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.

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

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.

Noha Gad

When a company aspires to expand, acquire another business, or finance a major strategic project, traditional sources of funding may not provide the full amount required. Senior lenders may be unwilling to increase their exposure, particularly when the company already has significant debt or lacks sufficient collateral. At the same time, raising additional equity can reduce existing shareholders’ ownership and influence over the business.

Mezzanine financing addresses this gap, as it consists of a hybrid form of capital that combines features of debt and equity and occupies an intermediate position in a company’s capital structure. Companies often use this financing to obtain additional capital beyond the amount available from senior lenders, while limiting the need for a substantial equity raise.

What Is mezzanine financing?

Mezzanine financing represents a strategic financial tool that bridges the gap between senior debt and equity. This hybrid form of financing enables lenders to convert debt into equity, fostering flexibility and higher returns. Frequently employed in acquisitions and company expansions, mezzanine financing captures both opportunity and risk, offering companies crucial capital for growth. Although it is considered one of the highest-risk forms of debt, mezzanine financing offers some of the highest returns in debt investments.

Companies may choose mezzanine financing to fund specific growth projects or acquisitions with short- to medium-term time horizons. Often, these loans will be funded by the company’s long-term investors and existing funders of the company’s capital.

Main forms of mezzanine financing

Mezzanine financing is not a single standardized product. It can be structured in several ways, depending on the company’s financing needs, repayment capacity, capital structure, and the investor’s return requirements. The most common forms include:

• Subordinated debt. This form is a loan that ranks below senior debt in the repayment hierarchy but above equity. If the company defaults or enters liquidation, senior lenders are paid first, while subordinated lenders are repaid only after those obligations have been satisfied.
• Second-lien financing. It is secured by the same assets that support a senior lender’s first lien, but the mezzanine lender has a lower priority claim over those assets. If the borrower defaults, the first-lien lender generally has priority in enforcing its security and recovering the outstanding debt.
• Preferred equity. Preferred equity is legally a form of ownership, but it has features that make it resemble debt or other types of mezzanine capital. Preferred shareholders typically receive priority over common shareholders when dividends are paid, or assets are distributed during a liquidation.

Mezzanine financing provides companies with additional capital and more flexibility than traditional senior debt. Its characteristics make it useful for established businesses pursuing acquisitions, expansion projects, recapitalization, or ownership transitions. Key advantages are:

• Providing access to additional capital by filling the gap between the amount of senior debt a company can obtain and the total capital it needs.
• Helping existing owners retain a larger share of the business than they might retain after a conventional equity raise.
• Offering more flexible repayment structures than conventional bank debt.
• Supporting companies in completing transactions that require more funding than senior debt alone can provide.
• Offering lower cost than pure equity, particularly when the potential cost of giving up a large share of the company’s future value is considered.

Although mezzanine financing can provide flexible capital with less immediate ownership dilution than equity finance, it also exposes the company to higher costs, repayment pressure, and contractual restrictions. These disadvantages mean that it is generally suited to established businesses with predictable cash flows rather than to companies with unstable or insufficient earnings. 

Finally, mezzanine financing is a flexible hybrid capital that can help companies bridge the gap between senior debt and equity. By combining debt-like features with equity-linked characteristics, it enables established businesses to secure additional funding for acquisitions, expansion, recapitalization, and other strategic initiatives while potentially limiting immediate ownership dilution. However, it is generally more expensive than senior debt, may involve equity dilution through conversion rights or warrants, and can put significant pressure on a company’s cash flow because of interest payments and repayment obligations. 

Noha Gad

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

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

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

What are fringe benefits?

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

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

Examples of fringe benefits

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

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

Why do companies offer fringe benefits?

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

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

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

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

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

Ghada Ismail

In this second part, Abu Zannad turns to the resource startups actually have plenty of: creativity. He explains why “out-noticing” the competition matters more than out-spending them, and why so many founders confuse visibility, reputation, and meaning when they talk about “building a brand.”

How can startups use creativity as a competitive advantage when they cannot compete with larger companies on advertising budgets, resources, or brand recognition?

I think we first need to stop treating creativity as incidental, as this magical thing that occasionally happens when a talented person walks into a room. Creativity is becoming a much more important competitive capability precisely because AI is making so many other capabilities abundant.

Today, almost everyone can produce more content, more variations, more designs, more headlines and more analysis, faster and cheaper than ever before. So producing more is becoming less interesting. The competitive advantage increasingly lies in seeing something other people did not see.

I often describe it as the difference between information and insight. Two companies can have access to exactly the same data and come to completely different conclusions. Same data. Different eyes. That difference is human judgement.

And I don’t think insight has to be left to luck. There are conditions that make it more likely. Experience gives you patterns. Curiosity makes you notice what does not fit. Scepticism stops you accepting the first explanation. Contradictions reveal where reality is behaving differently from the category’s assumptions. Connections allow two things that normally live separately to collide.

Sometimes even constraint helps. I call that creative desperation: when you genuinely cannot solve the problem in the conventional way, you are forced to find another path. That is why startups may actually have an advantage. A large incumbent can often buy another media plan. A startup cannot. It has to notice something the incumbent has stopped noticing.

Look at the extraordinary group of younger businesses emerging around us:

Dollar Shave Club did not beat the shaving establishment by producing a more expensive shaving commercial. It understood internet humour and attacked the seriousness of the category.

Liquid Death looked at bottled water and asked why water had to behave like bottled water at all. It borrowed from punk, heavy metal and entertainment culture.

PRIME understood that creator communities themselves could become an extraordinary distribution system.

Crumbl turned cookies into something closer to sneaker drops; weekly anticipation, scarcity, reviewing and participation.

Sleep or Die looked at the soft, calming visual language of the sleep category and contradicted it completely.

And Dubai Chocolate may be one of the most fascinating cases of all. Someone created an unusually sensory product: “the crack of the chocolate, the colour of the pistachio, the texture of knafeh and a platform discovered that people could not stop watching it”. The algorithm accelerated the phenomenon; it did not originate the human fascination.

I think we should stop treating cases like these as amusing stories about things that “went viral.” They are evidence. We are watching something close to a new applied science of cultural creativity develop in front of us.

Every platform is producing an enormous live laboratory of human behaviour. Every unexpected breakout gives us something to study. What was the human tension? What cultural code did the brand recognize? What category convention did it violate? What community carried the idea? What made somebody want to participate rather than merely watch? What behaviour did the platform reward? What made the idea travel from one subculture into another?

Those are not questions only for advertising people anymore. They are questions for founders, anthropologists, behavioural scientists, strategists and technologists. And over time, we can begin building frameworks around them; not formulas for producing virality, because culture will never be that obedient, but better places to look for the unexpected.

That distinction matters. Creativity is not a formula. But neither is it magic. We can study it. We can develop our intuition. We can accumulate cases. We can recognize patterns. We can learn the grammar of a platform, a category, a culture or a subculture; and then have the courage to break that grammar when the human insight tells us to.

This, to me, is where AI becomes enormously useful. Let the machine search wider. Let it retrieve more cases, make more connections, generate more possibilities and accelerate experimentation.

But the human still has to ask: Which one matters? Which contradiction is interesting? Which observation is merely strange… and which one reveals something genuinely human? Which idea deserves to exist?

Because AI can increasingly generate ten thousand possibilities. The scarce capability is knowing which possibility is worth pursuing.

So my advice to startups would be: don’t try to out-produce the large companies. You probably can’t. And increasingly, there is little advantage in doing so anyway. Out-notice them. Out-understand them. And then use creativity to turn what you noticed into something the culture cannot ignore.

What do you think startups misunderstand most about building a brand: is it about visibility, reputation, or creating an identity people want to associate with?

I think what startups misunderstand most is the word brand itself.

They often think the sequence is: Build the product. Acquire customers. Grow. And when we become big enough, we will “do the brand.” Usually that means a new logo, a brand book, perhaps a large campaign.

But the uncomfortable truth is that you are building the brand from the first day whether you intend to or not. The first product experience builds it. The first customer complaint builds it. The way your founder speaks builds it. The price builds it. The packaging builds it. The people who choose you build it. The things you repeatedly say, and the things you repeatedly do, build it. So visibility, reputation and identity are not really three competing answers. They are three different layers.

Visibility means: I know you exist.

You can buy visibility. You can hack it. You can go viral and acquire enormous visibility almost overnight. But visibility is not a brand. We are surrounded today by things that became very visible and disappeared six months later.

Reputation means: I have learned what to expect from you.

You deliver. The product works. You keep your promises. There is consistency between what you say and what actually happens. Reputation takes longer because it has to survive contact with reality. And then there is something more interesting.

Meaning.

At some point, the strongest brands begin to signify something beyond the immediate utility of the product. Choosing the brand says something. Sometimes it says something to other people. Sometimes, more importantly, it says something to ourselves.

That is very close to the argument I make in AdEntity. Modern advertising became powerful because it taught objects to carry meaning. A watch stopped being only an instrument for telling time. A car was not only transportation. A pair of shoes was not only protection for the feet. Commercial objects became signals through which ambition, taste, rebellion, belonging, care or achievement could become socially legible.

And AdEntity does not argue that brands invented those desires. It argues that the surrounding system; the brand, product, image, celebrity and media environment… helped teach people how those desires could be recognized.

That is why I would hesitate to tell a founder, “Create an identity people want to associate with.” It is almost right. But it can lead to another mistake: inventing a beautiful brand personality with no relationship to the actual business.

Meaning has to be earned through product truth.

If Liquid Death behaved like a rebellious entertainment brand but the product, packaging and every interaction reverted to conventional bottled-water behaviour, the mythology would eventually collapse.

If Apple talks about creativity but produces experiences that feel careless, the symbolism weakens.

A brand cannot indefinitely advertise a meaning that the business itself does not substantiate. And this is where I think startups face a particularly modern trap. Startups live inside dashboards: ‘CAC. ROAS. Conversion. Cost per click. Retention. Downloads. Funnels’.

These numbers matter enormously. I would never advise a founder to ignore them. But because they are visible every morning on a dashboard, they begin to acquire psychological authority. What we can measure immediately starts to look more important than what is accumulating slowly.

And brand accumulates slowly. Memory accumulates. Familiarity accumulates. Trust accumulates. Distinctive assets accumulate. Meaning accumulates. This is why performance marketing is so seductive. You spend today and something happens tomorrow.

Brand building is more like compound interest. For a while, it can look as though very little is happening. And then one day people search for your name instead of the category. They recommend you without being paid. They recognize you before they see the logo. They forgive you a small mistake because there is accumulated trust. They consider you before the performance ad arrives. They may even pay slightly more because the alternative does not feel equivalent.

That is an economic asset, not a communications indulgence.

Airbnb gave us a fascinating demonstration of this. When the company dramatically reduced marketing during the pandemic, traffic recovered to roughly 95% of its 2019 level before marketing expenditure fully resumed. By the fourth quarter of 2020, more than 90% of traffic was direct or unpaid. Brian Chesky’s conclusion was essentially that Airbnb had become culturally established enough that the brand itself was generating demand.

That is what founders should aspire to. Not necessarily becoming a verb. But getting to the point where every customer does not have to be rented again from an advertising platform. Because if every sale requires another paid impression, another promotion and another retargeting message, you may have built an efficient acquisition machine. You have not necessarily built a brand.

There is another problem that optimization culture creates for startups: they change too much. New headline. New proposition. New design. New tone. New campaign. New audience. New creative every week because something performed 4% better. Experimentation is essential for discovering what works. But once you discover something valuable, brand building requires the opposite capability: the discipline to repeat it.

Memory needs consistency. And let’s not confuse consistency with repetition.

The Ehrenberg-Bass work on distinctive assets is useful here. Colours, sounds, shapes, characters, packaging and other recognizable cues only become assets when people repeatedly learn to associate them with one brand. They are built and protected over time; they do not become distinctive because somebody declared them distinctive in a brand guideline.

So perhaps I would give founders a very simple architecture: Be visible enough to enter the mind. Be good enough to earn a reputation. Be consistent enough to become remembered. Be meaningful enough to stand for something.

And make sure the product continuously earns the story you are telling.

Because a brand, in the end, is not the campaign. It is not the logo. It is not the number of followers. It is not even what the founder says the company stands for. A brand is the memory and meaning that remain when the advertising disappears. That is what startups should start building from day one.

Ghada Ismail

Not every good startup is a venture capital startup.

That can be hard for founders to hear, especially when they have built a product people like, attracted their first customers, and started generating revenue. But venture capital is not simply looking for businesses that work. It is looking for businesses that could become much, much bigger.

That is what makes a startup “VC-backable.” It is less about having a well-prepared investor presentation and more about showing investors that there is a real opportunity to build something with significant scale.

Market Size and Growth Potential

One of the first questions investors will ask is how big the opportunity really is.

A startup can solve a genuine problem and still have limited room to grow if its potential customer base is too small. For a VC-backed company, the ambition usually needs to go beyond building a profitable small business.

This is particularly relevant for startups in Saudi Arabia and the wider GCC. A founder may begin with a solution designed for Saudi customers, but investors will want to understand whether that business can eventually expand across the region or into other markets.

The bigger question is not just, “Who will buy this?” It is, “How many people or businesses could eventually need it?”

Customer Demand and Market Traction

A great idea is still only an idea until someone is willing to use it or pay for it.

This is where traction matters. Revenue, customer numbers, repeat purchases, retention, and transaction volumes can all show whether a startup is gaining genuine momentum.

For an early-stage company, traction does not necessarily mean millions in revenue. A growing user base, successful pilots, strong engagement or commercial partnerships can also demonstrate demand.

But there is a difference between growth and meaningful growth. Adding customers through heavy discounts, for example, does not necessarily prove that they will stay.

The Problem and the Value Proposition

The strongest startups tend to begin with a problem rather than technology for technology’s sake.

If a company can help businesses reduce costs, make a complicated process faster, improve access to finance, or solve a problem customers face regularly, its value becomes easier to understand.

Saudi Arabia’s rapidly developing fintech, healthcare, logistics, and technology sectors offer plenty of opportunities. The challenge is proving that the solution is valuable enough for customers to change their existing habits.

Founder Experience and Execution

Investors are putting money into a company, but they are also betting on the people running it.

Founders do not necessarily need decades of experience or impressive corporate backgrounds. What matters is whether they understand the problem, know their customers, and can keep adapting when things do not go according to plan.

Startups rarely follow the original business plan perfectly. Markets change, products need to be rebuilt, and early assumptions can prove wrong. Being able to respond to those changes can be just as important as having the original idea.

Scalability and Business Economics

Rapid growth sounds impressive until you look at how much it costs.

Investors will want to understand how much it costs to acquire a customer, how long that customer stays, and how much value they generate. A startup does not need perfect economics from day one, but there should be a credible path toward becoming more efficient as it grows.

That is also where scalability comes in. A Saudi startup might expand from one city to the wider Kingdom, then into the GCC or other international markets. The opportunity does not have to be global from day one, but investors will want to see what the next stages could look like.

Ultimately, being VC-backable does not mean a startup has to be perfect. Very few early-stage companies are.

It means giving investors a reason to believe the business can become significantly larger than it is today, and that the founders have a realistic way of getting there.

Noha Gad

Companies increasingly turn to cost-control measures to safeguard their financial stability. Among the most common and visible of these measures is the hiring freeze. While often presented as a temporary, strategic pause, a hiring freeze carries significant implications for employees, job seekers, and the organization’s long-term growth trajectory.

A hiring freeze is a business decision that sounds simple on paper but ripples through every corner of an organization. At its core, it is a temporary pause on bringing new people on board, no new roles, no backfills for departing employees, and often a hard stop on most recruitment activity. Companies often take this decision when they need to tighten budgets, navigate economic uncertainty, or reevaluate their workforce strategy without resorting to layoffs.

For current employees, a hiring freeze can feel like a mixed signal: there is short-term reassurance that jobs are safe, but also the creeping reality of heavier workloads, stalled promotions, and growing anxiety about the company’s future. For job seekers, it can mean suddenly stalled offers or roles that vanish midway through the interview process. And for leadership, it’s a balancing act between preserving cash and protecting morale, productivity, and long-term talent pipelines.

Why do companies implement a hiring freeze? 

Leaders may implement a hiring freeze to protect company finances and keep the business operational. They may also freeze new hires if the organization is plateauing or declining. There are a few other reasons why a halt in hiring may be necessary:

• Budget deficit: If the process of hiring and paying new employees has the potential to cause overspending, leaders may halt recruitment. They may decide to delay hiring candidates until they improve the business's financial situation. 
• Emerging liquidity issues: Liquid assets are a type of capital businesses have, such as cash balances and bank deposits. If an employer is uncertain whether a company is maintaining enough liquid assets, it might stop hiring efforts.
• Upcoming layoffs: Layoffs are the discharge of temporary or permanent employees due to a lack of work or money available. Company leaders may implement a hiring freeze to save funds, preserve the fiscal stability of the business, and avoid layoffs.
• Changes in market conditions: The shifts in market conditions can have a notable impact on revenue generation and overall profitability. Thus, leaders may implement a hiring freeze to counter the impacts of these changing conditions.

A hiring freeze may have an impact on current employees, as they might be responsible for completing additional tasks and working longer hours to keep a business operational. Professionals can overcome the challenge of a hiring freeze by:

• Strengthening professional relationships with peers to position themselves as a valuable team member.
• Seeking leadership opportunities, as a hiring freeze may leave certain positions open, including leadership positions.
• Maintaining a positive mindset and attitude to be able to develop a positive reputation among colleagues and supervisors.  

Pros and cons

Although the hiring freeze delivers immediate financial relief, it sets off a chain of operational and cultural side effects that can last well beyond the freeze itself. Potential benefits include:

• Immediate cost control: Halting new hires quickly reduces cash outflow without the legal, financial, and reputational costs of layoffs.
• Preserving institutional knowledge: Because existing employees keep their jobs, a freeze avoids severance costs and the loss of expertise that come with mass redundancies.
• Signaling fiscal discipline to investors and lenders: A freeze can be read as a proactive, responsible move to protect the balance sheet and extend runway.
• Flexibility and reversibility: Unlike layoffs, a hiring freeze can be lifted relatively quickly when conditions improve, allowing the company to resume growth without rebuilding from scratch.

Key risks and downside include:

• Increased workload and burnout: Vacant roles and natural attrition mean remaining staff absorb extra responsibilities, which can reduce performance, quality, and customer service over time.
• Retention risks: Employees may interpret a freeze as a warning sign of deeper trouble, leading to disengagement or voluntary turnover.
• Talent pipeline damage: Prolonged freezes can harm the employer brand, making it harder to attract top candidates later and causing promising prospects to drop out of the funnel.
• Management challenges: Leaders may avoid addressing poor performance because removing an underperformer would leave a gap that can’t be filled, quietly lowering team standards.

To sum up, a hiring freeze can be a necessary, short-term response to financial pressure, but it is not a cost-free solution. While it buys time and preserves jobs in the near term, the hidden costs accumulate in heavier workloads, strained morale, stalled growth, and a weakened talent pipeline.

Ghada Ismail

Ahmad Abu Zannad is an advertising strategist and author of AdEntity, a new book arguing that advertising did more than sell products over the past century; it helped build modern identity itself, turning ancient human signals like status, belonging, and ambition into a shared language understood across cultures. The book’s central warning is that this role is now shifting to algorithms, which Abu Zannad argues are moving beyond shaping what we buy to shaping who we become.

In the first installment of Sharikat Mubasher’s interview with Abu Zannad, we explore the deeper forces shaping brand-building today: how culture actually works (and how startups misread it), what has really changed as advertising has moved from the TV era to the age of AI, and why an obsession with algorithms and data can quietly replace understanding people with merely measuring them.

Your book looks at how advertising became deeply connected to culture and technology. How can startups use culture to build brands that people genuinely connect with?

“I would start by changing one word in the question. I don’t think startups should use culture. I think they should understand it.

Culture is not putting a local celebrity in an advertisement, borrowing a dialect, or adding familiar symbols to a campaign. Those are expressions of culture. Culture itself is much deeper. It is the shared understanding of what success looks like, what generosity means, what makes somebody trustworthy, what feels prestigious, what feels embarrassing, what belongs and what does not.

And beneath culture sit very old human motivations.

People everywhere want belonging, recognition, security, status, love and hope. But cultures give those motivations different expressions. Ambition in Riyadh does not necessarily look exactly like ambition in London or Tokyo. The human motivation may travel; its cultural meaning changes.

And when I say culture, I don’t only mean national culture. Internet culture, youth culture, creator culture and subcultures are cultures too.

Look at Dollar Shave Club. They entered shaving against companies with enormous budgets and decades of category authority. They couldn’t outspend Gillette, so they understood something happening culturally: people were increasingly suspicious of over-engineered products, corporate language and inflated prices. Their famous low-budget 2012 launch film spoke in the irreverent language of internet culture and generated 12,000 new subscribers immediately after launch.

Or look at Liquid Death. They entered perhaps the ultimate commodity, water, and behaved nothing like a water brand. They borrowed the visual codes, humour and attitude of punk, heavy metal and energy-drink culture: tall cans, a skull, “Murder Your Thirst,” absurd entertainment. The founder has explicitly described the ambition as building an entertainment company that monetizes through beverages.

PRIME did something different again. Logan Paul and KSI entered one of the world’s most competitive beverage categories with something Coca-Cola and Pepsi could not simply manufacture overnight: an existing cultural relationship with millions of people. The product launched in 2022 out of creator culture, turning two former rivals into partners and their audiences into an extraordinary distribution network for attention.

None of these companies began with the advantages traditionally needed to enter such categories. What they possessed was cultural capital before they possessed category power.

That is where I think startups have an interesting advantage over very large companies. They may have less money, but they are often much closer to the tension they are trying to solve. The founder may have lived the frustration, spoken the language of the community and understood a behaviour long before somebody turned it into a market-research chart.

So I would tell a startup: don’t begin by asking, “How do we make our brand culturally relevant?”

Ask: What is already alive in culture that nobody in our category is seeing… and what legitimate role can our product play inside it?

Because if you cannot outspend the category, you may still be able to out-understand it culturally.

The best brands do not impose themselves on culture. They find something already alive within it… and become useful enough, meaningful enough and distinctive enough to belong there.”

From the traditional advertising era to today’s AI-driven landscape, what has changed most about how brands earn consumers’ attention, and what has remained the same?

“I think the biggest change is that we have moved from an age of persuasion to an age increasingly shaped by selection.

In the traditional advertising era, a brand would buy access to an audience through television, newspapers, magazines or outdoor, and then the creative idea had to do the difficult part: make people notice, remember, feel something and perhaps change their behaviour.

There was still a human author in the middle of that process. A strategist, a writer, a creative director, a filmmaker. Someone was making a judgement about people and culture and saying: I think this idea will matter.

Today, the system is very different.

The algorithm increasingly decides what reaches you, how often you see it, what disappears, what gets amplified and what comes next. And AI is accelerating this dramatically. We can now create hundreds or thousands of variations, personalize them, test them in real time and optimize continuously around whatever generates a measurable response.

So the machinery of attention has changed enormously.

But the strange thing is that the human being underneath it has changed very little.

We still want many of the things our grandparents wanted: belonging, recognition, status, love, security, hope, companionship, achievement. This is one of the central arguments in AdEntity. Advertising did not invent these desires. At its best, it understood them, translated them through culture and gave them contemporary symbols, products and stories.

A diamond could become a signal of commitment. A car could become a signal of achievement or freedom. A sports brand could turn effort into a story of personal transcendence.

The technology changed. The human motives did not.

And I think that distinction matters enormously now because AI creates a temptation to confuse response with meaning.

An algorithm can learn that outrage keeps me watching. It can learn which image makes me click, which headline makes me pause and which version converts 3% better. But a reaction is not necessarily a relationship. And attention is not necessarily admiration.

This is where I worry about the direction of advertising. We are becoming extraordinarily good at optimizing the signal while sometimes forgetting to ask whether the signal means anything.

The old advertising industry could certainly produce terrible work, but its greatest work tried to create something people would voluntarily carry into culture: a line, a story, an aspiration, a piece of music, an image, sometimes even a new way of seeing themselves.

The danger today is that we settle for something much smaller simply because we can measure it more precisely. So if I had to put the whole transition in one sentence:

The old challenge was, “How do I persuade you?” The new power is increasingly, “What will the system keep showing you?”

That is an extraordinary technological shift. But brands should remember that behind every data point is still a very old human animal trying to belong, aspire, love, achieve and make sense of the world. The screen keeps changing. The human being behind the screen changes far more slowly.”

Has advertising become too obsessed with algorithms and data at the expense of understanding people? What can startups learn from that?

“Yes, but I would be careful with the criticism because I am not against data, algorithms or AI. Quite the opposite. I think they are extraordinary tools.

The problem begins when we confuse efficiency with intelligence.

Years ago, in Adman vs. Chomsky, I challenged Noam Chomsky’s description of advertising as an industry whose prime task is to ensure that “uninformed consumers make irrational choices.” I still disagree with that as a definition of advertising. Human beings were irrational long before the first advertising agency existed. Behavioural economics has simply helped us understand some of the shortcuts through which people navigate a complicated world.

But there is a danger today that we take those cognitive biases; scarcity, social proof, loss aversion, immediacy, outrage, fear of missing out… and hand them to an algorithm whose only instruction is: find what produces a response and do more of it.

Then AI allows us to generate another hundred versions, test them faster, target them more precisely and optimize them continuously.

We should ask ourselves: is that progress?

Or have we simply become better at doing mediocre advertising faster, cheaper and more intrusively?

That, to me, is the misuse of the algorithm. Because there is another possibility.

Look at Sleep or Die, a young sleep brand that looked at an entire category filled with lavender colours, peaceful bedrooms and soft wellness language and basically said: this is not what insomnia feels like. The brand called exhausted customers “zombies,” used provocative cigarette-style packaging and built an entire irreverent world around the seriousness of sleep. One unconventional product post on LinkedIn reportedly reached more than 180,000 people, and around 4,000 people joined the waitlist before launch.

The algorithm did not come up with that idea. It discovered that people found the idea interesting.

And Dubai gave us an even more extraordinary example.

FIX Dessert Chocolatier did not begin with a dashboard saying, “Pistachio content has a high completion rate.” Sarah Hamouda began with a craving and created something genuinely different: chocolate, pistachio, tahini, and the crunch of knafeh. Then a creator filmed herself breaking the bar open. You could see the green filling, hear the crack and crunch, and almost experience the texture through the screen.

That video eventually exceeded 120 million views, and FIX reportedly received more than 30,000 orders after it took off. The phenomenon became so large that “Dubai chocolate” became a global food category, copied by some of the world’s biggest confectionery companies.

Again, the algorithm did not invent Dubai Chocolate. It recognized that human beings could not stop looking at it.

Crumbl is another useful example. Its weekly rotating cookie drops created anticipation and FOMO before TikTok amplified them; on the platform, its campaign reached 22 million people and grew followers by 1,500% in two months. The interesting part is that the platform was amplifying an existing behavioral idea, the weekly drop, not substituting for one.

And that is what I think startups should learn. Don’t ask AI to compensate for the absence of an idea. Don’t use behavioral science merely to locate the next vulnerability you can press.

Understand people first. Create something distinctive enough to deserve attention. Then let the algorithm do what it is exceptionally good at: find the people for whom that idea resonates and help it travel.

The algorithm should be an amplifier, not the strategist. And perhaps that is the simplest way I can put it: AI should help great ideas travel faster. It should not merely help mediocre ideas become cheaper.”

Ghada Ismail

Some investment funds just die quietly. They stop making new deals, their investment period comes to an end, and investors expect their money to be returned. But sometimes, the fund does not quite disappear. It keeps holding companies, waiting for the right moment to sell, while years continue to pass.

This is where the term “zombie fund” comes in.

A zombie fund is generally a private equity, venture capital, or similar investment fund that has reached or passed the end of its intended investment period but continues to exist because it still holds investments that have not been sold or exited. Instead of raising new capital and actively building a portfolio, the fund manager mainly focuses on managing existing assets and eventually returning whatever value can be recovered to investors.

Why Are Zombie Funds Created?

Most private investment funds operate on a defined timeline. A typical fund may spend its first few years identifying and investing in companies before entering a later period focused on managing and exiting those investments.

The problem begins when some portfolio companies cannot be sold within the expected timeframe.

For example, a private equity fund may have invested in a company expecting to sell it after several years. If market conditions deteriorate, valuations fall, or the company struggles to find a buyer, selling the investment may no longer make financial sense. The fund may therefore extend its holding period.

If this happens across several investments, the fund can remain active long after its original investment strategy has effectively ended.

How Does a Zombie Fund Work?

A zombie fund typically does not have the same level of activity as a new or actively investing fund. Its manager is primarily concerned with overseeing existing portfolio companies, making necessary decisions, and looking for opportunities to exit those investments.

The fund may still generate returns for investors, but capital is generally being returned gradually rather than being deployed into a new generation of investments.

For fund managers, this can create a difficult situation. Managing an older portfolio requires time and resources, while the management fees generated by the remaining assets may become less attractive as the fund shrinks.

For investors, meanwhile, capital can remain tied up for longer than originally expected.

Why Do Zombie Funds Matter?

Zombie funds can become particularly important during periods of weak investment activity or challenging exit markets.

When valuations decline or buyers become more cautious, private-market investors may struggle to sell portfolio companies at attractive prices. Rather than accepting a significant loss, a fund manager may decide to wait for market conditions to improve.

This can protect the potential value of an investment, but it can also delay the return of capital to investors.

A large number of aging funds can also affect the broader private equity ecosystem. Capital that remains locked in older investments cannot easily be recycled into new opportunities. This may reduce the ability of investors to commit capital to emerging companies and new fund managers.

Wrapping Things Up…

A zombie fund sits in an unusual space between life and closure. It is still legally and financially active, but its original investment mission has largely run its course.

For investors, understanding zombie funds is important because they highlight one of the less visible realities of private markets: investing does not end when the money is deployed. Exits, valuations, market conditions, and the timing of returns can keep capital tied up for years beyond expectations.

Ultimately, a zombie fund is not defined simply by its age. It is defined by what happens after its active investment life is supposed to have ended. In a market where patience can sometimes unlock value, staying alive may be strategic. But when there is no clear path to an exit or value creation, the same longevity can become a burden.

Kholoud Hussein 

Startup growth is often described as a linear process: build a product, acquire customers, generate revenue, raise capital, hire more people, and expand. But some of the most powerful startups grow differently. Instead of treating growth as a sequence of separate steps, they create a system in which each achievement strengthens the next. This is the idea behind the growth flywheel.

A flywheel is a self-reinforcing cycle. The more momentum it gains, the easier it becomes to keep moving. In a startup, this means using customers, data, product improvements, technology, talent, and capital to create a continuous loop of growth.

The concept differs from a traditional growth funnel. A funnel describes how potential customers move from awareness to purchase and retention. A flywheel focuses on what happens after those interactions: how each customer, transaction or improvement creates an advantage that helps attract the next customer.

How the startup flywheel works

Consider a software startup. It launches a product and attracts its first group of customers. Those customers provide revenue, but they also generate something equally valuable: feedback.

The startup uses that feedback to improve the product, making it more useful and easier to adopt. A better product can increase customer satisfaction, retention and referrals, helping the company attract more customers. More customers generate more revenue and more feedback, allowing the company to continue improving.

The cycle then repeats:

More customers → more feedback and data → better product → stronger customer value → more customers.

The important point is that growth is no longer simply an outcome. Growth becomes an input into future growth.

Why this matters for startups

This model is particularly important for startups because they typically operate with limited resources. They cannot always compete with established companies through larger marketing budgets, bigger sales teams or stronger brand recognition.

A flywheel can provide another source of advantage: compounding momentum.

For example, a marketplace can become more valuable as it attracts more buyers and sellers. More sellers create greater choice, which attracts more buyers; more buyers increase the opportunity for sellers, encouraging more suppliers to join.

Similarly, a fintech startup may use transaction data to improve its products and risk assessment. A SaaS company can use customer behavior to refine its software. A platform can benefit from network effects as each additional user increases its value to others.

Different businesses have different flywheels, but the principle is consistent: the business should become stronger because it is growing.

Beyond customers: Talent and capital

The flywheel can extend beyond the product itself.

As a startup grows, it can attract stronger talent, build relationships with larger customers and gain access to additional capital. Experienced employees may eventually become founders themselves, while successful investors can reinvest returns into new companies.

This creates a broader ecosystem in which one company's growth can contribute to future entrepreneurial activity.

Capital, however, should be viewed as fuel rather than the flywheel itself. Funding can accelerate hiring, product development, and expansion, but it cannot substitute for customer demand or a sustainable business model. If growth depends entirely on continuously raising more money, the flywheel has not necessarily been created.

Building a sustainable flywheel

The strongest startup flywheels are built around genuine value creation.

Companies need to identify what becomes more valuable as they scale. It could be customer data, network effects, brand recognition, distribution, technology, operational efficiency, or accumulated expertise.

The objective is not simply to grow faster. It is to build a business in which growth creates the conditions for further growth.

That is what makes the flywheel powerful. A startup stops relying exclusively on constant external inputs and begins generating its own momentum—turning customers into data, data into better products, products into stronger demand, and stronger demand into the next stage of growth.