You hear about them all the time as a young professional: Get educated. Stay relevant. Educate yourself on artificial intelligence (AI). Get a certification. Build a portfolio.
Amid all this well-meaning advice, however, is a little question missing: Will the skills I am learning actually improve how I perform my job?
Having a certificate means you have completed a course, and knowing an AI tool means you know how to use it. But neither explains how you would respond when an AI tool gives a bad answer or when a well-known method stops working.
As AI changes the way we work, the notion of employability needs to be broadened. Developing skills remains crucial, but so does understanding when our experience falls short, recognising what we lack, and applying that knowledge in new contexts. This is what constitutes a learning set.
Knowing a Skill Is Different from Applying It
Imagine a digital-native employee using AI to build a report. This person can quickly gather data and produce a well-drafted piece. But can they recognise an incorrect inference, verify the accuracy of the data, and explain why the report needs to be edited?
Access to a tool is not the same as evaluating the reliability of its results.
A learning set is demonstrated in how a person responds to a new challenge. Can they identify what they do not know, seek feedback, and learn from it? Can they connect their knowledge to other situations? Will they challenge the status quo when circumstances change?
Unlearning is necessary, too. Methods that worked at one time may lose their effectiveness as technology and business needs evolve. The challenge lies in retaining what works, questioning what does not, and adapting without compromising standards.
Employers Need to Allow Space for Learning
Of course, young professionals should also be motivated, flexible, and willing to take risks. However, some organisations offer little opportunity to question established practices or experiment responsibly.
Consider an employee who suggests using an AI solution to improve efficiency. One manager might agree to test the idea and assess its accuracy and potential risks. Another might reject it in favour of traditional methods.
The response helps determine whether the employee gets an opportunity to develop sound judgement or learns that following orders is safer than questioning them.
Experimentation requires safeguards for confidentiality, data security, and accuracy. However, sensible oversight should not impede progress. Employees need feedback, clarity about expectations, and opportunities to practise and apply what they learn.
It is unfair to expect young professionals to be flexible if workplaces reward them primarily for following procedures.
Employability Is a Collective Responsibility
Not everyone has the same opportunities to learn. Some professionals have mentors, access to investments in their skills, and opportunities to take on high-value assignments. Others must learn independently while managing demanding workloads and the responsibilities of their primary roles.
When evaluating potential employees, recruiters should look at how candidates tackle unfamiliar problems, respond to criticism, and explain their decisions. Doing this effectively requires consistent assessment methods that do not mistake confidence or privilege for competence.
Individuals must take ownership of their learning. Employers, in turn, need to create an environment where learning translates into better performance and career growth.
It is impossible to anticipate every skill that will be required in an ever-changing job market. What professionals can do is ask themselves: How adaptable am I when my needs change? And am I working in an environment that enables me to adapt?
Skills will continue to matter. Just as important is the ability to recognise when those skills need to change.
By Sonica Aron, Founder and Managing Partner, Marching Sheep