A joint study by Coface and the Observatory of Threatened and Emerging Jobs reveals how AI and employment patterns are shifting across the global labor market. More than three years after ChatGPT’s launch, the impact of artificial intelligence on employment remains largely invisible in aggregate statistics, yet it is beginning to emerge in certain segments, particularly in entry-level roles within vulnerable sectors.
The research provides a detailed mapping of where AI-driven automation is most likely to transform work. Unlike previous waves of automation, AI does not represent a continuation of technologies such as robotics or software. Instead, it shifts focus towards cognitive tasks that are complex and non-repetitive, posing a risk of upheaval in the structure of employment.
Methodology for Measuring Automation Exposure
The study analyzed 923 professions, breaking each down into tasks and elementary actions described as triplets: verb, object, and context. This granular approach addresses three limitations in existing analyses: lack of occupational detail, low reproducibility of assessments, and absence of forward-looking projections across different phases of artificial intelligence development.
Each elementary action received a score using explicit and reproducible rules. The methodology also weighted tasks based on their importance and frequency, refining forward-looking scenarios across five phases of AI development rather than providing a single snapshot. The assessment deliberately focuses on technical exposure to automation and does not account for demand dynamics, creation of new tasks, or deployment frictions that may slow actual AI rollout.
Cognitive Tasks Face Greatest Automation Risk
The study highlights a major break with previous automation waves. In the main scenario concerning agent-based AI deployment, approximately one in eight occupations crosses the 30% threshold of automatable tasks, which researchers identify as a threshold for profound transformation of the profession.
The most exposed professions concentrate in highly cognitive and information-intensive fields: engineering, IT, administrative roles, finance, law, and certain creative and analytical professions. More than a quarter of work content could be automated in management and administration, creative professions, law and finance, as well as engineering and IT sectors.
Conversely, the least vulnerable occupations remain largely manual or involve human interactions difficult to standardize: manufacturing, construction, maintenance, transport, catering, cleaning, and certain care and support activities. Face-to-face services and technical, craft and industrial production occupations remain below the 10% threshold.
Significant Disparities Between Countries
Countries’ exposure to AI-driven automation varies significantly, ranging from around 12% of work content exposed to automation in Turkey to nearly 20% in the United Kingdom. These differences are largely explained by economic structure, which determines employment composition and the proportion of tasks that can potentially be automated.
The wealthiest economies and those most oriented towards cognitive services appear most exposed to automation. In addition to the UK, the Netherlands, Ireland and Luxembourg have higher concentrations of information-intensive occupations. Countries where employment remains more oriented towards trade, personal services, construction, transport or physically intensive activities show more moderate exposure.
Mohamad Jomaa, CEO and Country Manager for GCC and Egypt at Coface, said the UAE and Saudi Arabia are positioning themselves as global AI and compute hubs through commitments measured in tens of billions of dollars, including multi-gigawatt data center campuses and advanced GPU capacity. He noted these represent long-term, sovereign-level investments designed to anchor economic diversification in a data-driven world.
Broader Implications Beyond Employment
The potential effects of AI rollout extend beyond employment alone. By automating tasks in skilled, well-paid occupations, AI could shift significant value added from labor to capital. For countries relying heavily on direct and indirect taxation of labor, this poses a dual budgetary challenge: reduced tax revenue from social security contributions, income tax, and VAT, while increasing public expenditure on unemployment insurance and training.
The study also raises questions about the value of education and qualifications. If some tasks for which lengthy courses of study prepare become more easily automatable, the link between educational attainment, pay and job security could weaken. Employers may place less emphasis on qualifications alone and instead focus on skills that remain complementary to AI, such as judgment, adaptability or the ability to oversee its use.
Additionally, the rise of AI could create new geopolitical, logistical and operational vulnerabilities due to concentration of critical assets—semiconductors, language models, data centers—among a limited number of companies and countries controlling the technologies.
Transformation of Work Structure Ahead
While the exact trajectory of these transformations remains uncertain, one point stands out clearly: AI is not being deployed on the fringes of work, but across cognitive, non-routine and skilled functions long perceived as the most secure. Because these functions form part of occupations that play major roles in generating income, added value and tax revenue, such transformation seems unlikely to occur without reshaping the nature of jobs and the balances that underpin them.
