In recruitment and talent management, the shift is already visible. Demand for AI competence is growing quickly, while the supply of experienced professionals is still limited. At the same time, AI is spreading beyond traditional tech roles and becoming relevant across many functions.
From my perspective working with talent acquisition, this is pushing companies to rethink how they build capability overall. Hiring alone isn’t enough. Organisations need to combine targeted recruitment, continuous upskilling, and a clearer view of how AI is applied in their business.
The demand for AI skills is outpacing the supply
The competition for AI talent has increased significantly in recent years, and we can clearly see that the demand is growing faster than the supply. Companies across industries are looking for people who can work with AI, not only within technology teams but also in many other roles.
But, the number of professionals with deep AI experience is still relatively small. This creates a situation where AI competence is highly sought after across the market.
Another change is how AI competence appears in job descriptions. Instead of existing as a standalone speciality, AI is often layered on top of other roles. Companies look for Developers, Data Engineers, or Architects who also understand machine learning, large language models, or AI infrastructure. In practice, AI is becoming part of existing roles rather than a separate function.
Strong technical background combined with AI skills is the real bottleneck
The most difficult profiles to recruit are those who combine strong AI expertise with solid experience in areas such as backend development or data engineering.
Many professionals entering the workforce today have good knowledge of AI tools and technologies such as PyTorch, TensorFlow, vector databases, or RAG architectures. However, building solutions that work in production requires experience across the full stack, from application development to infrastructure and cloud. This combination is where the real shortage is today.
At the same time, demand is increasing in areas beyond engineering. Competences related to responsible AI, governance, and compliance are becoming more important, while the supply remains limited.
Cross-functional teams or unicorn hires
One common pattern is that companies are looking for a single person who can do everything, combining data engineering, software development, AI modelling, infrastructure, and governance into one role.
Another way forward is to build cross-functional teams where different people bring different strengths. Instead of combining all expectations into one role, organisations can distribute them across a team.
Upskilling is the main driver of AI capability
Recruitment is important, but it will be very difficult to solve the AI competence challenge through hiring alone. For many organisations, the main driver needs to be upskilling.
Employees already understand the company’s data, processes, and customers, which often makes them well-positioned to apply AI in meaningful ways. Building on that existing knowledge can be more effective than bringing in entirely new profiles without that context.
This also means that organisations need to invest in continuous learning and create structures that support it. As Lasse Girs, our Head of AI Transformation, highlights, the most future-proof skill today is the ability to learn continuously and unlearn what no longer creates value. The pace of AI development challenges both individuals and organisations to keep updating their ways of working.
And hiring still plays an important role. Bringing in experienced AI specialists can help set direction, build the initial foundation, and support others. So there needs to be a balance between hiring and upskilling.
AI literacy and company culture will determine adoption
According to our recent AI survey, four out of five people across the Nordics believe AI will transform their work within five years, yet only one in ten currently sees AI literacy as important for their own career. This creates a clear gap between expectations and action.
At the same time, company culture has a strong impact on how AI is adopted. Employees in organisations with a positive attitude towards AI are far more likely to use generative AI tools in their daily work. Without that support, AI often remains something people experiment with on the side rather than something that is integrated into everyday processes.
This is why AI literacy needs to be treated as a core capability. Organisations that invest in learning, create clear development paths, and make time for experimentation are more likely to integrate AI into how they actually work.
AI talent is looking for real impact and room to explore
Expectations from AI professionals are evolving, and many candidates are no longer interested in working only on proof-of-concept projects. They are looking for environments where AI is used in production and where their work has a clear impact.
Also, continuous learning is a key factor. Because the field is evolving quickly, access to modern tools, opportunities to experiment, and the ability to stay up to date are important when choosing an employer.
We can also see stronger competition from startups again, where AI professionals are often attracted to environments that allow faster decision-making and closer collaboration across roles.
We need to rethink how we evaluate talent
As AI continues to evolve, it is likely to influence how we assess candidates as well. Experience will remain important, but the ability to learn, adapt, and work with new tools is becoming increasingly relevant.
Skills such as problem solving, curiosity, and the ability to understand the bigger picture are gaining importance, especially in a landscape where roles and technologies are continuously changing. This may also shift how we value experience compared to potential.
Focus on AI maturity, not just hiring
To stay competitive, organisations need to look beyond individual hires and focus on overall AI maturity.
This includes having a clear AI agenda from both a business and competence perspective, as well as building a culture where AI is part of everyday work. Organisations that support employees in developing AI skills and applying them in practice are more likely to capture the benefits of AI.
In the end, the question isn’t only who you hire, but how you build the capability to work with AI across the organisation.
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