Do You Know Where You Stand? Why Your AI Job Risk Status Matters Now

Short answer: you cannot manage a risk you have not measured. AI is not wiping out jobs overnight. It is changing them task by task, and the people who come out ahead are the ones who know which of their tasks are exposed before an employer, or the job market, tells them. That is what your AI job risk status is. Not a verdict. A starting point.

In part one of this conversation, I took on the question everyone is asking: will AI take my job? The honest answer was probably not this year, and probably not the way you are picturing it.

But that answer comes with a catch. "Probably not" is an average. You are not an average. Your job is a particular mix of tasks, in a particular industry, in a particular country, and that mix is what decides how AI lands on you. So the more useful question is not will it happen? It is where exactly do I stand?

The quiet shift you will not see coming

The headline version of AI and jobs is a cliff: one day the job is there, the next day it is gone. The evidence points to something slower, and much easier to miss.

Stanford's Digital Economy Lab tracks US payroll data covering millions of workers. In its August 2026 update, the researchers found no widespread, economy-wide job displacement from AI. Good news. But for workers aged 22 to 25 in the most AI-exposed occupations, employment now stands about 19% below where it would be if it had kept pace with their less-exposed peers. A year earlier that gap was 15%. And it is happening mainly through slower hiring, not firing.

Sit with that for a second. Nobody got a letter. Nobody was walked out of the building. The vacancies simply were not posted. And a school leaver or a fresh graduate never sees the job that was never advertised. They just find the door harder to get through, and they are not sure why.

The International Labour Organization reached a similar conclusion from a global view. Its 2025 index of occupational exposure found that one in four jobs worldwide is potentially exposed to generative AI, and that the more likely outcome is transformation, not replacement.

"Transformed" sounds gentle. In practice it means the tasks you were hired to do start to shrink, new ones show up, and whoever adapts first sets the standard everyone else is measured against.

Why guessing is the real risk

Almost everybody has a gut feeling about AI and their job. In my experience, that feeling is often wrong, in both directions. The office worker assumes they are safe because "a computer can't do what I do", when half their day is exactly the kind of work AI handles well. The electrician or the nurse worries for nothing, when most of what they do is hands-on work AI barely touches.

Guessing wrong is expensive either way. Too relaxed, and you are late. Too anxious, and you spend money and time on the wrong course.

The scale of the change makes this matter. The World Economic Forum's Future of Jobs Report 2025 puts it plainly: if the world's workforce were 100 people, 59 would need retraining or upskilling by 2030, and 11 of them are unlikely to get it. Nobody chooses to be one of the 11. You end up there by not knowing you needed to move.

Here at home, the gap between hearing about AI and understanding it is wide. A SALISES study at UWI Mona surveyed 1,072 Jamaicans across all 14 parishes. Awareness was high, scoring 76 out of 100. But only 6% had any kind of AI training, even though 62% said they wanted it. That is the exact condition in which people guess: everyone has heard about it, very few have measured what it means for them.

Employers are guessing too

This is not only an individual problem. A global study by KPMG and the University of Melbourne, covering more than 48,000 people in 47 countries, found that 57% of employees hide their use of AI at work and present AI-generated work as their own. Almost half (48%) admit to using it in ways that go against company policy.

So if you run a team and believe AI has not arrived in your workplace yet, it very likely has. You just cannot see it. And what you cannot see, you cannot manage: not the risk of customer data pasted into a free chatbot, and not the opportunity sitting in the hours your staff are already saving.

For the Caribbean, the stakes cut both ways

The Inter-American Development Bank previewed some striking numbers in September. As Reuters reported, the IDB estimates AI adoption could leave economic output in Latin America and the Caribbean 5.1% higher after a decade. But the effect on wages depends on one thing: whether workers can move into the jobs that are growing. If they can, wages could rise 2.3% to 5.3%. If they cannot, wages could fall 13.5% to 20.9%.

Same technology. Same region. Two very different futures. What separates them is movement, and you cannot move toward something you have not identified.

The World Bank adds an important detail. Its analysis for Latin America and the Caribbean finds that 30% to 40% of jobs in the region are exposed to generative AI in some way, but only 2% to 5% are at risk of automation. Between 8% and 12% of jobs could get a real productivity boost from AI, yet up to half of those, around 17 million jobs, may miss out because of weak digital access.

That changes the conversation. For much of the Caribbean, the bigger danger may not be replacement. It is being left behind while workers elsewhere use the same tools to get faster and better.

And "elsewhere" is closer than it looks. Caribbean workers compete for remote roles, outsourcing contracts and jobs in the US and Canada, in markets that already put a price on AI skills. Across the nearly one billion job ads PwC analysed on six continents for its 2025 Global AI Jobs Barometer, roles that require AI skills paid an average wage premium of 56% over similar roles that do not.

What your AI risk status actually tells you

Your AI risk status is not a pass-or-fail label stamped on your job title. Two people with the same title can have very different exposure, because exposure lives in the tasks, not the title.

Think of your working day in three piles:

• Tasks AI can already largely do. Keying in data, drafting routine emails, answering standard queries, formatting reports.
• Tasks AI helps you do more of. Analysis, planning, writing that needs your judgement, research.
• Tasks AI barely touches. Hands-on physical work, reading a room, calming an upset customer, making a call you will be held responsible for.

Now picture two accounts clerks in the same Kingston office. One spends most of the day keying invoices and reconciling statements. The other spends half the day on the phone sorting out supplier disputes and training the new hires. Same title, same salary band, very different exposure. The first clerk has something urgent to plan for. The second has a head start.

It works the other way too. An electrician whose quotes, scheduling and invoicing eat up several hours a week has exposed tasks as well, and those are exactly the hours AI can hand back for paid work.

A good measure turns a vague worry into something you can act on: a score, a picture of how your day splits between routine, thinking and physical work, and the one area where you are most exposed. That is a to-do list, not a fear.

What you can do as an individual

1. Measure first. Before you pay for a course or panic over a headline, spend five minutes finding out where you actually stand. Everything after that gets easier.
2. Get ahead of your exposed tasks. Use AI yourself on the routine parts of your job before someone else decides how they get done. The person who automates their own weekly report tends to become the person who runs the new process.
3. Learn the tools for your job, not "AI" in general. A bookkeeper, a teacher and a sales rep need different tools. Local routes such as HEART/NSTA Trust and UWI Open Campus are worth checking before you spend money elsewhere.
4. Double down on the human side. Relationships, judgement, hands-on skill and trust are the parts of your work that hold their value longest. Make them visible.
5. Check again every six to twelve months. AI tools change fast. Your score is a snapshot, not a life sentence.

If you are between 18 and 25, pay special attention. The Stanford findings say the pressure right now is at the entry door. Knowing how exposed your chosen field is before you spend years and school fees on it is one of the smartest moves you can make.

What organisations can do

1. Assess roles and tasks, not headlines. "AI will affect 40% of jobs" tells you nothing about your accounts team or your call centre. You need to know which tasks, in which roles, and how many hours of work are involved.
2. Bring AI use into the open. Given how many staff already use AI quietly, a clear policy, a short list of approved tools and basic training will do more for you than a ban.
3. Plan reskilling before restructuring. The World Economic Forum found that 85% of employers plan to prioritise upskilling. Often your best future hire is someone you already employ, pointed at new work.
4. Protect your entry-level pipeline. If AI takes over the tasks you used to give juniors, ask where your senior people will come from in ten years. Redesign those first roles; don't just cut them.
5. If you train people, check where they are headed. Colleges, TVET institutions and training programmes should know how exposed the occupations they prepare people for really are, and update what they teach.

None of this needs an army of consultants. Most of it starts with a list of your roles and an honest hour describing what each one actually does.

Where AI Proof Careers Hub comes in

This is the problem we built AI Proof Careers Hub to solve: turning "I think I'm fine" or "I think we're fine" into something measured.

For individuals, the free AI Career Exposure Diagnostic takes about five minutes. You describe what you actually do, and you get a score from 1 to 99, the split of your day between routine, thinking and physical work, your most exposed work area and one practical insight. If you want the full picture, the Personalised 90-Day AI Career Action Plan (US$4.99) breaks it down task by task, matches AI tools to your work, and gives you a 30/60/90-day plan.

For organisations, the Workforce AI Exposure Audit does the same thing across a whole workforce, role by role and task by task. It shows the hours of work at stake, your AI readiness, reskilling pathways and a 12-month roadmap. We assess roles, not people: no staff names, no personal data. You can read a full sample report before you talk to us, and the scoping call is free.

Whichever side you are on, the first step is the same. Find out where you stand. The decisions that follow are much easier once you know.

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Ewort Atkinson, MBA University of Leicester · Workforce & TVET researcher · DOI: 10.5281/ZENODO.19442958

Author of The AI-Proof Career: Why Skilled Trades Are the Future of Work and creator of the AI Proof Careers Hub diagnostic. Ewort writes about AI and work from Jamaica, for readers across the Caribbean and North America. Connect on LinkedIn.