Solving the AI Talent Shortage: Navigating Common Hiring Pitfalls

by Frosina Chankulovska

8 min read

With new tech daily, there's a booming demand for AI experts in various fields, from tech giants to car manufacturers and healthcare providers. This surge in interest is turning every industry on its head as finding enough skilled AI professionals becomes a growing challenge. 

The race is on for companies to win the best AI talent out there. Businesses compete with each other, and they race against time to find the talent that will lead to the next big innovations. 

To put things into perspective, job listings for AI roles have more than doubled in the last five years. However, the number of qualified candidates hasn't kept up, which highlights a critical need for more AI specialists to match the rapid advancements in technology.

Understanding the AI Talent Landscape

The Current State of AI Talent Demand and Availability

According to IBM, over 35% of businesses have already embraced AI. Start-ups, corporations, and tech giants are all seeking AI and ML talent, which is creating a highly competitive job market. According to the World Economic Forum’s “Future of Jobs Report,” ML and AI roles will be the most in-demand positions across all industries by 2025.

In a research conducted by SnapLogic, they found that 93% of US and UK organizations consider AI to be a business priority and have projects planned or already in production. However, 51% of them acknowledge that they do not have the right mix of skilled AI talent in-house to bring their strategies to life. 

Additionally, a study conducted by Access Partnership and Amazon Web Services reveals that by 2028, over 90% of employers will use AI. That being said, 73% of employers who took part in the study consider hiring AI talent a priority, which highlights the fact that AI is becoming the most sought-after skill in the tech job market. 

AI Talent stats

Challenges in Hiring Qualified AI Talent

SnapLogic's findings reveal that, in the UK, 56% of companies in the IT industry say a shortage of internal talent is their main obstacle. Similarly, in the US, 41% of companies in IT highlighted the same concern about internal talent shortages. 

Additionally, IT decision-makers in both the US and UK identify insufficient budgets (32%) as a significant barrier, followed closely by a lack of the necessary technologies and tools (28%) and inadequate access to relevant data (26%).

An additional challenge that was mentioned in the study conducted by AWS and Access Partnership is a lack of AI skills training. It found that 72% of employers are uncertain about the AI skills required within their organizations, and nearly 80% are unaware of available AI training options or how to implement AI training programs for their workforce.

Common Missteps in AI Talent Acquisition

Overemphasis on Technical Skills Over Potential

One common mistake companies are making when hiring AI talent is that they are seeking highly experienced candidates with lengthy experience, a plug-and-play candidate. 

However, what they fail to acknowledge is that AI and ML are relatively new (and constantly developing) fields, and while technical skills are undoubtedly important, an exclusive focus on them risks overlooking candidates with the capacity to grow alongside rapidly advancing technologies. 

Another misstep in AI talent acquisition is the tendency to hoard employees rather than align staff levels with actual resourcing needs. Companies often scramble to acquire expert-level talent, only to misuse these individuals in roles that do not fully utilize their capabilities or allow for professional growth. 

This approach stifles the creativity and independence of highly skilled professionals and overlooks the importance of adaptability in a dynamic field. Recognizing this, modern companies are now focusing on hiring high-profile talent not just for their expertise but for their potential to drive innovation within the organization. This strategy acknowledges that expert-level talent often thrives in environments that support autonomy and personal development, which aligns more with project-based engagements than traditional roles.

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The Pitfalls of Traditional Hiring Models

From what we have seen so far, the traditional recruitment approaches often fall short in the dynamic realm when hiring for AI and ML talent. Conventional hiring methods often fail to assess candidates' suitability properly for roles that demand a blend of technical expertise, adaptability, creativity, and problem-solving skills.

If you want to broaden your AI talent pool, you have to start incorporating some more unconventional, innovative methods of finding AI talent.

Innovative Strategies for Attracting AI Talent

Embracing Remote and Global Talent Pools

Following the COVID-19 pandemic, remote work has become the new norm, presenting companies with a global talent pool to tap into.

Companies can find the right candidate with the exact skills and experience they need, regardless of location. This is particularly beneficial for specific industries or for high-in-demand roles such as AI and ML. 

Whether it is a Machine Learning Engineer based in Madrid or an AI researcher halfway across the globe, geographical barriers no longer matter when it comes to securing top-tier talent.

Remote work provides companies with the ability to tap into a global talent network and highlights diversity and inclusivity within organizations. By embracing remote work, companies can promote diverse teams consisting of individuals from various cultural backgrounds, which can enrich the collective pool of perspectives and ideas.

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Leveraging Talent Platforms

One of the biggest challenges companies face in their AI initiative efforts is having access to skilled AI talent. To deal with this challenge, they often engage in lengthy internal talent searches and spend thousands of dollars in training. To make matters worse, studies show that companies don’t know how to implement AI training programs for their workforce.

This is where talent platforms like Adeva come into play, offering a strategic solution to the talent shortage challenge.

By leveraging on-demand talent platforms, companies can overcome the scarcity of skilled AI professionals. These platforms provide immediate access to top-tier AI talent and enable companies to focus on their core competencies and strategic goals rather than the complexities of talent management. 

In essence, on-demand talent platforms offer a direct and efficient bridge to the specialized skills required for AI projects. This saves companies the traditional challenges of talent scarcity, training costs, and the time lag of nurturing in-house expertise.

Moreover, platforms like the Adeva AI Talent Hub offer unparalleled flexibility compared to traditional hiring models. Companies can scale their AI talent pool up or down based on project requirements, without the financial and administrative burdens of permanent hires. This flexibility guarantees that businesses can respond more effectively to project needs, market changes, and new opportunities without the delay or cost of developing talent internally.

Building an Agile AI Workforce

Developing In-house AI Expertise

For companies that are looking to build their AI expertise from within, there are straightforward and practical steps they can take to achieve this. 

One effective approach is offering access to training in AI and machine learning. This could be through organized workshops, seminars, or online courses that are readily accessible. Using platforms such as Coursera, edX, and Udacity can provide employees with a wealth of knowledge in AI and related fields. These platforms feature courses taught by experienced professionals and are kept up-to-date with the latest industry developments.

Integrating such training opportunities into their development plan will bring new skills into the organization and will promote a culture of learning and adaptability. It's a practical step towards creating a workforce that is equipped to handle the demands of current and future AI projects. At the same time, you'll build and cultivate an AI-first culture in your company.

Partnering with Academia and AI Communities

Another effective AI strategy for getting access to AI talent is by building collaborations with academia and AI communities.

Partnerships between educational institutions and tech companies open doors to exciting possibilities. Companies gain early access to emerging talent and the forefront of research, while students benefit from hands-on experience and guidance from industry professionals.

This collaboration can take shape in various forms, including internships, cooperative education programs where academic learning is integrated with work experience, and guest lectures by seasoned industry figures.

AI talent stats


To close the AI talent gap, businesses must revamp their recruitment methods to attract and keep the best AI and ML professionals essential for their future. This involves moving beyond just looking at technical skills to also valuing potential for growth and adaptability.

Embracing flexible and innovative hiring strategies will help companies overcome the talent shortage and make sure they hire top AI talent. This proactive approach is vital for continuous innovation and staying competitive in a world increasingly driven by AI.

It's time for companies to update their strategies for finding AI talent. Exploring remote talent, using platforms like Adeva, or developing talent within the organization are key steps to take now to secure the AI expertise necessary for success in today's digital era.


Q: What specific AI skills are most in demand?
The most in-demand AI skills include machine learning, natural language processing, neural networks, computer vision, and data science. Proficiency in programming languages such as Python, R, and Java is also highly sought after, alongside expertise in TensorFlow, PyTorch, and other AI frameworks.
Q: How do companies measure the effectiveness of AI training programs?
Companies measure the effectiveness of AI training programs through employee performance metrics, project outcomes, and skill assessments. They also evaluate program impact by tracking the increase in AI-related project capabilities and the speed of implementation, as well as employee retention and satisfaction rates post-training.
Q: What are the long-term career prospects for AI professionals?
Long-term career prospects for AI professionals are robust due to the increasing integration of AI across industries. Opportunities for advancement into specialized roles or leadership positions are likely as technology evolves. Continuous learning and adapting to new AI developments are crucial for sustained career growth.
Frosina Chankulovska
Frosina Chankulovska
Client Partner, AI Specialization

Frosina, a Client Partner at Adeva, is leading an AI specialization, bridging the gap between global organizations and the world's top independent tech talent. Her track record spans across various industries, having honed her strategic talent acquisition and team-building skills at startups, scale-ups, and established industry players of the likes of McKinsey. Leveraging her expertise in AI and her exceptional relationship-building abilities, Frosina seamlessly aligns client needs with the spot-on tech talent, guaranteeing successful placements in today's competitive tech landscape.

  • Managed Accounts
  • Artificial Intelligence
  • Talent Sourcing
  • Talent Acquisition
  • Leadership
  • Strategic Leadership
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