- Data and AI literacy crucial to getting the most out of AI, report says
- Leaders are concerned about data quality, security and lack of agent experience.
- Budgets could be increasing and companies are spending on upskilling their employees.
AI adoption in Europe is increasing, but new research from Informatica suggests there is still a long way to go to establish adequate trust in the technology.
Most data leaders (96%) say their staff need more analytics to use AI responsibly, with data literacy (82%) proving to be more of a priority than AI literacy (71%) itself.
The report reveals the so-called “paradox of trust,” where employees trust AI tools and the data behind them, despite not having fully developed the skills necessary to use them responsibly.
AI is in full swing, but is held back by a paradox of trust
By the end of the first quarter of 2026, four in five (79%) European companies expect to have adopted generative AI in their workflows, and almost the same number (68%) will also begin piloting agent AI.
Primary use cases include improving decision making, driving collaboration, streamlining internal processes, and improving customer experience.
But despite this comprehensive approach, there is a clear lack of reflection on the bigger picture beyond actual implementation. Three-quarters (77%) of European companies admit that AI visibility and governance has not kept pace with employee usage, and the majority (55%) are purchasing off-the-shelf AI agents rather than creating their own.
More broadly, data leaders surveyed are also concerned about data quality, security, lack of expertise, especially around artificial intelligence, observability, and security barriers.
However, that could be about to change, as upskilling employees, privacy and security, and governance are seen as equally important in upcoming investments (with 23% projecting a significant increase in how much they will spend on AI).
“For AI to deliver transformative results and return on investment (ROI), organizations must prioritize data reliability, invest in rigorous AI governance, and upskill their workforce to help ensure their AI-powered decision making is based on high-quality, reliable data and that everyone in the organization knows how to use it responsibly,” summarized Chief Product Officer Krish Vitaldevara.
Looking ahead, it is clear that how quickly tools are deployed is not the only measure of success, and it is also imperative to know how reliably they can be relied upon. With AI now deployed at scale, successful businesses will address these broader factors to improve reliability and quality.
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