You may have spent years becoming good at your work. Now, AI tools can perform parts of it, expectations are shifting, and the advice to “keep learning” can feel frustratingly vague.
What should you learn?
Where does your experience still add value?
And how do you plan your next step when the work itself keeps changing?
Career development needs to address these questions.
AI can influence both the work people do and the opportunities available to them, through application screening, learning recommendations, and performance assessments. In a conceptual paper published in the European Journal of Training and Development, Martin Sposato proposes a framework for understanding this shift.
It focuses on three connected areas: collaboration, repeated adaptation, and professional identity.
For anyone thinking about their next career move, these ideas offer a useful starting point.
Build on your experience by learning to work effectively with AI.
Your professional value includes the judgment you bring to a task: recognizing missing information, understanding context, asking useful questions, and deciding whether an answer makes sense. Working with AI requires applying that judgment throughout the process. A project manager, for example, might use AI to draft a progress report while still checking whether the information is accurate, the deadlines are realistic, and the client’s concerns have been addressed.
A practical exercise is to choose one recurring task you are permitted to use AI for. Complete it with AI support, then examine the result. Where did the tool help? Where did it introduce errors or miss something important? What did you need to contribute? This helps you identify specific capabilities to develop, such as giving clearer instructions, verifying outputs, or interpreting results for other people.
Prepare to revisit your skills throughout your career.
Experience does not guarantee that the tools and methods you know will remain sufficient. Sposato describes careers shaped by repeated cycles of learning and renewal. A marketing professional, for instance, may need to adapt as advertising systems, content production, and analytics change. Someone with substantial experience can still become a beginner in part of their work.
You can make this manageable by keeping your learning priorities focused. Periodically review which tasks in your role are changing, what employers or clients increasingly expect, and where you need more practice. Choose a specific capability and apply it to a real task. “Learn AI” is difficult to act on. “Learn to use AI to organize project feedback, then verify the themes against the original responses” gives you something concrete to practise and assess.
Keep deciding what you want your career to become.
Algorithmic recommendations can influence which vacancies you see, which skills you are encouraged to develop, and which directions appear suitable. Those suggestions can be useful, but they offer a partial view of your possibilities. Your interests, circumstances, values, and ambitions also deserve attention.
Make time to ask yourself what work you want more of, which responsibilities you want to grow into, and what you want your working life to make possible outside the office. When considering a recommended role or learning path, examine whether it serves those priorities. Keeping a record of projects, decisions, and lessons can also help you recognize how your professional contribution is evolving.
Conclusion
Employers share responsibility for making this adaptation possible. People need time to learn, appropriate tools, support from colleagues, and opportunities to practise. They also need understandable explanations of how AI influences decisions about their development and advancement. Career support should address fairness, privacy, and autonomy alongside performance.
You do not need to predict every technological change to take a useful next step. Start with your current work, identify one development priority, and connect it to a direction that matters to you.
Main Reference: Martin Sposato (2026), “Rethinking career development for the AI era: a framework for training professionals in hybrid human–machine workplaces,” European Journal of Training and Development. DOI: 10.1108/EJTD-06-2025-0116. The framework is based on a narrative literature review and has not yet been empirically validated. The practical exercises above are adaptations of its ideas.
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