AI turns data science into a cross-disciplinary skill

August 16, 2026 3 min read

Universities are adapting their curricula as employers increasingly seek professionals who can combine technical skills with critical thinking, data interpretation and problem-solving.

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Artificial intelligence is reshaping the skills professionals will need over the next decade, as data and AI tools expand into fields such as healthcare, law, communications, marketing and economics.

The transformation also poses a challenge for universities: updating their programs at the pace of a technology that is evolving faster than traditional education models.

Artificial intelligence is redefining the professional profile the labor market will demand over the next decade. What until a few years ago was knowledge largely reserved for engineers and technology specialists is becoming a cross-disciplinary skill for professionals in fields ranging from law and communications to medicine, marketing and economics.

The shift is being driven by advances in generative artificial intelligence, which can analyze information, automate processes and support decision-making without requiring advanced programming skills.

In this environment, data science is moving beyond being an exclusive discipline for technology-oriented professionals to become a tool applicable across an increasing number of professions.

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”Rather than training data scientists, the challenge is to prepare professionals who can use artificial intelligence to solve real-world problems, interpret information and make better decisions,” explains Fernando Blázquez, director of the Bachelor’s Degree in Data Science and Artificial Intelligence and the Bachelor’s Degree in Full-Stack Development at the University of Design, Innovation and Technology (UDIT).

Over the past decade, data science education has evolved from programs focused primarily on statistics, programming and the processing of large volumes of information toward broader, more interdisciplinary degrees.

The incorporation of artificial intelligence, machine learning, cloud computing and generative tools has transformed both the content of these programs and the way students learn.

Students are no longer expected only to build models, but also to understand the context in which they are applied, evaluate their results, identify biases and translate data into useful decisions.

This evolution has brought the discipline closer to fields such as business, healthcare, communications and industry, while real-world projects are playing an increasingly important role in education.

Universities face technological change

Against this backdrop, universities face the challenge of adapting their academic programs to the pace of technological evolution. Rigid educational models are giving way to more flexible curricula capable of incorporating new tools and responding to changing labor-market needs.

Mastering tools such as Python, SQL and artificial intelligence platforms is only part of the required skill set. Companies are also seeking professionals who can communicate complex findings clearly, interpret large volumes of information and design solutions centered on people’s needs.

User experience (UX) is also becoming increasingly important in the development of AI-powered applications.

As conversational interfaces gain ground over traditional buttons and menus, understanding human behavior is becoming an important component in the design of these solutions.

Collaboration between universities and companies represents another challenge. Having students work with real-world projects and data helps bring academic training closer to organizations’ needs before graduates enter the labor market.

From technical training to problem-solving

One example of this evolution is Medscan, an application developed by first-year students in UDIT’s Bachelor’s Degrees in Data Science and Artificial Intelligence and Full-Stack Development. The project was recognized as one of the winning proposals at the international OdiseIA4Good 2026 hackathon.

The solution uses optical character recognition and artificial intelligence to identify medications from a photograph of their packaging and turn information from the official package leaflet into clear answers tailored to the user’s needs.

The project incorporates real-world problem-solving, accessibility, data reliability and the social impact of technology from the early stages of training, alongside its technical component.

”The biggest challenge for universities is no longer teaching a specific technology, but training professionals who can adapt to an environment that is constantly evolving. Beyond mastering tools, the real differentiator will be critical thinking, analytical ability and continuous learning,” Blázquez says.

This shift also requires universities to rethink how academic programs are designed. As technology evolves rapidly, curricula need to incorporate new tools, methodologies and labor-market needs without relying on complete restructurings every few years.

”In the coming years, we will see data science move from being a skill exclusive to technology-oriented professionals to becoming a cross-disciplinary capability. The most valuable professional will not be the one who uses the most AI tools, but the one who knows how to combine technology, judgment and a deep understanding of people’s needs,” Fernando Blázquez concludes.