Noha Gad

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

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

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

How could AI boost SMEs’ business?

Automating routine tasks

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

Improving customer service, communication, and marketing

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

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

Taking informed decisions

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

Managing inventory and supply chains

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

Eliminating errors and reducing costs

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

Providing new products and services

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

Enhanced accessibility and inclusion

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

Government support

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

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

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

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

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

Main barriers

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

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

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

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

Ghada Ismail

When a company decides to enter a new market or expand its operations, one of the first questions it faces is whether to build from scratch or invest in an existing business or facility. This decision often comes down to two approaches: greenfield investment and brownfield investment.

Both strategies can help companies expand their presence, reach new customers, and increase their market share. However, they differ in how businesses establish operations, how much control they retain, and the costs and risks they take on.

Understanding these differences can help investors choose the approach that best fits their growth plans.

What Is Greenfield Investment?

Greenfield investment occurs when a company establishes a new operation from the ground up in a foreign or domestic market. Instead of purchasing an existing facility or business, the investor develops the project from scratch, which may include acquiring land, constructing buildings, installing equipment, and hiring employees.

One of the main advantages of this approach is control. Investors can design facilities, choose technologies, establish company culture, and structure operations according to their specific requirements.

Greenfield projects can also introduce new technology, create jobs, and support the development of local industries.

However, starting from scratch can be expensive and time-consuming. Investors may need to navigate licensing requirements, secure land, build infrastructure, and establish relationships with suppliers and customers before generating revenue. These challenges can become more significant when entering an unfamiliar market.

What Is Brownfield Investment?

Brownfield investment involves investing in an existing facility, business, or industrial site to expand operations or develop new activities. Depending on the project, investors may renovate an existing property, upgrade production equipment, acquire an operating company, or repurpose an underused industrial site.

This approach can offer a faster route into a market because essential infrastructure may already be available. Existing facilities may have access to utilities, transport networks, suppliers, and an established workforce.

Acquiring an operating business can also provide an existing customer base and established market relationships.

Nevertheless, brownfield investments come with their own challenges. Older facilities may require costly upgrades, while existing businesses can have operational inefficiencies or outdated technology. Investors may also face difficulties integrating employees, systems, and corporate cultures after an acquisition.

Greenfield vs. Brownfield Investment: Key Differences

The main difference lies in how the investment is established.

Initial costs: Greenfield projects typically require substantial upfront spending on land, construction, equipment, and staffing. Brownfield investments may reduce infrastructure costs, although acquisition prices and renovation expenses can be significant.

Time to market: Greenfield investments usually take longer because operations must be developed from the beginning. Brownfield projects can begin operating sooner when the existing infrastructure is suitable.

Control and flexibility: Greenfield investors have greater freedom to design operations and introduce new systems. Brownfield investors may need to adapt existing facilities, processes, or organizational structures.

Risk profile: Greenfield investments carry construction, permitting, and market-entry risks. Brownfield investments involve risks related to existing assets, legacy systems, environmental conditions, and potential hidden liabilities.

Local presence: Greenfield investors build their workforce and business relationships over time. Brownfield investors may benefit from established employees, suppliers, and customers.

Neither strategy is automatically cheaper or safer. The outcome depends on the market, the condition of available assets, and the investor's objectives.

Which Investment Strategy Should Companies Choose?

The right choice depends on what a company wants to achieve.

Greenfield investment may be more suitable for businesses that need specialized facilities, advanced technology, or complete control over their operations. It can also make sense when suitable existing assets are unavailable.

Brownfield investment may appeal to companies seeking faster market entry, existing infrastructure, or an established customer base. It can be particularly attractive when a business can improve an underperforming operation or modernize an outdated facility.

To Wrap Things Up…

Greenfield and brownfield investments offer different routes to business expansion. Greenfield projects provide greater flexibility and control but generally require more time and upfront investment. Brownfield projects can accelerate market entry by building on existing assets, although they may involve costly upgrades and inherited risks.

Ultimately, investors should evaluate total costs, expected returns, operational requirements, and market conditions before committing to either approach. The goal is not simply to build something new or acquire something existing, but to choose the investment structure that offers the strongest long-term value.

Kholoud Hussein 

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

This is where AI orchestration is becoming increasingly important.

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

What is AI orchestration?

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

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

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

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

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

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

Why orchestration is becoming a business priority

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

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

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

AI orchestration addresses this coordination problem.

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

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

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

How startups can benefit

1. Building products without training foundation models

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

Orchestration offers an alternative.

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

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

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

2. Reducing operating costs

Not every task requires the most powerful or expensive model.

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

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

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

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

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

3. Scaling specialized AI solutions

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

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

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

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

The emerging role of AI agents

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

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

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

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

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

The challenges startups must address

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

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

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

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

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

From AI experimentation to business infrastructure

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

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

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

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

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

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. 

Ghada Ismail

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

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

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

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

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

From labeling images to training intelligence

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

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

That evolution is changing the value of the industry.

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

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

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

Why Saudi Arabia has a data advantage

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

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

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

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

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

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

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

A small but growing Saudi startup ecosystem

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

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

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

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

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

The gap between AI adoption and AI scale

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

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

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

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

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

Where the biggest opportunities may lie

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

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

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

Sayed A. sees the same potential across the GCC.

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

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

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

The human side of the AI data economy

Yet the sector faces its own challenges.

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

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

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

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

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

From data labeling to strategic infrastructure

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

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

For Saudi Arabia, that distinction matters.

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

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

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