Ghada Ismail

Imagine running a business without knowing how much you spent last month, which customers still owe you money, or whether your sales are actually covering your expenses? Making sound financial decisions under these circumstances would be difficult, if not impossible. Bookkeeping helps businesses avoid this uncertainty by providing an organized record of their financial activities and giving owners a clearer picture of where their money comes from and where it goes.

Whether a business is a small startup or a large corporation, keeping accurate financial records is essential for managing daily operations and supporting long-term growth.

What Is Bookkeeping?

Bookkeeping is the process of recording, organizing, and maintaining a business's financial transactions. These transactions include sales, purchases, payments, receipts, expenses, and other financial activities.

Its main purpose is to ensure that every financial transaction is documented accurately and systematically. This gives business owners a clear picture of their income and expenses over a specific period.

For example, when a startup purchases office equipment, receives a payment from a customer, or pays its employees, each transaction must be recorded in its financial records. Over time, these records help the business track its cash flow, monitor expenses, and assess its financial performance.

Why Is Bookkeeping Important for Businesses?

Maintaining accurate financial records is not simply an administrative task. It plays an important role in helping businesses operate efficiently and remain financially stable.

One of its main benefits is financial visibility. By tracking income and expenses, business owners can understand how much their companies generate and identify areas where costs could be reduced.

Bookkeeping also supports cash flow management by helping businesses monitor incoming and outgoing payments. This is particularly important for startups and small businesses, which may face financial pressure when customer payments are delayed or operating expenses increase.

Another benefit is tax preparation and compliance. Organized financial records make it easier to calculate taxable income, prepare financial statements, and meet applicable reporting requirements.

Furthermore, reliable bookkeeping helps businesses make informed decisions about budgeting, hiring, expansion, and investment. It also provides investors and lenders with a clearer understanding of a company's financial position.

What Are the Main Bookkeeping Tasks?

Bookkeeping involves several recurring activities that keep a company's financial records accurate and up to date.

These include:

• Recording transactions: Documenting all financial activities, including sales, purchases, payments, and receipts.
• Managing invoices: Tracking invoices issued to customers and payments owed to suppliers.
• Reconciling bank accounts: Comparing financial records with bank statements to identify discrepancies and missing transactions.
• Tracking expenses: Categorizing business spending to monitor operating costs and identify unnecessary expenses.
• Maintaining payroll records: Recording employee salaries, wages, deductions, and related payments.
• Preparing financial reports: Organizing transaction data to support the preparation of reports such as income statements and balance sheets.

Together, these activities provide the financial information needed for effective business management and more detailed accounting analysis.

What Is the Difference Between Bookkeeping and Accounting?

Although bookkeeping and accounting are closely related, they serve different purposes.

Bookkeeping focuses primarily on recording and organizing financial transactions. Accounting takes this information further by analyzing, interpreting, and reporting financial data to evaluate business performance.

For instance, a bookkeeper may record a company's monthly sales and expenses, while an accountant uses those records to prepare financial statements, assess profitability, and provide financial guidance.

In simple terms, bookkeeping creates the financial records, while accounting uses those records to understand what they mean.

Should Businesses Use Bookkeeping Software?

As businesses grow, managing financial transactions manually can become time-consuming and increase the risk of errors. Bookkeeping software offers a more efficient way to organize financial information.

These tools can automate tasks such as recording transactions, generating invoices, tracking expenses, and reconciling bank accounts. Some platforms also provide real-time financial dashboards, allowing business owners to monitor their financial position more easily.

However, choosing between manual bookkeeping, software, or professional bookkeeping services depends on the company's size, transaction volume, and operational needs.

To Wrap Things Up…

Bookkeeping is a basic yet essential part of running a successful business. By maintaining accurate and organized financial records, companies can monitor their financial health, manage cash flow, meet reporting obligations, and make better business decisions.

For startups and established businesses alike, effective bookkeeping provides the financial foundation needed to manage daily operations and support sustainable growth.

Noha Gad

Saudi Arabia’s event management industry is experiencing a period of remarkable growth, driven by Vision 2030’s agenda and its ambition to transform the Kingdom into a leading global destination for business, culture, entertainment, and sport. Analysts believe the growth in the event management market is largely attributed to substantial investments in venues and groundbreaking infrastructure.

According to a recent report released by Research and Markets, the event management market in Saudi Arabia is expected to grow from $2.59 billion in 2025 to reach $3.92 billion by 2031, making a compound annual growth rate (CAGR) of 7.14%. In addition, the IMARC Group estimated the market to reach $21.1 billion by 2034, exhibiting a CAGR of 8.08% between 2026 and 2034. This growth, the group said, is driven by rising tourism, government initiatives, increased corporate spending, and growing demand for entertainment and cultural experiences, as well as developments in event technology and social media integration.

The growing role of technology in shaping the Saudi event management industry encouraged organizers and emerging companies to use digital tools to improve planning, communication, guest management, and post-event analysis, thereby delivering larger, safer, and more personalized experiences.

Artificial intelligence (AI) is a key tool organizers and event management companies use to understand attendee preferences, recommend relevant sessions or exhibitors, answer guest questions through chatbots, and translate content for international audiences.

Data analytics is another driver behind the transformation that the event management market is witnessing. It helps organizers and event-tech companies to benefit from digital registration systems, mobile applications, and smart badges to collect information about attendance, movement, engagement, and feedback. Analyzing these data responsibly could help organizers measure the success of an event more accurately and improve future editions. Sponsors and exhibitors can also understand audience behavior, generate qualified leads, and demonstrate return on investment.

Automation is another tool that simplifies many routine tasks that previously required significant manual work, such as sending invitations, registration confirmations, reminders, check-in processes, surveys, and post-event reports. This will reduce administrative burdens, lower the risk of human error, and allow event managers to focus more on creativity, guest experience, and strategic planning.

Also, augmented reality (AR), virtual reality (VR), interactive screens, and holographic displays can make exhibitions, brand activations, and entertainment events more engaging.

Together, these advanced technologies are not replacing event managers; they are redefining their role. The most successful companies in Saudi Arabia’s event management market will be those combining strong local knowledge and operational expertise with digital tools that create smarter, safer, and more memorable experiences.

The event-management sector is no longer dominated solely by traditional agencies and venue operators. A layered ecosystem has emerged, spanning full-service event agencies, specialized production and AV companies, venue-management companies, ticketing platforms, and a growing cohort of event-tech startups. 

Companies such as SYNC, one of the leading event solutions providers, position themselves as end-to-end event solutions providers, handling everything from Saudi Conventions and Exhibitions General Authority (SCEGA) licensing and venue coordination to AV production and on-site execution. Serving government entities, corporate clients, and conference organizers, the competitive advantage of these companies lies in regulatory familiarity, supplier networks, and in-house production capability rather than software innovation.

In the AV and staging area, Al-Khalediya for Theatres and Cinema is one of the key players that has executed more than 1,000 projects across Saudi Arabia and the GCC, specializing in theatre design, acoustic systems, seating, and cinema fit-outs.

Hybrid-event and event-tech providers, such as INVE, do not execute events themselves, but provide software that organizers use to manage invitations, registration, guest data, and entry.

INVE is one of the Saudi-based startups that leverage technology to transform the event management industry in the Kingdom by offering digital invitations, RSVP tracking, guest profiles, registration, check-in, and follow-up communication in one platform. Serving both corporates and individuals, INVE reflects the industry’s move toward relationship-based and technology-enabled event management, seeking to turn a single event into a long-term guest-relationship asset.

Venttat is a leading Saudi event management company, offering expert services for organizers and exhibitors. It serves as a one-stop shop for corporate events, meetups, conferences, and global event technology services, exhibitor infrastructure, and digital logistics.

The growing event management market in Saudi Arabia is providing significant opportunities for investors, startups, and established companies, as the Kingdom hosts more international conferences, entertainment events, sporting competitions, and cultural festivals. This increases demand not only for event managers and organizers but also for the technology, services, and infrastructure that support them.

Startups and event planning companies will need to tailor AI-powered solutions and products for attendance forecasting, personalized recommendations, attendee matchmaking, chatbots, feedback analysis, and automated content creation. Smart venue technology presents significant opportunities as startups can focus on developing digital access control, crowd monitoring, security systems, digital signage, and real-time venue-management dashboards.

Despite its strong growth potential, the event management industry in Saudi Arabia faces several challenges that could affect its long-term development. One of these challenges is the scarcity of specialized talent, particularly in areas such as event production, technical operations, digital marketing, data analysis, and multilingual guest services. 

The growing number of event agencies, venues, and technology providers is increasing pressure on companies, which will need to differentiate themselves through quality, creativity, reliability, and strong client relationships rather than competing solely on cost.

Quality and consistency are another challenge, especially as Saudi Arabia seeks to host world-class events. Thus, organizers must meet high expectations for safety, logistics, guest experience, and operational precision. 

To sum up, Saudi Arabia’s event management industry is entering a new phase of development, driven by Vision 2030, major technological advancements, and the Kingdom’s growing position as a regional hub for business, culture, and entertainment.

Technology will be central to this growth, helping event organizers and managers change how they plan, deliver, and measure events, ultimately enabling them to focus on creativity, guest experience, safety, and strategic decision-making. Companies that can combine operational expertise with digital innovation will be best placed to meet the rising expectations of corporate clients, government entities, sponsors, and attendees.

Noha Gad

Have you ever dealt with the manager who asks you to Cc them on every email? The one who rewrites your work not because it is wrong, but because it is not exactly how they would have done it. They think they are protecting quality, meeting deadlines, and ensuring good results by focusing on minor details, demanding approval for small decisions, and leaving employees with little freedom to make their own choices.

A micromanager excessively supervises employees, focusing on controlling every task detail. Although this management style might yield short-term results, it often undermines morale and hampers team productivity in the long run. Instead of directing employees on how and when tasks can be accomplished, micromanagers watch employees’ actions closely, providing frequent criticism.

Key features of micromanagers

Micromanagers share several common behaviors. Although every situation is different, these signs usually appear:

• Lack of trust and delegation. These managers refuse to delegate tasks or take over work because they believe no one else can accomplish it right.
• Continuous supervision. Micromanagers demand frequent status updates, require approval for minor decisions, and track every step of a project.
• Excessive focus on minor details: They obsess over formatting, wording, or trivial errors instead of looking at overall progress. They may criticize a report because of its font or structure, even when the content is strong and the client is satisfied.
• CC'ing on everything: They insist on being copied on all emails and sitting in on routine communications. This makes employees feel unable to act without asking permission first.

Negative impact on the workplace

Micromanagement may seem like a way to maintain control and quality, but it often creates serious problems for employees and the wider business, such as: 

• Reducing employee morale. When employees feel constantly observed and corrected, they start losing enthusiasm for their work. They may feel that their manager does not trust their skills or respect their judgment.
• Increasing stress. Frequent check-ins, repeated revisions, and constant approval requirements can make employees feel pressured and insecure. They may worry about making mistakes or being criticized for small details.
• Lowering confidence. When a manager rewrites employees’ work, questions their choices, or takes over tasks, they may begin to doubt their own abilities.
• Discouraging creativity and new ideas. Micromanagers often prefer their own method, leaving little room for different approaches. This makes employees less likely to share ideas when they believe every suggestion will be rejected or changed. 
• Slowing down the work cycle. When every small decision needs approval, projects take longer to be done, as employees may spend more time waiting for feedback than doing actual work.
• Reducing productivity.  Although micromanagers often believe they are improving performance, their behavior can have the opposite effect. Employees may lose motivation to work efficiently because they know their manager will likely change the work anyway.

Micromanagers can take concrete steps to change their management. First, they should establish a few key metrics that define success for any project, ignoring every other detail that falls outside those parameters. They should delegate specific needs to be done, not control the way team members do it. An open-door policy should be in place so team members can seek coaching or further guidance whenever they feel they need it. Finally, managers should set a deadline for each stage of an assigned project.

To sum up, micromanagement may seem to be a commitment to quality, but it often creates stress, lowers morale, and reduces productivity. When employees feel constantly monitored and are given little freedom to make decisions, they may lose confidence, avoid sharing ideas, and become less engaged in their work. Successful leaders focus on clear goals, trust their teams, delegate responsibility, and provide guidance when needed.

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 

Artificial intelligence (AI) is no longer a tool for improving products and services; it has become a strategic asset that shapes economic competitiveness, national security, and digital independence. As governments and enterprises increasingly rely on AI for critical decision-making, questions are emerging about who controls the data, infrastructure, models, and rules behind these systems.

As AI deployment is accelerating worldwide, countries have concerns about their ability to build and run AI systems independently without the need for foreign technology providers, while maintaining the highest levels of security and privacy with regard to data, technology infrastructure, operations, and legal structures. This has brought sovereign AI to the center of global technology and policy debates.

What is meant by sovereign AI?

Sovereign AI refers to AI systems developed, deployed, and governed within a nation's legal and operational boundaries to ensure data and jurisdictional control. It helps governments and organizations address issues related to control, governance, and dependence. 

The difference between sovereign AI and global AI models lies in where the data resides and who holds the legal authority over the system. For Jurisdictional control, sovereign AI ensures that all data and operations remain under the legal authority of a specific country or region, perhaps within a sovereign cloud. Meanwhile, global AI models often operate on international cloud platforms where data may move across borders 

For data movement and privacy, sovereign AI restricts data movement, ensuring that sensitive information never leaves the local jurisdiction, unlike global AI models that use users' data to improve a centralized, multi-tenant model governed by a provider’s terms of service.

For alignment and autonomy, sovereign AI helps create models that are natively aligned with local dialects, legal codes, and social norms. In contrast, global models may reflect the cultural or linguistic biases of the region where they were developed. 

Types of sovereign AI deployment models

Sovereign AI is not a single architecture, but rather a spectrum of control based on data sensitivity, regulatory requirements, cost, scalability, and the need for operational control.

• Full sovereignty: This model hosts data, models, infrastructure, and personnel all within national borders, utilizing a domestic supply chain for hardware and software wherever possible.
• Hybrid sovereignty: Sensitive workloads remain on domestic, sovereign infrastructure, while non-sensitive tasks are offloaded to global platforms to take advantage of scale.
• Regional or bloc sovereignty: Sovereignty is defined at the level of a political alliance, such as the European Union, allowing for cross-border data flows within the member states while preserving regional legal control.
• Sector-specific sovereignty: Sovereignty requirements are applied only to critical industries like defense, healthcare, or finance, while other sectors continue to use global AI services.

Pros and Cons

Implementing a sovereign AI strategy provides the regulatory assurance and strategic control necessary for high-stakes environments. Other advantages include:

• Enhanced regulatory compliance: Systems adhere to national data protection laws from the point of deployment, reducing the risk of legal penalties.
• Greater data control: Sensitive government, financial, healthcare, and business data remain under local jurisdiction, with clear rules for storage, access, processing, and deletion.
• Data and intellectual property protection: Sensitive data and proprietary models are stored in isolated, controlled environments to prevent data leaks and model theft.
• Operational resilience: Local, hybrid, or disconnected deployment can reduce reliance on a single external provider and support continuity for critical services.
• Operational continuity: Localized infrastructure ensures that critical national and corporate services remain online regardless of international geopolitical events or trade restrictions.

Building and operating sovereign AI capabilities also involves high costs, talent constraints, and continued dependence on global supply chains. Key disadvantages are:

• High infrastructure and legal costs: Building local compute clusters and navigating complex, often conflicting regional regulations requires massive upfront capital investment.
• Talent shortage: There is a global shortage of AI engineers, data scientists, cybersecurity specialists, and infrastructure professionals required to build and maintain complex sovereign environments.
• Hardware dependence: Even with local infrastructure, many countries remain dependent on global supply chains for advanced chips, GPUs, and specialized components.
• Data lineage and provenance: Maintaining a verifiable, auditable record of where training data originated is essential for proving compliance with local privacy and copyright laws.

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.

Kholoud Hussein 

Artificial intelligence is moving beyond individual models that answer questions, generate content, or analyze data. As businesses adopt multiple AI tools, a new challenge is emerging: how to coordinate these technologies, connect them to business systems, and ensure they work together to deliver measurable results.

This is where AI orchestration is becoming increasingly important.

For startups, AI orchestration represents more than a technical development. It offers a way to build sophisticated AI-powered products without developing every underlying model from scratch, automate complex workflows, and compete in markets where the ability to integrate intelligence into business operations may matter more than the size of an individual model.

What is AI orchestration?

AI orchestration is the process of coordinating multiple AI models, agents, software tools, data sources, and business workflows within a unified system to accomplish a specific objective.

Rather than relying on a single model to perform every task, an orchestrated system determines which tools are needed, when to use them, how to transfer information between them, and how to evaluate the results.

Consider a startup developing an AI-powered financial research platform. One model could extract information from company announcements, another could analyze financial statements, and a third could summarize market developments. An orchestration layer would coordinate these tasks, retrieve relevant information, check whether the outputs meet defined requirements, and assemble the results into a structured report.

The distinction is important. An AI model generates an output; orchestration manages the process through which multiple capabilities contribute to a broader outcome.

AI orchestration can involve several components, including large language models, specialized machine-learning systems, APIs, enterprise databases, automation tools, and human approval mechanisms. In more advanced systems, AI agents can independently undertake subtasks within defined permissions, while an orchestration layer manages their interactions and monitors progress.

The concept is closely related to AI agents, but the two are not interchangeable. An agent is a system capable of taking actions toward a goal, while orchestration coordinates agents and other technologies within a larger workflow.

Why orchestration is becoming a business priority

The growing availability of AI models has lowered the barrier to developing AI-powered products. Startups can access models through APIs, use open-weight models, and integrate third-party services without bearing the full cost of training a foundation model.

However, access to these capabilities has created a different challenge: managing complexity.

A business might use one model for customer communication, another for document extraction, a separate system for fraud detection, and conventional software for transactions and recordkeeping. Each component may perform effectively in isolation, yet the overall process can remain inefficient if the systems cannot exchange information reliably.

AI orchestration addresses this coordination problem.

It can route tasks to the most suitable model, retrieve information from approved sources, manage dependencies between workflow stages, and apply checks before an output reaches a customer or employee. It can also incorporate conventional software rules, ensuring that AI-generated recommendations do not automatically trigger actions that require authorization.

For startups, this creates an opportunity to move beyond offering isolated AI features and develop complete business solutions.

The commercial value lies not simply in generating a better answer, but in reducing the time, cost, and complexity required to complete a business process.

How startups can benefit

1. Building products without training foundation models

Developing a competitive foundation model requires substantial investment in computing infrastructure, research, training data, and specialized talent. Most startups cannot justify that expenditure, particularly before establishing product-market fit.

Orchestration offers an alternative.

A startup can combine existing models with proprietary data, specialized software, and industry-specific workflows to build a differentiated product. Instead of competing directly with major AI laboratories, it can focus on solving a problem for a particular customer segment.

For example, a legal technology startup could coordinate document retrieval, clause extraction, contract comparison, and risk identification. A healthcare technology company could integrate transcription, administrative documentation, and information retrieval, subject to appropriate clinical oversight and data-protection requirements.

The underlying models may be available to competitors. The workflow design, integrations, specialized data, and customer experience can provide a stronger basis for differentiation.

2. Reducing operating costs

Not every task requires the most powerful or expensive model.

An orchestrated system can use a smaller, lower-cost model for routine classification or information extraction and reserve more capable models for complex reasoning. It can also use caching, reusable outputs, and rule-based automation to avoid unnecessary model calls.

For a startup operating on limited funding, these decisions can materially affect unit economics.

The relevant question is not simply how much an AI model costs per request. It is how much the entire system costs to deliver a successful business outcome.

That calculation includes model usage, data retrieval, integration, monitoring, error correction, and human intervention. A system that appears inexpensive at the model level may become costly if it frequently produces unreliable outputs or requires employees to repair its work.

Orchestration makes these costs easier to manage when the workflow is designed and monitored properly.

3. Scaling specialized AI solutions

Vertical AI startups focus on specific industries or business functions rather than building general-purpose assistants. Orchestration can strengthen this model by connecting intelligence to the systems and procedures customers already use.

A startup serving financial institutions, for instance, could coordinate regulatory document searches, customer-service workflows, transaction monitoring, and internal knowledge retrieval. In e-commerce, an orchestrated system could connect product discovery, inventory information, customer support, and order-management tools.

These applications depend on more than language generation. They require reliable access to data, clear task sequencing, permissions, and measurable performance.

This is where startups with deep industry knowledge can compete against larger technology providers. Their advantage may come from understanding a particular workflow and integrating AI into it more effectively, rather than owning the most powerful underlying model.

The emerging role of AI agents

AI orchestration is becoming more significant as businesses experiment with agentic systems: AI applications that can plan tasks, use tools, and take actions within specified boundaries.

An orchestrated workflow might assign research to one agent, analysis to another, and quality checks to a third. A coordinating layer would manage task allocation, exchange information, resolve failures, and determine when human intervention is required.

However, adding more agents does not automatically improve performance. Each additional component introduces potential failure points, including inconsistent outputs, duplicated work, delays, higher costs, and security vulnerabilities.

For startups, the objective should therefore be to orchestrate the minimum number of components necessary to deliver a reliable result. In many cases, a simple workflow with clearly defined steps will outperform a complex network of autonomous agents.

Human oversight also remains important, particularly in finance, healthcare, legal services, and other environments where errors can carry significant consequences.

The challenges startups must address

Despite its potential, AI orchestration does not eliminate the limitations of underlying models.

An orchestrated workflow can amplify errors if incorrect information passes from one component to another. Systems may also struggle with changing API costs, vendor outages, inconsistent model behavior, data privacy requirements, and the difficulty of explaining how a final decision was reached.

Startups must therefore treat orchestration as an engineering and governance challenge, not merely a way to connect AI tools.

That means testing individual components and complete workflows, monitoring latency and costs, protecting sensitive information, maintaining audit trails, and establishing clear rules for human approval. Where possible, systems should verify important outputs against authoritative data rather than relying on another model to confirm the first model's answer.

Vendor dependence is another strategic consideration. A startup built around a single provider may face price changes, product restrictions, or service disruptions. Designing flexible integrations can help, although switching providers is not always straightforward because models differ in performance, behavior, and technical requirements.

From AI experimentation to business infrastructure

The next phase of enterprise AI adoption is likely to depend less on how many models a company uses and more on how effectively those models are integrated into everyday operations.

For startups, AI orchestration offers a route from experimentation to commercially useful products. It allows small teams to combine existing intelligence with proprietary workflows, industry data, and established business systems, potentially creating value without competing directly in the capital-intensive race to build ever-larger models.

Yet orchestration itself is not a guaranteed competitive advantage. As integration tools become more accessible, the ability to connect models will become easier to replicate. Sustainable differentiation will increasingly depend on the quality of the workflow, the reliability of the system, the exclusivity of the data, and the business problem being solved.

Ultimately, AI orchestration shifts the competitive question from which model is the smartest to which system can deliver the most reliable outcome at the right cost.

For startups, that shift could be decisive. The winners may not be those that own every component of the AI stack, but those that know how to combine the right components into a product customers trust, use consistently, and are willing to pay for.

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.

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.