Libya AI Job Risk 2026: Which Occupations Are Most at Risk?
Libya's approximately 2.3 million formal sector workers score a weighted average AI exposure of 3.42/10 - one of the lower readings in the Tier 2 dataset, reflecting a workforce dominated by craft workers, elementary occupations, and service workers rather than the knowledge-economy concentration found in higher-scoring economies. The figure conceals a structural asymmetry: a small clerical and pr
Libya's approximately 2.3 million formal sector workers score a weighted average AI exposure of 3.42/10 - one of the lower readings in the Tier 2 dataset, reflecting a workforce dominated by craft workers, elementary occupations, and service workers rather than the knowledge-economy concentration found in higher-scoring economies. The figure conceals a structural asymmetry: a small clerical and professional class concentrated in the public sector and National Oil Corporation (NOC) carries very high exposure scores, while the majority of the workforce in construction, services, and elementary roles carries low scores. Libya operates under a dual-government configuration - the Government of National Unity (GNU) in Tripoli and the Government of National Stability (GNS) aligned with Tobruk and the Libyan National Army - which fragments the institutional infrastructure that would normally channel AI adoption and workforce adaptation.
Key Findings
Highest AI exposure: Clerical support workers at 8.5/10 - peak risk, covering ~92,000 workers (~4%)
~2.3M workers covered; weighted average 3.42/10 (ILO ILOSTAT / AICT Libya / National Planning Council 2024-2025)
Safest groups: Elementary occupations at 1.5/10 (~15%); craft/trades at 2.6/10 (~16%); armed forces at 2.3/10 (~7%)
Recovery resilience 3.2/10 - lowest in this batch; political fragmentation and oil dependency severely constrain adaptation
2.3M
Total workers (2024-25)
8.5/10
Highest AI score
3.42/10
Avg AI exposure
The most AI-exposed occupations in Libya
Libya occupation data comes from ILO ILOSTAT, the Libyan Authority for Information and Communication Technology (AICT), and the National Planning Council Libya Labour Statistics 2024-2025. Data collection in Libya is significantly hampered by political fragmentation between the Tripoli and Tobruk-aligned administrations, meaning estimates carry wider uncertainty ranges than single-government states. The figures below represent the best available synthesis of ILO harmonised data and Libyan statistical authority records covering approximately 2.3 million formal sector workers.
Occupation Group
AI Score
Workers (est.)
Share (est.)
Clerical support workers (ISCO 4)
8.5/10
~92,000
~4%
Professionals (ISCO 2)
6.5/10
~230,000
~10%
Technicians and associate professionals (ISCO 3)
5.8/10
~184,000
~8%
Managers (ISCO 1)
5.0/10
~69,000
~3%
Service and sales workers (ISCO 5)
3.1/10
~345,000
~15%
Craft and related trades (ISCO 7)
2.6/10
~368,000
~16%
Plant and machine operators (ISCO 8)
2.7/10
~276,000
~12%
Elementary occupations (ISCO 9)
1.5/10
~345,000
~15%
Skilled agricultural workers (ISCO 6)
2.8/10
~230,000
~10%
Armed forces (ISCO 0)
2.3/10
~161,000
~7%
Clerical support workers at 4% of the formal workforce score 8.5/10 - the economy's peak exposure. In Libya's context, clerical workers are concentrated almost exclusively in the public sector: ministries, the National Oil Corporation administrative apparatus, the Central Bank of Libya (CBL), and the fragmented municipal government structures across Tripoli, Benghazi, Misrata, and other major cities. Private sector formal employment in Libya is limited - the economy has historically provided public sector jobs as an oil revenue redistribution mechanism, creating a large administrative class with high AI substitution scores but limited private market alternatives in the event of disruption.
Professionals at 10% score 6.5/10. Libya's professional class is concentrated in healthcare (physicians, pharmacists, engineers), legal services, and education - all of which have meaningful AI exposure through documentation, diagnostics support, and research functions, though the physical and judgment-intensive components reduce the displacement timeline compared to pure administrative roles. Technicians at 8% score 5.8/10, reflecting the oil and gas sector's engineering technician workforce employed by NOC subsidiaries including Agoco, Waha Oil, and Mellitah Oil and Gas (the ENI-NOC joint venture). These roles involve equipment monitoring, production data recording, and maintenance scheduling - tasks where AI augmentation is actively deployed in peer petrostates including Kuwait, Qatar, and Oman.
Oil dependency and dual-government constraints on AI adoption
The National Oil Corporation (NOC) dominates Libya's economy to a degree unusual even by petro-state standards: oil and gas revenues account for approximately 95% of government revenue and 60% of GDP according to World Bank Libya Economic Monitor 2024. This extreme concentration means the structural health of the entire Libyan economy - and its capacity to invest in workforce development, technology adoption, or retraining - is directly tied to the production output and crude price that the NOC achieves. Libya's oil production capacity is further complicated by periodic pipeline closures and field shutdowns associated with political disputes between the two governmental configurations, tribal militia pressure on eastern fields, and infrastructure damage from the 2011 conflict that has never been fully repaired.
The Tripoli vs Tobruk political split - with the GNU controlling the CBL, the NOC headquarters, and most of western Libya's productive economy, and the GNS controlling eastern Libya under LNA protection - creates a fragmented institutional landscape. There is no unified ministry of labour, no single national skills development authority, and no coherent national digital strategy that applies across both jurisdictions. This makes systematic AI adoption - which requires institutional coordination, regulatory frameworks, and workforce policy - structurally more difficult than in single-authority states at comparable income levels. Egypt, Tunisia, and Morocco have national AI strategies; Libya does not have a single nationally legitimate government capable of ratifying and implementing one.
The banking sector provides another indicator of AI adoption capacity. Libya operates with two parallel central bank configurations following a 2014 split: the CBL in Tripoli and the Eastern CBL in Al-Bayda. International wire transfers, letters of credit, and trade finance have been complicated by the split. Major international banks significantly reduced Libyan correspondent banking relationships after 2011. This banking fragmentation directly limits the ability of Libyan businesses to access international technology vendors, cloud computing services, and SaaS platforms that form the infrastructure for commercial AI deployment. A Libyan company wanting to subscribe to Salesforce, Microsoft 365, or AWS services faces payment infrastructure barriers that comparable Egyptian or Tunisian firms do not.
The safest jobs from AI in Libya
Libya's physical economy - craft workers, elementary occupations, agriculture, and plant operators - employs approximately 53% of the formal workforce at below 2.9/10 AI exposure, anchoring the weighted average well below the regional mean.
Occupation Group
AI Score
Workers (est.)
Share (est.)
Elementary occupations (ISCO 9)
1.5/10
~345,000
~15%
Armed forces (ISCO 0)
2.3/10
~161,000
~7%
Craft and related trades (ISCO 7)
2.6/10
~368,000
~16%
Plant and machine operators (ISCO 8)
2.7/10
~276,000
~12%
Skilled agricultural workers (ISCO 6)
2.8/10
~230,000
~10%
Craft and trades workers (ISCO 7) at 16% of the workforce - the largest single occupation group - score 2.6/10. Libya's construction sector has been the primary driver of craft worker demand since the 2011 conflict, with reconstruction of damaged housing stock, infrastructure repair, and new construction in stable western cities providing sustained employment for plasterers, carpenters, electricians, and plumbers. The Libyan Construction Industry Chamber (LCIC) tracks tens of thousands of licensed contractors, the majority of whom employ craft workers on project-by-project bases. Elementary occupations at 15% score 1.5/10 - the lowest in the dataset - and are concentrated in cleaning, basic manual handling, and agricultural labouring roles where physical in-situ work makes near-term AI substitution economically impractical in a low-wage, infrastructure-constrained environment. Armed forces (ISCO 0) at 7% reflect the reality that Libya's conflict context has produced an unusually large security and military workforce across multiple factions - LNA, GNU-aligned forces, and various militia structures - scored at 2.3/10 for non-automatable command and physical security functions.
What this means for you
Libya's 3.42/10 weighted average is relatively low for a North African economy - comparable to Sudan and significantly below Egypt (4.5/10) and Tunisia (published separately). The low average reflects Libya's workforce composition rather than any inherent protection from AI: the dominant craft, elementary, and service workforce has genuinely low current AI exposure, but this is an artefact of the informal, oil-financed economy structure rather than a durable competitive advantage.
For Libyan workers in the public sector - the primary employer of clerical staff, administrative coordinators, and lower-level professionals - the structural exposure is real even if the near-term implementation probability is constrained by political fragmentation. When political normalisation occurs, whether through UN mediation or organic consolidation of government authority, the administrative modernisation push that follows will typically target exactly the roles currently carrying 8.5/10 and 6.5/10 scores: data entry clerks, registry administrators, document processors, and junior procurement officers. Libyan public sector workers in these roles should treat the current political delay not as permanent safety but as a window to develop complementary skills in supervision, client relations, or technical trades where AI substitution is slower.
Recovery resilience of 3.2/10 is the lowest in this batch. This reflects the absence of a functioning national skills development authority, limited access to international training platforms due to banking fragmentation, and an educational system that has been significantly disrupted by conflict. Workers who can access training through diaspora networks, UN development programme resources (UNDP Libya has active digital skills programmes), or cross-border proximity to Tunisia and Egypt will find materially better transition options than those relying solely on domestic infrastructure. The craft and construction sector remains the most reliable employment pathway for the medium term - demand for physical reconstruction work in a post-conflict economy with significant infrastructure deficit is not subject to AI displacement on any near-term horizon.
Explore Libya's Full Occupation Data
Interactive breakdown of every occupation group, sortable by AI exposure score and worker count.
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Methodology: AI exposure scores are assigned at ISCO-08 sub-major group level and aggregated to major groups using employment-weighted averages. Employment data is from ILO ILOSTAT, the Libyan Authority for Information and Communication Technology (AICT), and National Planning Council Libya Labour Statistics 2024-2025, covering approximately 2.3 million formal sector workers. Libya's political fragmentation means estimates carry wider uncertainty ranges than single-government states; figures should be interpreted as indicative ranges rather than precise measurements. Scores reflect task-level AI capability relative to occupation task profiles as of mid-2026. This analysis does not constitute career or financial advice.
Frequently Asked Questions
Clerical support workers score 8.5/10 AI exposure - the highest in Libya, covering roughly 92,000 workers or 4% of the workforce. Professionals score 6.5/10 across 230,000 workers. Technicians score 5.8/10 across 184,000 workers. Data from ILO ILOSTAT and AICT Libya National Planning Council 2024-2025.
Libya had approximately 2.3 million formal sector workers in 2024-2025 estimates. Weighted average AI exposure is 3.42/10 - relatively low due to the large craft, elementary, and service workforce shares. Risk velocity is 5.8/10. Recovery resilience is 3.2/10, constrained by political instability and oil-dependent revenue structure.
Elementary occupations score 1.5/10 and cover roughly 345,000 workers or 15% of the Libyan workforce. Craft and trades workers score 2.6/10 across 368,000 workers. Armed forces score 2.3/10. Agriculture scores 2.8/10 across approximately 230,000 workers or 10% of the workforce.
Employment data comes from ILO ILOSTAT, the Libyan Authority for Information and Communication Technology (AICT), and National Planning Council Libya Labour Statistics 2024-2025. Libya's data collection is hampered by political fragmentation, so estimates carry wider uncertainty ranges than single-government states.