Empowering
Global
Talent
MG Consulting Group
The Key Takeaways
- Two credible, conflicting datasets — Pearson’s 23% and the ILO’s 2.2% — are both right because they measure different technologies and different scopes
- Vision 2030 is directing the Saudi workforce toward knowledge-economy roles that Coface identifies as most exposed to generative AI — the same policy driving growth is concentrating new risk
- Job creation and displacement are running simultaneously in the same labor market, affecting different workers at different speeds
- Government programs — HADAF, Wa’ad, the National AI Strategy — are substantial; whether their pace matches automation’s pace is the open question
- The Saudi AI Job Risk Matrix exists to turn aggregate data into a role-level decision, not a forecast
Two headlines have dominated the conversation about AI and employment in Saudi Arabia.
One says nearly a quarter of Saudi jobs face high automation risk and that transition gaps are costing the Kingdom tens of billions of riyals annually. The other says AI will create net new jobs across Arab States by 2035, and the Kingdom’s AI market is growing tenfold by 2030.
Both are based on legitimate research. Both are true. But they are also incomplete without the Saudi-specific context that determines which workers and sectors sit in which category.
Before going further, it’s worth clarifying a point here. The terms below are often used interchangeably in this kind of conversation, but they describe very different things.
AI job displacement refers to the full or partial replacement of human job functions by AI or automation systems.
AI task automation refers to the automation of specific tasks within a job, not the entire job.
AI job augmentation refers to AI enhancing what a worker can do without replacing them.
Most current coverage on these topics treats them as one. This piece separates them.
The reason lies in the core of our argument throughout this article: that AI will not eliminate Saudi jobs at scale. But it will eliminate tasks within jobs, reshape skill requirements, and create a transition period that is genuinely costly for workers who are unprepared and employers who are uninformed.
Will AI Really Take Your Job in Saudi Arabia? The Short Answer Is More Complicated Than Yes or No
The two dominant findings on the issue seem to contradict each other. But they don’t. They only measure different things.
Pearson’s October 2025 “Lost in Transition” report found that 23% of Saudi jobs face high automation risk, and that career transition gaps are costing the Kingdom SAR 62 billion annually in earnings losses for Saudi nationals alone.
When total workforce losses (Saudi and non-Saudi) are included, that figure reaches SAR 196 billion.
On the other hand, the ILO’s September 2025 report on AI in Arab labour markets found something different: that only 2.2% of jobs across Arab States have high automation potential under generative AI specifically, while 14.6% have high augmentation potential.
On net, the ILO projects 118,000 additional jobs created across Arab States by 2035, not job losses.
Now here’s why these figures are not contradictory. Pearson measures broad automation risk across multiple technologies — robotics, robotic process automation, autonomous mobile systems, and AI.
Meanwhile, the ILO measures generative AI exposure specifically at the task level. Different scope and different methodology, but both are valid.
That said, an honest framing of the Saudi situation requires holding both in view at the same time. Displacement risk is real and documented. Job creation is also real and documented. The relevant question is not which report is right, but where your specific roles and skills sit within both.
The Numbers Behind the Fear: Which Saudi Jobs Are Actually at Risk?
The displacement narrative sits alongside a job creation story that is equally incomplete when read in isolation.
Saudi Arabia’s AI market is projected to grow from $513 million in 2023 to $5.1 billion by 2030. SDAIA projects AI will contribute 12% of GDP by 2030. These are meaningful numbers. Market growth and employment growth, though, follow different curves.
The ILO projects 118,000 net additional jobs across Arab States by 2035 — a figure covering the entire Arab region, not Saudi Arabia specifically. Saudi labor market dynamics as a GCC country differ significantly from non-GCC Arab States, and the ILO’s own report flags that distinction explicitly.
Where Saudi job creation is most substantiated:
- Tourism (400,000 to 600,000 additional jobs projected by Minister Al-Khateeb), green energy, specialized healthcare, and AI-related technical roles.
- Google-commissioned research projects 148,600 jobs from the Google Cloud region in Dammam by 2030.
The AI adoption pattern across GCC companies investing in the technology is consistent: specialized roles are created while routine task clusters contract. Net employment figures improve while specific workers face genuine transition costs.
Job creation does not cancel displacement. Both run simultaneously in the same labor market, affecting different workers at different speeds.
Who Is Most Vulnerable? Saudi Demographics and Displacement Risk
The aggregate employment picture in Saudi Arabia is strong on the surface. GASTAT’s Q1 2025 data shows overall unemployment at 2.8%, with Saudi national unemployment at 6.3% — both record lows and below the government’s 7% target.
But the distribution beneath those figures is uneven.
Youth unemployment stands at 14.8%, with a clear gender gap: 11.6% for men versus 20.7% for women. Overall, female unemployment (10.5%) remains more than double the male rate (4.0%).
Transition dynamics add further pressure. Pearson estimates that displaced Saudi workers take an average of 11.3 months to find re-employment, with around 40% out of work for over a year.
Graduates face similar friction, averaging 40 weeks to secure their first role. At the same time, the 20–24 population is projected to grow from 2.69 million in 2025 to 3.22 million by 2030.
Exposure is also uneven by sector. Female employment remains concentrated in administrative, customer service, and education roles — several identified by Coface as having above-average generative AI task exposure. While participation has risen to 36.3%, supported by flexible work programs exceeding 1 million contracts, access to reskilling is not advancing at the same pace.
The expat–Saudi distinction further shapes outcomes. Saudi nationals account for about 23% of employment (4.05 million of 17.6 million workers), with the remaining 77% non-Saudi. Automation affecting expatriates can often be absorbed through contract non-renewal.
For Saudi nationals, it carries policy implications under Nitaqat — making workforce shifts more constrained and more consequential.
The Policy Tension: Vision 2030 Wants AI Adoption. Nitaqat Wants Saudi Jobs.
Saudi Arabia is navigating a structural challenge at a scale most governments only theorize about: mandating adoption of technology that displaces labor while simultaneously mandating the expansion of domestic employment.
Vision 2030 drives AI investment through the National Strategy for Data and AI ($20 billion committed by 2030) and PIF’s wholly owned AI implementation arm, SCAI. The government’s target: 1 million AI specialists by 2030.
Nitaqat requires private sector employers to maintain specific Saudi national percentages by sector and company size. Minister Al-Rajhi reported in January 2026 that 2.5 million Saudis have entered the private sector since 2020, and that 92% of Labor Market Strategy targets have been achieved.
Workforce disruption in Saudi Arabia is structurally embedded in how this transition is being executed. Pearson’s 94-day reskilling benchmark and the 11.3-month re-employment gap sit on opposite sides of the same policy equation.
The Coface-Pearson bind makes it harder: construction roles face physical automation risk; the knowledge-economy roles Vision 2030 directs workers towards carry the highest GenAI task exposure. Both paths carry automation risk.
For organizations navigating recruitment strategy in Saudi Arabia under these conditions, the AI-Nitaqat tension is already presenting, in hiring cycles, which roles can shift toward augmentation, which face genuine displacement risk, and how to maintain compliance while managing technology adoption simultaneously.
The Saudi AI Job Risk Matrix
Understanding aggregate risk is useful. Knowing which quadrant a specific role sits in is actionable.
The framework below maps roles across two dimensions: automation exposure (how much of the work is susceptible to AI or automation) and skill transferability (how readily skills move to adjacent roles and sectors).
Every Saudi role — worker or employer — sits in one of four positions.
| Quadrant | Automation Exposure | Skill Transferability | Action |
| Reshape & Rise | High | High | Offload automatable tasks; move toward judgment and complexity |
| Reskill or Relocate | High | Low | Significant reskilling or sector relocation required |
| Augment & Advance | Low | High | Adopt AI to expand output and scope; advance into senior roles |
| Monitor & Maintain | Low | Low | Maintain current expertise; build adjacent awareness |
Reshape & Rise — Administrative coordinators shifting to operations management. Junior analysts moving toward business intelligence. IT support building into cloud systems. The path is upward, not out — 94 days of targeted reskilling, per Pearson’s benchmark.
Reskill or Relocate — Data entry specialists, routine document processing, template-driven work. The trajectory is clear even where immediate elimination is not. The 40% of displaced workers who remain out of work for more than a year are disproportionately here. Government programs — HADAF (1.5 million beneficiaries, SR3.8 billion) and Wa’ad (4.5 million training opportunities) — are the most relevant external resources.
Augment & Advance — Project managers, HR business partners, specialized nurses, senior technical specialists. AI expands the capabilities of these workers. Workers here are positioned to benefit materially from adoption, not defend against it.
Monitor & Maintain — Construction supervisors, regulatory compliance officers, Saudization program managers. Stable, but not permanently safe. Technologies evolve, and some roles in this quadrant will shift as AI capabilities develop.
The framework is deliberately qualitative rather than predictive. Specific job-loss forecasts for Saudi Arabia vary too widely across methodologies to cite with confidence.
What the matrix provides is a consistent logic for assessment — one that employers can apply at the role level, and workers can apply to their own situation.
Organizations facing this complexity — particularly where Nitaqat compliance limits how roles can be restructured — sometimes rely on external Middle East HR consultancies’ support for objective risk assessment and transition planning.
What to Do Next
For Saudi workers
Audit your tasks, not your job title. Tasks are what AI will automate. Identify which daily activities are routine, repetitive, or rule-based. That inventory locates your quadrant.
Build AI fluency. Using AI tools effectively within your specific field protects most roles — most workers do not need to become AI engineers. The Wa’ad campaign and Maharat Min Google (590,000+ trained since 2018) offer accessible entry points.
Seek augmentation before it is assigned to you. Workers who propose AI-assisted workflows proactively build the case for role expansion rather than replacement.
For Saudi employers
Audit roles before procuring AI platforms. Map roles against the Saudi AI Job Risk Matrix before signing technology contracts. Also, AI readiness for HR teams is a prerequisite for leading that assessment credibly.
Reskill or Relocate employees need the most support and the longest lead time. Finding them after procurement is reactive and expensive.
Communicate specifically, not reassuringly. PwC’s Middle East Workforce Hopes and Fears Survey 2025 found 75% of Saudi employees already use AI at work. What erodes trust is the gap between what workers observe and what leadership communicates.
Reskill before hiring externally. Reskilling existing Saudi employees is more cost-effective and more Nitaqat-compliant than replacing displaced workers with external hires.
For capability gaps that genuinely require external sourcing — particularly in specialized AI leadership — executive search and contract staffing agencies can help address the different parts of that challenge on different timelines.
Closing
AI will reshape Saudi employment — sector by sector, role by role, task by task. The aggregate numbers will look fine for years. Underneath them, specific workers will face genuine transition costs, specific employers will face compliance complexity, and the gap between reskilling timelines and displacement timelines will determine how much of this is managed versus absorbed.
The data is clear enough to act on. Pearson’s 23% names the risk. Coface names where GenAI is arriving next. GASTAT names those who carry the heaviest burden.
Our Saudi AI Job Risk Matrix is designed to help workers and employers turn those numbers into a decision — a map of where to look and what to do.
The organizations and workers who treat this as an ongoing assessment will find themselves better positioned each time the data moves.
Questions About AI and Jobs in Saudi Arabia
Will AI take my job in Saudi Arabia?
AI is more likely to change your job than eliminate it — but the impact is uneven. Pearson (2025) estimates 23% of Saudi jobs face high automation risk, driven largely by construction. At the same time, the ILO projects net job creation across Arab States by 2035. Your risk depends on your tasks, not your title.
Which jobs are most at risk?
For broad automation, Pearson identifies construction, production, and transport. For generative AI, Coface (2026) finds the highest task-level exposure in engineering, IT, administrative, finance, and legal roles. These are different layers of risk — and both apply.
Which jobs are safest?
Roles requiring physical skill, complex judgment, or deep human interaction are least exposed. Growth areas include tourism, green energy, specialized healthcare, and AI-related roles. Across sectors, AI fluency — using AI tools within your field — is the most practical advantage.
Is the Saudi government responding?
Yes. The Wa’ad campaign offers 4.5 million training opportunities, while HADAF has supported 1.5 million beneficiaries with SR3.8 billion in funding. The National Strategy for Data and AI commits $20 billion and targets 1 million AI specialists by 2030. The key question is whether reskilling keeps pace with automation.
Should I learn AI skills?
Focus on AI fluency within your current role before pursuing full specialization. Most jobs reward effective use of AI, not building it. Your position in the Saudi AI Job Risk Matrix is a more useful guide than generic advice.
Are Saudi women more at risk?
Female unemployment stands at 10.5%, with youth unemployment at 20.7%. Women remain concentrated in roles with above-average GenAI exposure, such as administrative and customer service work. Participation has risen to 36.3%, but reskilling access remains the critical variable.
What should Saudi employers do?
Assess roles against the Saudi AI Job Risk Matrix before adopting AI. Communicate clearly about task-level changes. Prioritize reskilling over external hiring where possible — it is often more cost-effective and aligned with Nitaqat. For specialized gaps, targeted external support can accelerate transition.