The AI Economy: New Careers, Changing Skills and How Students Can Prepare

170 Million New Jobs, With a Different Mix of Skills

By 2030, technological development, the green transition and demographic shifts are expected to create about 170 million jobs while displacing 92 million, according to the World Economic Forum's Future of Jobs Report 2025. The projected balance is an increase of 78 million jobs. For students choosing what to study and workers considering their next move, those figures point to an economy with new opportunities and changing requirements.

Artificial intelligence is a major force within that transformation. It is already changing how people work, learn and interact, automating routine tasks while creating demand for people who can build, guide, check and responsibly use AI systems. The employment forecast covers several economic trends, with AI operating alongside changes in energy, industry and populations.

Employers increasingly want technical knowledge combined with adaptability, creativity and ethical judgment. A person may need to understand an AI tool, decide whether its answer makes sense, explain the result to colleagues and take responsibility for how it is used. Knowing which button to press is only one part of that work.

For students, preparation therefore reaches beyond choosing a technology course. Programming and data analysis matter, but so do communication, teamwork and knowledge of a particular field. Healthcare, finance and agriculture all offer ways to combine subject expertise with AI skills.

The transition also carries risks. Routine work often gives newcomers their first opportunity to learn a profession. As software takes on more of those tasks, schools and employers face a practical challenge: helping young people gain experience while the jobs that traditionally provided it are changing.

Where AI Is Changing Work and Pay

Industries with greater exposure to AI are seeing changes in productivity, revenue and wages. PwC's 2025 Global AI Jobs Barometer found that industries using AI have seen revenue per employee triple since 2022. Revenue per employee describes how much income a business generates relative to the number of people it employs.

Wages in these sectors are rising twice as fast as in industries with less exposure to AI. PwC also found that workers with AI skills command a 56% wage premium, making the ability to work with these technologies an increasingly valuable part of a person's qualifications.

The speed of change matters as much as the financial rewards. Skills required for jobs exposed to AI are changing 66% faster than those required for other roles, according to PwC. Workers in these positions face a continuing need to update what they know as tools and working methods develop.

AI is consequently changing more than the number of jobs. It is reshaping the tasks within them and the abilities employers reward. Some routine activities can be automated, while other responsibilities increasingly involve checking results, making decisions and working with people.

Why the First Step Into Work Is Changing

Entry level positions in finance, journalism and customer service are among those facing pressure as AI takes on repetitive tasks. These jobs have traditionally served as training grounds, giving young professionals practical experience and a route toward more demanding responsibilities.

The World Economic Forum notes that AI could automate more than 50% of the tasks performed by market research analysts and sales representatives, compared with much lower rates for managerial roles. A task is an activity within a job, so changes at this level can alter what a worker does throughout the day.

Students entering these fields may therefore encounter a different career ladder from the one earlier generations followed. If routine assignments become less common, employers need other ways to help newcomers develop judgment, understand clients and learn how their industry works.

The concern extends to social mobility and the development of future talent. People who depend on a first job to gain professional experience could face a narrower route into work. Unequal access to education and digital infrastructure could make that transition harder.

AI also offers ways to make technical knowledge more accessible and support new forms of learning at work. Companies are beginning to rethink career structures through apprenticeships, further training and collaboration between people and AI. Those approaches can help workers build experience as their responsibilities change.

New Careers Around Building, Checking and Using AI

New kinds of work are emerging around AI systems, including roles that did not exist just a few years ago. Their common feature is the need to connect technical capabilities with human decisions about quality, usefulness and responsibility.

• AI prompt engineer: Develops effective instructions for tools such as ChatGPT, Claude and Midjourney. How a question or request is framed can affect the quality of the answer or image produced.
• AI output evaluator: Reviews material generated by AI, checks its quality, looks for bias and assesses whether it meets ethical standards before deployment.
• AI training data curator: Assembles and refines the datasets used to train models, with attention to data quality and ethical compliance.
• AI and human workflow designer: Organises working processes so that people and AI systems collaborate effectively, supporting productivity and job satisfaction.
• AI ethics consultant: Helps organisations address questions of fairness, responsibility and the ethical use of AI.
• AI user experience designer: Creates interfaces that make AI tools understandable and usable for people with different levels of technical knowledge.
• Creator working with AI: Uses AI as a creative partner in art, writing or design, supporting human creativity and opening new possibilities.

These roles involve different levels of technical expertise. Some centre on data and systems, while others focus on design, evaluation or communication. Across them, creativity, ethical reasoning and emotional intelligence remain valuable parts of the work.

The Technical Skills Students Need

AI literacy is becoming a basic part of preparation for work. It means understanding what AI can do, where its limits lie and how to judge its output. Familiarity with tools such as ChatGPT and Copilot is part of that knowledge, alongside concepts such as algorithmic thinking and data bias.

Algorithmic thinking involves approaching a problem through a clear sequence of steps. Data bias concerns patterns in information that can lead a system toward unfair or misleading results. Students need enough understanding to question an output rather than simply accept it.

Programming and data analysis remain valuable in many industries. Learning Python can help students develop programming skills, while analysing and interpreting data helps them turn information into useful conclusions. Not everyone needs to become a machine learning specialist, but a basic understanding of AI is increasingly relevant.

Digital skills cover a broader range of abilities, from everyday use of technology to managing and developing digital systems. Students can build these skills alongside their main subject, connecting technical learning to the kinds of problems they hope to solve at work.

Why Communication and Judgment Still Matter

Employers are also placing greater value on abilities often called soft skills or durable skills. A recent survey found that more than 70% of executives consider soft skills more important than highly technical AI skills. Communication is now the most requested skill across jobs on LinkedIn.

Critical thinking and problem solving help workers examine complex situations, develop ideas and choose among possible solutions. AI can contribute information or suggestions, while people still need to assess what is useful and decide how to act.

Communication and collaboration cover teamwork, negotiation and relationship building. An employee who understands an AI result must also be able to explain it to someone else, listen to concerns and work toward a shared decision.

Adaptability and continued learning help people respond when job requirements change. Ethical judgment and emotional intelligence involve making decisions guided by values, understanding social situations and responding appropriately to other people's emotions.

These abilities are increasingly valued as routine tasks become easier to automate. For students, group projects, leadership opportunities and practical problem solving can develop the human skills that complement technical learning.

How AI Literacy Can Fit Across School Subjects

Education systems and training providers are under pressure to keep pace with technological change. The AI Literacy Framework, discussed by the World Economic Forum, sets out four areas intended to help close the gap between AI development and education.

• Engaging with AI: Recognising when AI is present in everyday tools, understanding how it is used and critically evaluating its outputs.
• Creating with AI: Working with AI to support creativity and problem solving while considering ethical questions.
• Managing AI's actions: Delegating tasks responsibly and maintaining human oversight.
• Designing AI solutions: Exploring how systems work and how they can be built or adapted to address real problems.

The framework, also known as AILit, is intended to reach across disciplines rather than remain confined to computer science classes. Its approach connects technical knowledge with empathy, judgment and ethical reasoning.

For schools, that means treating AI literacy as more than familiarity with a particular application. Students need opportunities to consider when a tool is useful, how to check its work and which decisions require human responsibility. The aim is to combine practical knowledge with the human abilities that AI cannot replicate.

Learning Beyond a Degree

Workers also need opportunities to learn after formal education ends. Lifelong learning means continuing to acquire knowledge and skills throughout a career, an increasingly pressing need as the requirements of jobs exposed to AI change rapidly.

Coursera, edX and Udemy offer flexible opportunities to study AI, data science and other emerging fields. Educational institutions are also expanding courses in AI, robotics and advanced analytics, giving students and existing workers more routes into technical learning.

Governments and organisations are investing in workforce development as well. Michigan's AI and Workforce Plan, announced on May 29, 2025, aims to create 130,000 jobs with good pay and prepare workers for an economy increasingly shaped by AI through modern, accessible training programmes.

The figures and initiatives describe a transition already unfolding, with a longer employment forecast ahead:

• 2022: The starting point for the period over which PwC found that revenue per employee tripled in industries using AI.
• May 29, 2025: Michigan announced its workforce plan, including the goal of creating 130,000 jobs.
• 2030: The end of the World Economic Forum's forecast period for 170 million new jobs and 92 million displaced roles.

Training efforts cover both upskilling, or improving abilities for existing work, and reskilling, or learning abilities for different work. Both matter at different stages of a person's career.

Who Could Be Left Behind?

Students and workers without affordable education or reliable digital infrastructure face a harder transition. Access to tools and training can affect who is able to benefit from new opportunities, especially as routine jobs change.

Policymakers, educators and employers have several responsibilities. Expanding affordable, quality education can help reduce the digital divide. Modern training infrastructure can make learning more accessible, while support for small businesses can help them adopt AI.

Schools can also encourage students to combine disciplines. Knowledge of healthcare, finance or agriculture, paired with AI skills, can open routes into work that require an understanding of both the technology and the field where it is used.

Workplaces have a role in creating a culture of curiosity, experimentation and continuing development. Using AI to support human capabilities, with ethical considerations guiding deployment, can help organisations pursue innovation while addressing risks to workers.

What Students Can Do Now

Students can prepare by building a combination of technical, creative and human skills rather than concentrating on a single tool or job title. Practical experience helps connect what they learn in class with the decisions they may face at work.

Several steps can make that preparation more concrete:

• Learn AI basics through introductory courses in AI, machine learning and data analysis, and become familiar with widely used tools.
• Develop digital and programming skills, including Python and the ability to work with data.
• Practise communication, teamwork, critical thinking and problem solving through collaborative work and leadership opportunities.
• Apply learning through internships, personal projects, hackathons and competitions.
• Continue learning through online resources, workshops and professional communities.
• Consider how AI affects society and develop a sense of ethical responsibility in its use.

Curiosity and willingness to adapt matter throughout this process. Students who combine subject knowledge with technical competence, creativity and sound judgment will be better prepared for work in which people and AI increasingly collaborate.

Key Points

• The World Economic Forum projects 170 million new jobs and 92 million displaced roles by 2030 across several major economic trends, producing a net gain of 78 million.
• PwC reports a 56% wage premium for AI skills and skill requirements changing 66% faster in jobs exposed to AI.
• Routine and entry level work faces disruption, increasing the need for apprenticeships, practical experience and training.
• Emerging careers include prompt engineering, output evaluation, data curation, ethics, workflow design and creative work using AI.
• Students need AI literacy and digital skills alongside communication, adaptability, creativity and ethical judgment.
• Schools, employers and governments have roles in making continuing education accessible and helping workers prepare for changing responsibilities.