Stanford Index places Saudi Arabia first globally in training women in artificial intelligence: 666,000 trainees break the 20 percent gap

Saudi Arabia recorded training more than 666,000 women in data and artificial intelligence fields within a single year, according to the 2025 AI Index report released by Stanford University, which placed the kingdom at the top globally in this pathway. The figure reflects a shift in the composition of technical talent, as Saudi Arabia currently leads the world in the share of females in specialized AI training programs compared with males, at a time when the sector internationally suffers from a clear gap documented by UNESCO and UN Women reports, which indicate that women hold only one out of every five professional roles in this field, roughly 22 percent.

Building competence did not happen by chance, but was based on educational investments that accumulated over decades before culminating in a structural turning point in 2016.Areeb Al-Awish, vice-president of AI research at Humane, which leads efforts to develop AI models with an Arab priority, explains that the foundation stemmed from external scholarship programs that funded more than 100,000 men and women to pursue graduate studies, alongside the King Abdullah Scholarship Program, which alone has financed more than 250,000 citizens since 2005. Domestically, Princess Nourah bint Abdulrahman University, as the world’s largest women’s university, supplied a pipeline of female graduates in science, technology, engineering, mathematics and medicine, so that by 2017 the number of female university graduates in the kingdom had surpassed the number of male graduates.

Al-Awish, who has more than twenty years of experience in the sector and whose master’s research at King Saud University focused on designing a distributed system to locate individuals in large crowds during the Hajj seasons, notes that her path began with observing the absence of AI from early academic curricula and progressed to leading efforts to build the infrastructure for current models. The regulatory launch came with the rollout of Vision 2030, after noting that more than half of women held university degrees yet were outside the labour market, with the plan targeting an increase in women’s participation from 22 percent to 30 percent. Following the lifting of the driving ban in 2017, the issuance of anti-harassment regulations and the amendment of civil status rules to enable women to run businesses and travel independently, the share of women in the workforce rose to exceed 36 percent by 2025, ahead of the scheduled timeline.

Training hundreds of thousands creates the broad adoption layer that any genuine AI economy needs to operate efficiently.Al-Awish confirms that this expansion in qualification produces informed consumers and operators capable of managing systems, which is reflected in Humane’s work environment where specialist female researchers lead model projects with equal professional opportunities and expectations. This trajectory coincides with a rise in investment spending, after government expenditure on emerging technologies grew by more than 56 percent in 2024 and AI companies attracted financing totaling $9.1 billion, alongside Saudi Arabia’s accession to the Global AI Partnership as the first Arab country, Riyadh’s hosting of the International Center for AI Research and Ethics under UNESCO’s patronage, and the continuation of the Elevate initiative jointly run by the Saudi Data and AI Authority and Google Cloud to train 25,000 women in emerging markets over five years.

This trajectory directly impacts the work reality in the region for those who manage development and data teams in the Gulf, Egypt and the Levant. Injecting hundreds of thousands of AI-tool trained personnel expands the talent pool available to regional firms, enabling the creation of internal development centers and reducing reliance on recruiting external expertise at exceptional costs, while also requiring a recalibration of skill-assessment standards and hiring criteria in nearby labour markets. Moreover, leading the development of Arab-priority models shifts competition toward linguistic processing quality and local data accuracy rather than reliance on translated interfaces, and compels product managers in banks, financial institutions and service companies in the region to raise operational efficiency and accelerate integration of intelligent solutions based on operators and users who possess proven technical fluency.