What does the AI talent market look like from a recruiter’s perspective right now?

Recruiting for AI-related roles has impacted the dynamics of technology recruitment. This year, we at Avila have already carried out several fully AI-focused assignments for various organizations, and through these, a clearer picture has begun to form of what the AI talent market currently looks like in Finland and the Nordic countries.

In this article, we share observations from our entire team regarding the AI recruitments we have conducted, as well as what organizations should consider when building AI competence.

AI roles attract a lot of interest, but deep expertise is still rare

“The market for AI talent currently differs clearly from the rest of the technology field. While the recruitment market in the technology sector has generally partially stabilized over the past two years, the situation in AI roles is very different, and demand clearly exceeds supply,” comments Linda Tuominen.

According to LinkedIn’s Future of Work reports, demand for skills related to generative AI has grown extremely rapidly globally since 2023. Publications from McKinsey and the World Economic Forum have also highlighted the same phenomenon: AI expertise has become one of the fastest-growing skill areas in the labor market. This makes rapid building and implementation of companies’ AI strategies more challenging, as the available expertise is still limited for the time being.

In practice, this is visible in recruitments in many ways. AI roles attract an exceptionally high level of interest, and in many assignments the number of applicants has been large. However, a high number of applicants does not automatically mean a high number of qualified candidates.

There are many individuals in the market who have some degree of exposure and experience with AI, but significantly fewer who have experience in designing, implementing, and maintaining production-level AI solutions as part of business-critical systems. There are currently also large differences in skill levels among AI professionals, and active competition exists for experienced talent.

At the same time, it is worth noting that genuinely modern AI expertise cannot yet be expected to span a very long time.

“AI has only become widespread in working life in recent years, so there are naturally few professionals in the market with truly long experience. For this reason, recruitments particularly emphasize learning ability, applied skills, and the ability to quickly adopt new technologies,” shares Jarna Lehtola from her observations.

AI organizations are still finding their shape

One of the clearest observations at the moment is how different AI roles and organizations’ competence needs can be in practice. Although roles may appear similar from the outside, or positions share the same role titles, expectations regarding technical expertise, business understanding, and strategic responsibility can vary significantly depending on the organization.

“For example, under the title of AI Developer, very different things can be done depending on the organization. For some, AI development means leveraging code-assisted development, while in other organizations the role focuses heavily on building and integrating production-level AI solutions,” comments Jarna Lehtola.

Terminology and role definitions therefore vary greatly depending on the organization and its situation.

“For example, many applicants for AI roles have a background in machine learning. This expertise is extremely valuable, but in practice we have noticed that machine learning-based development and modern software development-focused AI development can be weighted very differently depending on the position and the organization’s needs,” comments Jarna Lehtola.

In addition, the market has seen a growing number of AI Lead-type roles, where the focus is not necessarily on hands-on development work, but on managing the AI entity as a whole, setting strategic direction, and building the organization’s AI capabilities. For this reason, organizations’ expectations toward AI roles also currently vary greatly.

There is currently still some uncertainty visible in AI organizations regarding tools, technologies, and entire AI stacks. Organizations are still actively considering which technologies to adopt, which to pilot, and at what stage solutions should be taken all the way to production. Tools and platforms are developing so rapidly that in many companies AI work is still partially in an experimental phase.

At the same time, organizations increasingly desire experience with AI solutions that have been taken to production, not just proof-of-concept level projects. This also creates a certain supply-demand challenge in the market: how can deep AI expertise be accumulated in practice if opportunities to build production-level solutions are still limited?

What have we observed in our assignments?

General observations

This year, we have carried out several AI-focused recruitments for various organizations. The assignments have included roles such as:

AI Data Lead
AI Developer
Senior Product Manager, AI
AI Automation Lead
AI Full Stack Developer

These are just examples, as the range of AI roles is currently growing rapidly as organizations’ needs become more defined, and is continuously expanding into other functions and role areas as well. In the assignments we have conducted so far, the following factors have been particularly emphasized:

Practical implementation experience with LLM solutions
Understanding of data architecture
Scalability of AI solutions
Information security and governance expertise
Ability to connect AI solutions to business objectives
Strong collaboration and communication skills

“In AI roles, in addition to technical expertise, collaboration and communication skills are strongly emphasized. In many organizations, AI professionals are expected to share their knowledge broadly across other functions as well, not just within the technology organization. This underscores the importance of business understanding and the ability to communicate in a practical manner with various stakeholders,” comments Linda Tuominen.

Concentration of expertise and rapid market changes

Following these searches, we have observed that the most experienced AI expertise is currently strongly concentrated in consulting and technology firms as well as AI-first environments, where practical AI projects have been built for a longer period. This is also reflected in competition for the same talent, as the market’s most experienced professionals are currently in extremely high demand.

The following conclusion can also be drawn from the observations: the market is currently fairly clearly divided into two distinct groups of professionals. On one hand, there are professionals who have worked with machine learning, data science, and earlier forms of AI for a long time before the breakthrough of generative AI. On the other hand, a very large number of new professionals have entered the market over the past 2–3 years with the rise of generative AI and LLM solutions.

In addition, the market is currently evolving extremely rapidly from the perspective of talent as well. In many organizations, AI teams are being built for the first time, new areas of responsibility are constantly emerging, and the content of roles is changing quickly as business and AI strategies become more refined.

“From the outside, this may appear as AI professionals spending a relatively short time in the same role, sometimes less than a year. However, this is not necessarily a case of traditional turnover, but rather that the market is currently moving extremely fast, which also affects, for example, internal transitions,” states Jarna Lehtola.

At the same time, experienced AI professionals are continuously finding new opportunities to build new teams, operating models, and AI solutions in different organizations. This naturally also increases movement in the market and the mobility of the workforce from one organization to another.

For this reason, organizations would benefit from considering already at the recruitment stage how AI professionals can be offered sufficient opportunities for development, learning, and growing responsibility over the longer term as well. A merely interesting title or a single AI project may no longer be enough to retain the most experienced professionals in a rapidly changing market.

Location of searches, language requirements, and speed of decision-making are emphasized

In many assignments, we have noticed that organizations should genuinely assess whether the Finnish language is a necessary requirement for that particular role.

“AI expertise is a global market. International recruitment, English-speaking teams, and remote models can open up a considerably broader talent pool than focusing solely on the Finnish market,” reflects Linda Tuominen.

This simultaneously raises a broader strategic question: will the AI organizations of the future be built in Finnish or in English? For many growing technology companies and international organizations, an English-speaking AI team may in practice be a necessity for achieving competitive expertise.

As with many other roles, AI professionals are also recruited from the market extremely quickly. In several situations, candidates have had multiple conversations ongoing simultaneously, and decisions are made considerably faster than in many traditional expert recruitments.

This also requires internal readiness from organizations. Decision-making must be swift, recruitment processes clear, and role definitions realistic in relation to the market. In addition, competitive compensation and the active commitment of recruiting managers to the process are particularly emphasized when several organizations are competing for the same talent simultaneously.

Summary

AI recruitment is currently one of the most rapidly changing talent markets. Although interest in AI roles is enormous, deep expertise is still limited and concentrated.

At the same time, the market is still finding its shape. Roles, responsibilities, technologies, and organizational structures are changing rapidly, and there is as yet no established common model for building AI organizations. This is visible both in the talent market and in organizations’ recruitment needs.

Based on the first AI recruitments, however, one thing is already clear: organizations that succeed in building a stable foundation for their AI strategy, making decisions quickly, and offering professionals opportunities for continuous learning and influence, are in a clear advantageous position in the talent competition of the coming years.

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