Across Europe, the appetite for AI has reached record levels. AI-related project demand has risen by more than 230% in a single year, as businesses look for ways to automate processes, improve decision-making and build new customer experiences. Yet, despite the surge, most of these projects (almost 95% according to the latest MIT report) never make it beyond the testing phase. And the reason is as practical as it is strategic - the people needed to build, test and scale them are in short supply.
From proof-of-concept to practical deployment
After several years of experimentation, organisations are now rapidly trying to embed AI within their everyday operations. While enthusiasm is high, the transformation calls for a workforce that understands data infrastructure, cloud environments, security frameworks and compliance obligations. However, there is a caveat.
Capabilities and requirements for reliable AI systems are changing fast and involve more than a single discipline. A growing number of roles now demand hybrid capabilities: engineers who understand both data pipelines and cyber risk, architects who can integrate AI tools with legacy systems, and product leaders who can translate technical complexity into business outcomes. These are the professionals who turn prototypes into sustainable tools, and they are becoming the hardest to hire.
The most sought-after abilities today sit around Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) - the building blocks of specialised AI assistants, or customer-facing chat solutions. Companies increasingly want to design their own versions of these systems, trained on internal data, but they often lack people who can do it safely and effectively. Around 40% of the most in-demand AI skills simply don’t exist in the current workforce, leaving thousands of roles unfilled across Europe.
As a result, organisations operate in a market where technology has evolved faster than talent. While general knowledge of AI is becoming common, expertise in the surrounding infrastructure remains rare. Employers are searching for specialists fluent in Python, TensorFlow, LangChain and PyTorch, but also for data engineers and security professionals who can make those models usable within complex corporate systems.
Low-code tools are widening participation
One of the more interesting developments is the renewed interest in low-code and no-code platforms, which allow non-technical employees to build or automate parts of a workflow without waiting for a development team. Demand for these projects grew by 40% over the past year, driven by tools such as n8n and Make that help business units integrate AI into their daily operations.
A growing number of developers are turning to vibe coding — a more conversational way of building software through natural-language prompts. It speeds up experimentation and opens coding to a wider pool of creators, but it also removes many of the safeguards that ensure the code is structure and secure. As a result, demand is rising for experts who can review, test and secure what these new tools produce.
For HR and recruitment teams, this broadens the definition of what an “AI-ready” organisation looks like. As marketing, finance, and operations departments begin to use these tools directly, employers need people who can combine digital literacy with an understanding of process design and governance.
Security and compliance take centre stage
As AI systems interact more closely with proprietary data and customer information, cybersecurity has become a central consideration. Over the past year, demand for security-related work has increased by 35%, with many new experts focusing on audits, identity management and risk governance. Some organisations have even postponed AI rollouts until they achieve ISO 27001 certification or complete a full identity-and-access-management review. This shows a growing importance of cross-functional awareness, where security professionals understand automation and data engineers anticipate compliance risks.
As the demand for platforms such as Scaleway almost doubled in 2024, there is a rising preference for regional infrastructure that offers greater transparency and alignment with European data regulations. For HR leaders, this means they need talent familiar with these environments - people who can build and manage sovereign systems while maintaining the flexibility of global platforms.
Implications for HR and recruitment professionals
The changing nature of AI work has completely redesigned future job roles. When discussing technical hiring, organisations don’t just need coders or data scientists - there is a rising demand for cloud engineers, automation experts, compliance officers and designers who understand user experience. The ability to coordinate these disciplines is becoming as valuable as deep expertise in any one of them.
For HR professionals, this means looking at the organisation’s AI capability through a wider lens. Workforce planning must consider not only the technical hires required. As employers compete for scarce technical talent, many are turning to independent specialists who already work at the frontier of AI, data and automation. Freelancers are often the first to master emerging tools or frameworks, moving between projects that expose them to different systems and challenges. That experience gives them a practical edge and provides organisations with a perfect opportunity to tap into this flexible workforce to ease immediate pressure while internal teams build longer-term capability.
What emerges from this year’s data is a clear picture of a market maturing fast but unevenly. Businesses are eager to scale beyond pilots and proofs of concept, yet they are constrained by a lack of the right skills in the right places. Workforce preparation will be key in tackling the challenge and will require targeted learning programmes, revised recruitment strategies and closer collaboration between HR, IT and business leaders. AI adoption is a matter of readiness, and that readiness begins with people.
