Ghada Ismail

As artificial intelligence continues to reshape education, edtech startups are exploring new ways to personalize learning, improve student performance, and support teachers with data-driven insights. AILA is among the companies working to transform the learning experience through AI-powered solutions tailored to students’ individual needs and the Saudi education system. In this interview with Sharikat Mubasher, AILA discusses its approach to personalized learning, the challenges of developing effective AI-driven educational tools, and the opportunities for edtech startups as Saudi Arabia advances its education and digital transformation goals.

AILA has recently secured a $3 million funding round. What does this investment enable the company to do that it could not do before?

Our earlier funding helped us build the product and reach product-market fit. We now have schools using AILA every day and results we can stand behind. This round is about scaling what already works. 

First, it lets us go deeper on our adaptive engine and our research. We want to keep proving, with rigorous evidence, that AILA improves how students learn, building on the work we have done with King Fahd University of Petroleum & Minerals. 

Second, it lets us scale our commercial and customer success teams so we can serve many more schools across the Kingdom, and support each one properly after launch. 

Third, it lets us expand beyond the Saudi curriculum. Today we are strongest around Qudurat, Tahsili and NAFES. By adding other curricula, we can take the same engine to new markets and serve many more students. 

Do you expect this round to take AILA to profitability, or is the priority still aggressive growth and market expansion? 

Our priority is growth, but disciplined growth. We have built a business with strong fundamentals, and our product runs at a gross margin of around 67%, which gives us a lot of room to grow efficiently. 

Reaching profitability is a clear goal for this round. We want to scale to more schools and new markets while becoming profitable, so that we go into our Series A from a position of strength. We are not buying growth at any cost. Every school we sign needs to make sense on its own. 

AILA describes its approach as personalized, AI-powered learning. What makes your personalization technology different from the growing number of AI tutoring and edtech platforms entering the market? 

Many new AI platforms are essentially content generators: you ask a question, you get an answer. That is useful, but it is not the same as teaching. AILA is built around diagnosis. We work out exactly where each student is, what they have mastered and what is holding them back, and then we guide them on the shortest path to improvement. 

Three things set us apart. First, we are built for the Saudi context, mapped to national learning outcomes and assessments such as Qudurat, Tahsili and NAFES, and available in both Arabic and English. Second, we work inside schools, so the insight does not stay with the student. Teachers, administrators and principals see the same picture and can act on it. Third, we measure results. Our benchmark shows students learning about 2.3 times more efficiently, and our pilot results have been validated through a research partnership with King Fahd University of Petroleum & Minerals. 

How does AILA use student data and learning behavior to determine what a student should learn next? 

Every interaction on the platform tells us something: whether the answer was right, how long it took, how many attempts were needed, and what kind of mistake was made. We map all of that to a structure of skills and learning outcomes, so we can estimate each student's level of mastery skill by skill. 

From there, the engine decides what comes next. It looks at the gaps that matter most, the prerequisite skills that need to be secured first, and how heavily each skill counts in the assessment the student is preparing for. It also brings back earlier material at the right moment, so learning sticks rather than fading. 

The same data flows to teachers and school leaders as clear, actionable reports, so they know which students need help and on what. 

What are the biggest technical challenges in building AI systems that can genuinely improve learning outcomes rather than simply generate educational content? 

The hardest part is measuring learning rather than activity. It is easy to build something students click on a lot. It is much harder to prove they actually know more afterwards. That is why we invest in validation and research, not just in features. 

The second challenge is quality and alignment. Content must be accurate, pedagogically sound and aligned with the Saudi curriculum, in both Arabic and English. Generative AI can produce a lot very quickly, but in education a confident wrong answer does real damage, so we keep strong quality control around everything the system shows a student. 

The third is getting the recommendation right from limited data, especially when a student is new to the platform, and keeping it right as they progress. 

And finally, the system has to fit how schools actually work. AI does not replace the teacher. It has to give teachers and school leaders insight they can act on in the time they have. 

What gaps in the Saudi education system did you identify when you first built AILA, and how have those needs evolved since the company launched? 

When we started, the biggest gap was that one classroom holds students at very different levels, yet they are largely taught the same way at the same pace. Teachers knew some students were falling behind, but they did not have the time or the granular data to know exactly why. At the same time, national assessments carry high stakes for students, and much of the targeted preparation was happening outside school. 

We built AILA to bring that personalized, data-driven support inside the school, where every student can access it. 

At the same time, AI itself has advanced enormously since we launched. Models today understand language, reasoning and student errors far better than they did a few years ago, in Arabic as well as English. That has let us do much more than we originally planned: sharper diagnosis, richer and more personal feedback, and a far better experience for students and teachers. Our offering today is stronger than what we could have built at the start. 

The need has also matured. Schools are no longer only asking how to prepare students for an exam. They want continuous visibility on learning outcomes across the year, aligned with national standards, so leaders can make decisions based on evidence. That shift, driven by Vision 2030 and the Human Capability Development Program, has shaped how our platform has grown. 

Saudi Arabia is investing heavily in education technology as part of its broader digital transformation. Where do you see the biggest untapped opportunities for Saudi edtech startups? 

I see three big opportunities. The first is assessment and learning analytics. The Kingdom has clear national learning outcomes, and there is huge value in tools that show schools, in real time, where students stand against them. 

The second is scale in the public sector. Private schools have moved fast, but the largest impact will come from solutions that work reliably across government schools, at national scale. 

The third is high-quality AI built for our own context. Global tools are not designed around Saudi curricula, assessments, and classroom realities. Startups that build for this market from the ground up, in both Arabic and English, have a real advantage. 

More broadly, anything that connects learning in school to what comes next, university and the job market, is still underserved. 

Looking three years ahead, what would success look like for AILA in terms of users, markets, and impact? 

Today we work with around 100 schools and roughly 65,000 students. In three years, success means having a proven adaptive engine, backed by published research, used by 300,000 students. 

On markets, Saudi Arabia will remain our home and our largest market, and we plan to extend across the GCC and the wider MENA region, supported by the new curricula we are adding. 

But the number that matters most is impact. We want to show, with independent evidence, that students on AILA achieve measurably better learning outcomes, and that schools using AILA make better decisions because of the data we give them. If we can prove that at scale, the growth will follow. 

Noha Gad

AI adoption is accelerating rapidly across Saudi Arabia, driven by Vision 2030’s ambitions and supported by significant national investment and strong executive commitment. As organizations move from pilots toward enterprise-wide deployment, attention is turning to whether the operational foundations are in place to deliver results at scale.

As the global process context platform for enterprise AI deployment, ARIS works with organizations across Saudi Arabia and the broader Middle East to build the process context and governance that AI success depends on. 

Sharikat Mubasher held an interview with Ayham Alzaaim, senior vice president for Asia Pacific and Middle East at ARIS, on the sidelines of LEAP 2026 to learn more about how the company helps organizations deploy AI responsibly at scale.

In this interview, Alzaaim discussed the importance of process context in achieving progress, how organizations can move from traditional process management to real-time operational intelligence, the common pitfalls they face when scaling AI and automation, and why execution discipline will define AI leadership in the Kingdom and beyond.

ARIS believes that progress starts with process. In practical terms, how does process context directly translate into operational resilience and productivity gains for organizations?

Process context gives organisations a clear, trusted picture of how their operations actually work - who owns what, how decisions are made, where risks exist, and where inefficiencies are hiding. That visibility is the foundation of resilience. You cannot protect what you cannot see, and you cannot improve what you have not measured.

In practical terms, organisations that have built strong process context are able to respond to disruption faster, identify bottlenecks before they become crises, and deploy technology, including AI, with far greater confidence. The productivity gains come not from adding more tools but from finally understanding the operations those tools are meant to serve. For organisations across the Kingdom, that understanding is what turns Vision 2030's digital ambitions into measurable day-to-day outcomes.

Based on your experience across the Middle East, what does the journey from traditional process management to real-time operational intelligence look like for leading organizations—and what can the GCC learn from it?

The journey has three distinct stages. First, organisations map and govern their processes, creating a single trusted view of how the business actually operates. Second, they connect that foundation to real execution data so they can see how work flows in real time, not just how it was designed to flow. Third, they use that dynamic intelligence to drive continuous improvement and, increasingly, to deploy AI agents on a governed foundation.

What the GCC can take from leading organisations globally is that the sequence matters enormously. The organisations that try to skip to the third stage without completing the first two are the ones struggling to show AI returns. Those that invest in the foundation first move faster, govern better, and scale with confidence.

AI adoption is accelerating rapidly across the Middle East. What are the most common pitfalls organizations across the region face when attempting to scale AI and automation, and how can they avoid them?

The most common pitfall is deploying AI before the operational foundation is ready. Organisations invest in the technology, run a successful pilot, and then find that when they try to scale it across the enterprise, it breaks down. The reason is almost always the same: the AI does not have the process context it needs to operate reliably in a complex, live environment.

The way to avoid it is to treat process context as a prerequisite, not an afterthought. Before deploying AI at scale, organisations need to know how their processes actually work, who owns them, what rules govern them, and how performance is measured. When that foundation is in place, AI can be deployed with confidence and scaled without losing control. That is the execution discipline Vision 2030 demands, and the organisations that build it now will be the ones defining AI leadership in the Kingdom

LEAP 2026 is one of the region’s most significant technology platforms. From your perspective, what role do events like LEAP play in accelerating digital transformation across Saudi Arabia and the broader Middle East?

LEAP brings together government decision-makers, enterprise leaders, technology innovators, and investors under one roof in the heart of the Kingdom. That convergence accelerates transformation in ways that individual conversations cannot.

For ARIS, LEAP is where we have some of our most important conversations with organisations across the region. It is where strategy becomes concrete, where leaders who are thinking about AI transformation move into planning and action. The energy and ambition in the room reflect exactly where Saudi Arabia and the broader GCC are in their transformation journey right now.

Saudi Vision 2030 aims to position the Kingdom as a global AI hub. What is driving that momentum, and what should organizations across the Kingdom prioritize to accelerate that vision?

What is driving it is a combination of factors that very few countries can bring together simultaneously: clear national direction, significant investment, world-class infrastructure, and genuine executive decisiveness at both government and enterprise levels. Saudi Arabia is not just talking about AI leadership; it is building for it.

What organisations should prioritise now is execution discipline. The investment and the ambition are both firmly in place. The next phase is to ensure AI initiatives deliver measurable outcomes rather than impressive announcements. That means building the operational and governance foundations that allow AI to be deployed at scale, responsibly, and with control. Organisations that get that right now will not just contribute to Vision 2030; they will help define what AI leadership looks like globally.

Kholoud Hussein

Saudi Arabia’s artificial intelligence ambitions are entering a new phase, with HUMAIN moving beyond its initial role as a state-backed AI infrastructure developer to build a broader business spanning data centers, computing capacity, Arabic-language models, enterprise software, strategic investments, and physical AI applications.

Since its launch by the Public Investment Fund (PIF) in May 2025, HUMAIN has assembled partnerships with some of the world’s largest technology companies, including NVIDIA, AMD, Cisco, Qualcomm, Microsoft, Amazon Web Services (AWS), and Mistral AI. It has also expanded its relationship with Elon Musk’s xAI, invested $3 billion in the company, and announced plans to develop large-scale computing infrastructure in Saudi Arabia.

The scale of its ambitions has increased sharply. In an interview published in October 2026, HUMAIN CEO Tareq Amin said the company was targeting 1.7 gigawatts (GW) of data center capacity in 2027, around 3 GW by 2030, and approximately 6 GW by 2034. The targets place HUMAIN at the center of Saudi Arabia’s effort to become a major global destination for AI computing. 

Yet the company’s long-term value will depend on more than the amount of computing power it can install. HUMAIN must also secure financing, convert infrastructure into contracted demand, develop commercially competitive AI products, attract technical talent, and help Saudi businesses adopt AI at scale. Its strategy reflects a broader economic question facing the Kingdom: how to turn substantial investment in technology infrastructure into a durable source of productivity, intellectual property, and non-oil growth.

From national AI platform to global technology business

HUMAIN was established to operate across the AI value chain, rather than concentrate on a single product or service. Its mandate encompasses next-generation data centers, high-performance computing and cloud platforms, advanced AI models, and AI-powered applications for businesses and government entities.

The company’s creation reflects the PIF’s wider strategy of investing in industries that can diversify the Saudi economy, attract international capital, and build domestic technological capabilities. It also brings AI infrastructure and software development into a coordinated commercial structure, linking the Kingdom’s energy resources and investment capacity with global semiconductor suppliers, cloud providers, and model developers. 

This approach addresses a structural challenge in the AI market. Building advanced models requires substantial computing resources, specialized chips, reliable power, and high-speed networking. Operating them commercially also requires cloud platforms, software, enterprise integration, and customers willing to pay for the resulting services.

HUMAIN is attempting to connect these layers within one business. Its partnerships provide access to technology and expertise that would be difficult to reproduce independently in the short term, while its Saudi operations are intended to establish a local base for computing, model development, and commercial deployment.

The strategy is not limited to serving Saudi government agencies. HUMAIN has repeatedly positioned the Kingdom as a potential exporter of AI computing and services, serving regional and international customers from infrastructure located in Saudi Arabia. The opportunity rests partly on the country’s access to energy, land, capital, and connectivity, as well as its ambition to develop locally relevant AI products. 

The latest deals: Infrastructure, models, and real-world applications

HUMAIN’s recent announcements show how its strategy is expanding from building computing capacity toward attracting customers, developing products, and investing in technologies with global markets.

One of the most significant developments came on August 31, 2026, when HUMAIN, AMD, and Cisco announced that AI computing infrastructure powered by AMD Instinct MI355X GPUs, AMD EPYC CPUs, and Cisco Silicon One networking was live in Saudi Arabia and serving customers.

The companies also outlined plans to deploy up to 250 megawatts (MW) of additional AI infrastructure in the next phase, beginning in 2027. Their joint venture remains on track to deploy up to 1 GW of AI infrastructure by 2030, according to the announcement. 

The commercial significance is that HUMAIN is beginning to demonstrate operational computing services rather than relying solely on future capacity announcements. Production workloads can help establish whether its infrastructure meets customer requirements for performance, availability, and cost. They can also generate demand data that informs subsequent investment decisions.

Another major development involves DataVolt, a Saudi-based developer and operator of sustainable digital infrastructure. On August 31, the companies announced an expanded partnership to develop 100 MW of an AI-ready data center project at Oxagon, NEOM’s industrial city, as part of a 360 MW first phase of DataVolt’s planned campus. Construction was reported to be underway. 

The project illustrates the importance of partnerships in delivering AI infrastructure. Data center development requires land, power connections, cooling systems, construction expertise, and specialized equipment, in addition to computing hardware. Sharing responsibilities across infrastructure and technology partners can help distribute the cost and execution risks of a capital-intensive buildout.

HUMAIN is also pursuing partnerships that connect infrastructure with the software layer. In August 2026, Microsoft and HUMAIN announced the first milestone in a long-term strategic collaboration focused on bringing HUMAIN’s ALLAM models into Microsoft’s AI ecosystem, including Microsoft Foundry and the Microsoft 365 Copilot ecosystem. The collaboration also envisages joint work between HUMAIN specialists and Microsoft’s Forward Deployed Engineers to identify and deploy AI solutions for organizations. 

The arrangement could help HUMAIN reach enterprise customers through an established global software ecosystem. It also highlights a commercial challenge: developing an AI model is only one part of building a sustainable business. Models must be integrated into workflows, connected to organizational data, secured, and adapted to the needs of individual customers.

A further partnership with French AI developer Mistral AI, announced on August 24, 2026, focuses on infrastructure, advanced model development, and AI deployment in Saudi Arabia and the wider region. Initial areas of interest include cybersecurity and voice applications, alongside the development and localization of advanced models with strong Arabic-language performance. The companies described the collaboration as involving hundreds of millions of euros and said Mistral would explore using HUMAIN’s data center infrastructure. 

These partnerships serve different purposes. AMD and Cisco support computing infrastructure; DataVolt supports data center development; Microsoft provides a route into enterprise software; and Mistral brings model-development capabilities. Together, they suggest that HUMAIN is seeking to build a connected commercial ecosystem rather than a collection of isolated technology projects.

The $3 billion xAI investment and HUMAIN’s global ambitions

HUMAIN’s investment strategy extends beyond infrastructure partnerships. In February 2026, the company announced a $3 billion investment in xAI’s Series E financing round, becoming a significant minority shareholder. Following xAI’s acquisition by SpaceX, HUMAIN’s holdings were converted into SpaceX shares, according to the company’s announcement. 

The investment builds on an earlier agreement between HUMAIN and xAI to develop more than 500 MW of AI data center capacity in Saudi Arabia and deploy Grok across the Kingdom. The partnership links access to advanced AI models with the development of domestic computing infrastructure. 

The $3 billion investment is particularly notable because it demonstrates that HUMAIN is prepared to deploy capital into international AI platforms, not just Saudi-based assets. It could provide financial exposure to the growth of a major technology business while strengthening the relationship between HUMAIN and a partner involved in advanced AI models and large-scale computing.

However, the transaction should be distinguished from direct investment in Saudi infrastructure. An equity investment in a global technology company does not automatically create domestic computing capacity, jobs, or technology transfer. Those benefits depend on the associated commercial arrangements, local operations, and the extent to which the partnership creates opportunities for Saudi engineers and businesses.

HUMAIN has also been expanding its investment plans. In September 2026, Semafor reported that Amin intended to launch a large global venture capital fund, potentially larger than a previously contemplated $10 billion vehicle. According to the report, the fund would have offices in Saudi Arabia, the United States, France, and the United Kingdom. Amin told the publication: “We don’t do passive investments; that will never happen.” 

The reported fund would focus on investments that contribute to HUMAIN’s strategic objectives, including companies willing to use Saudi data centers for some of their computing operations or bring staff to the Kingdom. If implemented on the scale reported, this could give HUMAIN a role in shaping where AI companies locate their computing workloads, technical teams, and commercial activities.

The fund remains a reported plan rather than a confirmed, fully capitalized investment vehicle. Its eventual size, investment mandate, and launch timetable will be important indicators of whether HUMAIN intends to become a major global AI investor alongside its infrastructure and software businesses.

Data centers: The backbone of HUMAIN’s expansion

Data centers sit at the center of HUMAIN’s business model because advanced AI requires large amounts of computing power. Training sophisticated models, operating generative AI applications, and serving large numbers of users all depend on access to specialized processors and the infrastructure needed to operate them reliably.

Saudi Arabia has been expanding its broader digital infrastructure base. According to the Saudi Press Agency, operational data center capacity rose from 68 MW in 2021 to more than 440 MW in 2025, while the Kingdom hosted more than 60 data centers developed by over 20 companies, with investments exceeding SAR16 billion. 

HUMAIN’s targets would represent a substantial expansion beyond that existing market. Its October 2026 target of 1.7 GW in 2027, followed by approximately 3 GW by 2030 and 6 GW by 2034, would require a combination of new facilities, computing hardware, power infrastructure, cooling systems, networking, and long-term customer demand. 

The cost implications are considerable. EnterpriseAM reported that Amin estimated AI data center capacity typically costs between $10 million and $12 million per MW. Applying that benchmark to 1.45 GW of additional capacity—the difference between 250 MW and a 1.7 GW target—would imply roughly $14.5 billion to $17.4 billion in infrastructure costs. This is an illustrative calculation, not a company-announced budget; actual costs would depend on project scope, equipment, financing, and what is included in the capacity target. 

The company has already taken steps to establish financing channels. In January 2026, HUMAIN and Saudi Arabia’s National Infrastructure Fund (Infra) announced a strategic financing framework of up to $1.2 billion to support AI and digital infrastructure projects. The non-binding framework covers the development of up to 250 MW of hyperscale AI data center capacity. The parties also agreed to explore an AI data center investment platform designed to facilitate participation by domestic and international institutional investors. 

Separately, Bloomberg reported in September 2026 that HUMAIN was exploring raising an initial $2.5 billion from global and domestic investors for a data center fund. The reported fund would support a 250 MW project being developed with Al Moammar Information Systems and could eventually expand its capacity. This remains a reported financing plan, distinct from the Infra framework. 

The financing challenge is therefore not simply how much capital HUMAIN can attract, but how it structures that capital across projects with different risk profiles. Equity, debt, institutional investment, joint ventures, and long-term customer contracts can all play a role. The critical factor will be whether projected demand and operating revenue justify the cost of building capacity at the pace the company has outlined.

The energy advantage—and the infrastructure constraints

Saudi Arabia’s energy resources are central to its proposition as a global AI computing destination. Data centers require reliable electricity around the clock, and AI workloads can create particularly high power and cooling requirements. The availability and cost of electricity can therefore influence the competitiveness of a data center as directly as its computing hardware.

In an interview published in October 2026, Amin described HUMAIN’s network of AI campuses as spanning approximately 65 square kilometers and backed by 14 GW of grid capacity. He argued that the Kingdom’s combination of energy, land, connectivity, and the ability to deploy infrastructure at scale gives it a competitive advantage. 

But available power capacity is not the same as operational data center capacity. Facilities must secure grid connections, cooling, networking, permits, equipment, and construction schedules. They must also be designed to maintain service reliability as workloads and hardware requirements evolve.

The economic opportunity extends beyond hosting servers. Building a domestic AI infrastructure industry can create demand for engineering, construction, cooling, power management, cybersecurity, and equipment maintenance. The PIF’s HUMAIN portfolio page also identifies local manufacturing opportunities in data center cooling equipment, power equipment, and server racks. 

Localizing these supply chains could help retain more of the economic value generated by infrastructure investment. Without that development, a large share of the expenditure could flow to international hardware and technology suppliers, leaving Saudi Arabia primarily as the location where the equipment operates.

HUMAIN will therefore need to balance rapid deployment with efforts to develop domestic suppliers, technical capabilities, and more efficient operations. Energy availability is a competitive advantage, but converting it into a lasting industrial advantage requires more than access to electricity.

Closing gaps in Arabic AI, sovereign computing, and enterprise adoption

HUMAIN’s ambitions are not limited to data centers. Its model strategy addresses another gap: the need for advanced AI systems that perform well in Arabic and reflect the language’s linguistic, cultural, and commercial context.

Arabic presents challenges for AI developers because of its regional dialects, morphological complexity, and the differences between formal written Arabic and everyday speech. Organizations also need models that can work with sector-specific terminology and sensitive data while meeting local requirements for security, privacy, and governance.

HUMAIN’s ALLAM models, developed in collaboration with the Saudi Data and AI Authority (SDAIA), are intended to address Arabic-language needs. In August 2026, Microsoft and HUMAIN announced plans to bring ALLAM into Microsoft’s AI ecosystem, potentially giving organizations a more direct route to use the models in enterprise applications. 

In September 2026, HUMAIN also unveiled humain-m3, an Arabic-language model commissioned by HUMAIN and developed by MiniMax. According to the announcement, the model has 428 billion parameters, uses a mixture-of-experts architecture, and was further pre-trained on more than one trillion tokens of Arabic-native content. It entered research preview through HUMAIN Node, the company’s platform for developers, researchers, and enterprises seeking access to AI models and inference capabilities. HUMAIN reported that the model achieved the highest average performance among the frontier models evaluated across seven public Arabic benchmarks. That performance claim is based on the company’s announcement and should be assessed alongside independent evaluations and real-world deployments. 

This illustrates HUMAIN’s hybrid approach: developing local AI capabilities while working with international model developers. The objective is not necessarily to build every component from scratch, but to use partnerships to accelerate development while retaining the ability to customize, deploy, and commercialize AI for Saudi and regional needs.

Another gap concerns the transition from experimentation to routine business use. Companies may have access to AI tools but lack the technical expertise, secure data environments, or internal processes required to deploy them effectively. HUMAIN’s collaboration with Microsoft is designed partly to address this challenge through direct engineering support for organizations identifying and implementing AI use cases. 

Its enterprise operating system, HUMAIN ONE, also reflects a shift toward AI agents: software systems designed to carry out tasks and coordinate workflows rather than simply generate text or answer questions. In May 2026, HUMAIN announced an expanded collaboration with AWS around HUMAIN ONE, aiming to support organizations in building, deploying, and governing autonomous AI agents at scale. 

The commercial test will be whether these products deliver measurable improvements in cost, productivity, service quality, and decision-making. A strong model or platform is not enough on its own; it must solve a problem that customers consider important enough to fund.

Beyond the data center: Physical AI and industrial applications

HUMAIN’s next stage could also involve extending AI from digital services into physical operations. This is important for an economy with substantial activity in energy, logistics, manufacturing, mining, and large infrastructure projects.

On August 31, 2026, HUMAIN and Applied Intuition announced a strategic collaboration to deploy physical AI in Saudi Arabia, beginning with autonomous trucking. The companies said they planned to build a national-scale autonomous logistics network, with thousands of autonomous trucks operating across key Saudi freight corridors by 2030. The partnership could later extend into robotaxis, ports, mining, manufacturing, agriculture, and construction. 

The collaboration links HUMAIN’s AI infrastructure with Applied Intuition’s autonomy technology. Its potential economic impact lies in applications such as increasing fleet utilization, improving freight capacity, reducing operating costs, and supporting logistics networks that can operate more efficiently.

The timeline and scale are ambitious, and the announced target should not be mistaken for an already operational national fleet. Deployment will depend on technical performance, road and logistics infrastructure, safety standards, regulation, and commercial economics.

Still, the move signals that HUMAIN is seeking opportunities where AI can influence physical productivity, not only digital services. If such applications prove commercially viable, they could help connect Saudi AI investment to improvements in existing industries rather than treating AI as a separate technology sector.

What comes after Vision 2030?

HUMAIN’s immediate targets are tied to the period leading up to 2030, but its 6 GW capacity ambition by 2034 points to a strategy extending beyond the Vision 2030 horizon. The company’s longer-term objective appears to be building a Saudi-based AI industry capable of serving customers globally, supported by infrastructure, models, software, and investment. 

For that ambition to become economically sustainable, several priorities will matter.

First, infrastructure must be matched with demand. Data centers are capital-intensive assets, and unused computing capacity can weaken returns. Long-term contracts, diversified customers, and strong utilization rates will be essential to support the expansion.

Second, HUMAIN must turn partnerships into capabilities that can be developed and commercialized locally. International technology providers can accelerate access to chips, models, and software, but the long-term objective should include Saudi expertise in system design, AI engineering, product development, and intellectual property.

Third, the company will need to demonstrate that its AI products can compete on performance, reliability, security, and cost. Arabic-language models and locally hosted infrastructure may provide differentiation, but their value will ultimately depend on the quality of the products and the problems they solve.

Fourth, financing needs to remain aligned with commercial returns. The $1.2 billion Infra framework, the reported $2.5 billion data center fund, and plans for a much larger global venture capital vehicle represent different funding channels and ambitions. They should not be treated as one consolidated investment commitment. HUMAIN has not publicly established a single, fully funded budget covering all its announced capacity targets and investment plans.

Finally, Saudi Arabia will need to deepen its talent base and domestic supply chain. Data centers can be built with international equipment, and models can be developed with global partners, but a competitive AI economy requires engineers, researchers, entrepreneurs, and product teams able to create and improve technology over time.

In a recent interview, Amin described the broader ambition in terms of building an industry from Saudi Arabia rather than merely bringing AI into the country. “Our ambition is not simply to bring AI into Saudi Arabia. It is to build an AI industry from Saudi Arabia that can compete globally,” he said. 

That distinction captures the economic stakes. If HUMAIN succeeds, Saudi Arabia could become more than a market for imported AI products or a location for international data centers. It could develop into a producer and exporter of computing services, Arabic AI models, enterprise applications, and industrial AI solutions.

If the company’s expansion is driven mainly by capacity targets without equivalent progress in customer demand, local capabilities, and commercially successful products, the economic returns could fall short of the investment scale.

The next phase will therefore be measured not only in megawatts and gigawatts, but also in contracted workloads, paying customers, productivity gains, locally developed intellectual property, and technology exports. Those outcomes will determine whether HUMAIN becomes a lasting pillar of Saudi Arabia’s post-2030 economy—or primarily an ambitious infrastructure platform in a rapidly changing global AI market.

Ghada Ismail

As artificial intelligence reshapes the business landscape, founders are rethinking how they scale their companies, improve efficiency, and respond to market uncertainty. In an interview with Sharikat Mubasher, Benjamin Owen, Business Coach, discusses how AI is creating new opportunities for entrepreneurs, what distinguishes founders in the GCC from those in other markets, and why building a self-managing company requires more than technological innovation. He also shares his views on leadership, talent, and the qualities that will define successful businesses over the next five years.

How has the current business environment changed the way founders think about growth?

Current technological shifts have empowered business owners to do more with less. A true 10x transformation in a business can only be achieved through technological innovation or by creating a new world for customers. AI has given founders the power to do both.

Founders now have more opportunities to do less while achieving better results, thanks to AI, disruptive technology, and, somewhat unfortunately, geopolitical disruption. Sometimes, a scaling company needs to disrupt its own established ways of working; other times, change is forced upon it by the economy. My opinion is that founders need to disrupt themselves before the market does.

Has the AI boom created too many startups chasing the same opportunity?

In my opinion, there is plenty of room for competition in any market. Founders can choose which part of the market they want to serve and become a leader in. The surge in AI startups is simply a signal that plenty of opportunities are available. The technology is advancing so quickly that many of the ideas, products, and services being created today will become obsolete or significantly better within a matter of months.

Are AI startups solving real problems or simply adding AI to existing products?

I believe AI startups are both solving problems and adding AI to existing products. Integrating AI into certain existing products can solve significant problems and create tremendous value for many people.

One of the partners in my mastermind group has an app called Talktokaya, which provides a safe space for people to talk to a virtual companion when they feel lonely. It is exceptional and is genuinely helping people who have nobody to turn to in moments of need. It is a transformational AI companion.

What stands out to you about GCC founders compared with founders in other markets?

What stands out about GCC founders is their strong abundance and growth mindset. Perhaps I can explain this by contrasting it with the UK, where I began my business journey. There, it sometimes felt as though the more successful I became, the heavier the burden I carried because of the taxation system.

Another sentiment my clients report is the desire here to see one another succeed. This is a stark contrast to the experience in some other countries.

What role can AI play in helping companies in the GCC region scale faster?

The GCC is one of the fastest-evolving regions in the world, and AI gives founders access to significant leverage, data, and resources. It can also make testing ideas and exploring different approaches much easier.

I am a strong advocate of the idea that the founders who win are those who test and iterate the fastest. AI is perhaps the most effective tool for this process.

How can founders build companies that operate without them?

Building a self-managing company is really about operationalizing an 80/20 cycle in your business. This means finding the right people, processes, and platforms to execute the work, allowing you to elevate yourself out of the day-to-day running of the business.

It is also essential to start with the end in mind. Many founders build themselves into their company's processes. They become the key decision-makers, hold on to roles and tasks for too long, and prioritize work over leverage.

Building a self-managing company is not just about creating a leveraged model. It is also about having a sufficiently ambitious goal to attract individuals with the highest level of talent, who can drive the business beyond the founder's own capabilities.

What mistakes do companies make when responding to times of uncertainty?

There is a distinction I work on with my clients that I consider fundamental to coaching: the difference between being a victim and being an owner.

A victim sees themselves as subject to circumstances and reacts to what is happening. An owner takes responsibility for their state of mind and chooses how to respond to a situation.

I believe the mistake people in companies make during uncertain times is becoming reactive or allowing themselves to become victims of uncertainty.

Leaders can also fall into the "when-then" paradox, waiting for circumstances to change before taking action.

What do you think the strongest companies will look like five years from now?

Five years from now, the strongest companies will be those with goals that attract the best talent, cultures built around a strong sense of purpose, and the ability to create a new world for their customers.

I believe the defining trait of the strongest companies will be their ability to achieve outsized results with small teams. They will have leveraged technological innovation so effectively that they can make a disproportionate impact relative to their size.

Noha Gad

The small and medium-sized enterprises (SMEs) sector in Saudi Arabia is becoming an important part of the national economy, driven by Vision 2030’s objective of increasing SMEs' contribution to the gross domestic product (GDP) to 35% by 2030. According to the ‘Vision 2030 Annual Report 2025,’ SMEs' contribution to the national GDP reached 22.9% in 2024, surpassing the 2024 target of 21.1%. This milestone, the report said, was supported by continued expansion in non-oil activity and firm domestic demand. With more than 1.7 million SMEs in the Kingdom employing nearly 8.8 million people, the SME sector has become a key driver of private sector growth, backed by a stronger and more accessible business ecosystem.

Artificial intelligence (AI) has reinforced the role and contribution of SMEs to the national economy by offering practical ways to work more efficiently and compete in a fast-changing market, from handling routine tasks to improving customer service and supporting business decisions. Thus, conversations shifted from whether SMEs should use AI to how they can apply it to real business needs. This was backed by the efforts of government entities, such as the Small and Medium Enterprises General Authority (Monsha’at), the Saudi AI and Data Authority (SDAIA), and others, aimed at connecting entrepreneurs with practical applications and supporting the shift from learning about AI to using it in the workplace

Today, most SMEs in Saudi Arabia integrate AI into their operations rather than adopting complex systems, as the value of AI lies in its ability to help businesses automate routine work, understand customers better, make faster decisions, and manage resources more efficiently. When used carefully and responsibly, AI tools can help owners and employees focus on higher-value activities such as serving customers, improving products, and growing the business.

How could AI boost SMEs’ business?

Automating routine tasks

AI can handle repetitive administrative work, such as drafting emails, summarizing documents, preparing reports, or responding to common customer questions. This can reduce manual work and help SMEs handle more activity without necessarily increasing headcount. Generative AI can also help in transcribing text or generating images and videos, freeing up people to focus on more complex and critical decisions.

Improving customer service, communication, and marketing

AI-powered chatbots and virtual assistants can respond to customers any time, provide basic information, and direct more complicated requests to the right employee. This can help SMEs offer faster responses and maintain better service quality, especially when staff numbers are limited. Deploying AI in areas like content creation, digital avatars, optimization of bandwidth for video conferencing, and the formulation of sophisticated virtual collaboration environments can drive enhanced communication and cooperation.

For marketing, AI tools can help SMEs draft social-media posts, product descriptions, and customer messages; however, they must revise AI-generated content before publishing to ensure it is accurate, clear, and consistent with their brand.

Taking informed decisions

Many SMEs collect data through sales records, invoices, websites, and customer interactions, but do not always have the time to analyze it. AI and data-analytics tools can assist in making decisions by analyzing and summarizing multi-format data, such as text, audio, and video, and simulating scenarios that could impact strategic choices. This capability provides an additional layer of information, fostering a data-driven approach to decision-making in diverse areas such as crisis management, healthcare, and education.

Managing inventory and supply chains

SMEs can use AI to forecast demand, monitor stock levels, and identify possible delays before they affect customers. This is particularly useful for businesses that sell physical products, where excess inventory ties up cash and shortages can lead to lost sales.

Eliminating errors and reducing costs

Manual processes can be slow and prone to mistakes, especially in areas such as data entry, invoicing, scheduling, and record-keeping. AI-supported automation can help reduce these errors by applying consistent rules across repeated tasks. This will enable employees to spend more time and effort on activities that require experience, creativity, or direct customer contact.

Providing new products and services

SMEs can leverage AI tools to build new applications or services. For instance, retail businesses could use it to personalize offers or improve online search. In some cases, AI becomes part of the product itself rather than simply an internal tool. 

Enhanced accessibility and inclusion

AI solutions can produce materials adapted for individuals with special needs or disabilities, ensuring that platforms and communications are universally accessible. Also, generating content that aligns with local dialects and traditions promotes clear communication between people from diverse backgrounds.

Government support

Saudi Arabia is building an environment that encourages SMEs to understand, access, and use AI effectively and responsibly. This approach combines national strategy and guidance from SDAIA with practical support, training, and business-development programs from Monsha’at. The aim is not simply to promote technology, but to help businesses apply AI in ways that improve efficiency, competitiveness, and long-term growth.

Driving the Kingdom’s national agenda for data and AI, SDAIA’s National Strategy for Data and AI aims to develop and adopt data and AI technologies across Saudi Arabia, thereby reinforcing the Kingdom’s position as a global hub for practical, sustainable, and ethical AI applications. The strategy focuses on strengthening the AI ecosystem, developing local talent, improving data governance, supporting entrepreneurship, and encouraging investment. The authority also launched the official guidelines for adopting AI, helping SMEs achieve outcomes that depend heavily on the quality and security of the data used.

On the other hand, Monsha’at is committed to making AI more accessible to entrepreneurs and SME owners. In August, it launched the AI Week, in partnership with SDAIA, to help SMEs adopt AI technologies, explore practical solutions, and connect with technology providers and ecosystem enablers.

The authority also launched the AI Incubator Program to support AI-focused startups through various workspaces, incubation facilities, mentoring, workshops, training, and access to technology experts.

To prepare Saudi talent and businesses to use technology effectively, the Saudi government launched different initiatives, notably SDAIA’s National Occupational Standard Framework for Data and AI. This initiative provided a reference for developing AI-related career pathways, job descriptions, workforce planning, and professional development, ultimately creating a more consistent skills base for organizations seeking to hire or train AI-capable staff.

Main barriers

Although AI can offer clear operational benefits, SMEs face various practical barriers when adopting it. These challenges are not only technical; they also relate to:

• Cost and returns. For small businesses, the cost of adopting AI tools, software subscriptions, cloud services, training, and technical support can be difficult to justify, especially when the expected return is unclear. Some tools may require integration with existing systems, which can add further expense.
• Skills and internal capacity. AI adoption requires people who can use the tools, interpret the results, and identify when an output is incorrect or unsuitable. Many SMEs may not have in-house data analysts, IT specialists, or employees with experience in AI.
• Data quality. AI systems depend on data to produce useful results. If an SME’s sales records, customer information, or operational data are incomplete, outdated, or inconsistent, the AI output may be inaccurate or misleading.
• Privacy and data security risks. AI tools can create risks if employees enter sensitive business, employee, or customer information into external platforms. In its guidelines, SDAIA advised organizations to establish clear policies on AI use, prohibit the entry of classified or sensitive information into third-party tools, and train employees on data governance, cybersecurity, and personal-data protection. 
• Biased outputs. AI tools can generate inaccurate, outdated, or biased information. Therefore, SMEs should keep human review in place, particularly for customer communications, pricing decisions, hiring-related tasks, or any output that affects customers.
• Intellectual property and legal compliance. SMEs using generative AI should also consider intellectual-property risks. AI-generated content may unintentionally resemble copyrighted material, while businesses may face questions about ownership and permitted use of the content they create.

Finally, the question facing SMEs in Saudi Arabia is no longer whether AI matters, but whether they can turn it into a lasting competitive advantage. The technology will not automatically raise productivity or expand market share; its impact will depend on how each business chooses, applies. and manages it. The next phase of adoption will likely reward SMEs that think like builders rather than buyers. Instead of asking, “Which AI tool should we purchase?” they should ask, “Which part of our business needs improvement, and can AI solve it better than our current process?” That shift can turn AI from a collection of separate applications into a practical capability embedded in how the business operates.

For Saudi Arabia, the opportunity lies in building a broad base of SMEs that are not merely using AI tools, but using them well. If smaller businesses can transform AI into faster service, sharper decisions, and more scalable operations, the sector’s contribution to GDP could grow in a sustainable way, backed not by technology alone, but by stronger, more capable enterprises.

Ghada Ismail

Artificial intelligence may be powered by chips and cloud infrastructure, but the quality of what AI produces increasingly depends on something much less visible: the data used to train it.

Data annotation, the process of labeling, classifying, transcribing, and evaluating data so that AI models can learn from it, is emerging as an important layer of the AI economy. In Saudi Arabia, the opportunity is particularly significant as the Kingdom invests heavily in sovereign AI infrastructure, Arabic-language models and the development of local digital talent.

“The AI race has moved from who has the most data to who has the best data,” said Sayed A., Chief Business Officer at Graystone Capital. “Data annotation is no longer back-office work. It is where model quality is decided.”

The shift is already visible globally. In 2025, Meta invested $14.3 billion for a 49% stake in Scale AI, the data-labeling company founded by Alexandr Wang, valuing the company at more than $29 billion. The deal underscored the growing strategic importance of high-quality training data as major technology companies compete to improve their AI models.

For Saudi Arabia, the opportunity is not simply to provide lower-cost labeling services. It is to build specialized data capabilities around Arabic, Saudi dialects, regulated industries and local cultural context.

From labeling images to training intelligence

At its simplest, annotation means adding information to raw data so an AI system can understand it. An image might be labeled to identify a vehicle or pedestrian. Audio can be transcribed and tagged according to speakers, accents or intent. Text can be classified according to sentiment, subject or meaning.

The role has expanded considerably with generative AI. Human workers can now compare AI-generated answers, identify inaccurate responses, write high-quality examples, assess safety risks, and help models understand specific professional or cultural contexts. This work is often referred to as human feedback, model evaluation, or reinforcement learning from human feedback.

That evolution is changing the value of the industry.

“Leading AI labs have largely used up the easily available text on the internet,” Sayed A. said. “Models now improve mainly through expert human judgment: doctors, lawyers, engineers and finance professionals who can tell a model when its answer is wrong.”

This matters because AI models can produce fluent answers while still making serious mistakes. A general annotator may identify whether an answer is grammatically correct, but a doctor may recognize a dangerous medical recommendation, while a banking professional can identify an incorrect interpretation of a financial rule.

The next generation of annotation businesses is therefore moving beyond volume toward expertise.

Why Saudi Arabia has a data advantage

Saudi Arabia's AI ambitions make this shift particularly relevant.

The Kingdom is building large-scale computing and AI infrastructure through initiatives involving HUMAIN, while the wider GCC is committing billions of dollars to AI infrastructure. Yet computing power alone cannot solve the data problem.

“Compute without quality data is an empty factory,” Sayed A. said.

The challenge is especially clear in Arabic. The language is used daily by more than 400 million people, according to UNESCO, but Arabic represents only 0.6% of websites whose content language is known, according to W3Techs' October 2026 data.

More importantly, Arabic is not a single linguistic market. Models have to deal with Modern Standard Arabic as well as regional varieties, slang, code-switching, and cultural references.

This creates an opening for Saudi companies that can produce data reflecting how people actually speak and communicate in the Kingdom. Sayed A. says: "The Gulf has committed to world-class computing power, from Stargate UAE in Abu Dhabi to HUMAIN in Saudi Arabia. But compute without quality data is an empty factory. Arabic is used daily by more than 400 million people, yet it makes up less than one percent of websites, and it spans more than 30 dialects that standard web data captures poorly. The region's leading models have all had to invest heavily in native, expert-reviewed data. Jais 2 was trained on 600 billion Arabic tokens, and HUMAIN's ALLaM model was refined with more than 600 domain experts and 250 evaluators. Curated Arabic data is a strategic asset, and the GCC is better placed than anyone to build it."

That process illustrates why data quality is becoming a strategic asset rather than a technical afterthought.

A small but growing Saudi startup ecosystem

Saudi Arabia's emerging annotation market includes companies pursuing different parts of the data value chain.

Annota8, a Riyadh-based startup, is developing an annotation platform covering image, video, text, audio and speech, documents and OCR, RAG, and large language model and agent workflows. Its operating layer supports project setup, task assignment, quality review, progress monitoring and data export. The company describes itself as Arabic-first and built in Saudi Arabia. It was also selected for the 500 Global Sanabil Startup Unlocked Program.

Tawsym is a Saudi startup specializing in AI training data and data annotation. Its services cover text, audio, image and video annotation, with a focus on language and cultural characteristics relevant to the Saudi market. The company says it works in partnership with the National Technology Development Program (NTDP) and Monsha'at's AI incubation program, and offers data hosting within Saudi Arabia to meet local data-storage requirements.

Bayanat Labs, based in Riyadh, focuses on Arabic data annotation, collection, alignment and evaluation across text, audio, image, and video. The company uses vetted native speakers, linguists and licensed domain professionals, while offering in-region data hosting by default. Its approach reflects demand for Arabic data that captures local linguistic differences while meeting the data-handling requirements of regulated sectors.

The emergence of these companies comes as Saudi Arabia's broader AI startup ecosystem expands. In June 2026, Monsha'at announced the graduation of 33 AI companies from its first AI incubator cohort, supported through a strategic partnership with the National Technology Development Program.

The gap between AI adoption and AI scale

Demand could increase as Saudi businesses move from experimenting with AI to deploying it across everyday operations.

A 2025 McKinsey survey found that 84% of GCC organizations were using AI in at least one business function, up from 62% in 2023. Yet only 31% had reached a level of AI maturity where AI was being scaled or fully deployed across the organization. That gap is important for data companies.

“GCC companies have embraced AI, but scaling it is another matter,” Sayed A. said. “The gap between pilot and scale is largely a data problem.”

A model that performs well in a demonstration may struggle when exposed to real customer conversations, local terminology, unusual cases or sensitive decisions. Businesses therefore need continuously updated datasets, evaluation systems and human review rather than a one-time training exercise.

This could create recurring demand for Saudi data companies, particularly in sectors where AI is moving into operational decision-making.

Where the biggest opportunities may lie

For Saudi startups, the strongest opportunities are unlikely to come from competing solely on the price of basic labeling.

Specialized Arabic dialects are one area. Saudi speech data, regional expressions, and conversational language are difficult to reproduce through generic global datasets.

Another opportunity is domain-specific annotation. Legal, healthcare, financial, and Islamic finance datasets require people who understand the underlying subject matter, not simply workers following basic labeling instructions.

Sayed A. sees the same potential across the GCC.

“We see a real opening for GCC-based providers of specialized, compliant annotation in areas such as Arabic dialects, Islamic finance, legal and healthcare, where local expertise and in-country data handling are advantages,” he said.

The investment environment is also becoming more supportive. AI startups in MENA raised $858 million in 2025, accounting for 22% of total VC funding in the region, according to MAGNiTT. The UAE and Saudi Arabia together absorbed 87% of AI funding.

For Saudi founders, this creates an opportunity to build an industry that sits underneath the more visible AI applications.

The human side of the AI data economy

Yet the sector faces its own challenges.

Basic labeling is increasingly being automated, putting pressure on companies whose business models depend on simple, repetitive tasks. Global providers also have enormous datasets, established customers, and large workforces.

At the same time, the treatment of annotation workers has become a major issue internationally. Low pay, inconsistent work and weak labor protections can create both ethical and reputational risks.

“The opportunity comes with hurdles,” Sayed A. said. “Automation is steadily commoditizing basic labelling, and global players are already setting up in the Gulf.”

That makes quality and trust increasingly important. Companies handling medical records, banking information or government datasets need strong controls around privacy, security, access and provenance.

Saudi Arabia’s focus on developing AI talent could provide another advantage. Under its National Strategy for Data and Artificial Intelligence, the Kingdom aims to develop 20,000 data and AI specialists and experts by 2030. Annotation could provide one route into that ecosystem, particularly as the work expands from basic labeling toward linguistics, quality assurance, model evaluation, red teaming and domain expertise.

From data labeling to strategic infrastructure

The biggest change may therefore be in how annotation itself is perceived.

What began as a labor-intensive task sitting behind the AI industry is becoming part of the model-development stack. The best providers are no longer simply asking people to label thousands of images or text samples. They are building systems for collecting, validating, evaluating, and governing the data that AI systems depend on.

For Saudi Arabia, that distinction matters.

The Kingdom has already invested heavily in computing capacity, AI models and digital infrastructure. The next layer is the human infrastructure that makes those systems useful in the local market.

“The winners will compete on expertise and trust, not on the lowest price per label,” Sayed A. said.

That may ultimately be the most important opportunity for Saudi Arabia's data annotation startups. Rather than becoming another low-cost outsourcing market, the Kingdom can build a specialized data industry around something global AI systems increasingly need but cannot easily manufacture themselves: high-quality human expertise rooted in the language, industries and culture of the region.

Ghada Ismail

In the final part of our interview, Abu Zannad turns to Saudi startups with international ambitions and closes with the one piece of advertising history he’d want every founder to know before they spend their first marketing riyal.

As Saudi startups look to expand internationally, how important is it to adapt their brand and messaging to different markets without losing their original identity?

“I think the wrong question for a Saudi startup is: “How much of our Saudi identity should we keep when we go abroad?”

The more useful question is: “What did being built in Saudi Arabia teach us that could make us more valuable somewhere else?”

Because origin by itself is not a strategy. It is raw material for a strategy.

And I think Saudi startups should be increasingly confident about this. The ecosystem has changed enormously. Saudi Arabia recorded $1.72 billion in venture-capital investment across 257 deals in 2025, the highest levels the market has seen. We are no longer only asking whether globally competitive startups can be created in Saudi Arabia. Increasingly, we are asking which of them can travel.

But travelling does not mean becoming culturally anonymous. The strongest global brands rarely erase where they came from. They understand what should travel intact and what needs to be translated.

I would ask a Saudi founder to think about four things.

First:

What is your Saudi core?

Not the flag. Not Arabic typography. Not putting a palm tree into the identity.

What capability, insight or sensibility did growing up inside this market actually give you?

Maybe you learned to design technology for Arabic-speaking consumers rather than adapting English technology afterwards. Maybe you became unusually good at operating in regulated and fragmented environments. Maybe your understanding of hospitality produced a different service standard. Maybe you grew up around a culture of family commerce and understood social selling differently. Maybe rapid transformation in the Kingdom taught your company to operate at a pace and scale that companies from more settled markets are not accustomed to. Maybe there is something in Saudi food, design, beauty, gaming, tourism, fintech or culture that the rest of the world has not encountered in this form before.

But the discipline is important:

Don’t ask what is Saudi about us. Ask 1-what is uniquely Saudi about us that is useful to somebody else.

Unifonic is an interesting example. It began in Saudi Arabia solving the difficult reality of reliable communications in fragmented, regulated markets and across Arabic language environments. Today it describes cultural fluency, trust and AI-native customer experience as part of its proposition. Something learned locally became a capability that could travel.

Foodics is another Saudi-born company that took its restaurant technology beyond the Kingdom into markets including the UAE and Egypt. The transferable asset was not “Saudi-ness” as decoration. It was a solution developed inside a sophisticated regional F&B environment that was relevant to restaurant operators elsewhere.

That brings me to the second question: 2-What are the qualifying factors in the market you are entering?

Strategists sometimes call these points of parity.

These are the things you have to get right simply to be taken seriously. If I enter Germany, Singapore, the UAE or Britain, what does the category expect? What is the regulatory standard? What does good customer service mean? What payment behaviour exists? What is the expected delivery time? What does trust look like? Which features are simply assumed? What tone belongs on the platform? What does the audience consider credible?

These are not necessarily reasons someone will choose you. They are the price of admission. A Saudi fintech company cannot enter another market saying, “We are proudly Saudi,” while failing to understand its financial regulation. A consumer startup cannot insist that its Saudi customer journey must be reproduced exactly in London.

Identity is not an excuse for irrelevance. You earn the right to be different only after you have demonstrated that you belong in the category.

Then comes the third question: 3-What are your winning factors?

These are your points of difference. Once I believe you can perform the basic job as well as the alternatives, why should I choose you? This is where Saudi origin can become strategically interesting. What can you offer that the incumbent cannot easily copy? A product insight? A technology? A cultural understanding? A design sensibility? A service model? A community? A particular form of hospitality? Access to a new cultural world? A way of solving complexity that your home market forced you to learn?

So I would separate very clearly:

Qualifying factors get you into the consideration set.
Winning factors give people a reason to choose you.

And both have to be understood at several levels: the market, the category, the audience, and increasingly, the platform.

Something that makes you distinctive on TikTok may be irrelevant in enterprise sales. Something that wins in Saudi food culture may need a different cultural translation in Paris. Something that works in B2C may signal the wrong things entirely in B2B.

So the identity stays coherent. The expression adapts.

This is where I think the national Saudi Made brand offers a very useful lesson. The program was deliberately created as a unified identity for Saudi products and services in domestic and international markets, and from the beginning it has been associated with quality, competitiveness, credibility and excellence. More recently that architecture has expanded: there is a Saudi Tech label supporting technology companies abroad, while the Saudi Crafts identity has been explicitly built around creativity, authenticity and quality presented in a contemporary way.

I find that combination interesting.

Quality and innovation on one side.
Authenticity and cultural confidence on the other.

Saudi Arabia does not have to choose between heritage and modernity. In many ways, the interesting story of Saudi Arabia today is precisely the tension between the two. That is something Saudi startups can borrow from; not necessarily the Saudi Made logo itself, because that has eligibility requirements, but the larger idea of what Saudi provenance can begin to mean.

A Saudi startup going abroad should ask:

4-What does “from Saudi Arabia” add to this particular proposition?

Sometimes the answer may be heritage. Sometimes technology. Sometimes design. Sometimes hospitality. Sometimes ambition. Sometimes the credibility of having solved a difficult problem in one of the world’s fastest-transforming economies. And sometimes, frankly, Saudi origin may add nothing relevant to the customer’s decision. Then don’t force it.

Because the objective is not to make every international customer admire where you come from. The objective is to give them a compelling reason to choose what you built. That is why I would never tell Saudi founders simply to “localize.” Localization can become another superficial exercise: change the language, hire a local influencer, swap some images and call the job finished.

I prefer the word trans-creation.

Trans-creation asks a much harder question:

5-How can the same meaning survive in a different cultural grammar?

This is actually one of the larger ideas behind AdEntity. Cultures have always survived contact not by remaining untouched, but by absorbing and translating what comes from outside while retaining enough coherence to remain recognizable.

Brands are not very different. The identity should have a centre. The expression should have flexibility. So if I had to give Saudi startups one framework for international expansion, it would be:

Know what is non-negotiably yours.
Learn what is non-negotiably theirs.
Meet the qualifying factors.
Protect the winning factors.
Then translate the expression for the market, category, audience and platform.

Don’t export the Saudi advertisement. Export the Saudi advantage. And perhaps the strongest global Saudi brands of the future will not be the ones that become less Saudi as they travel. They will be the ones that discover which part of being Saudi the world finds valuable.”

If you were advising a founder launching a startup today, what is one lesson from the history of advertising that you would want them to understand before spending their first marketing budget?

“My first advice? Please refer to my previous six answers. But if I had to reduce 100 years of advertising history to one lesson, it would be this:

Media amplifies.

It does not create meaning.

If you haven’t understood the human, the culture, the category, the product truth, and why anybody should care, spending more money will not solve the problem. Today, AI can simply help you waste that money faster.

So before buying attention, build something worth paying attention to. Then amplify the hell out of it.”

Noha Gad

Riyadh is rapidly emerging as a global hub for artificial intelligence (AI), driven by Saudi Vision 2030, massive investments, and a coordinated national strategy led by the Saudi Data and AI Authority (SDAIA). Designating 2026 as the “Year of AI” and committing over $14.9 billion in AI-related investments, the Kingdom is moving from strategy to execution, building one of the region’s most advanced digital infrastructures. With over 60 data centers, the world’s largest government data facility (Hexagon), and strategic partnerships with global tech giants, the Saudi capital now offers the infrastructure, capital, and talent pipeline needed to develop, deploy, and scale AI at pace. Additionally, initiatives such as the Riyadh Digital Innovation District, the national AI management standard (ISO 42001), and large-scale training programs signal a broader ambition: to make Riyadh a top-10 global technology district and a responsible, innovation-driven AI ecosystem by 2030.

Future-ready AI infrastructure

Riyadh’s emergence as an AI hub is underpinned by a rapidly expanding digital infrastructure designed to support the intensive computing, storage, and connectivity requirements of advanced AI systems. This infrastructure combines high-performance computing, large-scale data centers, cloud services, and specialized AI development zones, creating an integrated foundation for research, government applications, and private-sector innovation.

A key component is Shaheen III, the most powerful supercomputer in the GCC, operated by King Abdullah University of Science and Technology (KAUST). Consisting of two partitions: a CPU partition and a GPU-accelerated partition, Shaheen III is the fastest supercomputer in the Middle East and ranked among the world’s leading high-performance computers. Alongside it, the Hexagon Data Center in Riyadh is classified as the world’s largest government data center with a Tier IV facility and a planned capacity of 480 megawatts. Spanning over 30 million square feet in the Saudi Capital, Hexagon Data Center meets the highest international standards, aiming to ensure maximum levels of availability, security, and operational readiness for government data centers.

The Kingdom has also established nine cloud regions, in partnership with global tech leaders:

1. Google Cloud’s region in Dammam. Launched in November 2023, this region is Google’s official cloud hub in Saudi Arabia, obtaining a Class C License from the Communications, Space and Technology Commission (CST) and supported by $1 billion in strategic cloud and AI infrastructure investments by Google. 
2. Oracle (three cloud regions in Riyadh, Jeddah, and NEOM). Oracle launched its first Oracle Cloud Region in Jeddah in 2020 to provide over 100 core cloud and AI services, including Oracle Autonomous Database, OCI Compute, and enterprise SaaS apps. In October 2021, the company announced its partnership with NEOM Tech and Digital Hold Company to establish a hyperscale data center in NEOM to serve the ambitions of the public and private sector across the region and beyond. To further strengthen its commitment to the Kingdom, the tech giant launched a new cloud region in Riyadh in August 2024 to help businesses increase performance, protect data, and access Oracle's full array of cloud services. 
3. Huawei. Launched in 2023, the Huawei Cloud Riyadh Region is the company’s first region in the Middle East, offering three availability zones to promote digital-led economic growth in the Kingdom.
4. Alibaba. Launched in 2022, Alibaba launched its Cloud Region in Riyadh with two availability zones operated by the Saudi Cloud Computing Company (SCCC).
5. Tencent. Tencent Cloud launched its first Middle East Cloud Region in Riyadh in 2025, featuring two availability zones with full redundancy, advanced cloud services, and AI capabilities.
6. AWS. In 2024, Amazon Web Services (AWS) announced its plans to build three cloud regions in Saudi Arabia, including an AI Zone in collaboration with HUMAIN.
7. Microsoft Azure. Set to be launched in the fourth quarter (Q4) of 2026, Microsoft's Saudi Arabia East Azure datacenter region will enable government and private sector organizations to access supported Microsoft cloud and AI services and host eligible workloads and data locally in the Kingdom.

Together, these projects demonstrate Riyadh’s ambition to move beyond adopting AI technologies and become a major location for developing, hosting, and scaling them.

Key events powering Riyadh’s AI ecosystem

Riyadh’s AI ambitions are reinforced by a growing network of partnerships with global technology companies, the establishment of regional headquarters (RHQs), and the organization of major international technology and entrepreneurship events. These efforts are helping the city attract investment, expand access to cloud and computing infrastructure, develop local talent, and connect Saudi startups with global investors and technology leaders.

Major tech companies moved their regional headquarters to Riyadh to better serve the broader Middle East region. For instance, Lenovo opened its Middle East, Turkey, and Africa (META) RHQ in Riyadh this year, placing Saudi Arabia at the center of its regional leadership and supporting customers and partners across more than 60 countries. Similarly, other tech giants, including Google, Microsoft, Salesforce, Groq, and Tencent Cloud, announced strategic investments and initiatives to enhance AI, cloud computing, data centers, and skills development. 

The Kingdom is also strengthening its AI ecosystem by hosting and organizing flagship events and exhibitions that provide spaces for companies, policymakers, researchers, investors, and startups to exchange knowledge, announce partnerships, showcase emerging solutions, and reinforce Riyadh’s position as a meeting point for the global AI industry. Some of these leading events are:

• LEAP, the massive annual global technology event held in Riyadh, focusing on major tracks, notably AI, fintech, and cybersecurity.
• Black Hat, the leading cybersecurity conference and exhibition that gathers cybersecurity professionals, cutting-edge technologies, solution providers, and decision-makers from around the world. Black Hat MEA 2026 is scheduled to take place in Riyadh from 1 to 3 December.
•  Middle East Entrepreneurship AI & Analytics Summit, a global gathering for senior government, enterprise, and technology leaders to explore the technologies, strategies, and use cases shaping Saudi Arabia’s AI-powered future. The 15^th edition of the ME Entrepreneurship AI & Analytics Summit will convene in the Saudi capital on 28 October.
• NextGen2030 2.0 Youth & AI Summit Riyadh 2026, the international youth summit bringing together young leaders, innovators, entrepreneurs, and changemakers from around the world to explore the future of AI, youth leadership, innovation, and entrepreneurship.
• Global AI Show, Saudi Arabia’s biggest AI conference where the next generation of AI innovation meets real business opportunity.

Upskilling national AI talent

A sustainable AI hub needs more than investment; it needs people. That is why Saudi Arabia is focusing on developing local AI talent, improving digital skills, and preparing the workforce for AI-driven business environments. SDAIA is leading these efforts by launching major initiatives, notably the SDAIA Academy, which provides professional programs and practical bootcamps in areas such as machine learning (ML), large language models (LLMs), data engineering, computer vision, generative AI, AI-agent development, and responsible AI.

Global tech companies entering the Saudi market are also contributing to upskilling Saudi national talent in AI and other emerging technologies by transferring technical knowledge, delivering specialized training, and creating practical pathways for Saudi professionals to gain experience with widely used industry tools. For instance, Salesforce pledged to provide upskilling opportunities to 30,000 Saudi citizens by 2030 through its AI Center of Excellence (CoE) in Riyadh. Similarly, AWS partnered with the Saudi Ministry of Communication and Information Technology (MCIT) on a national program designed to qualify more than 20,000 Saudi citizens in artificial intelligence, machine learning, and cloud computing.

AWS also launched the AWS Builder Accelerator to provide Saudi graduates and early-career technology professionals with intensive training in cloud computing and AWS technologies. Additionally, Google’s Gemini unveiled an initiative to give one million students at Saudi universities access to advanced AI technologies, helping them develop digital skills and prepare for future jobs.

Meanwhile, Microsoft’s joint training programs have benefited more than one million beneficiaries over the past years. This included training over two-thirds of a million participants in SDAIA’s SAMAI initiative, empowering over 5,000 women through specialized programs, and training thousands of students and over 100,000 teachers.

Finally, Riyadh’s emergence as a global AI hub is being built on more than ambitious targets. Through large-scale computing and cloud infrastructure, partnerships with global technology companies, international events, and broad-based skills development, the Saudi capital is creating an ecosystem capable of turning AI investment into practical economic and social value. As the city continues to attract tech giants, develop local expertise, and expand its capacity to host and deploy advanced AI systems, its role in the global technology landscape is likely to become increasingly significant. 

Kholoud Hussein 

For years, the artificial intelligence race was largely conducted in English. The world’s most powerful foundation models could write, code, summarize, and reason across a growing range of tasks, but their performance often weakened when they encountered the realities of Arabic: its grammatical complexity, regional dialects, cultural references, code-switching, and the enormous gap between formal written Arabic and the language people actually speak.

Saudi Arabia increasingly sees that gap not simply as a linguistic problem, but as a technology and investment opportunity.

The Kingdom is now emerging as one of the most ambitious markets for Arabic artificial intelligence, with government-backed companies, startups, global technology groups and large enterprises building different layers of an Arabic AI ecosystem. At the center of that effort is the development of proprietary language models that can understand Arabic on its own terms rather than treating it as a translation layer on top of English-centric systems.

The scale of the opportunity is reflected in the capital flowing into the wider Saudi AI ecosystem. Saudi Arabia’s National Strategy for Data and AI targets $20 billion in local and foreign investment and at least 300 active AI startups by 2030. Meanwhile, the country’s broader AI market is estimated at $2.14 billion in 2025 and projected to reach $16.9 billion by 2032, according to MarketsandMarkets.

Yet the more important question is not how much Saudi Arabia spends on AI.

It is whether the Kingdom can turn that capital into proprietary intellectual property, globally competitive companies and models that become infrastructure for the Arabic-speaking digital economy.

The Arabic gap is bigger than translation

Arabic presents a distinctive challenge for AI developers.

The language exists simultaneously in Modern Standard Arabic, classical forms, and dozens of spoken dialects. A Saudi user, for example, may switch between formal Arabic, Saudi dialect, English terminology, and industry-specific language within the same conversation.

For AI models trained predominantly on high-quality English data, this creates a structural disadvantage.

Research cited by Arab News estimates that only around 15% of Arabic text available online is clean enough for large-language-model training, compared with more than 50% for English. Developers therefore face not only a shortage of data but also a shortage of high-quality, correctly labelled and culturally representative data.

That creates an opening for companies willing to build the data layer themselves.

This is where Saudi startups and emerging technology companies are becoming important. Their advantage is not necessarily the ability to spend hundreds of millions of dollars training a general-purpose model. It is their proximity to Arabic users, enterprise data, dialects and specific commercial problems.

The resulting market is therefore developing on several levels.

At one end are foundation models such as HUMAIN’s ALLAM, developed in Saudi Arabia for Arabic-first use cases. At another are companies such as Riyadh-headquartered Intella, which has built proprietary speech technologies and small language models designed around Arabic dialects and enterprise applications. Between them sits a growing ecosystem of startups developing voice agents, vertical models, translation systems, enterprise copilots and domain-specific AI applications.

Together, they are attacking the Arabic AI problem from different directions.

From consuming AI to owning the model

The launch of HUMAIN in May 2025 marked a major change in Saudi Arabia’s approach.

The PIF-owned company was established to operate across the entire AI value chain, from data centers and cloud infrastructure to advanced models and applications. Its flagship ALLAM model is positioned as one of the world’s most powerful multimodal Arabic large language models.

HUMAIN later launched HUMAIN Chat, powered by ALLAM 34B. The company said the model was trained on more than 500 billion Arabic tokens and refined using hundreds of domain experts and evaluators. The system was designed to support Arabic and English while incorporating regional and cultural context.

The significance extends beyond having another chatbot.

A proprietary foundation model gives Saudi Arabia control over an important part of the technology stack: the data, model weights, training processes, deployment environment, and intellectual property.

That matters particularly for government, financial services, healthcare, energy and other regulated industries where data residency, security and customization can be as important as raw model performance.

It also changes the economics of the market.

Instead of paying indefinitely for access to foreign foundation models, Saudi companies can increasingly build products on locally developed models or adapt them to specific requirements.

Startups are attacking the problem from the bottom up

The startup opportunity is not necessarily to compete head-on with the largest global AI laboratories.

It is to solve the problems those laboratories have historically struggled to solve.

Intella is one example.

The Saudi-headquartered company, founded in 2021, focuses on Arabic speech intelligence rather than trying to become another general-purpose ChatGPT competitor. Its technology covers speech-to-text, text-to-speech, analytics and industry-specific small language models, with support for more than 25 Arabic dialects. The company says its proprietary speech-to-text technology has reached 95.73% accuracy.

Its commercial proposition illustrates where startups can create value.

A bank does not necessarily need the world’s largest LLM. It needs an AI system that understands how its customers actually speak, recognizes local expressions, complies with data requirements, and can connect those conversations to a banking workflow.

The same logic applies to telecom operators, government agencies, insurers and retailers.

In September 2025, Intella raised $12.5 million in a Series A led by Prosus, taking its total funding to $16.9 million. The round included Saudi investors such as Wa’ed Ventures and Hala Ventures and was intended to support R&D, product development and regional expansion.

The figure is modest compared with infrastructure investments in Saudi AI, but strategically important.

It demonstrates that private capital is beginning to finance the specialist layers that make Arabic AI commercially useful.

The corporate-startup model is becoming more important

Saudi Arabia’s Arabic AI ecosystem is also developing through an unusual combination of startups, sovereign capital and global technology companies.

Google Cloud and PIF announced a $10 billion partnership to advance an AI hub in Saudi Arabia, with the initiative involving HUMAIN. The partnership includes research into Arabic-language models and Saudi-specific AI applications, including work to enhance the Arabic capabilities of Google’s Gemini models using additional Arabic datasets.

The model is significant because it illustrates how international technology companies can provide capabilities that startups and local companies may struggle to build independently: compute, cloud infrastructure, specialized chips, model-development platforms, and global distribution.

In return, Saudi Arabia offers something equally valuable: access to a rapidly digitizing market, large enterprise customers, government use cases, capital and a concentrated pool of Arabic data and talent.

The relationship is increasingly moving beyond conventional technology procurement toward co-development.

In August 2026, Microsoft and HUMAIN announced a long-term strategic collaboration under which ALLAM models are planned to become available through Microsoft Foundry and the Microsoft 365 Copilot ecosystem. The partnership also brings HUMAIN AI specialists together with Microsoft’s Forward Deployed Engineers to develop and deploy Arabic-language AI solutions for organizations.

This is strategically important for Saudi startups as well.

A local model becomes significantly more valuable when it can be distributed through a global enterprise platform.

The same principle is visible in AWS’s expanding relationship with HUMAIN. At LEAP 2026, AWS announced plans to make ALLAM available through Amazon Bedrock, while expanding infrastructure capacity for AI workloads in the Kingdom.

The emerging architecture is therefore not simply “Saudi versus Silicon Valley.”

It is increasingly a partnership model in which Saudi companies own local intelligence and context while international technology companies provide global infrastructure, platforms and distribution.

Aramco adds another dimension

Saudi Arabia’s AI ambitions are also being accelerated by its largest corporate institution: Aramco.

The company has developed its own industrial large language model, trained on decades of proprietary Aramco data. The model is designed for applications ranging from analyzing drilling and geological information to forecasting refined-product markets. Aramco has said its decision to develop its own generative AI capability reflects the need to capture the benefits of AI while managing technology and data risks.

That approach could become particularly influential in the next phase of Arabic AI.

The most valuable models may not be the largest models.

They may be the models that understand a particular industry better than a general-purpose system does.

Energy, banking, government, healthcare and legal services all contain large amounts of proprietary information that cannot simply be uploaded to a public AI platform.

This creates a market for smaller, highly specialized models trained or fine-tuned on proprietary datasets.

For startups, that is a much more realistic opportunity than attempting to reproduce the enormous capital expenditure of frontier-model developers.

The investment story is much larger than LLM funding

One of the biggest challenges in measuring the Arabic LLM opportunity is the lack of a separate investment category.

Saudi Arabia does not publish a single figure showing how much has been invested specifically in Arabic foundation models. Much of the disclosed capital is instead bundled into broader AI infrastructure, cloud computing, data centers, chips, venture capital, and AI applications.

The numbers nevertheless show the scale of the ecosystem being constructed.

Saudi Arabia announced nearly $15 billion of investments and agreements at LEAP 2026 across AI infrastructure, data centers, cloud computing, technology manufacturing and venture capital. AWS alone announced a planned investment of more than $5.3 billion in its Saudi cloud infrastructure region, while other agreements covered major data-center and AI projects.

Aramco has also committed approximately SAR9.9 billion ($2.6 billion) in cash contributions to HUMAIN in 2026, alongside the transfer of AI assets, according to Aramco.

Earlier commitments included the $10 billion PIF-Google Cloud AI hub and Saudi Arabia’s $1.5 billion commitment linked to AI-chip company Groq.

These figures should not be added mechanically: some represent partnerships or multi-year commitments, while others span infrastructure and AI rather than Arabic models specifically.

But collectively they illustrate the capital intensity of the market Saudi Arabia is attempting to build.

At the startup level, the numbers are smaller but equally revealing.

Wa’ed Ventures has a $500 million technology-focused fund and had deployed approximately $270 million across more than 75 companies by 2024. Its mandate includes AI and other deep technologies, while requiring certain international technology investments to localize operations in Saudi Arabia.

The Kingdom is therefore developing both sides of the capital equation: large strategic investment for infrastructure and smaller venture capital for experimentation and commercialization.

How much more money could follow?

The next investment cycle is likely to move from infrastructure toward monetization.

Saudi Arabia’s National Strategy for Data and AI targets $20 billion in local and foreign investment and at least 300 active AI startups by 2030. Meanwhile, the country’s AI market is forecast to grow from $2.14 billion in 2025 to $16.9 billion by 2032.

The enterprise AI segment alone is forecast to rise from $810.6 million in 2024 to more than $5.3 billion by 2030, representing a 37.8% compound annual growth rate.

These forecasts suggest that future capital will increasingly follow commercially proven applications.

That could benefit Arabic-model startups because the value of an LLM is ultimately determined by what sits on top of it.

A model that understands Saudi dialects becomes more valuable when it powers a bank’s customer service. A government-specific model becomes more valuable when it automates document processing. An industrial model becomes valuable when it improves maintenance, engineering or energy efficiency.

The transition is therefore likely to be from model building to model commercialization.

The next frontier: Arabic AI agents

The most important development over the next few years may not be larger Arabic LLMs, but more specialized AI agents.

Global AI development is already moving from systems that generate answers to systems that can perform tasks. In Saudi Arabia, Arabic-first agents could combine language models with enterprise databases, government systems, CRM platforms, and workflow tools.

That creates a much larger commercial opportunity.

An Arabic AI agent for a bank could understand a customer’s dialect, verify information, retrieve account data, and complete a transaction.

A government agent could interpret Arabic documents, identify regulatory requirements, and route applications.

An industrial agent could combine technical manuals, sensor data and historical operational information to assist engineers.

The underlying foundation model is only one component.

Data, security, retrieval systems, workflow integration, and domain expertise increasingly determine whether the technology produces economic value.

This is precisely where startups can complement the large capital providers.

The real competition will be over data and talent

The biggest constraint on Saudi Arabic AI may eventually cease to be funding.

It could be data and people.

Developing an Arabic model requires enormous quantities of high-quality training data, but collecting that data raises questions about copyright, privacy, ownership, dialect representation and governance.

Saudi Arabia has an advantage in that it can combine government datasets, corporate information, Arabic digital content and local linguistic expertise. But access to data does not automatically make it usable for training.

The country therefore needs a broader data economy alongside its AI economy.

Talent will be equally critical.

HUMAIN said its ALLaM team included more than 120 AI specialists, including 35 PhD holders, while Aramco has committed to training more than 6,000 AI developers through collaborations involving institutions such as Imperial College, Caltech and KAUST.

For startups, the competition for this talent could become intense.

The next generation of Arabic AI companies will require machine-learning researchers, computational linguists, data engineers, Arabic-language experts, cybersecurity specialists and enterprise software developers.

From linguistic gap to economic infrastructure

Saudi Arabia’s Arabic AI push is ultimately about more than language.

It is an attempt to establish ownership over a layer of digital infrastructure that could sit underneath the region’s future economy.

The opportunity is significant because Arabic is spoken by hundreds of millions of people, while businesses and governments across the region are accelerating digital transformation.

But building a competitive Arabic LLM does not automatically create a successful technology business.

The coming years will test whether Saudi companies can turn models into recurring revenue, whether startups can scale beyond government contracts, whether proprietary data can become a defensible advantage, and whether international partnerships create technology transfer rather than simple dependence on foreign infrastructure.

The strongest companies are likely to occupy the space between these worlds.

They will understand Arabic deeply enough to solve problems global models struggle with, but build products sophisticated enough to compete internationally.

That is where Saudi startups have a potentially decisive role.

The Kingdom does not need to build every component of the global AI stack itself. It needs to identify the layers where local knowledge creates an enduring advantage — Arabic language, regional data, industry expertise, sovereign deployment and culturally relevant applications — and build globally competitive businesses around them.

The first phase of Saudi Arabia’s AI strategy was about attracting infrastructure and capital.

The next phase is about turning that infrastructure into intellectual property.

And the ultimate test will be whether Arabic AI becomes something Saudi Arabia merely helped develop — or an industry in which Saudi companies own the models, data, applications, and businesses that serve the next generation of the Arabic-speaking digital economy.

Noha Gad

Offering a strong salary is no longer enough to attract and retain top talent in today’s competitive job market, as employees increasingly look beyond base pay to evaluate the full value of a job offer, and that is where fringe benefits come in.

Fringe benefits are forms of non-wage compensation provided to employees in addition to their regular salary, including cash equivalents, property, services, or other privileges, such as health insurance, retirement contributions, company cars, tuition assistance, or paid time off.

Although they are viewed as extras, fringe benefits play a pivotal role in modern compensation packages for both employers and employees. For employers, they serve as powerful tools to enhance employer branding, boost employee morale and productivity, and gain tax advantages when structured correctly. For employees, they can significantly increase the real value of their compensation while improving financial security, health, and work-life balance.

What are fringe benefits?

Fringe benefits are additional remuneration that employees receive from their employers. They are designed to enhance the overall employee experience and provide added value beyond monetary compensation, serving as incentives that attract top talent and boost employee morale and satisfaction. By offering these extras, companies aim to create a positive work environment where employees feel valued and motivated.

Fringe benefits encompass a wide range of non-wage compensation that add another layer of appeal to any employment package, while creating a supportive workplace culture where employees feel appreciated for their hard work and dedication without only relying on financial remuneration.

Examples of fringe benefits

There are various types of fringe benefits that companies can offer to their employees, including:

• Health insurance: Many employers offer comprehensive health insurance plans, covering medical, dental, and vision expenses for employees and their dependents.
• Retirement plans: Companies may contribute to retirement savings accounts or offer pension schemes to ensure financial security for employees after they retire.
• Paid time off: In addition to statutory holidays, companies often provide vacation leave, sick leave, personal days off, or paid parental leave to support employee well-being and family needs.
• Employee Assistance Programs (EAP): These programs offer confidential counseling services for employees dealing with personal issues such as stress management or substance abuse problems.
• Education reimbursement: Some organizations support continuous learning through tuition reimbursement programs or scholarships for further education or professional development courses.
• Wellness programs: These initiatives promote employee health through gym membership discounts, wellness challenges, on-site fitness classes, or access to mental health resources.

Why do companies offer fringe benefits?

Offering fringe benefits gives companies a competitive edge in the job market, helping them to attract and retain top talent. Some advantages of providing fringe benefits include:

• Increasing employee satisfaction. These benefits make employees feel valued and appreciated, leading to higher job satisfaction and making them more likely to be loyal and committed to their work.
• Improving morale and motivation. Through fringe benefits, employers show they prioritize employees’ well-being, thereby boosting their morale and motivation.
• Attracting top talent: A comprehensive package that includes attractive fringe benefits can be a major draw for highly skilled professionals.
• Enhancing productivity: Offering fringe benefits helps create a positive work environment where individuals are motivated to excel. 
• Reducing turnover: Investing in fringe benefits can help reduce employee turnover rates as individuals are less likely to leave an organization that provides valuable perks beyond salary alone.
• Saving costs for employees: Some fringe benefits, like health insurance or retirement plans, may come with cost savings for employees compared to purchasing these services individually.

To sum up, fringe benefits have evolved from optional extras into a core component of strategic compensation, enabling employers to differentiate their offers, strengthen retention, and build a culture where employees feel genuinely supported.

These non-wage benefits can materially raise the real value of employees’ compensation while improving health, financial security, and work-life balance. For employers, a well-designed mix, aligned to workforce needs and local tax rules, can drive morale, productivity, and long-term cost efficiency.

Fringe benefits become a genuine investment in employees and a real advantage when it comes to winning and keeping great talent. For employers, all what they need to do is to choose benefits that truly fit their team and their goals, understand the full cost and tax picture, explain them in plain language, and revisit them often to see how they stack up.

Noha Gad

Saudi Arabia is ramping up investment in artificial intelligence (AI) and digital infrastructure, led by the Public Investment Fund (PIF), as the Kingdom seeks to attract technology capital and build domestic AI capabilities. Driven by Saudi Vision 2030, technology in the Kingdom has evolved from an operational tool into an innovative national industry, establishing a competitive digital ecosystem that attracts significant domestic and international capital.

Marking 2026 as the Year of Artificial Intelligence underscores the Kingdom’s commitment, following achievements by 2025 in which Saudi Arabia ranked first globally in digital governance and cybersecurity indicators, and third in AI models. 

Voice AI is emerging as one of Saudi Arabia’s most visible and practical AI applications, moving from pilots in 2024–2025 to scaled deployments across government, banking, healthcare, and customer experience in 2026.

A report released by Markets and Markets anticipated the Saudi AI voice market to grow with a compound annual growth rate (CAGR) of 32.3% to reach $149.05 million in 2030. This growth was mainly driven by growing demand for secure authentication methods across vital sectors.

Key cities such as Riyadh, Jeddah, and Dammam dominate the market thanks to their status as major economic and technological hubs. The concentration of financial institutions, telecom companies, and government agencies in these cities fosters a conducive environment for the growth of AI voice biometrics solutions.

The Saudi voice AI market is segmented into various types, notably speaker verification, speaker identification, voice authentication, voice analysis, and others. Each sub-segment plays a crucial role in addressing specific needs within the voice biometrics landscape.

Speaker Verification

This sub-segment currently dominates the market because of its widespread application in secure access control systems, particularly in banking and financial services. Organizations are increasingly adopting this technology to authenticate users based on their unique voice patterns, which enhances security while providing a seamless user experience. The growing trend of remote banking and digital services has also accelerated the demand for speaker verification solutions, making it a preferred choice among enterprises.

Speaker Identification

This technology is used to determine which known person from an enrolled pool is speaking, without the user first stating their identity. Most enterprises and contact centers in Saudi Arabia use this technology to automatically tag callers or meeting participants with their profiles in CRM or HR systems, thereby streamlining routing and supporting fraud‑detection workflows by matching suspicious voices against databases of known bad actors.

Voice Authentication

This term refers to the practical use of voice biometrics as the authentication mechanism, effectively turning a person’s voice into a “password” for accessing accounts, applications, or services. It serves as a key enabler of digital transformation in Saudi financial services and public‑sector channels, allowing banks, fintechs, and government agencies to replace cumbersome security questions with seamless voice‑based login and transaction approval.

Voice analysis 

This biometric encompasses a broader set of techniques that extract insights from speech without necessarily identifying the speaker. This allows Saudi banks, telecommunication companies, and government to use voice analysis to monitor customer satisfaction, flag potentially fraudulent or high‑risk interactions, optimize agent scripts, and improve overall service quality.

Arabic voice AI across vital sectors

Arabic Voice AI in Saudi Arabia is moving from isolated pilots to core infrastructure across government, financial services, healthcare, and customer‑facing industries, driven by Vision 2030. What ties these efforts together is more than just Arabic support; it is the use of smart, bilingual agents that can understand local dialects, operate on sovereign or in‑Kingdom infrastructure, meet Personal Data Protection Law (PDPL) and sectoral compliance rules, and integrate with existing CRM, core banking, and e‑government systems.

In government and public services, Arabic voice AI appears in hotlines, ministry contact centers, and digital‑government channels, handling FAQs, appointment booking, status checks, and basic transactions in Saudi dialects.

Also, financial institutions are among the most advanced users of Arabic Voice AI, deploying bilingual voice agents for onboarding, account servicing, payment reminders, collections, and handling complaints in line with consumer‑protection and conduct rules. Typical use cases include instant balance and mini‑statement queries, card blocking/unblocking, instalment and due‑date reminders, cheque‑book or PIN requests, and automated follow‑ups on failed payments, all delivered in natural Saudi dialect rather than translated scripts. 

In the healthcare sector, Arabic voice AI is adopted by several hospitals, clinic groups, labs, and telehealth platforms across the Kingdom for non‑clinical workflows, including appointment scheduling, rescheduling, reminders, insurance verification, and patient communication via phone and WhatsApp. Some providers are piloting clinical documentation tools, such as AI scribes, that listen to doctor–patient conversations and auto‑generate notes in Arabic, reducing administrative burden while keeping diagnosis and treatment decisions with clinicians. 

Beyond regulated sectors, Arabic voice AI is being embedded in retail, e‑commerce, real estate, and hospitality to automate high‑volume inbound and outbound calls in natural Arabic. Use cases include outbound payment reminders for BNPL and credit products, insurance renewal calls, lead qualification for property projects, and post‑visit follow‑ups for malls, resorts, and giga‑projects.

Key Voice AI companies in Saudi Arabia

Several AI-powered companies in Saudi Arabia are developing and scaling AI models to better understand the Arabic language, particularly the Saudi dialect, helping organizations integrate Arabic voice AI agents to streamline their operations and support their business growth. Key examples are:

• Nabrah. A Saudi-built platform that combines autonomous voice agents with a full studio for text‑to‑speech, speech‑to‑text, and voice cloning, all tuned for realistic Saudi Arabic. Its “Nabrah Agents” product automates inbound and outbound calls for lead qualification, support, appointment booking, order confirmation, and surveys, with 24/7 operation, multilingual support, and seamless handoff to human agents when needed. 
• Lahjati. An all‑in‑one Arabic voice AI platform for content creation, with more than 600 professional voices, supporting more than 192 Arabic dialects and performance styles. Its core tools include high‑quality text‑to‑speech for voice‑overs, audiobooks, ads, and e‑learning; speech‑to‑text with up to 99% accuracy for Arabic dialects; and custom voice design to create brand‑specific avatars.
• Mawj. A leading AI voice agent platform in Saudi Arabia that focuses on enterprise reliability and large‑scale call automation. It handles sales, renewals, inquiries, surveys, ticket creation, verifications, order intake, and bookings across calls, WhatsApp, and other digital channels.
• Tzamun. A key provider of AI call‑center and voice‑agent solutions designed for Saudi businesses seeking to automate high‑volume phone interactions, while staying aligned with local regulations and customer expectations.

Challenges:

Even as adoption accelerates, Arabic Voice AI in Saudi Arabia still faces a set of practical, technical, and regulatory hurdles that shape how fast and how deeply it can be embedded in everyday services.

Ibrahim Jabarin, CEO of Hamsa, an AI company specializing in developing advanced models that understand the Arabic language and dialects, stated in an exclusive interview with Sharikat Mubasher that the Arabic voice AI market in the Kingdom faces five main challenges: the limited availability of high-quality voice data; the high cost of graphics processing units (GPUs) and sovereign infrastructure; the scarcity of specialists in deep learning and speech processing technologies; securing significant investment to develop models; and long procurement cycles and preference for global suppliers, along with the absence of unified Arab references to measure model performance.

In addition, regulatory and compliance complexity is a key pitfall facing Arabic voice AI in Saudi Arabia. Requirements around multi‑factor authentication, call recording retention, auditability, data residency, and cross‑border transfers can constrain architecture choices, pushing many companies toward on‑premise or in‑Kingdom cloud deployments and more conservative rollout plans.

User trust and acceptance are another challenge facing this innovative industry as customers and employees remain cautious about fully automated voice interactions, especially for sensitive topics like finance, health, or government services. Building trust requires transparent disclosure that the caller is an AI, clear options to reach a human, and consistent, natural‑sounding Arabic.

 Finally, Saudi Arabia’s ambitious AI agenda is finding one of its most tangible expressions in voice. From government hotlines and bank call centers to hospital appointment lines and retail customer service, Arabic voice AI is moving from pilot projects to everyday infrastructure, powered by local platforms that understand Saudi dialects and operate within the Kingdom’s regulatory boundaries.

The growth in Arabic voice AI in the Kingdom reflects more than a technological shift; it signals a change in how citizens, residents, and customers interact with institutions through natural, bilingual conversations instead of rigid menus and forms. 

The way Saudi Arabia navigates key challenges facing the market, either through investing in local data, talent, and sovereign infrastructure, or through clear, human‑centered design, will determine whether Voice AI becomes a background utility or a defining feature of the Kingdom’s digital identity in the post‑2030 era.

Noha Gad

As Saudi Arabia accelerates its digital transformation agenda, artificial intelligence (AI), cloud computing, cybersecurity, and intelligent infrastructure are becoming central to the Kingdom’s economic and technological development. Its ambition to become a global technology and AI hub is creating new opportunities for organizations that can help businesses and government entities move from experimentation to secure, scalable, and commercially valuable digital solutions.

NTT DATA is playing a pivotal role in this transformation, supporting public and private organizations across Saudi Arabia with digital infrastructure, cloud adoption, data and AI solutions, cybersecurity, and managed services. 

On the sidelines of LEAP 2026, Sharikat Mubasher held an interview with Ahmad Qarout, Technology Solutions Leader at NTT DATA Saudi Arabia, to learn more about the company’s business in the Kingdom, how its solutions support organizations’ digital transformation and cloud adoption, and its long-term strategy to expand in Saudi Arabia and the broader region.

First, could you walk us through NTT DATA's business in Saudi Arabia, and explain how your solutions contribute to accelerating digital transformation and advancing the technology industry in the Kingdom?

Saudi Arabia is one of NTT DATA's most strategic markets globally, and we are proud to support public and private sector organizations across their digital transformation journeys. We help clients modernize infrastructure, accelerate cloud adoption, strengthen cybersecurity, harness data and AI, improve customer experiences, and operate complex digital environments at scale. 

As the Kingdom moves from digital transformation ambition to large-scale execution, organizations are increasingly looking for partners that can not only design and deploy technology, but also operate, secure, and continuously optimize it. This is where NTT DATA differentiates itself. We combine global expertise with strong local engagement, helping organizations turn innovation into measurable business outcomes. 

Our work directly supports Vision 2030 by enabling organizations to leverage AI, cloud, data analytics, intelligent automation, and cybersecurity to improve productivity, accelerate innovation, and develop new capabilities. We are equally committed to knowledge transfer, skills development, and building a sustainable local digital ecosystem that supports the Kingdom's long-term growth ambitions. 

NTT DATA is participating in LEAP 2026 to showcase AI-powered intelligent infrastructure and cutting-edge solutions. How do these technologies work together to create a truly intelligent ecosystem within Saudi Arabia?

A truly intelligent ecosystem requires much more than AI applications alone. It depends on the integration of data, infrastructure, networking, cloud, security, governance, and operational expertise working seamlessly together. At LEAP 2026, NTT DATA showcased how these elements combine to create an environment where AI can move from experimentation to enterprise-wide value. 

Our approach brings together AI solutions, modern digital infrastructure, advanced networking, cybersecurity, and intelligent operations to help organizations automate workflows, improve decision-making, enhance customer experiences, and unlock greater value from enterprise data. This creates a secure foundation for scaling AI responsibly and effectively. 

A key example is the new NTT DATA AI Factory Lab in Riyadh, which will provide organizations with hands-on experiences and practical demonstrations of real-world AI use cases. The lab will feature technologies including the Cisco Secure AI Factory with NVIDIA, allowing organizations to explore how AI workloads can be built, deployed, governed, and scaled on an enterprise-grade foundation while maintaining visibility, security, compliance, and operational resilience. 

How do you assess the role of premier events such as LEAP 2026 in strengthening the Kingdom's position as a global AI and technology hub?

LEAP has become one of the world's most influential technology events and plays a critical role in advancing Saudi Arabia's position as a leading global AI and innovation hub. It provides a platform that brings together technology leaders, policymakers, investors, startups, hyperscalers, and enterprise customers to exchange ideas, showcase innovation, and accelerate partnerships. 

What makes LEAP particularly important in 2026 is that it reflects the evolution of the Saudi technology market. The conversation has shifted from digital ambition to practical execution, with organizations focused on scaling AI, building resilient infrastructure, and delivering measurable outcomes. Events such as LEAP help facilitate these conversations and drive collaboration across the ecosystem. 

For NTT DATA, LEAP is an opportunity to engage directly with customers and partners, demonstrate real-world innovation, and contribute to the development of a thriving technology ecosystem aligned with Saudi Arabia's Vision 2030 goals. 

Does NTT DATA plan to announce any strategic initiatives or partnerships during LEAP 2026?

NTT DATA continues to invest in strategic partnerships and ecosystem collaboration across Saudi Arabia and the wider region. The growing number of partnerships and MOUs reflects the direction of the Saudi market itself, where collaboration between global technology leaders, local organizations, and government stakeholders is becoming increasingly important. 

One of our key initiatives and announcements is the launch of the NTT DATA AI Factory Lab in Riyadh, which brings together NTT DATA's AI expertise with technologies from leading partners including Cisco and NVIDIA. The lab is designed to help organizations move from AI exploration to practical implementation through executive workshops, demonstrations, and real-world use case development. 

What is NTT DATA's long-term strategy for expanding its business within Saudi Arabia and the broader region?

Our long-term strategy is centered on supporting the next phase of growth in Saudi Arabia and the Middle East, where digital transformation is increasingly becoming an ongoing operational capability rather than a one-time project. We are investing in local presence, local talent, and in-country delivery capabilities to help customers manage increasingly complex and mission-critical technology environments. 

We see significant opportunities in AI, cloud, cybersecurity, intelligent infrastructure, data-driven transformation, and managed services. As organizations scale AI and modernize their operations, they require trusted partners that can help them operate securely, meet sovereignty requirements, and continuously optimize performance. 

The launch of the AI Factory Lab in Riyadh is one example of this commitment. More broadly, our goal is to help organizations across the region build resilient, secure, and future-ready digital foundations while supporting national priorities around innovation, skills development, and economic diversification. Ultimately, we want to help clients transform ambitious digital investments into sustainable business outcomes and long-term value creation.