AI Job Risk in Singapore
Singapore concentrates finance, logistics, administration, and technology inside a small, highly connected market, so the effects of AI tend to become visible quickly. At the same time, the country puts heavy weight on precision and regulatory discipline, which means faster processing does not easily erase human responsibility.
Average AI Risk
48.25 / 100
Jobs Analyzed
204
How to read this page in practice
The notes below explain how to interpret the country score, what kinds of sector mix usually raise or lower it, and what this comparison can and cannot tell you.
How to Read This Country
This country page is most useful when it shows how dense finance, logistics, administration, and technology can combine high processing speed with heavy accountability. Singapore has unusually high concentrations of finance, logistics, technology, and public administration, and AI adoption tends to move quickly there. But because many roles inside a compact market carry several functions at once, the labor market cannot be read through a simple replacement story alone.
What Drives the Score
Document work, comparison, analysis, and monitoring are all highly exposed to AI support in Singapore. Still, work tied to international logistics, financial regulation, and administrative operations keeps a stronger human role because precision and explainability remain central.
What Holds Up Better
What stays strongest in Singapore is work where accuracy matters more than speed and work that has to align several stakeholders at once. Even where efficiency rises, people continue to carry responsibility in roles where the cost of error remains high.
What This Page Does Not Claim
This page is designed to capture national direction rather than separate financial work from on-the-ground service work in fine detail. Read the score together with both the density of advanced data work and the weight of regulatory and operational responsibility.
Jobs Most At Risk from AI
This table is a current snapshot of the jobs that appear on the higher-risk side within this country profile. It is useful as a directional comparison, not as a permanent national ranking.
| Rank | Job | Risk Score |
|---|---|---|
| 1 | Data Entry Clerk | 81 |
| 2 | Retail Cashier | 78 |
| 3 | Bookkeeper | 77 |
| 4 | Truck Driver | 77 |
| 5 | Software Tester | 77 |
| 6 | Accounting Clerk | 76 |
| 7 | Data Analyst | 76 |
| 8 | Court Reporter | 75 |
| 9 | Proofreader | 75 |
| 10 | Receptionist | 74 |
| 11 | Insurance Underwriter | 73 |
| 12 | Paralegal | 73 |
| 13 | Translator | 73 |
| 14 | Civil Drafter | 73 |
| 15 | Taxi Driver | 71 |
| 16 | Travel Agent | 70 |
| 17 | Bank Teller | 69 |
| 18 | QA Engineer | 69 |
| 19 | Tax Preparer | 68 |
| 20 | Mobile App Developer | 68 |
Jobs Safest from AI
This table shows the jobs that currently appear on the lower-risk side within this country profile. Read it as a structural comparison of work, not as a guarantee that these roles will stay unchanged.
| Rank | Job | Risk Score |
|---|---|---|
| 1 | Surgeon | 10 |
| 2 | Therapist | 11 |
| 3 | Plumber | 11 |
| 4 | Judge | 12 |
| 5 | Psychologist | 12 |
| 6 | Electrician | 12 |
| 7 | Paramedic | 14 |
| 8 | Nurse | 15 |
| 9 | Dentist | 15 |
| 10 | School Counselor | 16 |
| 11 | Psychiatrist | 16 |
| 12 | Athletic Coach | 16 |
| 13 | Veterinarian | 17 |
| 14 | Professor | 18 |
| 15 | Doctor | 19 |
| 16 | Air Traffic Controller | 20 |
| 17 | Machine Learning Engineer | 20 |
| 18 | Social Worker | 20 |
| 19 | Fitness Trainer | 20 |
| 20 | Elevator Technician | 21 |
Industry Risk
This table compares the industries that shape the country score today. It is most useful for seeing which parts of the economy pull the average up or down.
| Industry | Industry Average Risk Score |
|---|---|
| Media | 62.33 |
| Retail | 61.5 |
| Finance | 59.47 |
| Technology | 52.22 |
| Transportation | 45.3 |
| Legal | 43 |
| Hospitality | 35.62 |
| Construction | 34.33 |
| Education | 31.67 |
| Healthcare | 26.2 |