Could the ‘AI trust paradox’ be holding your business back? It’s time to get real on just what you need, report finds
- Data and AI literacy training are crucial to get the most out of AI, report claims
- Leaders are worried about data quality, security and a lack of agentic expertise
- Budgets could be increasing, and companies are spending on employee upskilling
European AI uptake is on the rise, but new Informatica research suggests there’s still a long way to go to establish proper confidence in the tech.
Most (96%) data leaders say their staff need more staining to use AI responsibly, with data literacy (82%) proving more of a priority than AI literacy (71%) itself.
The report reveals a so-called ‘trust paradox’, where employees trust AI tools and the data behind it, despite not actually having fully developed the skills needed to use it responsibly.
AI is in full swing, but it’s held back by a trust paradox
By the end of 2026’s first quarter, four in five (79%) European businesses expect to have adopted generative AI in their workflows, with nearly as many (68%) also starting to pilot agentic AI.
Top uses cases include improving decision-making, boosting collaboration, optimizing internal processes and improving customer experience.
But despite this all-in approach, there’s a clear lack of thought for the bigger picture beyond actual implementation. Three-quarters (77%) of European firms admit AI visibility and governance hasn’t kept up with employee use, and most (55%) are buying off-the-shelf AI agents instead of building their own.
More broadly, the surveyed data leaders are also worried about data quality, security, a lack of expertise particularly around agentic AI, observability and safety guardrails.
That could be about to change though, with employee upskilling, privacy and security, and governance all considered equally important in upcoming investments (with 23% projecting a significant increase in how much they’ll spend on AI).
“For AI to deliver its transformative outcomes and ROI, organizations must prioritize data reliability, invest in rigorous AI governance and upskill their workforce to help ensure their AI-driven decision making is based on trusted, high-quality data and everyone in the organization knows how to use it responsibly,” Chief Product Officer Krish Vitaldevara summarized.
Looking ahead, it’s clear that how quickly tools are deployed isn’t the only measurement of success, with how confidently they can be trusted also imperative. With AI now deployed at scale, successful businesses will tackle these broader factors to improve reliability and quality.
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