Ghada Ismail

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Visibility means: I know you exist.

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

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

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

Meaning.

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

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

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

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

Meaning has to be earned through product truth.

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

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

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

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

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

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

That is an economic asset, not a communications indulgence.

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

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

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

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

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

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

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

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

Noha Gad

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

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

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

Why do companies implement a hiring freeze? 

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

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

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

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

Pros and cons

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

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

Key risks and downside include:

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

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

Ghada Ismail

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

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

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

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

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

And beneath culture sit very old human motivations.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Today, the system is very different.

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

So the machinery of attention has changed enormously.

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

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

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

The technology changed. The human motives did not.

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

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

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

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

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

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

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

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

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

The problem begins when we confuse efficiency with intelligence.

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

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

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

We should ask ourselves: is that progress?

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

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

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

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

And Dubai gave us an even more extraordinary example.

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

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

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

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

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

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

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

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.

Ghada Ismail

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

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

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

Why Are Zombie Funds Created?

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

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

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

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

How Does a Zombie Fund Work?

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

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

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

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

Why Do Zombie Funds Matter?

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

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

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

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

Wrapping Things Up…

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

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

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

Noha Gad

As Saudi Arabia accelerates its digital transformation agenda, artificial intelligence (AI), cloud computing, cybersecurity, and intelligent infrastructure are becoming central to the Kingdom’s economic and technological development. Its ambition to become a global technology and AI hub is creating new opportunities for organizations that can help businesses and government entities move from experimentation to secure, scalable, and commercially valuable digital solutions.

NTT DATA is playing a pivotal role in this transformation, supporting public and private organizations across Saudi Arabia with digital infrastructure, cloud adoption, data and AI solutions, cybersecurity, and managed services. 

On the sidelines of LEAP 2026, Sharikat Mubasher held an interview with Ahmad Qarout, Technology Solutions Leader at NTT DATA Saudi Arabia, to learn more about the company’s business in the Kingdom, how its solutions support organizations’ digital transformation and cloud adoption, and its long-term strategy to expand in Saudi Arabia and the broader region.

First, could you walk us through NTT DATA's business in Saudi Arabia, and explain how your solutions contribute to accelerating digital transformation and advancing the technology industry in the Kingdom?

Saudi Arabia is one of NTT DATA's most strategic markets globally, and we are proud to support public and private sector organizations across their digital transformation journeys. We help clients modernize infrastructure, accelerate cloud adoption, strengthen cybersecurity, harness data and AI, improve customer experiences, and operate complex digital environments at scale. 

As the Kingdom moves from digital transformation ambition to large-scale execution, organizations are increasingly looking for partners that can not only design and deploy technology, but also operate, secure, and continuously optimize it. This is where NTT DATA differentiates itself. We combine global expertise with strong local engagement, helping organizations turn innovation into measurable business outcomes. 

Our work directly supports Vision 2030 by enabling organizations to leverage AI, cloud, data analytics, intelligent automation, and cybersecurity to improve productivity, accelerate innovation, and develop new capabilities. We are equally committed to knowledge transfer, skills development, and building a sustainable local digital ecosystem that supports the Kingdom's long-term growth ambitions. 

NTT DATA is participating in LEAP 2026 to showcase AI-powered intelligent infrastructure and cutting-edge solutions. How do these technologies work together to create a truly intelligent ecosystem within Saudi Arabia?

A truly intelligent ecosystem requires much more than AI applications alone. It depends on the integration of data, infrastructure, networking, cloud, security, governance, and operational expertise working seamlessly together. At LEAP 2026, NTT DATA showcased how these elements combine to create an environment where AI can move from experimentation to enterprise-wide value. 

Our approach brings together AI solutions, modern digital infrastructure, advanced networking, cybersecurity, and intelligent operations to help organizations automate workflows, improve decision-making, enhance customer experiences, and unlock greater value from enterprise data. This creates a secure foundation for scaling AI responsibly and effectively. 

A key example is the new NTT DATA AI Factory Lab in Riyadh, which will provide organizations with hands-on experiences and practical demonstrations of real-world AI use cases. The lab will feature technologies including the Cisco Secure AI Factory with NVIDIA, allowing organizations to explore how AI workloads can be built, deployed, governed, and scaled on an enterprise-grade foundation while maintaining visibility, security, compliance, and operational resilience. 

How do you assess the role of premier events such as LEAP 2026 in strengthening the Kingdom's position as a global AI and technology hub?

LEAP has become one of the world's most influential technology events and plays a critical role in advancing Saudi Arabia's position as a leading global AI and innovation hub. It provides a platform that brings together technology leaders, policymakers, investors, startups, hyperscalers, and enterprise customers to exchange ideas, showcase innovation, and accelerate partnerships. 

What makes LEAP particularly important in 2026 is that it reflects the evolution of the Saudi technology market. The conversation has shifted from digital ambition to practical execution, with organizations focused on scaling AI, building resilient infrastructure, and delivering measurable outcomes. Events such as LEAP help facilitate these conversations and drive collaboration across the ecosystem. 

For NTT DATA, LEAP is an opportunity to engage directly with customers and partners, demonstrate real-world innovation, and contribute to the development of a thriving technology ecosystem aligned with Saudi Arabia's Vision 2030 goals. 

Does NTT DATA plan to announce any strategic initiatives or partnerships during LEAP 2026?

NTT DATA continues to invest in strategic partnerships and ecosystem collaboration across Saudi Arabia and the wider region. The growing number of partnerships and MOUs reflects the direction of the Saudi market itself, where collaboration between global technology leaders, local organizations, and government stakeholders is becoming increasingly important. 

One of our key initiatives and announcements is the launch of the NTT DATA AI Factory Lab in Riyadh, which brings together NTT DATA's AI expertise with technologies from leading partners including Cisco and NVIDIA. The lab is designed to help organizations move from AI exploration to practical implementation through executive workshops, demonstrations, and real-world use case development. 

What is NTT DATA's long-term strategy for expanding its business within Saudi Arabia and the broader region?

Our long-term strategy is centered on supporting the next phase of growth in Saudi Arabia and the Middle East, where digital transformation is increasingly becoming an ongoing operational capability rather than a one-time project. We are investing in local presence, local talent, and in-country delivery capabilities to help customers manage increasingly complex and mission-critical technology environments. 

We see significant opportunities in AI, cloud, cybersecurity, intelligent infrastructure, data-driven transformation, and managed services. As organizations scale AI and modernize their operations, they require trusted partners that can help them operate securely, meet sovereignty requirements, and continuously optimize performance. 

The launch of the AI Factory Lab in Riyadh is one example of this commitment. More broadly, our goal is to help organizations across the region build resilient, secure, and future-ready digital foundations while supporting national priorities around innovation, skills development, and economic diversification. Ultimately, we want to help clients transform ambitious digital investments into sustainable business outcomes and long-term value creation. 

Ghada Ismail

When people talk about venture capital and private equity, two terms appear repeatedly: Limited Partners (LPs) and General Partners (GPs). While both are essential to an investment fund, they play very different roles.

In simple words, LPs provide the capital, while GPs manage and invest it. Understanding this relationship is key to understanding how venture capital and private equity funds work.

What is a Limited Partner?

A Limited Partner is an investor who commits money to an investment fund but generally does not participate in its day-to-day management.

LPs can include pension funds, sovereign wealth funds, family offices, insurance companies, endowments, banks, and high-net-worth individuals. In the venture capital ecosystem, they provide the majority of the capital that funds use to invest in startups.

LPs typically commit a specific amount to a fund, but they do not necessarily transfer the entire amount upfront. Instead, the GP can make capital calls when investments or other fund expenses require funding.

In return, LPs receive a share of the fund's returns. Their potential liability is generally limited to the amount they have committed to the fund, which explains the term "limited" partner.

What is a General Partner?

General Partners are responsible for running the investment fund.

The GP is typically the venture capital or private equity firm managing the fund. Its responsibilities include identifying investment opportunities, conducting due diligence, negotiating deals, supporting portfolio companies, and deciding when to exit investments.

GPs also manage the fund's relationship with LPs, provide performance updates, and oversee the fund's overall strategy.

Unlike LPs, GPs are actively involved in investment decisions and typically commit some of their own capital to the fund.

The basic financial structure behind LP and GP partnerships

LPs and GPs usually make money in two main ways: management fees and carried interest.

GPs typically charge a management fee to cover the costs of running the fund, such as salaries, office expenses, and other operating costs. They can also earn carried interest, or “carry,” which is a share of the profits made from the fund’s investments.

For example, if a venture capital fund invests in several startups and those investments become highly successful, the GP can receive a percentage of the profits once certain conditions are met.

LPs receive most of the profits generated by the fund after management fees and carried interest are deducted. In simple terms, LPs provide most of the capital, while GPs manage the fund and earn fees plus a share of the profits if the investments perform well.

LP vs. GP: The Key Difference

The easiest way to remember the distinction is:

LP = supplies capital
GP = manages capital

LPs typically do not choose individual startups or companies for investment. Instead, they select funds based on factors such as the GP's track record, investment strategy, team, geographic focus, and expected returns.

GPs then deploy the capital according to the fund's investment strategy.

Why the Relationship is Important

A strong LP-GP relationship can be critical to a fund's success.

LPs want GPs to generate attractive returns while managing risk responsibly. GPs, meanwhile, rely on LPs for the capital needed to execute their investment strategy and often seek to build long-term relationships that can support future funds.

For startups, this relationship may seem distant, but it can have a direct impact. A well-capitalized VC fund has the resources to back promising startups through multiple funding rounds and potentially provide additional support as they scale.

To Wrap Things Up…

LPs and GPs are two sides of the same investment structure. LPs provide the financial firepower, while GPs provide the investment expertise and management.

The model allows institutions, family offices, and other investors to gain exposure to private markets without managing individual investments themselves, while giving professional fund managers the capital needed to identify and build the next generation of companies.

For anyone looking to understand how venture capital works, knowing the difference between LPs and GPs is one of the best places to start.

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.