Ghada Ismail

As artificial intelligence reshapes the business landscape, founders are rethinking how they scale their companies, improve efficiency, and respond to market uncertainty. In an interview with Sharikat Mubasher, Benjamin Owen, Business Coach, discusses how AI is creating new opportunities for entrepreneurs, what distinguishes founders in the GCC from those in other markets, and why building a self-managing company requires more than technological innovation. He also shares his views on leadership, talent, and the qualities that will define successful businesses over the next five years.

How has the current business environment changed the way founders think about growth?

Current technological shifts have empowered business owners to do more with less. A true 10x transformation in a business can only be achieved through technological innovation or by creating a new world for customers. AI has given founders the power to do both.

Founders now have more opportunities to do less while achieving better results, thanks to AI, disruptive technology, and, somewhat unfortunately, geopolitical disruption. Sometimes, a scaling company needs to disrupt its own established ways of working; other times, change is forced upon it by the economy. My opinion is that founders need to disrupt themselves before the market does.

Has the AI boom created too many startups chasing the same opportunity?

In my opinion, there is plenty of room for competition in any market. Founders can choose which part of the market they want to serve and become a leader in. The surge in AI startups is simply a signal that plenty of opportunities are available. The technology is advancing so quickly that many of the ideas, products, and services being created today will become obsolete or significantly better within a matter of months.

Are AI startups solving real problems or simply adding AI to existing products?

I believe AI startups are both solving problems and adding AI to existing products. Integrating AI into certain existing products can solve significant problems and create tremendous value for many people.

One of the partners in my mastermind group has an app called Talktokaya, which provides a safe space for people to talk to a virtual companion when they feel lonely. It is exceptional and is genuinely helping people who have nobody to turn to in moments of need. It is a transformational AI companion.

What stands out to you about GCC founders compared with founders in other markets?

What stands out about GCC founders is their strong abundance and growth mindset. Perhaps I can explain this by contrasting it with the UK, where I began my business journey. There, it sometimes felt as though the more successful I became, the heavier the burden I carried because of the taxation system.

Another sentiment my clients report is the desire here to see one another succeed. This is a stark contrast to the experience in some other countries.

What role can AI play in helping companies in the GCC region scale faster?

The GCC is one of the fastest-evolving regions in the world, and AI gives founders access to significant leverage, data, and resources. It can also make testing ideas and exploring different approaches much easier.

I am a strong advocate of the idea that the founders who win are those who test and iterate the fastest. AI is perhaps the most effective tool for this process.

How can founders build companies that operate without them?

Building a self-managing company is really about operationalizing an 80/20 cycle in your business. This means finding the right people, processes, and platforms to execute the work, allowing you to elevate yourself out of the day-to-day running of the business.

It is also essential to start with the end in mind. Many founders build themselves into their company's processes. They become the key decision-makers, hold on to roles and tasks for too long, and prioritize work over leverage.

Building a self-managing company is not just about creating a leveraged model. It is also about having a sufficiently ambitious goal to attract individuals with the highest level of talent, who can drive the business beyond the founder's own capabilities.

What mistakes do companies make when responding to times of uncertainty?

There is a distinction I work on with my clients that I consider fundamental to coaching: the difference between being a victim and being an owner.

A victim sees themselves as subject to circumstances and reacts to what is happening. An owner takes responsibility for their state of mind and chooses how to respond to a situation.

I believe the mistake people in companies make during uncertain times is becoming reactive or allowing themselves to become victims of uncertainty.

Leaders can also fall into the "when-then" paradox, waiting for circumstances to change before taking action.

What do you think the strongest companies will look like five years from now?

Five years from now, the strongest companies will be those with goals that attract the best talent, cultures built around a strong sense of purpose, and the ability to create a new world for their customers.

I believe the defining trait of the strongest companies will be their ability to achieve outsized results with small teams. They will have leveraged technological innovation so effectively that they can make a disproportionate impact relative to their size.

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.

Kholoud Hussein 

For years, the artificial intelligence race was largely conducted in English. The world’s most powerful foundation models could write, code, summarize, and reason across a growing range of tasks, but their performance often weakened when they encountered the realities of Arabic: its grammatical complexity, regional dialects, cultural references, code-switching, and the enormous gap between formal written Arabic and the language people actually speak.

Saudi Arabia increasingly sees that gap not simply as a linguistic problem, but as a technology and investment opportunity.

The Kingdom is now emerging as one of the most ambitious markets for Arabic artificial intelligence, with government-backed companies, startups, global technology groups and large enterprises building different layers of an Arabic AI ecosystem. At the center of that effort is the development of proprietary language models that can understand Arabic on its own terms rather than treating it as a translation layer on top of English-centric systems.

The scale of the opportunity is reflected in the capital flowing into the wider Saudi AI ecosystem. Saudi Arabia’s National Strategy for Data and AI targets $20 billion in local and foreign investment and at least 300 active AI startups by 2030. Meanwhile, the country’s broader AI market is estimated at $2.14 billion in 2025 and projected to reach $16.9 billion by 2032, according to MarketsandMarkets.

Yet the more important question is not how much Saudi Arabia spends on AI.

It is whether the Kingdom can turn that capital into proprietary intellectual property, globally competitive companies and models that become infrastructure for the Arabic-speaking digital economy.

The Arabic gap is bigger than translation

Arabic presents a distinctive challenge for AI developers.

The language exists simultaneously in Modern Standard Arabic, classical forms, and dozens of spoken dialects. A Saudi user, for example, may switch between formal Arabic, Saudi dialect, English terminology, and industry-specific language within the same conversation.

For AI models trained predominantly on high-quality English data, this creates a structural disadvantage.

Research cited by Arab News estimates that only around 15% of Arabic text available online is clean enough for large-language-model training, compared with more than 50% for English. Developers therefore face not only a shortage of data but also a shortage of high-quality, correctly labelled and culturally representative data.

That creates an opening for companies willing to build the data layer themselves.

This is where Saudi startups and emerging technology companies are becoming important. Their advantage is not necessarily the ability to spend hundreds of millions of dollars training a general-purpose model. It is their proximity to Arabic users, enterprise data, dialects and specific commercial problems.

The resulting market is therefore developing on several levels.

At one end are foundation models such as HUMAIN’s ALLAM, developed in Saudi Arabia for Arabic-first use cases. At another are companies such as Riyadh-headquartered Intella, which has built proprietary speech technologies and small language models designed around Arabic dialects and enterprise applications. Between them sits a growing ecosystem of startups developing voice agents, vertical models, translation systems, enterprise copilots and domain-specific AI applications.

Together, they are attacking the Arabic AI problem from different directions.

From consuming AI to owning the model

The launch of HUMAIN in May 2025 marked a major change in Saudi Arabia’s approach.

The PIF-owned company was established to operate across the entire AI value chain, from data centers and cloud infrastructure to advanced models and applications. Its flagship ALLAM model is positioned as one of the world’s most powerful multimodal Arabic large language models.

HUMAIN later launched HUMAIN Chat, powered by ALLAM 34B. The company said the model was trained on more than 500 billion Arabic tokens and refined using hundreds of domain experts and evaluators. The system was designed to support Arabic and English while incorporating regional and cultural context.

The significance extends beyond having another chatbot.

A proprietary foundation model gives Saudi Arabia control over an important part of the technology stack: the data, model weights, training processes, deployment environment, and intellectual property.

That matters particularly for government, financial services, healthcare, energy and other regulated industries where data residency, security and customization can be as important as raw model performance.

It also changes the economics of the market.

Instead of paying indefinitely for access to foreign foundation models, Saudi companies can increasingly build products on locally developed models or adapt them to specific requirements.

Startups are attacking the problem from the bottom up

The startup opportunity is not necessarily to compete head-on with the largest global AI laboratories.

It is to solve the problems those laboratories have historically struggled to solve.

Intella is one example.

The Saudi-headquartered company, founded in 2021, focuses on Arabic speech intelligence rather than trying to become another general-purpose ChatGPT competitor. Its technology covers speech-to-text, text-to-speech, analytics and industry-specific small language models, with support for more than 25 Arabic dialects. The company says its proprietary speech-to-text technology has reached 95.73% accuracy.

Its commercial proposition illustrates where startups can create value.

A bank does not necessarily need the world’s largest LLM. It needs an AI system that understands how its customers actually speak, recognizes local expressions, complies with data requirements, and can connect those conversations to a banking workflow.

The same logic applies to telecom operators, government agencies, insurers and retailers.

In September 2025, Intella raised $12.5 million in a Series A led by Prosus, taking its total funding to $16.9 million. The round included Saudi investors such as Wa’ed Ventures and Hala Ventures and was intended to support R&D, product development and regional expansion.

The figure is modest compared with infrastructure investments in Saudi AI, but strategically important.

It demonstrates that private capital is beginning to finance the specialist layers that make Arabic AI commercially useful.

The corporate-startup model is becoming more important

Saudi Arabia’s Arabic AI ecosystem is also developing through an unusual combination of startups, sovereign capital and global technology companies.

Google Cloud and PIF announced a $10 billion partnership to advance an AI hub in Saudi Arabia, with the initiative involving HUMAIN. The partnership includes research into Arabic-language models and Saudi-specific AI applications, including work to enhance the Arabic capabilities of Google’s Gemini models using additional Arabic datasets.

The model is significant because it illustrates how international technology companies can provide capabilities that startups and local companies may struggle to build independently: compute, cloud infrastructure, specialized chips, model-development platforms, and global distribution.

In return, Saudi Arabia offers something equally valuable: access to a rapidly digitizing market, large enterprise customers, government use cases, capital and a concentrated pool of Arabic data and talent.

The relationship is increasingly moving beyond conventional technology procurement toward co-development.

In August 2026, Microsoft and HUMAIN announced a long-term strategic collaboration under which ALLAM models are planned to become available through Microsoft Foundry and the Microsoft 365 Copilot ecosystem. The partnership also brings HUMAIN AI specialists together with Microsoft’s Forward Deployed Engineers to develop and deploy Arabic-language AI solutions for organizations.

This is strategically important for Saudi startups as well.

A local model becomes significantly more valuable when it can be distributed through a global enterprise platform.

The same principle is visible in AWS’s expanding relationship with HUMAIN. At LEAP 2026, AWS announced plans to make ALLAM available through Amazon Bedrock, while expanding infrastructure capacity for AI workloads in the Kingdom.

The emerging architecture is therefore not simply “Saudi versus Silicon Valley.”

It is increasingly a partnership model in which Saudi companies own local intelligence and context while international technology companies provide global infrastructure, platforms and distribution.

Aramco adds another dimension

Saudi Arabia’s AI ambitions are also being accelerated by its largest corporate institution: Aramco.

The company has developed its own industrial large language model, trained on decades of proprietary Aramco data. The model is designed for applications ranging from analyzing drilling and geological information to forecasting refined-product markets. Aramco has said its decision to develop its own generative AI capability reflects the need to capture the benefits of AI while managing technology and data risks.

That approach could become particularly influential in the next phase of Arabic AI.

The most valuable models may not be the largest models.

They may be the models that understand a particular industry better than a general-purpose system does.

Energy, banking, government, healthcare and legal services all contain large amounts of proprietary information that cannot simply be uploaded to a public AI platform.

This creates a market for smaller, highly specialized models trained or fine-tuned on proprietary datasets.

For startups, that is a much more realistic opportunity than attempting to reproduce the enormous capital expenditure of frontier-model developers.

The investment story is much larger than LLM funding

One of the biggest challenges in measuring the Arabic LLM opportunity is the lack of a separate investment category.

Saudi Arabia does not publish a single figure showing how much has been invested specifically in Arabic foundation models. Much of the disclosed capital is instead bundled into broader AI infrastructure, cloud computing, data centers, chips, venture capital, and AI applications.

The numbers nevertheless show the scale of the ecosystem being constructed.

Saudi Arabia announced nearly $15 billion of investments and agreements at LEAP 2026 across AI infrastructure, data centers, cloud computing, technology manufacturing and venture capital. AWS alone announced a planned investment of more than $5.3 billion in its Saudi cloud infrastructure region, while other agreements covered major data-center and AI projects.

Aramco has also committed approximately SAR9.9 billion ($2.6 billion) in cash contributions to HUMAIN in 2026, alongside the transfer of AI assets, according to Aramco.

Earlier commitments included the $10 billion PIF-Google Cloud AI hub and Saudi Arabia’s $1.5 billion commitment linked to AI-chip company Groq.

These figures should not be added mechanically: some represent partnerships or multi-year commitments, while others span infrastructure and AI rather than Arabic models specifically.

But collectively they illustrate the capital intensity of the market Saudi Arabia is attempting to build.

At the startup level, the numbers are smaller but equally revealing.

Wa’ed Ventures has a $500 million technology-focused fund and had deployed approximately $270 million across more than 75 companies by 2024. Its mandate includes AI and other deep technologies, while requiring certain international technology investments to localize operations in Saudi Arabia.

The Kingdom is therefore developing both sides of the capital equation: large strategic investment for infrastructure and smaller venture capital for experimentation and commercialization.

How much more money could follow?

The next investment cycle is likely to move from infrastructure toward monetization.

Saudi Arabia’s National Strategy for Data and AI targets $20 billion in local and foreign investment and at least 300 active AI startups by 2030. Meanwhile, the country’s AI market is forecast to grow from $2.14 billion in 2025 to $16.9 billion by 2032.

The enterprise AI segment alone is forecast to rise from $810.6 million in 2024 to more than $5.3 billion by 2030, representing a 37.8% compound annual growth rate.

These forecasts suggest that future capital will increasingly follow commercially proven applications.

That could benefit Arabic-model startups because the value of an LLM is ultimately determined by what sits on top of it.

A model that understands Saudi dialects becomes more valuable when it powers a bank’s customer service. A government-specific model becomes more valuable when it automates document processing. An industrial model becomes valuable when it improves maintenance, engineering or energy efficiency.

The transition is therefore likely to be from model building to model commercialization.

The next frontier: Arabic AI agents

The most important development over the next few years may not be larger Arabic LLMs, but more specialized AI agents.

Global AI development is already moving from systems that generate answers to systems that can perform tasks. In Saudi Arabia, Arabic-first agents could combine language models with enterprise databases, government systems, CRM platforms, and workflow tools.

That creates a much larger commercial opportunity.

An Arabic AI agent for a bank could understand a customer’s dialect, verify information, retrieve account data, and complete a transaction.

A government agent could interpret Arabic documents, identify regulatory requirements, and route applications.

An industrial agent could combine technical manuals, sensor data and historical operational information to assist engineers.

The underlying foundation model is only one component.

Data, security, retrieval systems, workflow integration, and domain expertise increasingly determine whether the technology produces economic value.

This is precisely where startups can complement the large capital providers.

The real competition will be over data and talent

The biggest constraint on Saudi Arabic AI may eventually cease to be funding.

It could be data and people.

Developing an Arabic model requires enormous quantities of high-quality training data, but collecting that data raises questions about copyright, privacy, ownership, dialect representation and governance.

Saudi Arabia has an advantage in that it can combine government datasets, corporate information, Arabic digital content and local linguistic expertise. But access to data does not automatically make it usable for training.

The country therefore needs a broader data economy alongside its AI economy.

Talent will be equally critical.

HUMAIN said its ALLaM team included more than 120 AI specialists, including 35 PhD holders, while Aramco has committed to training more than 6,000 AI developers through collaborations involving institutions such as Imperial College, Caltech and KAUST.

For startups, the competition for this talent could become intense.

The next generation of Arabic AI companies will require machine-learning researchers, computational linguists, data engineers, Arabic-language experts, cybersecurity specialists and enterprise software developers.

From linguistic gap to economic infrastructure

Saudi Arabia’s Arabic AI push is ultimately about more than language.

It is an attempt to establish ownership over a layer of digital infrastructure that could sit underneath the region’s future economy.

The opportunity is significant because Arabic is spoken by hundreds of millions of people, while businesses and governments across the region are accelerating digital transformation.

But building a competitive Arabic LLM does not automatically create a successful technology business.

The coming years will test whether Saudi companies can turn models into recurring revenue, whether startups can scale beyond government contracts, whether proprietary data can become a defensible advantage, and whether international partnerships create technology transfer rather than simple dependence on foreign infrastructure.

The strongest companies are likely to occupy the space between these worlds.

They will understand Arabic deeply enough to solve problems global models struggle with, but build products sophisticated enough to compete internationally.

That is where Saudi startups have a potentially decisive role.

The Kingdom does not need to build every component of the global AI stack itself. It needs to identify the layers where local knowledge creates an enduring advantage — Arabic language, regional data, industry expertise, sovereign deployment and culturally relevant applications — and build globally competitive businesses around them.

The first phase of Saudi Arabia’s AI strategy was about attracting infrastructure and capital.

The next phase is about turning that infrastructure into intellectual property.

And the ultimate test will be whether Arabic AI becomes something Saudi Arabia merely helped develop — or an industry in which Saudi companies own the models, data, applications, and businesses that serve the next generation of the Arabic-speaking digital economy.

Ghada Ismail

When a company wants to grow, buying another business can sometimes be easier than building something from scratch. Instead of spending years developing a new product, entering a new market, or hiring a specialized team, a company can acquire a smaller business that already has what it needs.

This is the idea behind a bolt-on acquisition.

A bolt-on acquisition is when an established company buys a smaller business and adds it to its existing operations. The acquired company usually brings something specific to the table, such as new technology, customers, talent, products, or access to a particular market.

The focus is not necessarily on changing the entire business. It is about adding another useful piece to what is already there.

How does a bolt-on acquisition work?

It usually starts with a company identifying an area where it wants to grow.

Take a software company that has a large customer base but does not offer cybersecurity services. Rather than spending years developing those services internally, it could acquire a smaller cybersecurity company that already has the technology, employees, and customers.

The buyer can then add those capabilities to its existing business.

The acquired company may keep its own name and management team, or it may be fully integrated into the larger company. That depends on the businesses involved and what the buyer believes will work best.

What matters is that the acquisition fills a specific gap or creates an opportunity for further growth.

Why do companies choose bolt-on acquisitions?

Speed is one of the biggest reasons. Building a new product or entering a new market takes time. Companies need to hire people, develop products, find customers, and build relationships. Buying an established business can shorten that process considerably.

Bolt-ons can also give companies access to new markets. A business looking to expand into another country, for example, could acquire a local company that already understands the market and has an established customer base.

Technology and talent are another major attraction. In areas such as artificial intelligence, fintech, and software, smaller companies often develop highly specialized products or expertise that larger businesses may want to bring in quickly.

There can also be financial benefits. Once the businesses are combined, the buyer may be able to share infrastructure, eliminate overlapping costs, and introduce the acquired company's products to a much larger customer base.

How is it different from a major acquisition?

Not every acquisition is a bolt-on.

A large or transformational acquisition can significantly change the direction of a company. It could involve buying a major competitor, entering a completely new industry, or acquiring a business that becomes a central part of the company's future.

A bolt-on is usually more focused.

The buyer already has an established business and is looking for smaller companies that can strengthen it in specific areas. In simple terms, it is less about rebuilding the business and more about adding to it.

That can make bolt-ons easier to manage than very large deals, although integration still requires careful planning.

What is the challenging part here?

Smaller acquisitions are not automatically easy acquisitions.

One of the biggest challenges is making the two businesses work together. Different company cultures, technology systems, and ways of working can create problems if they are not handled properly.

There is also the question of price. A company may look like a perfect fit, but if the buyer pays too much, the deal may not generate the expected returns.

Then there are the promised synergies. Buyers often expect an acquisition to increase sales or reduce costs, but those benefits do not happen automatically. They need to be planned and executed.

To Wrap Things Up…

For companies with ambitious growth plans, bolt-on acquisitions can offer a practical way to expand without making one huge bet. Instead of spending a large amount on a single transformational deal, a company can make several smaller acquisitions over time. Each one can add something different, whether that is technology, customers, talent or geographic reach.

This approach is particularly common among private equity-backed companies. An investor may acquire a larger “platform” business and then use a series of bolt-on acquisitions to expand it.

Ultimately, a successful bolt-on acquisition comes down to one simple question: Does the smaller company add something the buyer genuinely needs?

If the answer is yes, and the two businesses can work well together, a bolt-on can be a relatively straightforward way to accelerate growth without starting from zero.

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.

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.

Noha Gad

Saudi Arabia is ramping up investment in artificial intelligence (AI) and digital infrastructure, led by the Public Investment Fund (PIF), as the Kingdom seeks to attract technology capital and build domestic AI capabilities. Driven by Saudi Vision 2030, technology in the Kingdom has evolved from an operational tool into an innovative national industry, establishing a competitive digital ecosystem that attracts significant domestic and international capital.

Marking 2026 as the Year of Artificial Intelligence underscores the Kingdom’s commitment, following achievements by 2025 in which Saudi Arabia ranked first globally in digital governance and cybersecurity indicators, and third in AI models. 

Voice AI is emerging as one of Saudi Arabia’s most visible and practical AI applications, moving from pilots in 2024–2025 to scaled deployments across government, banking, healthcare, and customer experience in 2026.

A report released by Markets and Markets anticipated the Saudi AI voice market to grow with a compound annual growth rate (CAGR) of 32.3% to reach $149.05 million in 2030. This growth was mainly driven by growing demand for secure authentication methods across vital sectors.

Key cities such as Riyadh, Jeddah, and Dammam dominate the market thanks to their status as major economic and technological hubs. The concentration of financial institutions, telecom companies, and government agencies in these cities fosters a conducive environment for the growth of AI voice biometrics solutions.

The Saudi voice AI market is segmented into various types, notably speaker verification, speaker identification, voice authentication, voice analysis, and others. Each sub-segment plays a crucial role in addressing specific needs within the voice biometrics landscape.

Speaker Verification

This sub-segment currently dominates the market because of its widespread application in secure access control systems, particularly in banking and financial services. Organizations are increasingly adopting this technology to authenticate users based on their unique voice patterns, which enhances security while providing a seamless user experience. The growing trend of remote banking and digital services has also accelerated the demand for speaker verification solutions, making it a preferred choice among enterprises.

Speaker Identification

This technology is used to determine which known person from an enrolled pool is speaking, without the user first stating their identity. Most enterprises and contact centers in Saudi Arabia use this technology to automatically tag callers or meeting participants with their profiles in CRM or HR systems, thereby streamlining routing and supporting fraud‑detection workflows by matching suspicious voices against databases of known bad actors.

Voice Authentication

This term refers to the practical use of voice biometrics as the authentication mechanism, effectively turning a person’s voice into a “password” for accessing accounts, applications, or services. It serves as a key enabler of digital transformation in Saudi financial services and public‑sector channels, allowing banks, fintechs, and government agencies to replace cumbersome security questions with seamless voice‑based login and transaction approval.

Voice analysis 

This biometric encompasses a broader set of techniques that extract insights from speech without necessarily identifying the speaker. This allows Saudi banks, telecommunication companies, and government to use voice analysis to monitor customer satisfaction, flag potentially fraudulent or high‑risk interactions, optimize agent scripts, and improve overall service quality.

Arabic voice AI across vital sectors

Arabic Voice AI in Saudi Arabia is moving from isolated pilots to core infrastructure across government, financial services, healthcare, and customer‑facing industries, driven by Vision 2030. What ties these efforts together is more than just Arabic support; it is the use of smart, bilingual agents that can understand local dialects, operate on sovereign or in‑Kingdom infrastructure, meet Personal Data Protection Law (PDPL) and sectoral compliance rules, and integrate with existing CRM, core banking, and e‑government systems.

In government and public services, Arabic voice AI appears in hotlines, ministry contact centers, and digital‑government channels, handling FAQs, appointment booking, status checks, and basic transactions in Saudi dialects.

Also, financial institutions are among the most advanced users of Arabic Voice AI, deploying bilingual voice agents for onboarding, account servicing, payment reminders, collections, and handling complaints in line with consumer‑protection and conduct rules. Typical use cases include instant balance and mini‑statement queries, card blocking/unblocking, instalment and due‑date reminders, cheque‑book or PIN requests, and automated follow‑ups on failed payments, all delivered in natural Saudi dialect rather than translated scripts. 

In the healthcare sector, Arabic voice AI is adopted by several hospitals, clinic groups, labs, and telehealth platforms across the Kingdom for non‑clinical workflows, including appointment scheduling, rescheduling, reminders, insurance verification, and patient communication via phone and WhatsApp. Some providers are piloting clinical documentation tools, such as AI scribes, that listen to doctor–patient conversations and auto‑generate notes in Arabic, reducing administrative burden while keeping diagnosis and treatment decisions with clinicians. 

Beyond regulated sectors, Arabic voice AI is being embedded in retail, e‑commerce, real estate, and hospitality to automate high‑volume inbound and outbound calls in natural Arabic. Use cases include outbound payment reminders for BNPL and credit products, insurance renewal calls, lead qualification for property projects, and post‑visit follow‑ups for malls, resorts, and giga‑projects.

Key Voice AI companies in Saudi Arabia

Several AI-powered companies in Saudi Arabia are developing and scaling AI models to better understand the Arabic language, particularly the Saudi dialect, helping organizations integrate Arabic voice AI agents to streamline their operations and support their business growth. Key examples are:

• Nabrah. A Saudi-built platform that combines autonomous voice agents with a full studio for text‑to‑speech, speech‑to‑text, and voice cloning, all tuned for realistic Saudi Arabic. Its “Nabrah Agents” product automates inbound and outbound calls for lead qualification, support, appointment booking, order confirmation, and surveys, with 24/7 operation, multilingual support, and seamless handoff to human agents when needed. 
• Lahjati. An all‑in‑one Arabic voice AI platform for content creation, with more than 600 professional voices, supporting more than 192 Arabic dialects and performance styles. Its core tools include high‑quality text‑to‑speech for voice‑overs, audiobooks, ads, and e‑learning; speech‑to‑text with up to 99% accuracy for Arabic dialects; and custom voice design to create brand‑specific avatars.
• Mawj. A leading AI voice agent platform in Saudi Arabia that focuses on enterprise reliability and large‑scale call automation. It handles sales, renewals, inquiries, surveys, ticket creation, verifications, order intake, and bookings across calls, WhatsApp, and other digital channels.
• Tzamun. A key provider of AI call‑center and voice‑agent solutions designed for Saudi businesses seeking to automate high‑volume phone interactions, while staying aligned with local regulations and customer expectations.

Challenges:

Even as adoption accelerates, Arabic Voice AI in Saudi Arabia still faces a set of practical, technical, and regulatory hurdles that shape how fast and how deeply it can be embedded in everyday services.

Ibrahim Jabarin, CEO of Hamsa, an AI company specializing in developing advanced models that understand the Arabic language and dialects, stated in an exclusive interview with Sharikat Mubasher that the Arabic voice AI market in the Kingdom faces five main challenges: the limited availability of high-quality voice data; the high cost of graphics processing units (GPUs) and sovereign infrastructure; the scarcity of specialists in deep learning and speech processing technologies; securing significant investment to develop models; and long procurement cycles and preference for global suppliers, along with the absence of unified Arab references to measure model performance.

In addition, regulatory and compliance complexity is a key pitfall facing Arabic voice AI in Saudi Arabia. Requirements around multi‑factor authentication, call recording retention, auditability, data residency, and cross‑border transfers can constrain architecture choices, pushing many companies toward on‑premise or in‑Kingdom cloud deployments and more conservative rollout plans.

User trust and acceptance are another challenge facing this innovative industry as customers and employees remain cautious about fully automated voice interactions, especially for sensitive topics like finance, health, or government services. Building trust requires transparent disclosure that the caller is an AI, clear options to reach a human, and consistent, natural‑sounding Arabic.

 Finally, Saudi Arabia’s ambitious AI agenda is finding one of its most tangible expressions in voice. From government hotlines and bank call centers to hospital appointment lines and retail customer service, Arabic voice AI is moving from pilot projects to everyday infrastructure, powered by local platforms that understand Saudi dialects and operate within the Kingdom’s regulatory boundaries.

The growth in Arabic voice AI in the Kingdom reflects more than a technological shift; it signals a change in how citizens, residents, and customers interact with institutions through natural, bilingual conversations instead of rigid menus and forms. 

The way Saudi Arabia navigates key challenges facing the market, either through investing in local data, talent, and sovereign infrastructure, or through clear, human‑centered design, will determine whether Voice AI becomes a background utility or a defining feature of the Kingdom’s digital identity in the post‑2030 era.

Shaimaa Ibrahim

Arabic voice AI technologies are at the forefront of digital transformation in the GCC region, driven by growing demand for intelligent solutions that understand local dialects and interact with users spontaneously and instantly, as well as the increasing need for data sovereignty and compliance. Against this backdrop, Hamsa, a US-listed company headquartered in Amman, stands out as an AI company specializing in developing advanced models that understand Arabic language and dialects; an integrated voice AI system; and intelligent agents capable of interacting with users, implementing tasks, and integrating with enterprise systems.

In an exclusive interview with Sharikat Mubasher, Ibrahim Jabarin, CEO of Hamsa, discussed the company’s strategy, its vision for the future of voice AI in the region, its competitive position among international peers, and its expansion plans across Saudi Arabia, the UAE, and other Gulf and Arabian markets.

Jabarin highlighted major pitfalls in the sector and unveiled Hamsa’s roadmap that includes supporting more than 16 languages, developing a new generation of intelligent agents, and enhancing security and compliance, thereby strengthening its presence regionally and globally.

First, tell us more about Hamsa, what distinguishes it in the Arabic AI technologies market, and the key solutions and services that the company provides for enterprises?

Hamsa is a voice AI company that develops its proprietary models capable of understanding and processing the Arabic language. We developed our Arabic model from scratch rather than relying on models originally developed for English and subsequently adapted for Arabic. This approach positively impacted performance; the accuracy of Hamsa’s models reached about 94% in transcribing Saudi and Gulf dialects and about 92% in standard Arabic. 

The company is also developing an integrated ecosystem that features speech recognition, voice synthesis, noise cancellation, speaker recognition, and integration with enterprises’ communication systems and operational infrastructure. This provides a quick response of up to 280 milliseconds to the first audio byte, with intelligent agents’ response time ranging from 0.8 to 1.2 seconds.

For enterprises, Hamsa provides a wide spectrum of comprehensive solutions, including real-time voice processing for calls and web applications; a Low-Code platform dedicated to designing chat agents and executing operations; APIs that help developers build their own solutions; and the ‘Hamsa Media’ product that processes voice content at large scale, including transcription, voice-over, and dubbing.

All these solutions can be deployed within customer data centers or via a private cloud hosted within the country to meet enterprises’ need for data sovereignty and compliance. 

To what extent have the strategic partnerships forged by Hamsa contributed to expanding the company’s business, deepening its regional presence, and attracting new customers?

For Hamsa, partnerships are not merely an additional sales channel; they represent a fundamental pillar for entering markets and accelerating the adoption of voice AI solutions, particularly in regulated sectors, such as banking and government entities that choose trustworthy suppliers with established experience and relationships. 

We adopt four main partnership tracks: systems integration and consulting firms, infrastructure and hardware partners, customer experience platforms and contact centers, as well as telecommunications operators

These partnerships help accelerate sales cycles, strengthen Hamsa’s ability to implement projects and expand in the market without a significant increase in the teams, and unlock access to strategic enterprises and accounts that are otherwise difficult to reach directly.

The company also relies on integration with customers’ existing technical infrastructure through open protocols and standards that reduce transformation complexities and shorten implementation time. Therefore, Hamsa’s strategy for entering any new market begins with searching for the right partner before the first customer. This underscores our belief that a strong partnership is the cornerstone for building a sustainable presence and accelerating growth.

Hamsa recently concluded a strategic agreement with OmniOps. In your opinion, how will this partnership accelerate the adoption of voice AI technologies within government and private organizations?

The significance of this partnership lies in its ability to address the most prominent barriers to voice AI adoption in the Kingdom, which are no longer related to model quality, but rather revolve around three key questions: where is the data stored? Who operates the solutions within the Kingdom? And how are they integrated with existing systems? The partnership provides comprehensive answers to all these requirements by keeping sensitive voice data within the Kingdom, with an accredited local authority responsible for operations, integration, and support, in compliance with the Personal Data Protection Law (PDPL) and data localization requirements.

This ecosystem enables enterprises to transition from limited pilot phases to full-scale production deployment by providing models, infrastructure, integration, and support within an integrated framework and a single accountable entity, rather than dealing with multiple suppliers and technologies.

Based on Hasma’s experience, this approach could shorten project implementation timelines to between six and nine months, while delivering intelligent Arabic voice services all day long, with all data remaining within the Kingdom's borders.

Why does Saudi Arabia represent a priority in Hamsa’s expansion strategy, and where do you see growth opportunities you are targeting over the upcoming period?

Saudi Arabia is the top market for Hamsa for several reasons. First, language and dialects. The company’s technologies have been built from the ground up to understand Arabic and its dialects, particularly the Saudi dialect, rather than adapting a global product to meet local market needs.

Second, the market size. The Kingdom hosts the largest call center operations in the region, especially in the banking, telecommunications, and healthcare sectors, which handle millions of calls per month. This offers significant opportunities to automate repetitive tasks using intelligent voice agents.

Third, the regulatory and strategic environment. Vision 2030 and the National Data and AI Strategy have made AI adoption a national priority, accelerating transformation and uptake.

Fourth, data sovereignty requirements. Though these requirements represent a challenge for many solution providers worldwide, they represent a strength for Hamsa. We designed our solutions to operate within customers’ data centers or via a private cloud hosted within the Kingdom, in line with compliance and data localization mandates.

We see significant growth opportunities in the banking and financial sector, particularly in customer services, card management, collections, and identity verification; in telecommunications, government services, and healthcare, in areas such as patient follow-up and preliminary screening; as well as retail and e-commerce, in order management and delivery services.

Beyond Saudi Arabia, which other GCC markets does Hamsa target, and what are your expansion plans for the next few years?

The United Arab Emirates is the second most important strategic market for Hamsa, as it is one of the fastest countries globally in AI adoption, particularly within the government sector, along with its position as a regional innovation hub. Hamsa enables the deployment of its solutions within the country, in line with the regulatory requirements and data sovereignty mandates.

Qatar represents another significant market for the company, notably in the healthcare and government services sectors, while Bahrain and Oman are considered promising markets, where Hamsa relies on local partnerships to reach customers and implement projects efficiently.

Beyond the GCC, Hamsa aims to expand in Egypt, Jordan, and Morocco, given the substantial operational scales these markets offer in communications centers, government services, and the financial sector. The next phase will focus on expanding into global markets by strengthening the platform to support more than 16 languages, leveraging the company’s expertise in developing models that can understand Arabic dialects and switch between languages despite limited data availability.

In all markets it enters, Hamsa adopts a unified approach that depends on three main principles: a local partner with deep market knowledge and established relationships; hosting solutions within the country to ensure compliance with sovereignty and data protection requirements; and providing technical and operational support in accordance with local time.  

Amidst the growing competition with global companies, where does the competitive advantage of Hamsa’s Arabic voice AI solutions lie?

It is important to acknowledge that global companies have extensive expertise and substantial budgets to develop AI technologies; however, our competition is not built on scale, but on delivering value that resonates with the needs of the Arab market. We believe Hamsa excels in four key areas: 

1. Building Arabic models from the ground up. Most global solutions rely on models originally developed in English, with Arabic support added as an afterthought. This limits their ability to understand local dialects and switch between Arabic and English. At Hamsa, we trained our models from the beginning on this linguistic reality.
2. Owning the full technology stack. Hamsa develops core components of the technology stack through a single platform, from speech recognition and voice synthesis to telecommunications, which ultimately reduces complexity and costs. This enables us to optimize performance, adjust response time, and deliver a stable, reliable experience.
3. Data sovereignty and compliance. Hamsa’s solutions are designed to operate within customers’ data centers or via a private cloud hosted within the Kingdom, fulfilling the requirements of banks and government entities. Our solutions comply with personal data protection laws in Saudi Arabia and the UAE.
4. Deep market knowledge. Our teams across the region deeply understand enterprises' needs, procurement dynamics, and regulatory requirements. This enables us to develop solutions tailored to the local market, including models specifically designed for local dialects.

How do you see the future of AI Agents in the GCC region?

The voice AI market in the region is moving toward three major shifts, the first of which has already begun:

1. From pilot phases to full-scale production: Organizations are moving beyond exploring potential and are now seeking scalable, production-ready solutions with high reliability, compliance, and auditability. 
2. From providing answers to executing procedures: The current generation of intelligent assistants can complete transactions, such as checking balances, booking appointments, opening tickets, and implementing procedures through integration with enterprise systems.
3. From voice-only to multi-interface experiences. The future points toward intelligent agents that combine voice conversation with visual interfaces, offering option display, sending confirmations, and visualizing order or transaction status. I expect government entities to lead this shift ahead of the private sector, given their focus on improving service quality and enhancing accessibility. The biggest challenge will not be developing the models themselves, but rather integrating them with legacy systems, ensuring compliance with regulatory frameworks, and measuring their business impact through clear, measurable metrics.

Based on your experience, what are the key challenges facing Arab AI companies today, and what does the sector need to accelerate its growth and enhance competitiveness regionally and internationally? 

Voice AI companies in the region face five main challenges. The first is the limited availability of high-quality voice data, especially for Arabic dialects, which forces companies to build their own database from scratch, ultimately slowing model development. Second, the high cost of graphics processing units (GPUs) and sovereign infrastructure, which imposes financial burdens on local companies.

Third, the scarcity of specialists in deep learning and speech processing technologies. This places regional companies in direct competition with global companies for top-tier talent. Securing finance is the fourth challenge, as model development companies require significant investment before generating revenue. 

Fifth, long procurement cycles and preference for global suppliers, along with the absence of unified Arab references to measure model performance, collectively hinder the expansion of local companies.

To accelerate the sector’s growth, the region needs to:

1. Create common, open Arabic databases and references that support model development.
2. Provide a sovereign computing infrastructure with competitive costs to promote local innovations.
3. Expand the presence of specialized investment funds that understand the nature and cycle of developing AI models.
4. Strengthen regulatory coordination among Gulf countries to reduce the variability of compliance requirements, enabling companies to expand regionally within a unified, more efficient framework.

What are Hamsa’s ambitions for the next few years, either on geographical expansion, launching new products, or establishing partnerships?

Hamsa’s roadmap for the upcoming years is centered on four key pillars. Geographically, we focus on strengthening our presence in Saudi Arabia and the UEA, then expanding into other GCC countries, notably Qatar, Kuwait, and Bahrain. Later, we will enter Morocco before expanding into Europe and the US through our multilingual platform.

At the product level, we are pursuing three strategic tracks: expanding the platform to support over 16 languages while preserving Arabic’s positional excellence; developing intelligent agents that integrate voice capabilities with visual interfaces; and advancing custom voice solutions, advanced analytics, and model fine-tuning tailored to the specific needs of various sectors.

On the compliance and security side, we aim to achieve ISO 27001 certification and transition to SOC 2 Type II compliance, while expanding the deployment of voice agents to web applications, smart kiosks, and other environments where voice-based interaction offers superior efficiency.

Hamsa will continue to forge comprehensive partnerships with infrastructure and digital sovereignty partners, system integrators, and customer experience platforms, thereby accelerating our expansion and ensuring implementation quality.

Our ambition for Hamsa is to become the premier choice for Arabic voice AI and subsequently strengthen its position globally through a multilingual platform.

Translation: Noha Gad

Ghada Ismail

As artificial intelligence becomes increasingly embedded in business, not everything an AI system generates should be taken at face value.

Two concepts often create confusion in this context: synthetic data and AI hallucination. Both involve information generated by AI rather than directly collected from the real world, but their roles could not be more different.

One is a tool that can help businesses overcome data limitations. The other is a reliability problem that can undermine trust in AI systems.

What Is Synthetic Data?

Synthetic data is artificially generated information designed to replicate the characteristics and patterns of real-world data.

Instead of collecting thousands of real customer transactions, for example, a startup could generate synthetic transactions that mimic realistic purchasing behavior. Similarly, an AI developer could create synthetic images, customer profiles or financial scenarios to train and test an AI model.

This can be particularly valuable for startups that lack access to large datasets or operate in areas where data is sensitive.

Synthetic data can help companies reduce data-collection costs, accelerate AI development and limit exposure to sensitive information. It can also allow developers to test AI systems across scenarios that may be difficult or expensive to reproduce in the real world.

However, synthetic data is only useful when it is representative and properly validated. Poor-quality synthetic datasets can reproduce errors, biases or unrealistic patterns.

What Is AI Hallucination?

AI hallucination is something very different.

It occurs when an AI model generates information that sounds convincing but is factually incorrect, unsupported, or completely fabricated.

An AI chatbot, for instance, might invent a statistic, cite a research paper that does not exist, or provide an incorrect explanation with complete confidence.

Hallucinations can occur because generative AI models are designed to predict and generate likely sequences of information. They do not automatically distinguish between what is true and what merely appears plausible.

For businesses, this can become a serious issue. An inaccurate AI-generated answer may be inconvenient in a consumer application but potentially damaging in areas such as financial services, healthcare, legal technology or enterprise decision-making.

Synthetic Data vs AI Hallucination

The simplest way to distinguish the two is intention and purpose.

Synthetic data is deliberately created. AI hallucination is an unintended output.

Synthetic data is generated for a specific purpose, such as training, testing, or simulating scenarios. It can be reviewed, measured, and validated before being used.

Hallucinations, by contrast, emerge during an AI system's operation and need to be detected, corrected, or prevented.

In other words, synthetic data can be an AI development asset, while hallucination is an AI reliability risk.

Why Does This Matter for Startups?

The distinction is especially important for startups building AI products.

Early-stage companies often face limited access to high-quality data. Synthetic data can provide a way to experiment and develop models without relying exclusively on costly or sensitive real-world datasets.

At the same time, startups must ensure that their AI products do not generate unreliable information. A hallucination can quickly erode customer confidence, particularly when an AI product is being used to make business or financial decisions.

Importantly, synthetic data does not automatically cause hallucinations. However, if synthetic datasets are poorly designed or contain unrealistic patterns, they can affect the quality of the models trained on them.

That makes data validation, testing, and human oversight critical throughout the AI development process.

One Is a Tool, the Other Is a Risk

Synthetic data and AI hallucination may both involve AI-generated information, but treating them as interchangeable misses a crucial distinction.

Synthetic data can help startups solve one of AI's biggest challenges: access to useful, scalable, and privacy-conscious data.

Hallucinations represent another challenge: ensuring that AI systems remain accurate and trustworthy.

As businesses move beyond experimenting with AI and begin deploying it in real-world operations, knowing the difference between data that was intentionally generated and information that was unintentionally invented will become increasingly important.

By: Ahmed Rashad, Sr. AI Specialist, Middle East & Africa at Nutanix

Have you seen this film before? A new technology arrives, powerful and effortlessly accessible. Departments spin up projects with minimal oversight from IT or finance. The first efforts reproduce old ways of working, and then somebody rethinks the workflow entirely, and the pace picks up. Then the invoice arrives, and the organization discovers it must bring things under control without cutting off access, because access is now how the work gets done.

That was the cloud, twenty years ago. It is gen AI today, on fast forward. What took cloud most of a decade is taking enterprises about eighteen months.

We watch this from two seats. We run our own AI workloads on our own GPUs, so we have made these mistakes with our own money. We also sit alongside a great many organizations making them at the same time, in different industries and under different regulatory regimes. The striking thing is how little the story varies.

Everyone’s first question is the wrong one

It is almost always “which model?”, and it is the question that matters least, because the answer changes every quarter.

The question that survives contact with production is what a unit of work costs. Not cost per token, but cost per resolved support ticket, per merged pull request, per document retrieved. The unit price keeps falling while total spend keeps climbing, because cheaper inference simply means more inference. Jevons would have recognized it immediately.

The same discipline applies to the benefit side. Where organizations measure carefully, the gains tend to land in a recognizable range: on the order of 10 to 15 percent for support teams, and 20 to 25 percent in feature delivery velocity for engineering teams. Those numbers are only worth quoting when they have been instrumented beforehand, against a baseline captured before deployment. Worth knowing: a randomized trial by METR found that experienced developers completed real tasks 19 percent slower with AI tools, while believing they had been 20 percent faster. If you cannot say how you measured, you have a feeling rather than a result.

Agents are not chatbots, and they do not fail like chatbots

This is the shift most organizations are unprepared for. A person using an assistant makes a request and receives an answer, and both the cost and the blast radius are bounded by their attention. An agent decides for itself how many steps to take, which systems to touch, and what to do with whatever it finds. The same instruction on a different day produces a different number of tool calls, a different bill, and a different set of side effects.

Which means the controls that work are the ones you would apply to a new joiner with production access, not the ones you would apply to software licenses. An identity for every agent, distinct from the human who launched it. Permissions scoped to each tool and each system, because MCP support is table stakes now, but speaking MCP and letting you grant

an agent read access there and write access nowhere are very different things. Budget ceilings that are enforced rather than alerted on. Traces detailed enough to reconstruct why an agent took eleven steps rather than three. And a human gate on anything irreversible.

The organizations getting this right have arrived at the same architectural conclusion independently. Those decisions cannot live inside each application. They belong at a single point that every agent’s requests pass through, so that policy, spend and audit are answered once for the whole estate rather than reimplemented project by project.

Running inference in production is a different discipline from running a pilot

A demo needs one model to work once. Production needs many models to work continuously, at predictable cost, while the field moves underneath you. Every organization we work with has replaced a model in production faster than it expected to, whether because of a cheaper open weight release, a regulatory constraint, or a change in vendor pricing. The ones who suffered were those who had welded a specific model to a specific location and a specific set of applications.

Flexibility here is not a luxury; it is the whole game: serving different models for different tasks, sizing endpoints to demand, and sharing GPUs across workloads through partitioning and scheduling rather than dedicating them. And, unfashionably, batch. Document classification, index rebuilds and evaluation runs do not care whether they complete at 14:00 or at 04:00. Defer them, and interactive workloads get the daytime capacity they need. Banks ran on this logic throughout the mainframe era. It was never wrong. It merely stopped being necessary when compute was cheap.

Location is becoming a variable, not a decision

Public cloud wins on speed and on access to the newest hardware. Other forces push the opposite way. Data residency and sovereignty requirements are no longer a compliance checkbox to be satisfied at the end of a project. For a growing number of organizations, they determine which workloads can exist at all, and where. Add data gravity, latency to customers, and the economics of sustained utilization, and owned or collocated infrastructure starts to look like the sensible home for a meaningful share of inference.

Meanwhile, a new class of specialized GPU providers has appeared, and some of the organizations we work with are becoming those providers themselves, turning regional advantage and spare capacity into a business of their own.

Nobody gets this allocation right at the first attempt. What matters is that getting it wrong stays cheap to correct: that a workload can move between owned, rented and regional infrastructure without being rewritten, and that governance follows it when it moves.

Do not build a walled garden

The temptation is to stand AI up as a separate estate, with its own tooling, its own rules and its own team, deliberately quarantined from everything else. There are two problems with that.

The first is that agents produce nothing of value until they can reach the systems and the data where your business actually runs. A wall built for safety very often becomes the reason a promising pilot never becomes production. The capability works. It simply is not allowed near anything that matters.

The second is the arithmetic of running everything twice. Two sets of policies, two audit trails, two places to look during an incident, and two opportunities for them to contradict each other, while the people who understand your controls best sit on the far side of the wall from the workloads that need them most.

The organizations moving fastest treat AI as a workload like any other, subject to the same access model, the same operational discipline and the same teams, with the controls that are specific to AI layered on top rather than rebuilt alongside.

Where that leaves us

There is no magic bullet for a technology moving this fast, and anyone selling one is selling something else. But the discipline transfers even when the tools do not. Measure cost per unit of work. Instrument your claims before you repeat them. Give agents identities, budgets and boundaries, enforced in one place. Keep models and workloads free to move. And govern all of it with your estate rather than beside it.

The film is on fast forward, and none of us gets to slow it down. But you can learn the genre well enough to see the twists coming, and avoid being the character who loses the plot.

Ghada Ismail

Saudi Arabia’s long-established relationship with precious metals is entering a new digital phase. As financial technology reshapes the way consumers save, invest, and manage wealth, gold is increasingly moving beyond traditional jewelry markets and physical bullion transactions into digital platforms and banking channels.

The emergence of digital gold services is creating a new intersection between fintech, wealth management, and precious-metals markets. Consumers can increasingly purchase gold digitally, track their holdings, automate savings, and, in some cases, convert digital ownership into physical metal. At the same time, the expansion of these services from fintech startups to major Saudi banks suggests that digital precious-metals investing is evolving from a niche proposition into a broader financial-services category.

The concept behind digital gold is relatively straightforward. Instead of requiring consumers to purchase and physically store a gold bar, digital platforms allow them to acquire ownership of gold while managing their holdings through a mobile application or digital banking platform.

This can lower the traditional barriers associated with precious-metals investment, particularly for consumers who may want to build their holdings gradually rather than make a large one-time purchase.

Startups adopting digital gold trading

GrowK is one example of this model in Saudi Arabia. The platform allows users to save in 24-karat digital gold, with automated savings options that can be structured on a daily, weekly, or monthly basis. It also allows users to buy, sell, and monitor their gold holdings digitally.

The significance of such a model goes beyond convenience. By introducing automated and recurring purchases, platforms can transform gold from an asset typically purchased periodically into a digital savings product.

This is where the model begins to resemble fintech.

Rather than simply digitizing the traditional gold-buying process, digital platforms can introduce features that are familiar from modern financial applications, including automated saving, portfolio monitoring, real-time pricing, and fractional ownership.

Banks are validating the model

The development is not limited to startups.

Saudi Arabia’s established banking sector is also incorporating precious metals into its digital financial services, potentially giving the category a much broader consumer reach.

SNB’s Gold Account enables customers to buy and sell investment-grade gold through the bank’s digital channels, including SNB Mobile and AlAhliOnline. The bank states that its gold is 999.9 purity and provides customers with the option of requesting physical gold bars through selected branches.

The model effectively combines digital access with physical ownership. Customers can manage their gold electronically while retaining a pathway to physical bullion.

Al Rajhi Bank’s Gold Wallet follows a similar approach, allowing customers to buy, sell, and store gold digitally while monitoring gold prices and managing their holdings through the bank’s digital ecosystem.

The involvement of major banks is significant because it moves digital gold beyond the realm of specialist investment applications.

When precious metals become integrated into mainstream digital banking, consumers can potentially view gold alongside their other financial products rather than as a separate physical asset requiring a visit to a jeweler or bullion dealer.

The infrastructure opportunity

While consumer-facing applications are attracting attention, another part of the market is developing behind the scenes.

Nexus Global’s Mithqal is designed as infrastructure for banks, fintech companies, wealth managers, and other institutions seeking to offer digital precious-metals products.

The platform provides capabilities related to digital gold accounts, metal wallets, pricing, trading, physical allocation, custody, settlement, and reporting. Its offering also extends beyond gold to other precious metals, including silver and platinum.

This infrastructure layer could become particularly important as demand grows.

Rather than every financial institution building its own technology and operational infrastructure for precious-metals products, platforms such as Mithqal can potentially provide the underlying technology needed to bring these services to market.

This mirrors developments elsewhere in fintech.

Payments infrastructure enabled companies to build digital wallets and payment applications without developing payment rails from scratch. Banking infrastructure has similarly allowed fintech companies to launch financial products without building a complete banking system.

Precious-metals infrastructure could play a comparable role, creating the technological rails for a broader digital bullion ecosystem.

Why gold, and why now?

Saudi Arabia has a particularly strong foundation for this market because gold already occupies an important position in the country’s consumer and investment culture.

The World Gold Council reported that Saudi Arabia’s bar and coin investment demand increased from 15.5 tons in 2024 to 17.5 tons in 2025, representing a 13% increase. Saudi Arabia was also the largest bar and coin investment market in the GCC during 2025.

At the same time, jewelry demand declined. Saudi jewelry consumption fell 10% to 44 tons in 2025, while its value declined 28% to $8.9 billion, according to the World Gold Council.

The shift is important because it suggests that high gold prices may be changing how consumers approach the metal.

Rather than purchasing gold primarily as jewelry, some consumers may increasingly view it through an investment lens.

Digital platforms are well positioned to serve this behavior because they can make smaller purchases more accessible.

The same consumer who may find a large physical gold purchase expensive can potentially accumulate smaller quantities over time.

Silver could expand the opportunity

Gold is likely to remain the primary asset in the digital precious-metals market, but silver could provide the next stage of growth.

Silver has a different investment profile from gold. Alongside its role as a precious metal, it has significant industrial applications, including electronics, solar technology, and manufacturing.

That gives digital platforms an opportunity to move beyond single-asset products toward multi-metal investment portfolios.

A consumer could eventually use one application to allocate a monthly amount between gold and silver, monitor the performance of both assets, and potentially redeem holdings physically.

This would represent a significant evolution from the concept of a digital gold wallet.

It would become a digital precious-metals portfolio, combining the accessibility of fintech with the characteristics of physical commodities.

Trust will determine the winners

Despite the opportunity, digital precious-metals platforms face a fundamental challenge, which is trust.

When a customer sees one gram of gold displayed in an application, they need to know exactly what that balance represents.

Is the gold physically allocated? Where is it stored? Who is responsible for custody? Is the inventory independently verified? Can the customer redeem the gold? What are the transaction spreads, storage fees, and withdrawal costs?

These questions become particularly important as the market expands beyond established banks and into startups and specialist platforms.

Regulatory clarity will also matter.

The distinction between a platform facilitating the purchase and custody of physical precious metals and one offering investment products or regulated capital-market activities can be significant.

For companies operating in this space, regulatory compliance could therefore become a competitive advantage rather than simply a legal requirement.

The strongest platforms will likely be those that combine technology with transparent ownership structures, credible custody arrangements, clear pricing, robust compliance, and reliable physical redemption mechanisms.

Is digital gold the new fintech?

The answer depends on how “new fintech” is defined.

Digital precious metals are unlikely to replace the established pillars of Saudi fintech, such as payments, lending, insurance, and financial infrastructure.

But they could represent something equally important: the next stage of Saudi wealthtech.

The Kingdom’s fintech market is gradually moving beyond simply making financial transactions digital toward helping consumers save, invest, and manage their wealth through technology.

Gold provides an unusual advantage in this transition.

Unlike many emerging financial products, it does not require consumers to understand an entirely new asset. Gold is already familiar. The innovation lies in changing how consumers access, accumulate, manage, and potentially redeem it.

The four Saudi examples illustrate the different layers of this emerging ecosystem. Together, they suggest that digital precious metals are developing into something broader than a collection of investment apps.

The next opportunity could be the creation of a fully integrated digital commodities ecosystem in which consumers can save in gold, diversify into silver, automate purchases, monitor portfolios, and access physical assets through a single digital experience.

For Saudi Arabia, the opportunity is particularly compelling because the digital future is being built around an asset with a very long history.

Gold may be one of the oldest stores of wealth, but the way Saudi consumers own it could be entering a distinctly digital era.

By: Christian Reilly, Field CTO EMEA at Cloudflare

Artificial intelligence has rapidly moved from experimentation to strategic priority. Across the Middle East and Africa, governments, financial institutions, telecommunications providers, and enterprises are investing heavily in AI to improve efficiency, enhance customer experiences, and unlock new growth opportunities. From national AI strategies and smart city initiatives to AI-powered customer service and operational automation, organizations are increasingly viewing AI as a critical driver of competitiveness.

Yet despite the enthusiasm and investment surrounding AI, many initiatives fail to deliver meaningful business outcomes. While pilot projects often demonstrate promising results, organizations frequently struggle to move beyond proof-of-concepts and scale AI across the enterprise. The challenge is rarely a shortage of data, talent, or ambition. More often, the real obstacle lies in the technology environment supporting these initiatives.

Legacy applications, fragmented infrastructure, and accumulated technical debt are preventing many organizations from realizing the full value of AI.

The Foundation Problem Most AI Strategies Overlook

Much of the conversation around AI focuses on models, algorithms, and use cases. However, AI success depends just as much on the underlying technology foundation as it does on the AI itself.

AI workloads require fast access to data, scalable infrastructure, seamless connectivity between applications, and the ability to process information in real time. Many existing enterprise environments were not designed for these requirements. Instead, they were built to support traditional business applications, often through architectures that have evolved over many years.

As organizations attempt to integrate AI into these environments, they encounter challenges that slow progress and increase costs. What begins as an innovative AI initiative can quickly become a complex modernization project involving application integration, infrastructure upgrades, security enhancements, and data transformation.

This issue is particularly relevant in the Middle East and Africa, where many organizations are simultaneously pursuing ambitious digital transformation goals while managing long-established technology environments. While modernization efforts have accelerated significantly in recent years, critical business processes often continue to depend on legacy systems that were never designed to support AI-driven operations.

When Technical Debt Becomes a Business Challenge

Technical debt has traditionally been viewed as an IT concern. In today's AI-driven economy, it has become a strategic business challenge.

Many technology teams spend considerable time maintaining aging applications, resolving system issues, and managing complex integrations. These activities consume resources that could otherwise be focused on innovation and AI deployment.

The result is what many organizations are beginning to experience as an innovation tax. Instead of investing time in creating new AI-enabled services, teams are forced to devote significant effort to making existing systems work together.

As AI initiatives expand, this burden grows. New applications introduce additional data requirements, integration points, security controls, and operational complexity. Without a clear modernization strategy, organizations risk creating even more fragmented environments that become increasingly difficult to manage.

The organizations achieving the greatest returns from AI are often not those spending the most on technology. They are the ones that have simplified their environments and created a foundation capable of supporting innovation at scale.

Data Silos Continue to Hold Back AI Progress

AI depends on access to high-quality, connected, and consistent data. Unfortunately, this remains one of the biggest challenges facing many organizations.

Over time, businesses often accumulate multiple applications, platforms, and databases that operate independently of one another. Information becomes trapped within departmental systems, creating data silos that limit visibility and reduce operational efficiency.

For AI initiatives, these silos create significant obstacles. Models require access to reliable information from across the organization to deliver meaningful insights and accurate outcomes. When data is fragmented, incomplete, or inconsistent, AI performance suffers.

This challenge is becoming increasingly important as organizations across the Middle East and Africa continue expanding their digital ecosystems. Cloud platforms, SaaS applications, edge environments, and on-premises systems must work together seamlessly to support modern business operations.

Organizations that successfully connect these environments gain a significant advantage. They can move data more efficiently, accelerate AI deployment, and generate value faster than competitors operating within fragmented infrastructures.

Security Must Be Built Into AI From the Beginning

As organizations scale AI adoption, cybersecurity becomes increasingly important.

AI systems often process sensitive business information and interact with multiple users, applications, and data sources. This expanded connectivity creates new risks that organizations must address proactively.

Many legacy architectures were developed before modern security frameworks such as Zero Trust became widely adopted. As a result, they often struggle to provide the visibility, control, and protection required for today's AI-powered environments.

Security therefore cannot be treated as an afterthought. It must be embedded throughout the AI lifecycle, from development and deployment to operations and governance.

Organizations that integrate security into their architecture from the outset are better positioned to scale AI safely and confidently. Those that attempt to retrofit security controls later often encounter delays, increased costs, and unnecessary risk.

Building a Platform for Long-Term AI Success

Organizations that successfully scale AI tend to share several characteristics. They focus on simplifying complexity, consolidating fragmented environments, and modernizing their infrastructure in parallel with their AI initiatives.

Cloud-native and API-first architectures have become increasingly important because they provide the flexibility, scalability, and connectivity required for modern AI workloads. Integrated platforms also help eliminate operational silos, allowing development, operations, security, and business teams to work from a shared foundation.

Most importantly, successful organizations recognize that AI is not a standalone technology project. It is part of a broader application and business strategy.

The future of AI in the Middle East and Africa will be shaped not only by the sophistication of AI models but by the strength of the digital foundations that support them. Organizations that modernize their infrastructure, reduce complexity, strengthen security, and connect their data environments will be best positioned to transform AI investments into measurable business value.

For many organizations, the path to successful AI adoption does not begin with the model. It begins with the platform on which that model runs.