Artificial intelligence (AI) is rapidly reshaping the IT industry. Automation and machine learning are taking over repetitive programming, analytical, and administrative tasks while simultaneously creating demand for entirely new types of skills. As a result, companies adopting AI increasingly need specialists capable of designing and overseeing intelligent systems, analyzing massive datasets, and ensuring cybersecurity in an increasingly automated environment. At the same time, we are seeing a talent shortage – there simply aren’t enough professionals who can build and improve AI algorithms or data engineers able to manage the flood of information. This is good news for young people entering the field – AI and data science specialists are in high demand, and their salaries rank among the highest in IT. Importantly, the rise of AI does not mean that all jobs will vanish. On the contrary – forecasts suggest that technological progress will create more new roles globally than it eliminates, provided that workers continue learning and adapting. Below, we outline which academic fields are gaining importance in the era of AI, how the world is responding to these educational shifts, and how to choose your best career path in IT.
How AI Is Transforming the IT Industry and Employer Expectations
AI has become an integral part of digital transformation – companies increasingly use artificial intelligence to optimize business processes: from data analysis and automating routine tasks to supporting decision-making. This has shifted the focus in the IT job market. Traditional roles (e.g., front-end developer) are being partially simplified by low-code tools and generative AI, reducing demand for some skills (for instance, job postings for front-end developers in Poland have dropped by ~20% year-over-year due to automation). On the other hand, demand is rising for new specializations. Polish market data shows strong growth in job postings for IT security (+39%), data analysis (+34%), and artificial intelligence (+22%). Employers seek professionals with knowledge of machine learning, big data, cybersecurity, cloud technologies, and DevOps methodologies.
At the same time, companies expect broader competencies than before. AI now supports not only software engineers but also managers, recruiters, and business analysts. Being able to combine technical expertise with understanding business needs – and the ability to work effectively with AI tools – is becoming a major advantage. Soft skills such as creativity, communication, and adaptability are also gaining importance, as these are much harder to automate. It’s worth remembering that roles requiring high qualifications and social intelligence are the least threatened by automation, while routine jobs with lower skill requirements are the most vulnerable. In other words, the more advanced the qualifications and the more complex the tasks, the more likely AI will become your tool, not your competitor.
Academic Fields and Specializations of the Future
As the IT job market evolves, university programs evolve with it. Below we analyze academic majors and specializations gaining prominence in the age of AI. Choosing one of these paths can significantly improve future career prospects.
Computer Science: Computer Science has long been the foundation of the IT industry, and its importance only grows in the era of AI. It provides strong foundations in programming, algorithms, databases, and system architecture – skills that are useful in every technical specialization. It is also one of the most competitive majors: at top Polish universities, around 10 applicants compete for a single spot. Universities are expanding CS curricula with AI modules – for example, the Warsaw University of Technology has introduced new specializations such as Internet of Things Engineering and Cybersecurity, and has integrated AI-related content across all CS programs. Computer Science offers broad versatility, preparing graduates for careers ranging from software development to data analysis.
Data Science: In the era of big data and machine learning, demand is soaring for specialists who can extract insights from data. Data Science combines computer science, applied mathematics, and statistics, teaching Python/R programming, machine learning methods, data exploration, and data visualization. Job postings for data analysts and BI specialists in Poland have grown by over 30% in the past year, and experienced data experts are among the highest-paid in IT (senior-level often above 20–25k PLN monthly). Universities are expanding in this area – for instance, University of Warsaw launched the interdisciplinary Artificial Intelligence and Cognitive Science program, Kozminski University offers a Master’s in Data Science, and Jagiellonian University and others now include DS tracks within computer science. Success in this field requires continuous learning – technology changes quickly, and the most in-demand specialists combine technical skills with business understanding.
Cybersecurity: The more AI and digital systems we use, the more critical it becomes to protect them. Cybersecurity is one of the fastest-growing fields – experts predict rising demand for information and network security professionals as digitization accelerates. The trend is already visible: job postings in cybersecurity have risen by nearly 40% year-over-year. Cybersecurity studies cover cryptography, system and application security, penetration testing, security audits, and threat analysis. New programs have been introduced at multiple institutions, including the Warsaw University of Technology (Cybersecurity Engineering). Entry-level salaries typically range from 8–12k PLN, with experienced certified professionals earning 20–30k PLN per month. Cybersecurity requires not only technical knowledge but also an understanding of business risk and legal aspects, making it ideal for people with broad analytical interests.
AI Engineering: The rapid rise of AI has led to dedicated academic programs training AI engineers. These programs focus on machine learning, neural networks, natural language processing, and computer vision – in short, creating intelligent systems. A pioneering program in Poland was ALK Kozminski’s Management and AI, combining business and artificial intelligence. Now, many technical universities have added AI specializations within computer science. For example, Gdańsk University of Technology introduced Artificial Intelligence (within biomedical engineering) and Machine Learning (within computer science) even before the current AI boom. Students learn practical model training, programming in AI-focused languages (Python, R, Julia), using libraries like TensorFlow, and exploring AI ethics. Experts predict that demand for AI specialists will grow exponentially over the next decade – the role of AI Engineer is poised to become one of the defining professions of the future.
Applied Mathematics: The theoretical foundations of AI lie in mathematics – which is why Applied Mathematics and related fields (data analytics, numerical methods) are also gaining importance. Strong mathematical training is essential for machine learning (requiring linear algebra, statistics, optimization), cryptography and security (number theory, combinatorics), computational simulations, and fintech. Math graduates increasingly join the IT sector as data analysts, predictive modelers, AI engineers, or algorithm developers. Their theoretical expertise enables them to understand and improve AI systems and solve unusual problems using mathematical modeling. The field is often less competitive than pure CS but provides universal competencies, such as abstract reasoning and problem-solving – invaluable in AI. Combined with programming skills, applied mathematics can lead to careers in algorithmic finance, machine learning, or AI system design at the algorithmic level.
Cognitive Science: Cognitive science is an interdisciplinary field combining computer science, cognitive psychology, neuroscience, linguistics, and philosophy of mind. In the context of AI, its role is unique – it studies human cognition, helping build AI systems that better align with human needs. Students learn cognitive modeling, fundamentals of AI, human–computer interaction (UX/UI), and user behavior analysis. This versatile skill set opens many career paths: designing intelligent interfaces and voice assistants, UX research, customer behavior analytics, neuroinformatics, or academic research on the brain and AI. For example, the Computational Cognitive Science track combines cognitive psychology and neuroscience with programming and AI. As AI becomes more integrated into daily life, understanding the human mind and user experience becomes crucial – cognitive scientists may become human-centered AI specialists, AI ethicists, or UX experts in AI-driven projects.
Bioinformatics: Advances in biotechnology and personalized medicine are driving demand for bioinformaticians – professionals who combine biological knowledge with data analysis and programming. Modern technologies such as genome sequencing produce enormous datasets requiring analysis and interpretation. Bioinformatics teaches how to use algorithms and AI to perform biological analyses – identifying genes linked to diseases, designing drugs via computer simulations, or analyzing medical data to predict disease progression. Experts believe that genetic engineering and molecular biology will fuel strong demand for bioinformatics engineers. Today, bioinformaticians work in pharmaceutical companies, research centers, hospitals, and startups focused on personalized medicine. The field requires strong foundations in biology and chemistry alongside technical skills, making it ideal for those interested in both natural sciences and coding. Bioinformatics allows the use of AI to improve healthcare through protein modeling, AI-assisted diagnostics, and epidemiological analysis.
Of course, this list of future-oriented fields is not exhaustive. Other promising areas include robotics engineering, automation and mechatronics (AI in Industry 4.0), aerospace engineering (autonomous vehicles, space exploration), and game design (increasingly driven by AI). The key point is that the future job market will favor fields offering skills complementary to AI. Jobs requiring creativity, complex problem-solving, teamwork, or caring for others will remain essential – and often strengthened by AI support. Among the "professions of the future" expected to grow are specialists in renewable energy, online education, and mental health, as well as AI ethics experts and data curators responsible for ensuring data quality for AI systems.
Examples from Around the World: How Other Countries Are Responding to the AI Revolution
The rapid development of AI is a global challenge — countries around the world are adapting their education systems to prepare young people for the new realities of the job market. Here are several examples of national strategies:
United States: The U.S. has long invested in technological education, and recently it has placed strong emphasis on AI. Major American universities (including MIT, Stanford, and Carnegie Mellon) have launched dedicated AI departments and institutes, offering specialized programs in machine learning, robotics, and data science. AI is also entering primary and secondary schools — in many states, programming and basic AI literacy are being integrated into the K–12 curriculum. In 2025, the U.S. government announced a national strategy to promote AI literacy, integrating AI across all educational levels, from early schooling to intensive teacher training and “Reskilling Revolution” initiatives for workers upgrading their skills. The goal is to build an AI-ready workforce that maintains America’s innovation advantage. Tech companies actively support education as well — Google and Microsoft provide free AI courses online, while Silicon Valley startups run hackathons and internships for students. This ecosystem has made the U.S. a fertile ground for AI talent, with graduates often transitioning directly into top tech firms to work on cutting-edge solutions.
Germany: Germany places strong emphasis on linking academic research with practical training. New AI-focused university programs are emerging — for example, the University of Applied Sciences in Offenburg offers a 7-semester bachelor’s in Applied Artificial Intelligence, combining theory with a mandatory internship semester. The Technical University of Munich introduced the interdisciplinary “Robotics, Cognition, Intelligence” program, merging AI with automation and cognitive science. The German government also funds leading AI research hubs such as DFKI (German Research Center for AI) and Cyber Valley — Europe’s largest AI consortium involving universities (Stuttgart, Tübingen), the Max Planck Institute, and companies like BMW, Bosch, and Amazon. Importantly, Germany is also working to educate the wider public about AI. In Heilbronn, the massive Innovation Park Artificial Intelligence (IPAI) is being built — a technology campus combining workspaces, labs, startups, and public education zones. Designed as an “AI city,” it aims to attract top global talent and host training, workshops, and outreach programs centered on ethical and responsible AI. These initiatives show that Germany is taking a comprehensive approach to AI — spanning higher education, research, and public awareness — to remain economically competitive in the digital era.
India: India is known for producing large numbers of engineering and IT graduates, and is now rapidly pivoting toward AI. The Indian government launched the National AI Strategy under the motto “AI for All”, focusing on large-scale education and upskilling. AI is set to enter school curricula — under the new education policy, AI fundamentals will be taught starting from 6th grade. National frameworks have been developed to standardize AI education in schools and universities, alongside extensive teacher training programs. Demand for AI and data science specialists is enormous — estimates suggest that India’s need for such talent will exceed 1 million in 2024. Yet according to NASSCOM data, the country still faces a 51% skills gap in AI-related roles. To address this, aside from modernizing university curricula (many institutions are launching new AI/ML programs), India is expanding online courses and bootcamps in partnership with industry and government (e.g., FutureSkills Prime). The country is also establishing its first dedicated AI university — the University of AI in Karnataka. The government encourages tech firms such as TCS, Infosys, and Wipro to offer internships and structured training programs for students. As a result, India is rapidly becoming a global AI talent hub — each year, hundreds of thousands of young engineers acquire machine learning, data analytics, and automation skills that fuel both local startups and multinational corporations.
Key takeaway: Countries that want to lead the economy of the future are investing heavily in AI education at all levels. Global trends show that integrating AI into curricula — from primary school to university to professional reskilling — is becoming the norm. For Polish students, this means it’s worth observing these initiatives and learning from global best practices. Polish universities are also adapting (new programs, specializations, international collaborations), but much depends on individuals — taking advantage of opportunities such as exchanges, online courses, and AI competitions.
How to Choose a Field of Study in the Age of AI
Choosing a field of study is a major decision, especially in the rapidly evolving IT sector. It’s worth approaching it strategically, considering both your interests and labor market signals. Here are some practical guidelines:
Follow your passion and natural strengths: Consider what truly interests you and what you excel at. Do you enjoy programming and creating applications? Are you fascinated by data and analytical problem-solving? Or are you drawn to the human mind, looking to combine psychology with technology? Choosing a field aligned with your passions increases your chances of enjoying your studies and finding satisfaction in your future career. Developing your strengths (e.g., mathematical talent, creativity, communication skills) will help you stand out. Don’t choose a field just because it’s trendy if it doesn’t suit you — in IT, passion often drives long-term success because it fuels continuous learning.
Analyze the job market and emerging trends: Research which professions are in demand now — and which will grow in the future. Read reports and forecasts (e.g., the World Economic Forum’s Future of Jobs Report, PARP reports, and insights from the Ministry of Education). Today, IT is seeing rising demand for AI/ML specialists, data scientists, cybersecurity experts, and cloud engineers. Some roles are shrinking due to automation (e.g., fewer openings for manual testers as automated testing spreads). Choose fields that offer transferable skills — for example, a computer science graduate can become a programmer, but with additional training could also work as a data analyst or ML engineer. Avoid overly narrow specializations at the start — it’s better to build a broad foundation and specialize later (during master’s studies or postgraduate courses). Also consider automation forecasts — routine roles are the most at risk, while jobs requiring creativity, high-level thinking, and interpersonal skills are far safer. Data from Statistics Poland and Eurostat indicate that only ~3% of the Polish workforce consists of IT specialists (below the EU average), and there are around 50,000 IT vacancies in Poland — strong evidence that demand for technical graduates is high, especially those aligned with emerging technologies.
Think long-term (futureproofing and automation): The job you want in 10–15 years may not even exist yet — or may look completely different. Therefore, prioritize flexibility. Assess the future growth potential of any field you consider. For example, today’s AI specialist is often an ML engineer, but in a decade roles such as AI trainer, AI ethicist, or data curator may be in high demand. Choose fields that teach how to learn and adapt (e.g., computer science, cognitive science, automation engineering). Consider interdisciplinary combinations — many innovations arise at the intersection of fields. If you have two interests (e.g., biology and programming), you might find a niche like bioinformatics; if you like both technology and law, consider AI policy or ethics; if you enjoy design and coding, UX engineering may be ideal. You don’t need to choose a single lifelong direction — many people start with a broad degree and refine their path later (through master’s programs, postgraduate studies, or specialized courses).
Account for automation and AI development: As highlighted earlier, awareness of technological trends should guide your choice. For example, if you dream of a career in financial analysis, remember that many tasks in this field can already be automated by AI. It may be wise to complement financial studies with data science or programming so you can create or supervise these systems rather than compete with them. Roles related to creating and managing AI are expected to grow — here automation will support the specialist, not replace them. Meanwhile, routine administrative roles (e.g., data entry, basic office work) may largely disappear. Regardless of your major, ensure that your program includes elements of data analysis, statistics, or digital tools. It’s widely said that AI literacy will become as essential as basic computer skills. When choosing a university, check whether the curriculum keeps up with technological trends (e.g., whether AI, big data, or cloud technologies are included). This will help ensure that your knowledge remains relevant five years from now.
Finally — stay up to date. The IT job market is extremely dynamic. Read tech blogs, listen to podcasts about future professions, and track skills rankings. Even during your studies, observe which technologies are rising in popularity and tailor your electives, projects, and internships accordingly.
Alternative Career Paths in IT
University education is not the only way to enter the IT field. In recent years, alternative pathways have grown increasingly popular — and for many people, they have become an effective gateway to the tech industry. Here are the most important options:
Coding Bootcamps: These are intensive, practical courses lasting from a few weeks to several months, focused on specific skill sets such as web development, data science, or UX design. Bootcamps are typically run by industry practitioners and emphasize project-based learning — participants build applications, websites, or ML models in a short time. Many Polish coding schools have successfully trained job-ready graduates even without prior programming experience. Employers increasingly value real skills and project portfolios over formal degrees, meaning that a strong bootcamp graduate can compete for junior roles alongside computer science graduates. Bootcamps are demanding and require high personal commitment, but they provide hands-on, job-relevant skills and often recruitment support. Schools such as Kodilla, InfoShare Academy, and Kozminski Academy (Python/AI courses) have produced hundreds of career changers — from teachers and forklift operators to waitresses and managers — proving that anyone can learn to code with enough effort.
Online Courses and Self-Learning: The internet offers abundant resources for learning nearly any technology, often for free or at low cost. MOOC platforms like Coursera, edX, Udemy, and Udacity host courses taught by professors from top universities and industry experts. You can earn certifications in machine learning (e.g., Andrew Ng’s course), data analytics (e.g., Google Data Analytics Certificate), or cloud administration (AWS, Azure certifications). Nanodegree programs provide concise, targeted training (e.g., Front-End Developer, AI Engineer). Self-learning requires discipline but can effectively replace formal education, especially when paired with personal projects published on GitHub or in a portfolio. Many successful IT professionals are self-taught, and the industry values skill above credentials. Even if you’re a university student, online courses can supplement your curriculum with the latest technologies. Documentation, forums (Stack Overflow, Reddit), and local meetups are also invaluable learning resources.
Internships and Practical Training: For young people, internships or student placements are one of the best ways to enter IT. Many employers — especially large corporations, banks, consulting firms, and software houses — offer structured internship programs for final-year students and recent graduates. These programs provide exposure to real-world projects, production code, and professional mentorship. Even low-paid or unpaid internships can significantly boost your CV. Positions such as junior developer, data analyst assistant, or QA intern often lead to full-time roles. Take advantage of your university career office, job portals (Pracuj.pl, JustJoin.it), and student organizations. Participation in student projects, research groups, and hackathons can also substitute for early professional experience.
Professional Certifications: Numerous industry-recognized certifications validate technical skills — often globally. Examples include Cisco (CCNA, CCNP) for networking, Microsoft/AWS/Google certifications for cloud competencies, Certified Ethical Hacker, CompTIA, and project management certifications like Scrum Master or PRINCE2. In cybersecurity, certifications such as CISSP, CISM, and CEH are highly valued. Certifications cannot replace experience but can help beginners stand out. For example, earning AWS Cloud Practitioner or Oracle Java certifications without commercial experience signals strong motivation and knowledge. Study materials are widely available online, and exam fees often pay off through improved job prospects.
In summary, you can enter IT without a formal computer science degree — many people have successfully done so. What matters most is acquiring practical skills and continually developing them. Whether you choose university studies, a bootcamp, or self-learning, remember: your career is a marathon, not a sprint. In IT, learning never stops, and any certificate or diploma is just the starting point. What truly counts is your ability to apply knowledge in practice.
**Conclusions and Recommendations for the Future ** The IT industry in the age of AI offers enormous opportunities, but it also demands continuous development. For young people — high school students, graduates, and university students — this means staying flexible and ready for constant learning. As experts emphasize, nearly 40% of essential job skills will change within the next five years. In other words, what you learn today may require updating or even replacing within a few years. This shouldn’t be discouraging — it should be motivation to develop a habit of lifelong learning.
The key advice is: stay open to change. Follow technological trends, even once you start working — don’t rely on one programming language or a single specialization when the world keeps moving. Invest in your development throughout your life (lifelong learning). Learn foreign languages — English is a must in IT, but knowing Chinese or German can open additional doors. Develop soft skills: teamwork, communication, presenting ideas, critical thinking. In the era of AI, it is precisely these human qualities that will be most valuable. Equally important is adaptability — if you see a technology losing relevance, don’t be afraid to reskill or expand your competencies. For example, if you’re a manual tester, start learning test automation; if you’re a developer, consider exploring machine learning; if you work in network administration, dive into cybersecurity and cloud technologies.
Remember that a career is a long-distance run. You don’t need to achieve everything at once — what matters is choosing a direction and developing consistently. Every experience (studies, projects, internships, courses) builds your skillset. Don’t be discouraged by early difficulties — the IT industry is full of people who began with setbacks (struggling with programming, failing certification exams, being rejected from internship programs), but persistence pays off.
Finally, embrace two mindsets: that of a specialist and that of an explorer. Be a specialist in what you do (whether it’s coding, data analysis, or system administration) — strive for mastery and professionalism. But at the same time, keep the curiosity of an explorer — pay attention to new areas, even those outside your comfort zone. In a few years, new IT fields may emerge, such as prompt engineering (training generative models) or AI-powered VR world design. Who knows? If you stay curious and open to new challenges, you’ll find your place in the future digital landscape more easily.
In summary: the IT job market in Poland and globally is developing dynamically under the influence of artificial intelligence. It is worth investing in future-oriented education (AI, data science, cybersecurity, etc.), but an equally important factor is adopting a mindset of continuous development and adaptability. As recent years have shown, technology is not slowing down — companies will continue to need innovative solutions and highly skilled experts who can keep up with this pace. By staying informed, developing both hard and soft skills, and remaining flexible, you significantly increase your chances of building an interesting and successful career. Regardless of whether you choose an academic path or an alternative one, the most important thing is not to fear change — but to use it consciously to your advantage. Good luck!
Sources: Reports by PARP and the World Economic Forum; GUS/Eurostat data; articles from Bankier.pl, Antal, Speednet, Perspektywy; government portals (Gov.pl, White House); IndiaAI; Interia/GeekWeek; and university materials. All referenced sources appear in the text.