Despite years of discussions and pilot projects, only 25% of EU workers use artificial intelligence (AI) for work purposes, compared to 43% of employees in the US. The gap is not simply about access to better tools or frontier models but reflects how organisations manage change.
Too many European organisations make the same fundamental mistake, conflating access to AI with its adoption. Often, this comes across as broad mandates to employees, asking them to be more productive through AI but then leaving them to figure out what that means for their role. Research has found that over a third of employees are still looking for clear reasons to adopt AI.
If Europe wants to persuade its workforce that AI is an enabler rather than a threat, then it needs to rethink how it builds confidence, trust and motivation across the workforce.
Understand workers’ concerns
Every employee approaches work, risk and technology in unique ways. Their fears and concerns vary depending on stage of career, level of tech skill and more.
Despite these differences, 41% of workers in the EU are at least somewhat concerned about job displacement due to AI, with some of the highest fear appearing among younger individuals preparing to enter the job market.
Meanwhile, current workers are split on attitudes towards the technology. Our research found over 60 different personas, grouped into seven broad archetypes. Some readily adopt AI tools, while others are more cautious or need more persuading. There are also workers who are simply unsure where to begin their journey or how it fits into their working life.
Age and career stage also shape these attitudes. As Europe’s workforce grows older, some experienced employees may worry about becoming obsolete or question the value of learning entirely new ways of working late in their careers. This makes it harder to change long-established working habits.
Finally, even when workers do want to use AI, 82% report they are regularly unsatisfied with the tools they are actually given. This can quickly lead to them being put off further adoption due to these bad experiences and slow future roll-outs further.
Without intentional support, no matter how many tools businesses buy, they will still fail to see a return on investment.
Get the workforce onside
Leaders need to understand workers’ anxiety is not unfounded. Cognizant predicted in 2023 that by 2032, 90% of jobs would be disrupted by AI. But more recent research found that that target was reached six years ahead of schedule.
Leaders can’t gloss over that, and if they want to bridge the gap between employees and AI initiatives, they need to start building trust, which starts with honesty. Executives should be clear about what has been decided, what remains uncertain and where automation is still being tested. This gives employees the information needed to understand where they fit, how their role could change and what support will be available.
This must also be supported by reskilling, support and development pathways to ensure enterprises are preparing their workforces for these new ways of working. For instance, Cognizant’s Skillspring programme provides personalised learning and development courses based on what skills workers need in their role.
Similarly, its Bluebolt initiative allows employees to share ideas with the whole company to help improve how work gets delivered. This doesn’t mean a complete change to a person’s career but instead ensuring they have the skills necessary to combine AI capabilities with their existing domain expertise.
The conversation around AI also needs to be reframed. Rather than focusing on automation, leaders should prioritise improving effectiveness, expanding capabilities and elevating the quality and impact of human work.
Build for behaviour, not technology
Businesses need to change how they approach transformation. Principally, this means moving from individual tech projects to business wide change across all teams. The first step of this process is leadership teams adapting their mindset from AI being the solution to their problems to building for outcomes which are enabled by AI.
This can be done in three simple steps:
- Define an intention architecture. Instead of focusing the programme on what the technology can do, successful organisations anchor it in what they want people to do differently. They design from desired behavioural outcomes backward, clarifying the human actions, decisions and workflows AI should change.
- Normalise experimentation. Most teams don’t feel psychologically safe enough to try, fail and iterate in the open. High‑performing organisations create structured spaces where experimentation is expected, supported and de‑risked. This makes learning part of the job, not a side project.
- Model the behaviour through managers and influencers. People adopt what they see, not what they’re told. When managers and respected peers actively use AI tools in their day‑to‑day work, the shift becomes cultural rather than policy‑driven.
Shifting the mindset for the future
Europe will not close its AI adoption gap simply by giving more employees access to more powerful tools. It will close it when organisations give people a clear reason to use AI, the confidence to experiment and an honest account of how work will change. The technology is ready. The determining factor will be whether leaders can bring their workforce with them.
