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

Large investment opportunities often require more than capital. They demand industry expertise, strategic insight, and effective risk management. Through a club deal, investors combine resources and experience to pursue opportunities that may be difficult to access individually. This approach is particularly common in private equity and real estate, where high-value transactions often benefit from collaboration.

What Is a Club Deal?

A club deal is an investment transaction in which a group of investors joins forces to finance or acquire an asset, company, or business. Rather than relying on one investor to provide all the necessary capital, participants contribute funds and share the investment according to agreed terms.

Participants may include private equity firms, institutional investors, family offices, and high-net-worth individuals. Beyond capital, they may contribute industry knowledge, management experience, or access to new markets.

Unlike a traditional investment fund, which pools capital to build a portfolio of assets, a club deal typically brings together a select group for a specific transaction.

For example, three private equity firms seeking to acquire a company valued at SAR 1 billion could each contribute part of the purchase price. They would then agree on ownership stakes, decision-making responsibilities, and how future returns would be distributed.

How Does a Club Deal Work?

The process usually begins when an investor identifies an opportunity that requires more capital or expertise than it can provide independently. It then approaches other investors with similar objectives to form a group.

The participants assess the opportunity, conduct due diligence, and negotiate the terms, including each investor's contribution, ownership share, and management responsibilities. They also establish how major decisions will be made, how disputes will be resolved, and what happens if a participant wants to exit.

Once the agreements are finalized, the investors provide the necessary funding and proceed with the transaction. Depending on the structure, they may manage the investment jointly or appoint a team to oversee operations.

Why Do Investors Use Club Deals?

Access to larger opportunities is a major reason why investors would pursue club deals. Pooling capital allows them to participate in transactions that might otherwise exceed their individual investment capacity.

Risk sharing is another advantage, as each participant assumes only part of the financial exposure. However, this does not eliminate the possibility of losses.

Club deals also bring together complementary expertise. One investor may understand a particular industry, while another offers operational experience or knowledge of a specific market. Combining these strengths can improve investment assessment and management.

For private equity firms, this approach can also enable larger acquisitions without concentrating too much capital in a single transaction.

What Are the Risks of a Club Deal?

Despite their advantages, club deals can become complicated when investors have conflicting priorities or management approaches.

Participants may disagree on business strategy, additional funding, the timing of an exit, or whether to sell an asset. Such differences can delay decisions and create tension without clear agreements.

The structure also involves legal negotiations and coordination among multiple parties. Investors must assess their partners' financial capacity, experience, and willingness to cooperate.

Control is another consideration. Smaller investors may have limited influence over major decisions despite committing substantial capital.

Clearly defined ownership rights, voting arrangements, exit provisions, and dispute-resolution mechanisms are therefore essential before a transaction closes.

To Wrap Things Up…

Club deals allow investors to pursue larger opportunities by combining capital, expertise, and market knowledge. However, success depends on more than financial contributions. The quality of the investment, alignment among participants, and clarity of their agreements all play a role. Ultimately, choosing the right partners is just as important as identifying the right opportunity.

Noha Gad

Every product starts as an idea, but turning that idea into a profitable, sustainable offering requires far more than good design or effective marketing. Companies must coordinate research and development (R&D), manufacturing, supply chains, quality control, sales, customer support, and eventual retirement, often across multiple teams, markets, and systems. When this coordination breaks down, it can lead to delayed launches, inconsistent product information, rising costs, compliance risks, and lost customer trust.

This is where Product Lifecycle Management (PLM) becomes essential. PLM is a strategic business approach that manages a product throughout its entire life, from the initial concept and design stages through production, launch, growth, service, and end-of-life. Rather than treating each stage as a separate task handled by a different department, PLM creates a connected framework for managing product data, processes, decisions, and collaboration across the organization.

This approach is not important only for large manufacturers; it is also pivotal to consumer brands, technology companies, industrial firms, and even digital-first businesses that heavily rely on lifecycle management to bring products to market faster, maintain quality, control costs, and respond to changing customer expectations. 

Why does product lifecycle management matter?

At the core of PLM is the end-to-end integration of product data, processes, and systems along the entire value chain. Implementing PLM consistently can shorten decision paths, reduce errors cost, and bring innovations to market faster and more reliably. Increasing product complexity, shorter innovation cycles, and globally distributed value chains make structured PLM a decisive competitive factor that allows companies to manage product processes across departmental and site boundaries, while meeting regulatory, environmental, and economic requirements.

An integrated PLM approach creates the necessary transparency: development, production, quality assurance, sales, and aftersales all work from a shared data foundation. Therefore, decisions become faster, better informed, and more traceable. Also, a consistent, quality-assured product data landscape is a prerequisite for modern technologies such as AI, machine learning, and automation to deliver their full potential.

PLM approach also helps organizations keep up with the evolving complexity and engineering challenges of developing new products. It is one of the four cornerstones of a manufacturing corporation's information technology structure, alongside customer relationship management (CRM), supply chain management (SCM), and enterprise resource planning (ERP). 

For marketing, determining a product’s lifecycle stage guides the team’s strategy. For instance, a new product in the introduction stage needs to be explained, while a mature product needs to be differentiated.

Stages of PLM

PLM follows a structured sequence of phases that guide a product from its earliest idea to its eventual withdrawal from the market. Most PLM frameworks include the following core stages: 

• Concept stage. The concept stage involves initial ideas and planning for a new product, and includes market research, identifying customer needs, and determining the feasibility of the product. Often led by the R&D department, this stage kicks off the product lifecycle, as it is where the ideas are generated.
• Design stage. In this stage, the product is planned, developed, and tested. It involves creating product prototypes, refining the design, and ensuring that it meets all regulatory and safety requirements. 
• Production stage. This stage starts when the company feels confident in the new product and feels there is a market for it. This stage involves manufacturing the product, including sourcing raw materials, assembling components, and testing the final product. At this point, the company should have a fully completed product and should not be continually tweaking the design.
• Sales stage. After the product is made, it moves to the sales stage that involves promoting and selling the product to customers. This includes advertising, sales promotions, and pricing strategies. In many cases, the sales stage and production stage occur concurrently, as a company must try to forecast how many sales will occur.
• Support stage. The support stage involves providing ongoing support to customers after they have purchased the product. This includes customer service, warranties, and repairs. It may also include ongoing training or services provided to new owners to enhance user experience.
• Retirement Stage. Whether competitors have delivered a better product or the product is simply no longer demanded by the market, the product lifecycle ends with the retirement stage. This stage involves the end-of-life of the product, including disposal, recycling, or repurposing of the product.

Finally, product lifecycle management plays a pivotal role in providing companies with a structured approach to manage products from initial idea through design, production, sales, customer support, and retirement. By connecting teams, standardizing product data, and improving collaboration across the value chain, PLM helps businesses launch products faster, reduce errors and costs, maintain quality, and respond more effectively to market changes.

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.