
TL;DR
Our UK Job Market Visualiser maps 412 occupations covering roughly 35.3 million jobs and shows a labour market splitting into very different risk profiles. The biggest conclusion is not that "AI will take all jobs," but that workers now face a more uneven mix of income fragility, health strain, automation pressure, and regional career risk than headline employment data suggests.
Key takeaways
- The visualiser covers 412 occupations and around 35.3 million jobs, with a job-weighted average pay of about £39.6k.
- Only 52 occupations score at 8/10 or above for digital AI exposure, but 39 already score 7/10 or above for automation potential and 22 for high offshoring risk.
- Some of the UK's biggest occupations combine low pay with high income or health vulnerability, making the main protection problem resilience rather than just technology disruption.
- The market is splitting between occupations that are hard to automate but physically or emotionally demanding, and occupations that are better paid but more exposed to desk-based AI and offshoring pressure.
- The most useful response is not panic but a practical protection plan: stronger savings, role-specific Income Protection, and where relevant faster healthcare access through PMI.
The UK labour market is often described in broad, reassuring averages. Employment is up or down. Wages are rising or falling. AI is either about to wipe out white-collar work or is just another technology panic. But those headlines flatten the one fact that matters most to individual households: risk is not evenly distributed.
That is exactly why we built the UK Job Market Visualiser. It is designed to show the UK labour market as it actually feels from the worker's side: not as one national trend, but as a patchwork of occupations with very different combinations of pay, growth prospects, digital AI exposure, automation potential, offshoring risk, health strain, and income vulnerability.
Based on the current visualiser dataset, the tool tracks 412 occupations covering around 35.3 million jobs. Across roles with pay data, the job-weighted average annual pay is about £39.6k. But that average hides a much more fractured picture. Some roles are relatively secure because they combine decent pay, persistent demand, and low offshoring risk. Others are hard to automate but financially fragile because pay is low and illness or reduced hours would create pressure almost immediately. Others still are highly paid and still vulnerable, not because the role disappears overnight, but because AI, software-led workflow redesign, and remote global labour markets can steadily squeeze bargaining power.
The striking conclusion from the data is this: the UK's real work crisis is not a single "future of work" story. It is a layered protection problem.
What the Visualiser Actually Measures
The visualiser lets you explore occupations using several different lenses:
- Median pay
- Growth outlook
- Digital AI exposure
- Automation potential
- Offshoring risk
- Skills shortage
- Education pathway
- Income vulnerability
- Health vulnerability
Some of these layers come directly from the occupation dataset, such as pay, education, and growth outlook. Others are WeCovr scoring layers built to answer a more practical question: if something goes wrong in this job, how exposed is the worker likely to be?
That distinction matters. A role can be relatively safe from AI and still be a poor resilience profile if it is physically punishing, low paid, and dependent on consistent attendance. Equally, a professional desk-based role can look comfortable on the surface but carry substantial long-term disruption risk if its workflows are highly repeatable, globally transferable, and increasingly software-assisted.
The First Big Conclusion: The UK Labour Market Is Splitting Into Different Risk Regimes
The most obvious pattern in the visualiser is not one clean hierarchy from "good jobs" to "bad jobs." It is a split between different kinds of vulnerability.
One broad cluster includes roles that are hands-on, place-based, and difficult to offshore, but often score higher for health vulnerability or income fragility. These are roles where physical presence, dexterity, care work, manual handling, customer contact, or shift work still matter. AI cannot directly replace most of the core labour, but workers may still face unstable finances if they are on lower pay, have weaker sick-pay buffers, or work in roles where injury, fatigue, or burnout can interrupt earnings quickly.
Another cluster includes more digital, process-heavy, desk-based occupations. These often have better pay and lower short-term health strain, but they can carry far higher AI exposure, automation potential, and offshoring risk. The threat here is less about a robot appearing tomorrow and more about job redesign: fewer people doing more, routine tasks being compressed into software, and employers having more options about where the work gets done.
That split is one of the most useful insights in the tool, because it helps explain why public arguments about work so often talk past each other. People in care, teaching, warehousing, retail, and hospitality are not imagining the same labour market as people in back-office administration, coding, finance operations, or digital support roles. They are living inside different risk systems.
The Second Big Conclusion: "Low AI Risk" Does Not Mean "Low Worker Risk"
This is where the data becomes especially useful for insurance and financial-planning decisions.
A simplistic reading of the future of work says the safest jobs are the ones AI cannot do. The visualiser shows why that is incomplete.
Consider some of the UK's largest occupations by job count:
| Occupation | Approx. jobs | Median pay | AI exposure | Automation | Offshoring | Health vuln. | Income vuln. | Outlook |
|---|---|---|---|---|---|---|---|---|
| Sales and retail assistants | 1.23M | £26.3k | 4/10 | 4/10 | 0/10 | 5/10 | 8/10 | Negative |
| Care workers and home carers | 1.03M | £30.1k | 2/10 | 1/10 | 0/10 | 7/10 | 6/10 | Positive |
| Cleaners and domestics | 593k | £25.6k | 1/10 | 3/10 | 0/10 | 7/10 | 8/10 | Negative |
| Warehouse operatives | 530k | £29.1k | 1/10 | 7/10 | 0/10 | 7/10 | 8/10 | Negative |
| Kitchen and catering assistants | 507k | £25.2k | 1/10 | 3/10 | 0/10 | 5/10 | 8/10 | Negative |
The pattern is hard to ignore. Several huge occupations have very low AI exposure and no real offshoring risk, but that does not make them secure in any household-finance sense. In fact, many combine lower pay with elevated health or income vulnerability, which means the worker is often one illness, accident, childcare shock, or hours cut away from immediate financial pressure.
That is a different type of labour-market risk from the one dominating headlines, but for many families it is more urgent. A low-paid role that cannot be offshored can still be highly exposed to:
- sickness absence
- reduced shifts
- weak savings capacity
- limited employer sick pay
- higher physical wear and tear
- restricted pricing power in the labour market
This is one of the most important conclusions the visualiser supports: resilience is not the same thing as technological defensibility.
The Third Big Conclusion: Many Better-Paid Digital Roles Are Not Immune, Just Exposed in Different Ways
At the other end of the labour market, the tool shows why many white-collar workers should be careful about assuming they are safe simply because their earnings are higher.
Take one notable example from the dataset:
- Programmers and software development professionals
- around 477k jobs
- median pay about £57.5k
- 8/10 AI exposure
- 7/10 automation potential
- 7/10 offshoring risk
- income vulnerability only 3/10
- growth outlook neutral
This is a useful case study because it captures the new professional tension. Software roles remain relatively well paid and not especially income-fragile on a month-to-month basis compared with lower-paid frontline work. But the direction of structural pressure is obvious. These roles sit close to the tools that can standardise, accelerate, and globalise their own workflows.
That does not automatically mean large-scale unemployment. More realistically, it can mean:
- fewer entry-level roles
- more output expected per worker
- pressure on routine coding and support tasks
- more winner-takes-more dynamics for top performers
- weaker insulation from remote competition
This is why the visualiser is more useful than a single "AI risk" chart. It forces a more adult conclusion: some high-skilled roles have good income buffers today, but rising medium-term bargaining pressure. That matters for career planning, retraining choices, and how much emergency resilience a household should hold.
The Fourth Big Conclusion: The UK's Biggest Occupations Are Not Necessarily the Most Attractive Ones
Another striking feature of the visualiser is the mismatch between occupation size and occupation quality.
The labour market does not allocate the most people to the roles with the best combined profile of pay, growth, and resilience. Instead, many of the largest occupational blocks sit in roles that are economically essential but structurally pressured.
For example:
- Sales and retail assistants remain the single biggest occupation in the dataset, but combine low pay, high income vulnerability, and negative growth outlook.
- Care workers and home carers show a more positive growth story, but still combine moderate pay with high health vulnerability.
- Other administrative occupations and book-keepers/payroll managers/wages clerks illustrate a different problem: very large white-collar job groups that are exposed to AI, workflow automation, and in some cases meaningful offshoring risk.
This points to a broader economic conclusion. The UK labour market still relies heavily on roles that are either:
- hard, human, and underpaid
- or scalable, digital, and increasingly compressible
That is not a healthy structure if your goal is broad-based middle-class resilience.
The Fifth Big Conclusion: Skills Shortage Matters, but It Is Not a Guaranteed Shield
The dataset flags 128 occupations as being in relative UK skills shortage. That is useful, because shortage is one of the few factors that can materially improve a worker's bargaining position. If demand is strong and supply is thin, workers tend to have:
- more job mobility
- more pay leverage
- more ability to negotiate conditions
- lower short-term unemployment risk
But the visualiser also shows why skills shortage should not be treated as a magic defence.
A role can be in shortage and still involve:
- long shifts
- burnout
- physical strain
- emotional load
- chronic understaffing
Teaching and care work are classic examples of this broader pattern. The occupation may be clearly needed and difficult to replace, but the worker can still carry substantial health strain and finite resilience. In other words, demand for the role does not automatically mean good protection for the person doing it.
That distinction is central to how WeCovr thinks about the job market. A resilient occupation is not just one employers want. It is one where the worker has a plausible combination of decent pay, manageable health exposure, sustainable demand, and enough bargaining power to absorb shocks.
What the Aggregate Numbers Suggest
At a dataset level, several headline figures stand out:
- 412 occupations
- Around 35.3 million jobs
- Job-weighted average pay of roughly £39.6k
- 52 occupations scoring 8/10 or above for digital AI exposure
- 39 occupations scoring 7/10 or above for automation potential
- 22 occupations scoring 7/10 or above for offshoring risk
- 104 occupations with positive growth outlook
- 134 occupations with negative growth outlook
The most important inference from those numbers is not that most jobs are about to disappear. It is that the occupational map is already uneven enough to force materially different strategies between worker groups.
If you are in a low-paid, physically exposed, frontline role, your problem is often not AI but interruption. If you are in a digital, repeatable, process-led role, your problem may be slower but more structural: compression of demand, role redesign, and intensifying competition. If you are in a shortage role with reasonable pay, your challenge may be sustainability rather than employability.
That is why a single national conversation about "the future of jobs" is so often unsatisfying. The right question is narrower and more practical:
What is the actual mix of risks attached to my occupation, and what does that mean for how I protect my income and health?
The Protection Implications Are More Important Than the Tech Debate
For WeCovr, this is where the visualiser becomes genuinely useful rather than merely interesting.
The labour market data points toward three protection realities.
1. Income Protection matters most where income stops hurting immediately
If your occupation scores high for income vulnerability, a relatively short interruption in work can create pressure quickly. That pressure is often highest in:
- lower-paid roles
- roles with variable hours
- self-employed or contractor-heavy roles
- occupations with weaker employer benefits
- jobs where physical capacity is central to earning power
For those workers, Income Protection is not a luxury add-on. It is often the most direct financial hedge against the real risk in the occupation.
2. PMI matters where delay can compound career damage
High health vulnerability does not just mean "a greater chance of feeling unwell." In practical terms it can mean:
- musculoskeletal strain
- fatigue
- stress and burnout
- emotionally demanding work
- injury exposure
- repetitive wear and tear
Where NHS delays are long, time-to-diagnosis and time-to-treatment can make the difference between a contained disruption and a major income shock. That is the argument for private medical insurance: not replacing the NHS, but shortening the career damage that untreated issues can cause.
3. Better-paid professionals still need buffers
A worker in a highly digital occupation may not need the same protection mix as a warehouse operative or care worker, but they still need resilience. Where AI exposure, automation potential, or offshoring risk is elevated, the right response may be:
- bigger emergency savings
- more active retraining
- less dependence on a single narrow workflow skill
- a stronger focus on career mobility
- realistic cover for illness or incapacity while transitions happen
In other words, higher income does not remove the need for protection. It often just changes the shape of it.
What Workers Should Do With the Visualiser
The tool is most useful when you stop asking "Is my job safe?" and start asking a more specific set of questions.
If your role is low AI but high health or income risk
Focus on:
- your sick-pay gap
- emergency savings
- Income Protection affordability
- how quickly you could access treatment if a physical or stress-related issue hit you
If your role is high AI, high automation, or high offshoring
Focus on:
- whether the repeatable part of your role is growing or shrinking
- what higher-value work in your field is harder to commoditise
- whether your income depends too heavily on one employer or workflow niche
- whether you have enough runway to adapt if the market changes faster than expected
If your role is in shortage but high strain
Focus on:
- sustainability, not just employability
- burnout prevention
- whether your current income reflects the actual toll of the role
- how to reduce the financial damage from time off work
This is also why the UK Job Market Visualiser is best used as a decision-support tool, not a prophecy machine. It gives a clearer picture of structural pressure, but the useful step is what you do next.
The Most Striking Overall Conclusion
After looking across the dataset, the most striking conclusion is this:
The UK's jobs problem is less about mass replacement and more about uneven exposure.
Some workers face low-tech but high-fragility roles. Others face good pay today but rising medium-term digital pressure. Others remain in shortage but at a physical or psychological cost that can undermine long-term earning power anyway.
That means the smartest labour-market response is not a universal slogan such as "learn to code" or "AI won't replace humans." It is a more grounded strategy:
- understand your occupation properly
- separate technology risk from income fragility
- separate job demand from health sustainability
- build financial protection around the specific weak points in your role
That is the real point of the visualiser. It helps move the conversation from abstract future-of-work commentary to an immediate household question:
How exposed is my occupation, and what should I do about it before something goes wrong?
If you want to explore your own role, compare your occupation against adjacent careers, or pressure-test how exposed your current work profile looks, start with the UK Job Market Visualiser.
What does the UK Job Market Visualiser actually measure?
Does a low AI score mean an occupation is safe?
Why do some high-paid jobs still score as risky?
How should someone use this data in practice?
Sources
- WeCovr UK Job Market Visualiser dataset and occupation scoring model, covering 412 occupations and approximately 35.3 million jobs.
- UK Job Market Visualiser
- Office for National Statistics (ONS): Standard Occupational Classification (SOC 2020) reference materials.
Disclaimer: This is general guidance only and does not constitute formal tax or financial advice. Tax treatment depends on individual circumstances, policy terms, and HMRC interpretation, which cannot be guaranteed in advance. Whenever applicable, businesses and individuals should always consult a qualified accountant or tax adviser before arranging such policies.
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