The race against time to fill the AI talent gap

Spanish universities are reacting urgently to a demand that has overwhelmed them: Artificial Intelligence has emerged so rapidly that up to 50% of specialized positions in this field remain unfilled. And the major paradox is that there aren’t even enough experts to train the next generation. This situation raises a critical question: how can we adapt training and talent acquisition models in technology—and especially in AI—particularly in sectors like pharmaceuticals, where innovation cannot be put on hold?

The gap is no longer just about talent, but about trainers

The shortage of AI talent is not a new phenomenon, but what has changed in the last two years is the pace: the labor market is moving faster than the education system. Official master’s programs are overwhelmed, top professionals are already working in the industry, and universities are even competing to recruit qualified professors.

“There are not enough PhDs with AI experience to teach the programs companies are demanding,” say several academic institutions.

In this context, the urgent question is not just how to train AI talent, but: who is going to do it?

Companies need hybrid profiles, but the system continues to produce vertical specialists

A data engineer, a computer scientist, and a pharmacist with machine learning knowledge might have three different educational backgrounds and never cross paths. Yet the pharmaceutical industry needs these worlds to come together:

• Predictive models for clinical development
• AI algorithms for personalized treatment
• Automation of quality, production, or supply chain processes

The kind of talent the sector needs no longer fits into traditional profiles. This requires a deep review of how academic and corporate training programs are being designed.

What are universities doing to keep up?

Some institutions have begun taking drastic measures:

• Rapidly updating master’s programs to include generative AI, cloud, and MLOps
• Partnering directly with tech and pharmaceutical companies to bring in active professionals as guest lecturers
• Outsourcing part of the training through bootcamps or private certifications

But even with these efforts, the challenge remains: training talent without enough qualified trainers creates a structural bottleneck.

“We’re training at full speed, and we’re still behind,” admitted the director of a master’s in AI.

And from the company’s perspective, what can be done now?

While the education system catches up, large companies cannot afford to wait. That’s why many are choosing to collaborate with specialized technology partners in AI, who can provide up-to-date talent, flexible teams, and integrated solutions. This approach allows companies to:

• Reduce the average vacancy cost (AVC) by quickly filling critical roles
• Embed continuous learning and expert support into real business projects
• Avoid increasing fixed structure by using more scalable and adaptive collaboration models

This approach is becoming one of the most viable solutions to keep innovation moving and protect competitiveness, especially in highly regulated industries like pharmaceuticals.

The pharmaceutical sector is especially impacted by this shortage

In a context where breakthroughs increasingly depend on large-scale data processing, AI talent is not just a competitive advantage—it’s a business-critical need. Pharmaceutical companies must bring in professionals who understand AI, regulatory frameworks, biostatistics, and clinical knowledge. That makes the talent equation even more complex.

How do you train someone who needs to understand machine learning, healthcare regulations, and product lifecycle management?

This is where internal reskilling programs, partnerships with research centers, and investment in young talent become essential.

A model that falls short in the face of a shifting demand

What academia is teaching today may already be outdated in less than two years. The nature of AI itself demands professionals who are:

• Agile in continuous learning
• Equipped with critical thinking and tech ethics
• Aware of the cross-functional role of data and AI within organizations

Meanwhile, the average vacancy cost (AVC) for AI roles is rising rapidly, directly affecting innovation and operational performance. Failing to fill an AI vacancy is not just an HR problem—it’s a real loss in competitiveness.

Who is really leading the future of training?

The challenge is no longer just about creating more master’s programs or bootcamps. The real question organizations must ask is: how does continuous training become part of business strategy?

• Can a pharmaceutical company afford to wait for universities to catch up?
• Should companies launch their own internal AI training programs?
• Can external training keep pace with the speed of technological disruption?

“The professionals we need don’t exist yet. We have to create them, and that requires partnerships and strategic vision,” is increasingly heard in tech talent forums.

While universities struggle to update their programs and find qualified trainers, companies must move beyond traditional recruiting. The solution lies in growing talent from within, investing in flexible learning models, and understanding that AI training is no longer an academic task—it’s a business necessity.

Because in the end, the question remains the same: who will train the trainers… when there aren’t enough professionals to fill the key positions?

References and key documentation:

• OECD (2024). AI and the Future of Skills.
• World Economic Forum (2025). Future of Jobs Report.
• Fundación Cotec (2024). Talento para la era de la Inteligencia Artificial.
• El País (2025). Las universidades forman a contrarreloj expertos en IA.