences clearly show that trust in AI does not rest on a single factor: it depends on data ownership, the technologies used, and the level of control retained by the company.
Beyond these national differences, some concerns are common across several markets. Data privacy, loss of human control, dependence on non-European suppliers, the explainability of decisions, and regulatory compliance shape the questions raised by decision-makers. The latter, in particular, ranks among the top four concerns in each of the countries studied, confirming that trust in AI depends as much on its uses as on companies’ ability to manage them.
The issue of control also gains importance as systems become more autonomous. Being able to understand how they work, oversee their actions, and intervene when necessary becomes essential to prevent increasing automation from resulting in a loss of control or an increase in risks.
These concerns lead us to look beyond mere technological performance and focus on the framework within which AI is deployed. It is precisely here that the conditions for trustworthy AI are established.
Data, transparency, control: the conditions for trustworthy AI
Trust in artificial intelligence does not depend solely on its performance. For a company to integrate it sustainably into its processes, it must also be able to understand how it works, what data it uses, and under what conditions it can be monitored.
Data governance is a key pillar. The quality, source, protection, and conditions under which data is processed directly influence the reliability of AI systems. High-performing technology cannot inspire lasting trust if the company does not have control over the data on which it relies.
Also transparency plays an essential role. Identifying a system’s capabilities and limitations, understanding its role in a process, and ensuring sufficient traceability help to better regulate its use. This approach is at the heart of the European strategy: for high-risk AI systems, the European framework, through the AI Act, includes requirements regarding data quality, transparency, traceability, robustness, and cybersecurity.
Finally, automation does not necessarily mean eliminating human intervention. The more AI is involved in sensitive decisions, the more important it becomes to monitor its operation, question its results, and intervene in the event of a problem. Human oversight is, in fact, one of the requirements stipulated for high-risk systems in the AI Act.
Trust thus depends as much on the performance of AI as on the company’s ability to oversee its use and maintain control over its processes.
AI and sovereignty: the issue of technological dependence
Trust in artificial intelligence also raises a more strategic issue: that of technological dependence. AI systems rely on a range of resources: models, cloud infrastructure, computing power, and data, and control over these resources can become critical when they are integrated into sensitive or high-risk processes.
This concern is particularly evident in Germany and Italy, where dependence on non-European suppliers ranks among the main obstacles cited regarding AI. The choice of a solution is therefore no longer based solely on its performance: the supplier’s origin, data processing conditions, and the ability to maintain control over one’s technological environment are now key considerations.
This issue extends beyond individual corporate strategies. The European Union is now seeking to reduce its dependencies on critical digital technologies, particularly in the cloud and AI. In June 2026, the Commission proposed, among other things, the “Cloud and AI Development Act”, which establishes a European framework for assessing cloud and AI sovereignty.
For a company, sovereignty does not necessarily mean seeking total technological autonomy. Rather, it involves the ability to assess its dependencies, maintain control over its data, and retain sufficient choice and control over the technologies to which it entrusts its processes.
As AI plays an increasingly important role, the question therefore also becomes one of what degree of dependence is acceptable. This is a particularly important issue when it is no longer simply a matter of assisting with certain tasks, but of integrating artificial intelligence into critical and strategic processes.
What role should AI play in critical processes?
The more artificial intelligence is involved in sensitive processes, the higher the standards it must meet. A company does not expect the same guarantees from a tool used to assist with content creation as it does from a system involved in a decision, a digital transaction, or a process with direct consequences for the organization or its customers.
The expected level of trust must therefore evolve in line with the level of risk. The more significant the potential consequences of an error, the more precisely the company must define what the AI can do autonomously and the situations in which human intervention remains necessary.
This need for control becomes all the more important as companies gradually transition from experimentation to more integrated uses. Recent studies on AI adoption in businesses highlight precisely the growing importance of governance, accountability, and human oversight as AI becomes more closely integrated with core business activities.
The real challenge, then, is to adapt the level of control as applications evolve. To sustainably integrate AI into critical processes, companies will need to go beyond mere technology adoption and build a framework capable of inspiring sufficient trust to scale up.
Building trust to move from adoption to maturity
As the applications of artificial intelligence expand and become more closely tied to strategic activities, the ability to govern the technology becomes just as important as its performance. This trend is particularly evident in European companies, where the advancement of AI is still accompanied by gaps in governance and risk management.
Moving from experimentation to sustainable use thus requires meeting several conditions: controlling the data used, clearly defining responsibilities, adapting the level of human oversight to the risks, and ensuring that systems remain sufficiently transparent and controllable. Maturity is therefore no longer measured solely by the number of applications deployed, but by the company’s ability to integrate them into a controlled framework of trust.
This evolution is also changing the way innovation is approached. Governance, security, and control are not necessarily obstacles to the development of AI; on the contrary, they can enable its use to be gradually expanded to processes where reliability requirements are higher. Organizations that succeed in scaling up tend to combine their deployments with investments in data, governance, and team training.
The challenge, therefore, is no longer simply whether companies will adopt artificial intelligence. It is now a matter of determining how they can create the necessary conditions to place greater trust in it, without losing control over their data, decisions, and processes, while improving performance.
To learn more and explore detailed data on AI adoption, trust levels, and the concerns of decision-makers in France, Germany, Italy, and Spain, check out Namirial’s 2026 European Digital Trust Barometer.
