When a job advert asks for “generative AI skills”, what does that actually mean?
Employers are asking for AI skills across a range of roles, but the level of knowledge expected can vary considerably.
For some roles, it may mean drafting an email, summarising a document or preparing meeting notes with AI assistance. For others, it could involve improving a team’s workflow or building technical systems.
For applicants, the challenge is understanding the expectation and demonstrating relevant ability. For existing employees, it is learning how to use these tools effectively while remaining responsible for the work they produce.
Recent reporting highlights why this deserves attention.
Employers Are Redesigning Work Around AI
On 15 October 2026, City AM reported that British businesses are redesigning roles to support human–AI collaboration, drawing on KPMG’s 2026 CEO Outlook.
The report said 87% of British bosses expected a return on their AI investment within three years. It also identified skills gaps and employee upskilling among the workforce challenges facing business leaders. These are employers’ expectations and reported priorities, rather than proof that every investment will succeed. Nevertheless, they show why practical AI capability is becoming relevant to workforce planning.
For employees and applicants, a useful response is to ask a concrete question: which tasks in my occupation could AI support, and how would I check the results?
The Employment Picture Needs Careful Reading
Research publicised by King’s College London on 6 October 2026 offers another perspective.
The study examined 1.25 billion job advertisements and 158 million employee positions across 41 countries. It reported an overall recruitment increase of around 3.3% following firm-level AI adoption, alongside a 6.7% increase in senior employment and a 2.5% fall in junior employment.
Importantly, the researchers identified adoption through job adverts stating that applicants would need to use generative AI. This provides evidence about changing workforce patterns within the firms studied. It does not settle the long-term effect of AI on total global employment.
The findings also raise a practical concern: how do people build experience if some junior opportunities become harder to access? For learners, developing clear examples of relevant skills is one constructive step, although training alone cannot resolve that wider employment challenge.
What Does Practical AI Competence Look Like?
Opening a chatbot and asking a question is a starting point. Workplace competence involves a more considered process.
Imagine preparing a customer update about a delayed order. AI might help produce the first draft, but someone still needs to check that it contains the correct information, makes no unauthorised promises and explains the next step clearly.
Or consider summarising meeting notes. A useful summary must distinguish confirmed decisions from suggestions. It should not invent an owner for a task or turn a proposed deadline into an agreed commitment.
These examples show what an applicant can demonstrate: clear instructions, careful review and an understanding of the consequences of errors.
An employer can learn more from a specific explanation of that process than from an unsupported claim to be “proficient in AI”.
Build Evidence You Can Explain
If you are starting out, choose a familiar, manageable task and practise using fictional information.
You could turn sample meeting notes into an action list, prepare an announcement from supplied facts or summarise a public document.
Keep the original information, your prompt, the generated response and your corrected version. Record what you checked and why you changed it.
That gives you something concrete to discuss:
“I used AI to organise fictional meeting notes, then checked the actions against the source. I corrected a suggested task that had been presented as an agreed decision.”
This is a modest claim, but it demonstrates judgement. Label practice work accurately and avoid presenting it as employment or client experience.
Experience Still Matters
Many existing workplace skills help people evaluate AI output.
Customer service experience helps you recognise an inappropriate promise. Administrative experience helps you spot a missing deadline. Subject knowledge helps you identify when a summary has misunderstood something important.
Start by applying AI to work you understand well enough to review. As your experience grows, you can take on more demanding tasks within the appropriate guidance and approval arrangements.
The aim is to produce work you understand and can stand behind.
A New Course to Help You Develop These Skills
Our new course, Generative AI Skills for Work and Job Applications, connects workplace practice with the challenge of demonstrating competence to employers.
Across twelve lessons, it explores prompting, communication, document summaries, research, planning and responsible use. It then applies those foundations to job applications, interviews and the preparation of a practical skills portfolio.
The course is designed for an international audience and requires no programming background. Exercises use fictional or suitable public information, allowing learners to practise without relying on confidential workplace material.
Its purpose is to help learners move beyond broad claims about AI familiarity. By completing and reviewing realistic tasks, they can begin developing evidence of what they can do, how they check their work and where they still need guidance.
If an employer asks about your AI skills, being able to explain a genuine example is a useful place to start.
Explore Generative AI Skills for Work and Job Applications
Published: 15th October 2026.