- According to a report, data and AI training is crucial to getting the most out of AI.
- Executives are concerned about data quality, security and the lack of agent expertise.
- Budgets could increase and companies are spending to upskill their employees.
AI adoption in Europe is on the rise, but a new study from Informatica suggests there is still a long way to go to build true trust in the technology.
Most (96%) of data leaders say their staff need more knowledge to use AI responsibly, with mastering data (82%) a higher priority than mastering AI itself (71%).
The report reveals a so-called “trust paradox,” in which employees trust AI tools and the data behind them, even though they haven’t actually developed the skills to use them responsibly.
AI is booming, but it’s held back by a trust paradox
By the end of the first quarter of 2026, four in five European companies (79%) plan to have adopted generative AI into their workflows, and almost as many (68%) will also begin testing agentic AI.
Key use cases include improving decision-making, strengthening collaboration, optimizing internal processes, and improving customer experience.
But despite this comprehensive approach, there is a distinct lack of thinking about the broader vision beyond actual implementation. Three-quarters (77%) of European companies admit that AI visibility and governance has not kept up with employee usage, and most (55%) are purchasing ready-made AI agents instead of creating their own.
More broadly, the data leaders interviewed are also concerned about data quality, security, and the lack of expertise, particularly in terms of agentic AI, observability and security safeguards.
This may be about to change, however, with employee upskilling, privacy, security and governance all seen as equally important in future investments (with 23% planning a significant increase in their AI spending).
“For AI to deliver its transformative results and ROI, organizations must prioritize data reliability, invest in rigorous AI governance, and upskill their workforce to ensure that their AI-driven decision-making is based on reliable, high-quality data and that everyone in the organization knows how to use it responsibly,” summarized Krish Vitaldevara, Chief Product Officer.
Looking ahead, it is clear that the speed with which tools are deployed is not the only measure of success, the confidence in which one can use them is also imperative. With AI now deployed at scale, successful businesses will address these broader factors to improve reliability and quality.
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