Artificial intelligence (AI) is software that does tasks that usually need human judgement, such as writing text, spotting patterns or making predictions. Generative AI, the kind most people have now used, produces new text, images or code from a prompt.

The most useful way to think about AI and your career is in tasks, not whole jobs. A job is a bundle of tasks. AI changes some of them a lot, some a little and some not at all, so most jobs change shape rather than disappear. That is what UK graduate employers say. In the Institute of Student Employers' (ISE) Student Development Survey 2026 (144 employers, surveyed in January and February 2026), 87% expected AI to reshape graduate and apprentice roles, and 43% said their entry-level roles had already changed, though not yet been formally redesigned. Yet 40% didn't expect AI to replace any entry-level roles over the next three years. The ISE summed it up as "reshaped not replaced": routine admin, basic data tasks and basic writing are expected to matter less.

That matters most at the start of a career, because entry-level jobs have often been built from exactly those routine tasks. What is left, and what grows, is the work that needs a person: judging what matters, checking whether something is right, talking to customers and colleagues, and deciding what to do.

The global picture is similar. The World Economic Forum's Future of Jobs Report 2025 surveyed over a thousand employers worldwide. They expected 170 million jobs to be created and 92 million displaced by 2030, a net gain of 78 million, with nearly two-fifths of the skills workers need changing over the same five years. These are employers' projections, not facts about the future. Treat any confident prediction about AI and jobs, including a headline figure, as an estimate.

So don't ask "will AI take my job?" Ask "which tasks in the role I want will change, and what does that mean for the evidence I need?" A task audit answers it. List the main tasks of your target role. For each, decide whether AI could help (you do it faster, with AI's help), AI could do much of it (with a person checking), or it needs a person, and say why. The why is the point: it shows which of your strengths the role will rely on more.

Software has sorted applications for years: the applicant tracking system you met in Module 1 is one. What has changed is how much of the sorting can now be scored, ranked and decided automatically, and how often AI is involved.

Here is where you may meet it. Application screening: software reads your CV or your answers to application questions, looks for words, skills or qualifications, and scores, ranks or filters you. The Information Commissioner's Office (ICO), the UK's data protection regulator, warns that an application "may be rejected before a human recruiter can review it". Chatbots answer your questions or ask screening questions. Online tests are often scored automatically. Game-based assessments and recorded video interviews are sometimes scored partly by software. And employers are wary of AI-written or misleading applications: in the ISE's Student Development Survey 2026, two-thirds of employers believed graduates and apprentices use AI to misrepresent their skills, up from around half in 2025.

Keep it in proportion. In the ISE's Student Recruitment Survey 2026 (155 employers, reported in October 2026), only 16% of employers said they were using AI in recruitment, and only 10% of game-based assessments were fully AI-driven. Automatic scoring is more common: in the 2025 survey, 57% of employers scored numerical tests automatically, while in-person interviews and group tasks stayed almost wholly human. Automated isn't always AI, but both can decide what happens to you.

What to do. Read the employer's careers pages and privacy notice for what they say about AI and automation. Write for both readers: a plain layout, one clear piece of evidence for each criterion, the advert's words only where they are true. Don't try to trick the software: hidden keywords and inflated claims are dishonest, and the next reader is a person.

Your rights. Since 5 February 2026, the UK GDPR (amended by the Data (Use and Access) Act 2025) says that when a significant decision about you, such as rejecting your application, is made with no meaningful human involvement, the employer must have safeguards: telling you about it, letting you give your view, letting you get a person to look at it again (human review), and letting you challenge it. In its Recruitment rewired report (31 March 2026), the ICO found many employers were probably making such decisions with weaker safeguards than the law requires. The Equality Act 2010 still applies whatever tool is used, including reasonable adjustments, which Module 4 covers. This is general information, not legal advice; other countries' rules differ.

If AI takes on more routine tasks, what are employers looking for instead? Two sources agree closely. In the World Economic Forum's Future of Jobs Report 2025, seven in ten employers called analytical thinking essential, the core skill most often named, while AI and big data were among the fastest-growing skills. In the ISE's Student Development Survey 2026, UK employers named critical thinking, AI literacy, communication and adaptability as growing in importance, with sound judgement alongside.

You already have a way to assess these. Each maps onto the eight employability components of Andrew Hirst's Employability Success Framework from Module 1. Critical and analytical thinking is problem solving and creativity: defining the real problem and testing ideas and assumptions. Communication is communication skills. Adaptability sits in professionalism, whose basic behaviours include adapting to change. AI literacy is part of IT and numeracy, where using AI tools responsibly and checking what they produce is already a basic behaviour.

AI literacy means knowing what AI tools can and can't do, using them well on real tasks, checking what they produce and protecting data. Four habits show it. Know the limits: AI tools can state wrong facts confidently and can't know what they weren't given. Use it on real work: drafting, summarising, suggesting options, practising questions. Check everything: facts, figures, sources and tone, against something you trust. Protect data: never paste in other people's personal details or anything confidential, and follow your organisation's or course's rules.

There is good news for anyone who feels behind. The same ISE survey found employers almost twice as likely to build digital literacy on the job as to recruit for it. You don't need to be an expert. You need to show you can learn, that you use AI with care, and that you bring the judgement it lacks.

The skills that hold their value are also the hardest for AI to fake in an application: a true story of how you defined a problem, persuaded someone or changed course when a plan failed. Your STAR stories already carry this kind of evidence.

You now know how AI is likely to change the role you want, where it sits in recruitment and which skills matter more. This lesson turns that into a plan, in two parts: how you'll use AI in your job search, and how you'll build evidence that you use it well.

Using AI in your job search. Most employers now accept some use of AI: in the ISE's Student Recruitment Survey 2026, 64% did, mostly for CVs and covering letters, and 82% gave applicants guidance on it, up from 55% a year before. The line is firm at online tests, interviews and assessment centres: undeclared AI in interviews was the misconduct employers reported most. Guidance differs between employers, so write your own rules. A sensible set: follow the employer's guidance where there is any; use AI to draft, check, research an organisation and practise interview questions; never let it add a claim you can't back with your own evidence; never use it where it's banned, such as in many online tests; and keep other people's personal details out of it. If an employer asks whether you used AI, tell the truth.

Building evidence. Saying "I'm good with AI" proves no more than saying "I'm good with people". The evidence is a story: a real task where you used an AI tool, checked what it produced, caught what it got wrong and decided what to keep. It can be small, such as a club's spreadsheet, a summary of feedback for a volunteer group, or a faster way to do a routine job at work. Plan it so it ends as a STAR story with a clear Action, told in "I", and a Result.

Making it happen. Turn your plan into dated actions and add them to the FIT section of your Career Planning Canvas, beside the actions you already have. Revisit your rules each time you apply: the tools change quickly, and employers' guidance is changing too.

Plans like this work for everyone. A student can use a seminar project, a returner a community group, a career changer a task from their current job, and someone in work can volunteer for a team's first trial of a new tool.