Jordan and HUMAIN Sign MoU to Advance Arabic AI Models and Talent Development

Jordan’s Ministry of Digital Economy and Entrepreneurship (MoDEE) and Saudi Arabia’s artificial intelligence company HUMAIN have signed a strategic Memorandum of Understanding (MoU) to advance Arabic AI models, grow regional AI talent, support startups and expand AI infrastructure. The agreement was signed by Eng. Sami Smeirat, Vice Chairman of Jordan’s National Council for Future Technology and Minister of Digital Economy and Entrepreneurship, and HUMAIN CEO Tareq Amin on the sidelines of LEAP 2026 in Riyadh.

“This partnership represents an important step in Jordan’s journey from adopting artificial intelligence technologies to actively contributing to their development. Our focus is on investing in talent, supporting practical AI use cases, and ensuring that Jordanian companies and professionals are part of the global ecosystem shaping the future of artificial intelligence.” — Eng. Sami Smeirat, Vice Chairman, Jordan’s National Council for Future Technology and Minister of Digital Economy and Entrepreneurship

The MoU sets out five principal areas of cooperation designed to move Jordan beyond passive adoption of AI tools toward active participation in AI creation and deployment. Partners will work as strategic members of the Arabic AI Models Alliance to co-develop advanced models for Arabic language processing, including ALLAM — described as a family of advanced large language models intended to expand Arabic language technologies and their applications across sectors.

• Advancing Arabic AI models, exemplified by collaboration on the ALLAM family of large language models.
• Accelerating AI adoption across the public sector by identifying priority sectors and building scalable solutions.
• Supporting startups and investment in the digital economy to foster commercialisation and regional partnerships.
• Developing Jordanian AI talent and skills through training, upskilling, fellowships and practical exchanges.
• Strengthening AI infrastructure and specialised computing to enable model training, development and deployment.

MoDEE and HUMAIN say the partnership will explore sovereign AI strategies and data localisation in line with applicable legislation, signalling a focus on regulatory compliance and national control over sensitive datasets. The arrangement also aims to open channels for Jordanian companies to access advanced AI products, platforms and computing resources, while mapping opportunities for investment and collaboration with regional and global technology players.

Eng. Smeirat reiterated the domestic priorities behind the agreement and linked them to national directives: “Jordan’s strength lies in its people. We are focused on creating greater opportunities for Jordanian talent and companies while attracting global and regional technology leaders to build, innovate and invest in Jordan.” That framing positions talent development and inward collaboration as twin pillars of the country’s AI strategy.

Operationally, the MoU foresees concrete capacity-building measures — training programmes, fellowships, talent exchange and practical on-the-job training — alongside explorations into dedicated AI computing infrastructure to support both research and commercial model deployment. By combining HUMAIN’s technology platform and Jordan’s policy and talent base, the partnership aims to accelerate the translation of strategic ambitions into deployable AI solutions for government and industry.

Outlook

The agreement anchors Jordan within a collaborative regional effort to produce Arabic-language AI capabilities and places emphasis on talent pipelines and sovereign infrastructure. If executed, the MoU could lead to expanded access to advanced AI models like ALLAM for Jordanian organisations, a stepped-up public-sector AI rollout in priority areas, and increased regional investment and startup activity. The success of the initiative will hinge on operationalising the planned training programmes, securing computing capacity for model development, and aligning data localisation measures with practical deployment needs.