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.

Ghada Ismail

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

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

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

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

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

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

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

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

First:

What is your Saudi core?

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

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

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

But the discipline is important:

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

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

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

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

Strategists sometimes call these points of parity.

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

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

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

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

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

So I would separate very clearly:

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

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

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

So the identity stays coherent. The expression adapts.

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

I find that combination interesting.

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

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

A Saudi startup going abroad should ask:

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

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

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

I prefer the word trans-creation.

Trans-creation asks a much harder question:

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

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

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

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

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

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

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

Media amplifies.

It does not create meaning.

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

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

Noha Gad

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

Future-ready AI infrastructure

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

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

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

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

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

Key events powering Riyadh’s AI ecosystem

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

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

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

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

Upskilling national AI talent

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

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

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

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

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