Getting a job could become harder as AI improves and employers raise their expectations. Basic prompting is a useful starting point, but applicants may increasingly need to demonstrate practical AI skills at work alongside a sound understanding of their profession.
The pressure comes from two directions. More capable AI can reduce the staffing needed for some tasks, while candidates may face tougher assessments for the opportunities available. Employers will want to know what applicants can achieve with AI and whether they understand the work themselves.
Employers are looking beyond prompting
Workday’s October 2026 Global Workforce Report analysed job requisitions across more than 550 employers. Demand for basic AI skills, including simple prompting, peaked in January 2026 before falling 25% over subsequent months.
Meanwhile, Workday’s report found that demand for building AI tools, automating workflows and AI engineering rose 51% between September 2025 and July 2026. These figures represent employers within its dataset, rather than the whole global job market.
Prompting still matters, but employers may increasingly regard it as a basic skill. Applicants who can show how they have improved a process, saved time or solved a problem have more to offer than those who simply list familiarity with AI on their CV.
What could change in 2027?
Gartner predicts that by 2027, 75% of hiring processes will include certifications and testing for workplace AI proficiency. It expects practical assessments to become particularly important in roles involving gathering, retaining and summarising information.
This remains a forecast. However, it suggests that applicants may increasingly have to demonstrate their abilities during recruitment, rather than rely on claims about their experience.
Gartner also argues that employers should assess critical thinking, professional knowledge, creativity and communication alongside AI proficiency. Candidates may need to show that they can use AI effectively and exercise judgement independently.
Can employees explain the work themselves?
AI can help someone produce work that appears more knowledgeable than they actually are. An employee who secretly relies on it for guidance throughout a task may struggle when asked to explain a decision, recognise an error or handle an unfamiliar situation.
The issue is whether they understand what they submit and are honest about the assistance they use. A polished report offers limited evidence of competence if its author cannot explain the findings.
Practical exercises and follow up questions can help employers establish that understanding. Where independent knowledge is essential, part of an assessment can take place without AI. Asking someone to explain their reasoning can reveal more than reviewing the finished document alone.
Better AI raises the stakes
AI tools continue to evolve, with new releases and improvements arriving month by month. Where those improvements reduce errors and produce more accurate reports, they also reduce the time spent checking, correcting and rewriting. Progress varies between tools and tasks, but employers have reason to reassess what newer systems can handle.
That changes the employment calculation. If a team can complete more work with the same number of people, an employer may decide to recruit fewer staff. Some organisations may expand instead, but higher productivity does not automatically create more vacancies.
Applicants could therefore face both stronger competition and higher expectations. Knowing how to complete a task manually may remain important, while knowing how to complete it effectively with AI becomes an additional requirement.
What practical competence looks like
A useful example is meeting administration. Asking AI for a summary is straightforward. Turning that summary into confirmed decisions, assigned responsibilities and actions in a planning system requires a better understanding of the whole task.
The same applies elsewhere. A finance professional needs to interpret the figures behind an AI generated report. A compliance professional needs to recognise whether a requirement applies to the correct jurisdiction. A marketing professional needs to identify unsupported claims before publication.
These skills do not all require programming. They require professional knowledge, familiarity with suitable tools and the ability to judge the result. Employees also need to understand which information they are authorised to share and which decisions require human approval.
Give employers something concrete
For someone developing their AI skills at work, a sensible next step is to improve one recurring task and record the result. Include the time spent reviewing the output, rather than counting only how quickly AI produced it.
An applicant who can explain how they reduced a reporting task, improved accuracy or made information easier to use has a concrete example for an interview. They can discuss what worked, what needed correction and how they protected sensitive information.
Employers must support this learning too. Providing access to AI without clear guidance or time to practise is unlikely to produce dependable competence.
Preparing for a tougher job market
AI training cannot guarantee employment or protect every existing role. Employers may use automation to reduce recruitment, reorganise teams or cut costs. That possibility should be part of an honest discussion about workplace AI.
However, applicants can strengthen their position by combining professional knowledge with practical AI ability. Being able to demonstrate both may become increasingly important as recruitment changes.
At AI Tuition Hub, our career focused courses explore how AI applies to different professional roles. As employer expectations rise, developing AI skills at work can help you build competence you can explain, demonstrate and apply.
Published: 11th October 2026.