AI in the European workplace in 2026: Expert insights on the future of AI in HR

AI in the workplace has moved beyond its initial hype and pilot phase, with more organisations across Europe adopting the technology and integrating it into daily work and long-term planning. However, progress is not necessarily yielding meaningful impact yet. 

One reason for this uneven result is that many organisations are not redesigning work for the AI era or bringing their employees along in a way that engenders trust and clarity. Only half of employees across Europe trust how AI is used within their companies. That is a critical issue for HR and payroll leaders hoping to embed AI effectively.  

In our 2026 HR & Payroll Pulse report, experts from the SD Worx Research Institute explore how companies are using AI, whether hype has translated to practical impact and what we may expect from advancements in automation. 

This article provides a glimpse into Pulse research, including expert insights from contributor Jason Pridmore, a professor of Human Centric AI and Society at Erasmus University Rotterdam.   

Organisations across Europe are busy figuring out ways to use AI to make work more efficient, moving from disparate experiments to a more integrated approach. The excitement around AI seems to be translating finally to concrete action.  

In 2026, 54% of organisations are actively exploring the potential of AI in the workplace, while 50.5% are actually investing in the technology, a substantial increase from last year. Employee data reinforces this ambitious trend. 

For Pridmore, this signals the start of a new era in human-AI collaboration in European workplaces.  

“I think we’ve hit the moment where AI has shifted to the reality of a much more practical and pragmatic approach to AI in the workforce,” he says. “[Organisations are] thinking about how different types of AI can be applied across different components of an organisation.” 

Despite the encouragement around AI, there is still a gap between that implementation push and real impact and trust. Fewer than half of organisations (46%) say their employees have the skills to work effectively with AI tools, and 43.8% say they’re investing in reskilling and upskilling employees for AI-driven work.  

Also, organisations tend toward optimism while employees are a bit hesitant or skeptical in many cases. Transparency and trust are significant hurdles for organisations hoping to create real value.   

It is also not necessarily about the technology itself, but the way organisations approach it. Pridmore confirms that organisations are still struggling to yield concrete, scalable results from their AI investments, often because they use AI in fragmented ways, apply the technology to small, ad hoc tasks, and have yet to define clear strategies for where AI belongs.  

“What is still happening is that large language models are predominantly used from one tool to another, rather than being systemised and integrated into daily tasks,” says Pridmore, making it challenging to build that long-term trust and transparency.  

“Trust in technology is built through repeated, consistent experiences,” he says, warning that consistency in AI is more challenging because of its variability. Organisations need to help employees understand what these technologies do and how they affect their jobs in an open and transparent way.  

“In a European context, people don’t just ask if this is good for me,” Pridmore continues, “they also ask if it’s good for my colleagues. There’s more recognition that work is only part of life, and that technology should contribute to a better overall balance.” 

If there is one topic that gets a lot of traction, it is whether AI will lead to widespread and inevitable job loss. The reality is more nuanced, and for Pridmore, organisations should be framing AI differently.  

In practice, AI does not simply take work away from people. It often shifts work into other sorts of activities, automating some while urging the resurgence of others.  

Employees still largely need to review and interpret outputs, run quality checks and make sound judgements. Workloads change shape, rendering the simple calculation “AI = efficiency” an already obsolete narrative.  

As Pridmore states, “we haven’t made that full shift yet. [AI] may save time in specific cases, but much of that work still needs to be double-checked and reviewed.” More lasting value may come from rethinking workflows and roles in anticipation of more AI and automation, rather than layering technology on top of existing work.  

Reshaping how we work also comes with the risk of losing human knowledge. As roles disappear and others get more complex, there should be investment in retaining that insight and the “why” behind work.  

“We will have a longer-term process of losing valuable knowledge and experience if we embed AI in everything,” says Pridmore. “The experience people had before AI, the ‘why” behind things, may no longer be shared with new employees.” 

One mistake that many companies have made and continue to make is to design AI initiatives from the top down. AI adoption should be a collaborative process between leaders and employees, rather than features rolled out to workers.  

Our 2026 Pulse data shows that a possible outcome of this is fewer people actively using AI daily or long-term, despite the organisational enthusiasm.  

Pridmore suggests that organisations that involve employees too late miss out on valuable insights around inefficiency, friction, potential improvements and whether there are already workarounds in practice that do not make it up to higher-level decision makers. 

He says, “workers are already figuring out how to make their lives easier. They have workarounds, they understand inefficiencies, and they are often open to sharing that. The problem is that we involve them too late in the process.” 

HR leaders and departments have their work cut out for them in shifting toward AI-enabled workforces and workplaces. Pridmore suggests three practical ways to tackle what’s to come: start with people, preserve expertise and do not be afraid to experiment.  

• Start with people: Instead of asking what you can automate, focus on identifying work that needs improvement and start fixing things for the people doing that work. Understand what is actually happening and the context and people behind it, and then improve those aspects before thinking about automation.
• Preserve expertise: Human knowledge will always be valuable. Do not lose it. Companies should not seek to extract employee expertise and convert it into AI processes.
• Experiment: Do not be afraid to fail (and fail well). Opt for more experimentation and responsible experimentation at that. Try things, learn from mistakes swiftly, always be transparent and keep people as informed as possible.  

HR leaders are facing a myriad of challenges in 2026, with smarter AI integration as only one of these pressures. Our 2026 HR & Payroll Pulse report, powered by SD Worx Research Institute research, explores these issues and best practices for tackling them.  

For the latest evidence-based insights, head to the SD Worx Research Institute hub to better understand and address your unique AI in HR and payroll challenges.  

Looking for the full story and the data to back it up? The 2026 HR & Payroll Pulse Europe report explores how workplaces are changing, what employees expect today, and what HR leaders can do to make work better.