While AI promises significant benefits, European enterprises are encountering a range of challenges in implementing AI at scale. Many companies are still learning how to integrate AI effectively into their existing systems and workflows. One major issue is the fragmentation of AI tools within organizations. In these early days of enterprise AI, it’s common for different departments to independently adopt their own AI or analytics solutions without a unified strategy. 

This can lead to a patchwork of point solutions. Many of these applications have overlapping capabilities, but because they were adopted in silos, the company is not benefitting from any synergy between them. This fragmented approach has been called the “hidden AI tax”, as mentioned by Glean (2025), which refers to the extra costs and inefficiencies that accumulate when companies layer multiple unintegrated AI tools on top of each other.

Closely related to fragmentation are integration issues and data silos. Most AI systems need to connect with a company’s data sources and software infrastructure to be effective. If every department’s AI tool is separate, integrating them to get a unified view is difficult. Data may remain locked in departmental databases and not shared across the organization. European companies often cite legacy IT systems and inconsistent data formats as obstacles when trying to deploy AI. 

The AI algorithms might be powerful, but connecting them to existing business processes is a non-trivial task. Additionally, strict data protection regulations mean companies must be very careful about how data is used and shared by AI systems, which sometimes slows down integration across silos.

Another challenge is the cost and efficiency issue of managing multiple AI solutions. Many AI tools, especially enterprise-grade ones, may have licensing or subscription costs. If a company is licensing several different AI platforms, the expenses add up quickly. Unlike some traditional software, AI tools often charge based on usage or require a cloud infrastructure, which can surprise companies with high bills. 

There are also costs for training employees on each tool and for maintaining them. Another issue is inflexible licensing models, for example, some vendors require an organization-wide license even if only one department needs the AI feature, or demand a high minimum number of users. A company might have to pay for a generative AI feature in a collaboration app for all employees, even if only a handful actually use it. In essence, organizations risk paying for AI tools that overlap in functionality or remain underutilized, which is an inefficient allocation of resources. 

This contributes to “AI bloat”, as mentioned by Glean (2025), which refers to spending a lot on many AI initiatives without a proportional business return. Indeed, despite growing investment in AI, many companies have yet to see the payoff.

Beyond technology and cost, organizational and skill challenges also play a role. Adopting AI at scale requires new skills and ways of working, which many European companies are still developing. There is a shortage of AI competencies in many organizations. This skills gap makes it hard to progress beyond pilot projects, as companies may rely heavily on external consultants or vendors for AI expertise. 

Moreover, internal resistance or cultural hurdles can slow adoption: employees might be unsure how to use AI tools or even distrust them, especially if not adequately trained. There are also governance and ethical concerns. Uncertainty around the EU AI Act is cited by about 68% of businesses as a factor making them hesitant or unclear about how far to push AI initiatives (AWS, 2025). Companies want to avoid legal risks, so some take a cautious approach, which can be a barrier to fast adoption.