Key findings
- Clerical support workers score 8.5/10 on AI exposure - Senegal's highest-risk group. Around 78,600 workers in data entry, administrative coordination, and office roles in Dakar and other urban centres face growing pressure from administrative AI tools entering West African businesses.
- Professionals score 6.5/10, covering 154,300 Senegalese doctors, lawyers, engineers, accountants, and teachers - 9.9% of the workforce. Senegal's expanding tertiary education sector and Dakar's growing tech scene are producing a professional class that AI augmentation tools will reach first.
- Craft and related trades workers score just 2.5/10 on AI exposure, covering 417,900 workers - 26.9% of employment, the largest single occupation group. Senegal's artisanal economy in textiles, construction, food processing, and metalwork is structurally AI-resilient.
- Elementary occupations score 2.0/10, covering 327,200 workers - 21.1% of employment. Manual labour, domestic work, and street commerce dominate this group, with near-zero near-term AI displacement risk.
- Senegal's weighted average AI exposure of 3.44/10 reflects the large craft and elementary workforce share that keeps formal AI risk concentrated in a minority of total employment.
1.55 million workers, ILO ILOSTAT 2024 data
Employment data comes from ILO ILOSTAT (CC BY 4.0), based on ANSD Senegal's Enquete sur l'Emploi au Senegal (EES), using ISCO-08 major group structure. The 2024 data covers approximately 1.55 million formally tracked Senegalese workers. Senegal's total labour force is substantially larger - the ILO figure captures formal and semi-formal employment; a large informal economy operates alongside it, particularly in agriculture, trade, and artisanal production not fully captured in formal labour surveys.
Senegal's economy is transitioning rapidly. The discovery of offshore oil and gas reserves (Sangomar field, GTA project) is reshaping the fiscal base and creating demand for technical and professional workers. The Services Senegal digitisation initiative and the expansion of mobile money (Wave, Orange Money) are pushing digital tools further into urban commerce. Both dynamics will increase AI exposure in the formal professional and clerical workforce over the medium term.
The most AI-exposed jobs in Senegal
Clerical support workers score 8.5/10 on AI exposure - the same peak score seen across all economies we analyse. The 78,600 Senegalese clerical workers perform data entry, document processing, scheduling, and administrative coordination in formal-sector organisations. These roles are concentrated in Dakar's government ministries and public agencies, in the financial sector (Societe Generale Senegal, Ecobank, UBA), and in the growing private sector across telecoms, logistics, and retail.
AI adoption in Senegalese businesses is accelerating faster than in many peer economies because Dakar serves as a regional hub for West Africa. Multinationals operating across the region often base their Francophone Africa administrative functions in Dakar, exposing Senegalese clerical workers to the same AI productivity tools their counterparts in Paris or Casablanca are already using. The timeline for meaningful displacement is 5-8 years for most clerical roles, but the direction is clear.
Professionals at 6.5/10 cover 154,300 workers - the second-largest high-exposure group by headcount. Senegal's professional class includes teachers, engineers, accountants, doctors, and a fast-growing tech sector concentrated around Dakar's Technopole and the Gainde 2000 digital economy cluster. AI tools for legal research, financial modelling, medical diagnostics, and software development are reaching Senegalese professionals through international platforms. The dominant effect is augmentation now - making professionals more productive - but competitive pressure on entry-level professional work is building.
| Occupation Group (ISCO-08) | AI Score | Robotics Risk | Workers | % of Total |
|---|---|---|---|---|
| Clerical support workers (4) | 8.5/10 | 2.5/10 | 78.6k | 5.1% |
| Professionals (2) | 6.5/10 | 1.5/10 | 154.3k | 9.9% |
| Managers (1) | 5.5/10 | 1.5/10 | 16.6k | 1.1% |
| Technicians and assoc. professionals (3) | 5.5/10 | 3.5/10 | 48.9k | 3.2% |
| Service and sales workers (5) | 3.5/10 | 4.5/10 | 183.9k | 11.9% |
| Skilled agricultural workers (6) | 3.0/10 | 6.5/10 | 157.8k | 10.2% |
| Plant and machine operators (8) | 3.0/10 | 7.5/10 | 132.1k | 8.5% |
| Craft and related trades workers (7) | 2.5/10 | 4.5/10 | 417.9k | 26.9% |
| Elementary occupations (9) | 2.0/10 | 5.5/10 | 327.2k | 21.1% |
| Armed forces (0) | 2.5/10 | 3.0/10 | 33.9k | 2.2% |
Craft and trades workers at 26.9% are Senegal's single largest occupation group - and at 2.5/10 AI exposure, they represent the backbone of the economy's AI resilience. Dakar's informal artisanal sector is where AI pressure does not reach.
The safest jobs in Senegal
Elementary occupations score 2.0/10 on AI exposure in Senegal, covering 327,200 workers - 21.1% of employment. This group includes domestic workers, street cleaners, building labourers, market porters, and subsistence vendors. The physical, relational, and informal nature of these roles means near-term AI displacement risk is essentially zero. In Senegal's context, many of these workers are also outside the formal labour market, making AI adoption in their sectors structurally irrelevant in the near term.
Craft and related trades workers score 2.5/10 on AI exposure at 417,900 workers - the largest single occupation group. Senegal's artisanal economy is extensive: tailors producing boubous and traditional clothing, construction workers building across the rapidly urbanising Dakar banlieue, mechanics, electricians, and food processors. Many operate in the informal sector under family arrangements or artisanal cooperatives. These roles require manual dexterity, materials knowledge, and client relationships that AI tools cannot readily replicate or substitute.
| Occupation Group (ISCO-08) | AI Score | Robotics Risk | Workers | % of Total |
|---|---|---|---|---|
| Elementary occupations (9) | 2.0/10 | 5.5/10 | 327.2k | 21.1% |
| Craft and related trades workers (7) | 2.5/10 | 4.5/10 | 417.9k | 26.9% |
| Skilled agricultural workers (6) | 3.0/10 | 6.5/10 | 157.8k | 10.2% |
| Plant and machine operators (8) | 3.0/10 | 7.5/10 | 132.1k | 8.5% |
What this means for Senegalese workers
Senegal's AI exposure profile is favourable in aggregate - a 3.44/10 weighted average reflects the dominance of craft, elementary, and agricultural work that constitutes the majority of employment. The risk is real but concentrated: for the 78,600 formal-sector clerical workers and 154,000 professionals in Dakar and secondary cities, AI augmentation and eventual displacement pressure is as real as in Morocco or Cote d'Ivoire - applied to a smaller absolute headcount but a workforce that is growing fast as Senegal formalises.
The medium-term structural shift to watch is Senegal's Plan Senegal Emergent (PSE) which targets services, tourism, agriculture, and hydrocarbons as growth drivers. The hydrocarbon sector will create professional and technician roles that are AI-exposed from inception. The services expansion - particularly in financial services, logistics, and business process outsourcing - will add clerical workers to the AI-exposed category. Workers entering the formal economy over the next decade should treat AI literacy as a baseline professional skill.
See Senegal's full occupation breakdown
Explore AI exposure, robotics risk, and employment data for all Senegalese occupation groups - or compare Senegal against 205 other countries.
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Methodology
Employment figures are from ILO ILOSTAT (CC BY 4.0), based on ANSD Senegal's Enquete sur l'Emploi au Senegal (EES), using ISCO-08 major group classifications. Data year: 2024. Covers approximately 1.55 million formally tracked Senegalese workers. AI exposure scores are research-based estimates per ISCO-08 group, informed by Frey-Osborne (Oxford), OECD, and IMF studies on task-level automation. They reflect the proportion of an occupation's core tasks that current AI can perform or significantly augment - not predictions of job loss rates.
Frequently asked questions
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Related analyses
Data sources
- ILO ILOSTAT - Employment by sex, occupation (ISCO-08), Senegal 2024 (CC BY 4.0)
- ANSD Senegal - Agence Nationale de la Statistique et de la Demographie, Enquete sur l'Emploi au Senegal
- Frey, C.B. and Osborne, M.A. (2017). The future of employment. Technological Forecasting and Social Change.
- OECD - The Future of Work and Skills
- IMF - Gen-AI: Artificial Intelligence and the Future of Work (2024)