Noha Gad

The process of taking a company public traditionally involved significant challenges, including regulatory requirements, market volatility, and high costs. Initial public offerings (IPOs) have long served as the primary method, enabling companies to achieve substantial growth. However, the rapid rise of startups in sectors such as fintech, artificial intelligence (AI), and sustainable technology increased demand for more efficient routes to capital markets. Special Purpose Acquisition Companies (SPACs) address this need.

With no commercial operations, a SPAC is essentially a shell company established to acquire companies by purchasing their shares. They are formed specifically to raise capital through an IPO, which can be used to acquire or merge with another private operating company. This approach enables private companies to become publicly traded in a matter of months rather than years, without the full burdens of a conventional IPO.

How do SPACs work?

A SPAC is created by experienced investors, known as sponsors, with the sole purpose of acquiring or merging with an unidentified private business. Unlike traditional IPOs, where a company directly lists its shares, a SPAC raises capital first and identifies a target later. This structure provides a streamlined path to public markets. 

The SPAC transaction process encompasses several key stages:

• Formation and IPO. Sponsors form a team of industry experts and file for an IPO, then investors purchase units, typically comprising one share of common stock and a fraction of a warrant. Proceeds from the IPO are placed in a trust account to earn interest.
• Finding a target. The SPAC has 18 to 24 months to find and negotiate a merger with a private company. During this period, the SPAC remains listed on an exchange, offering its shares for trading.
• Merger announcement. After identifying the target, the SPAC announces the proposed deal and takes shareholders’ votes on the transaction.
• De-SPAC and public listing: If approved, the merger will be completed, and the target emerges as a public company under a new ticker symbol.

Advantages of SPACs 

SPACs offer several benefits over traditional IPOs, providing efficiency and access for private companies seeking capital and investors pursuing opportunities. Key advantages are:

• Fast access to public markets. The process usually takes 3 to 6 months from merger announcement to completion, compared to more than 12 months for a standard IPO.
• Price stability: The SPAC sets a fixed share price during its IPO, reducing exposure to pricing volatility common in direct listings.
• Expert guidance: Sponsors, often executives or investors with proven track records, offer strategic advice, networks, and credibility. 
• Attractiveness in emerging markets: This model can support fintech and tech startups in emerging markets, providing liquidity without full IPO infrastructure.

While SPACs offer distinct advantages, most notably speed and efficiency, they also carry specific risks for investors and target companies. These include share dilution, inconsistent post-merger performance, potential conflicts among sponsors, and high redemption rates.

In essence, SPACs present a compelling alternative to traditional IPOs as they provide faster access to public markets and engage experienced sponsors. However, their success ultimately depends on careful evaluation at every stage. Ongoing regulatory developments continue to strengthen transparency and investor protections, contributing to a more stable environment. For investors, the key is to study sponsor track records, merger terms, and the realism of financial projections. Target companies, in turn, must ensure alignment with long-term strategic goals to mitigate potential drawbacks. As the SPAC model evolves alongside moderating deal volumes, it remains a relevant pathway for growth-oriented companies seeking to enter public markets. 

Mohamed Ramzy

The technology landscape in the Middle East is witnessing an unprecedented boom, driven by massive investments in digital infrastructure and a strategic shift towards the adoption of artificial intelligence (AI). With this momentum, cybersecurity is becoming increasingly important as the primary defense line protecting data and ensuring sustainable economic growth.

Spire Solutions emerged as a key player in this rapidly growing landscape and one of the leading distributors of cybersecurity, data, and AI solutions in the region, with a robust presence across the Gulf and Egyptian markets over nearly two decades. As Saudi Vision 2030 targets accelerate and Egypt advances digital transformation pathways, the company aims to deepen its operations and expand investments in both markets.

Sharikat Mubasher held an interview with Anas Dawud, General Manager for Saudi Arabia, Egypt, and Lavent at Spire Solutions, to discover the company’s expansion plans, its role in supporting Saudi Vision 2030 and driving digital transformation in Egypt, the role of the regulatory ecosystem in bolstering growth, and the company’s plans across the cybersecurity and AI sectors.

Spire Solutions has a long-standing record in the region. Could you highlight the company's key milestones and current solutions it focuses on?

Spire Solutions was founded nearly 19 years ago, with an initial focus centered on cybersecurity as a value-added service across the GCC, Egypt, and North Africa. To meet the needs of these rapidly evolving markets, we expanded our portfolio to include data and AI solutions, establishing a strategic integration between information security, data analytics, and intelligent technologies. This comprehensive package now defines our IT services offered to clients.

Speaking about the Saudi market, how do you assess the company’s operations amid the Kingdom’s significant momentum?

Saudi Arabia is our largest market. We have been operating in the Kingdom for over 15 years, and now serve a huge client base of more than 1,000 customers, including diverse government entities and large companies. Our main goal is to ensure robust protection of the IT environments of these organizations.

Geographically, we currently cover 18 cities across the Kingdom through a specialized team of engineers and consultants. We also aim to expandi our team in the Western Region, in line with our plan to open a regional headquarters in Riyadh soon.

How has Saudi Arabia’s regulatory environment contributed to driving Spire Solutions’ growth in this sector?

Governance and regulations put in place by Saudi regulators have been the cornerstone for our business growth, setting clear frameworks for cybersecurity and data protection standards. This boosted demand for our solutions across pivotal sectors, whether financial, banking, or governmental, while ensuring companies operate within a protected, regulated market.

The sector is witnessing a great momentum, backed by several supporting entities, notably the Saudi Data and Artificial Intelligence Authority (SDAIA), the Saudi Ministry of Communications and Information Technology, and many other entities.

Spire Solutions recently participated in the inaugural AI Everything MEA 2026 summit. How do you see the growth opportunities in the Egyptian market compared to other markets in the region? 

The Egyptian market is very promising, driven by a genuine, tangible shift from the government and the private sector towards comprehensive digital transformation.

Companies and banks have shown significant awareness of the need to integrate AI into their work cycles. Spire Solutions has been operating in the Egyptian market for more than 10 years, and we believe that the current movement in digital transformation and AI paves the way for us to boost investment and expand the reach of our technical services and consultancies in the coming period.

Does Spire Solutions plan to launch new projects in Egypt soon?

Spire Solutions is in ongoing talks with local partners to form successful partnerships. We recently held high-level meetings with leading Egyptian entities and have several strategic projects in the pipeline that we will announce soon.

I would like to emphasize a key point regarding our business approach in Egypt. We do not engage directly with the end-user; rather, we work through strategic partnerships with system integrators. However, we provide support to end-users through consultancy services and technical training.

The entrepreneurship sector in the region is experiencing significant momentum. What role does Spire Solutions play in supporting startups?

Supporting startups is at the heart of our strategy. We are committed to providing them with specialized technical training, enabling access to markets, and helping them build effective customer relationships.

We believe in the importance of working alongside entrepreneurs who are developing innovative solutions. Through this approach, we have supported a number of startups that have successfully established a strong presence across the region.

How do you see the future of AI and data sectors in key markets in the region by 2030?

Data volume is growing continuously and rapidly, making data science and analytics an essential pillar of organizational resilience, serving as the engine that guides companies' investments and helps navigate emerging challenges.

Today, AI plays a crucial role in promoting this process by providing faster and more accurate data protection. We aim to expand beyond Egypt and the GCC to enter specific markets in North Africa within the coming period.

With the accelerated pace of digital transformation in Saudi Arabia and Egypt, opportunities are abundant for cybersecurity and AI companies. Spire Solutions, guided by a clear regional expansion strategy, focuses on building local partnerships, knowledge exchange, and upskilling talent, ultimately enhancing organizations’ readiness to address evolving challenges in the digital landscape.

Translation: Noha Gad

Kholoud Hussein 

Saudi Arabia’s mega projects were conceived as symbols of economic diversification. Today, they are becoming test beds for something even more transformative: artificial intelligence embedded at scale.

From predictive construction systems to AI-managed urban mobility, the Kingdom’s flagship developments are not merely large in size or investment value. They are increasingly designed as intelligent ecosystems. Backed by the policy framework of Saudi Vision 2030 and coordinated through institutions such as the Saudi Data and Artificial Intelligence Authority (SDAIA), AI is moving from experimental pilot to core infrastructure layer.

With mega projects collectively valued in the trillions of dollars, Saudi Arabia is positioning artificial intelligence not as a supporting tool, but as an operating system for next-generation cities, tourism hubs, logistics corridors, and industrial zones.

AI as a National Priority

Saudi Arabia’s AI ambitions are not confined to individual developments. In 2020, the Kingdom launched the National Strategy for Data and AI, aiming to position the country among the top 15 global AI leaders by 2030. Officials have repeatedly emphasized that artificial intelligence is central to economic competitiveness.

Crown Prince Mohammed bin Salman has described technology and innovation as pillars of the Kingdom’s diversification strategy. Meanwhile, SDAIA President Abdullah Alghamdi has stated that data and AI are “key enablers of economic growth and digital transformation.”

According to official projections, AI could contribute an estimated $135 billion to Saudi Arabia’s GDP by 2030, representing roughly 12 percent of the national economy. These figures underscore why mega projects are being built with AI integration from inception rather than retrofitted later.

NEOM: Building an AI-Native City

Perhaps the most visible example is NEOM, the $500 billion smart city development in northwestern Saudi Arabia. Designed as a fully connected urban environment, NEOM integrates AI across energy management, transportation, water systems, and security infrastructure.

Within NEOM, THE LINE represents an ambitious experiment in AI-driven urban planning. The linear city will rely on predictive analytics to manage traffic flows, optimize energy consumption, and coordinate autonomous transport systems. Digital twins—virtual models of physical infrastructure—allow planners to simulate real-world conditions before construction is completed.

AI algorithms will monitor energy demand in real time, automatically adjusting renewable energy generation and storage systems. In practice, this reduces waste and improves grid resilience. In urban mobility, AI-enabled platforms are expected to manage autonomous vehicles and high-speed transit networks with minimal human intervention.

The result is an environment where infrastructure decisions are driven by continuous data analysis rather than static planning assumptions.

The Red Sea Project: AI in Sustainable Tourism

Sustainability is another arena where AI is reshaping Saudi mega developments. Red Sea Global, developer of the Red Sea Project, has embedded AI into environmental management systems.

The destination aims to operate on 100 percent renewable energy. AI-powered monitoring systems analyze weather patterns, guest flows, and energy consumption to optimize operations while minimizing ecological impact. Smart desalination plants use machine learning to improve efficiency and reduce carbon intensity.

By using predictive analytics, operators can anticipate peak visitor demand and adjust services accordingly, limiting overuse of sensitive natural environments. This model reflects a broader shift: AI is not only about efficiency but also about environmental stewardship.

Qiddiya and Predictive Operations

Entertainment and sports infrastructure are also being transformed. Qiddiya Investment Company is developing one of the Kingdom’s largest entertainment cities, integrating AI for crowd management, safety monitoring, and real-time operational analytics.

Advanced camera systems and computer vision technologies help detect congestion patterns and enhance security oversight. Predictive maintenance tools monitor ride systems and facilities to reduce downtime and prevent mechanical failures.

For mega venues hosting international events, AI-driven analytics enable dynamic pricing strategies, optimized staffing, and personalized visitor experiences.

AI in Construction and Project Management

Beyond the final user experience, AI is reshaping how mega projects are built.

Saudi Arabia’s construction sector faces the challenge of delivering projects at an unprecedented scale. AI-enabled project management platforms analyze supply chains, labor allocation, and procurement timelines to mitigate delays. Predictive analytics help identify bottlenecks before they escalate into costly overruns.

Drone-based imaging combined with machine learning allows real-time monitoring of construction progress. This data feeds into centralized dashboards, enabling developers to compare projected timelines with actual performance.

Given that Saudi giga-projects represent investments exceeding $1 trillion collectively, even marginal efficiency gains through AI can translate into billions of dollars in savings.

The Startup Ecosystem: Local Innovation at Scale

While global technology providers are active in the Kingdom, Saudi startups are increasingly contributing to the AI ecosystem, supporting mega projects.

Companies such as Mozn specialize in AI-driven analytics and risk management platforms. Originally focused on financial crime detection, firms like Mozn are expanding into broader data analytics solutions relevant to infrastructure and enterprise clients.

Another emerging player is Quant Data & Analytics, which develops AI tools for predictive analytics and data intelligence. Such companies are well-positioned to serve government agencies and mega-project operators requiring localized AI solutions.

Saudi Arabia’s venture capital ecosystem has grown significantly, with AI startups attracting increasing funding rounds. Government-backed funds and accelerators are prioritizing artificial intelligence as a strategic vertical.

As mega projects mature, demand for specialized AI applications—ranging from logistics optimization to energy modeling—creates a substantial addressable market for domestic startups.

Human Capital and Workforce Transformation

AI integration also has labor market implications. Mega projects are serving as training grounds for Saudi engineers, data scientists, and AI specialists.

Under Vision 2030, workforce localization initiatives aim to equip Saudi nationals with advanced digital skills. Universities and research centers are partnering with mega-project developers to create AI-focused training programs.

Officials have emphasized that AI adoption is not about workforce replacement but productivity enhancement. SDAIA leadership has noted that building local AI talent is essential for long-term sustainability.

Economic Impact and Investment Outlook

The economic implications are profound. With AI projected to contribute $135 billion to GDP by 2030, mega projects act as catalysts, accelerating this contribution.

Investment in digital infrastructure, cloud computing, and data centers is expanding alongside physical construction. Saudi Arabia has announced multi-billion-dollar investments in cloud services partnerships to support AI workloads.

Moreover, foreign direct investment linked to technology partnerships continues to grow as global firms view Saudi mega projects as large-scale test environments for advanced AI applications.

Industry analysts estimate that AI-related spending in Saudi Arabia could grow at compound annual rates exceeding 25 percent through the end of the decade, driven largely by giga-project deployment.

Challenges and Governance Considerations

Despite momentum, challenges remain. Integrating AI across complex, multi-stakeholder projects requires strong governance frameworks. Data privacy, cybersecurity, and algorithmic accountability are critical concerns.

Saudi authorities have introduced regulatory standards governing data protection and AI ethics to ensure responsible deployment. This regulatory clarity may enhance investor confidence.

Finally, Saudi Arabia’s mega projects were initially defined by scale—record-breaking budgets, ambitious architecture, and expansive geography. Increasingly, however, they are defined by intelligence.

Artificial intelligence is embedded in planning models, operational systems, sustainability metrics, and security frameworks. It is shaping not only how projects are built but how they function long after completion.

If current trajectories continue, Saudi Arabia’s giga-projects may become global reference models for AI-integrated urban development. In doing so, they reinforce the Kingdom’s broader ambition: to transition from an economy built primarily on natural resources to one powered by data, technology, and intelligent systems.

Ghada Ismail

Most startups don’t fail because they lack ideas. They fail because they misjudge how efficiently they turn what they own into revenue.

In the rush to grow, founders often focus on how fast money is coming in, while paying far less attention to how hard their assets are actually working. Office space sits half-used. Software tools pile up. Teams expand faster than output. On paper, the startup looks like it’s growing. In reality, its engine may be inefficient.

This is where the Asset Turnover Ratio quietly steps in. It doesn’t care about hype, valuation, or future promises. It asks one simple, uncomfortable question: How much revenue are you actually generating from the assets you already have? For startups operating on limited capital and tight runways, the answer can be revealing, and sometimes alarming.

What Is Asset Turnover Ratio?

The Asset Turnover Ratio measures how efficiently a business uses its assets to generate revenue. It shows how much revenue is produced for every unit of assets owned by the company.

The formula is simple:

Asset Turnover Ratio = Revenue ÷ Average Total Assets

If a startup generates SAR 2 million in revenue and holds SAR 1 million in total assets, its asset turnover ratio is 2. This means the company generates SAR 2 in revenue for every riyal invested in assets.

In general, a higher ratio indicates stronger operational efficiency, while a lower ratio suggests that assets may not be used to their full potential.

Why Asset Turnover Ratio Matters for Startups

Startups rarely have excess resources. Capital is limited, margins are thin, and every investment—whether in people, technology, or infrastructure—needs to prove its value quickly.

The asset turnover ratio helps founders understand whether their business model is genuinely efficient or simply growing heavier over time. It highlights whether assets are actively contributing to revenue or quietly becoming cost centers.

For investors, this metric offers insight into execution quality. A startup that generates strong revenue relative to its asset base signals discipline, thoughtful scaling, and smarter capital allocation, qualities that matter far more than growth alone.

Interpreting High and Low Asset Turnover Ratios

A high asset turnover ratio often reflects a lean, well-optimized business. Digital startups, SaaS platforms, and marketplace models typically perform well because they generate revenue without heavy physical infrastructure. High turnover suggests that the startup is maximizing output from minimal resources.

A low asset turnover ratio is not necessarily a red flag on its own. Asset-heavy startups in sectors such as manufacturing, logistics, or hardware development often show lower ratios, especially in early stages. The real concern arises when assets continue to grow while revenue lags behind, signaling inefficiencies or premature expansion.

What matters most is what happens next. Improving turnover over time indicates that the startup is learning how to scale more efficiently.

How Startups Can Improve Asset Turnover

Improving asset turnover is not about cutting costs aggressively. It is about making smarter decisions with existing resources.

Startups can focus on increasing revenue before acquiring new assets, delaying major capital expenditures until demand is validated, and outsourcing non-core functions instead of owning everything in-house. Regularly reviewing underperforming assets—whether tools, systems, or physical resources—also helps prevent unnecessary drag on performance.

Ultimately, the goal is not to own fewer assets, but to ensure that every asset actively supports growth.

Putting Asset Turnover in Context

No single metric tells the full story. Asset turnover should be viewed alongside profitability, cash flow, and growth indicators. A startup can be efficient but unprofitable, or profitable but inefficient. The real insight comes from understanding how these metrics work together.

For founders, asset turnover serves as a reality check. It keeps ambition grounded in execution and encourages smarter scaling rather than reckless expansion.

Wrapping Things Up…

At its core, the asset turnover ratio is not just a financial metric, but rather a discipline check.

It forces founders to ask whether growth is being built on smart execution or on accumulating more resources than the business can justify. High turnover reflects a startup that knows how to extract value before spending more. Low turnover, if ignored, quietly erodes runway long before cash flow problems become obvious.

In a startup landscape where capital is no longer unlimited, the businesses that survive will not be the ones that own the most assets, but the ones that use what they own best.

Kholoud Hussein 

In the startup world, building a product is only half the battle. The other half is making sure it works in the real world. That’s where alpha and beta testing come in.

For early-stage companies, these testing phases are not technical formalities. They are risk-management tools. They help founders validate assumptions, uncover weaknesses, and refine user experience before a full public launch. Done properly, alpha and beta tests can mean the difference between a controlled rollout and a costly failure.

What Is Alpha Testing?

Alpha testing is the first structured round of product testing. It usually happens after internal development is complete but before the product reaches external users.

At this stage, the product may be functional but not polished. Features might be incomplete. The interface may still need refinement. Bugs are expected.

Alpha testing is typically conducted internally by the startup’s team, along with a small, controlled group of trusted users. These may include employees, close partners, advisors, or early supporters. The goal is to identify major technical flaws and usability issues before exposing the product to a broader audience.

This phase focuses on stability and core functionality. Does the platform crash? Do essential features work as intended? Are there obvious friction points in navigation?

Because the testing environment is controlled, feedback tends to be direct and detailed. Developers can observe user behavior closely, fix bugs quickly, and release multiple iterations in a short period of time.

For startups, alpha testing is about protecting reputation. Launching publicly with obvious technical failures can damage trust early. Alpha testing minimizes that risk.

What Is Beta Testing?

If alpha testing is internal, beta testing is external.

Beta testing involves releasing the product to a limited group of real users outside the company. These users represent the target market more accurately. They are not part of the founding team, and they interact with the product in real-world conditions.

Unlike alpha testing, beta testing examines more than technical performance. It evaluates user experience, product-market fit, and perceived value.

Key questions during beta testing include:

• Do users understand the product without explanation?
• Are they willing to pay for it?
• Which features do they actually use?
• Where do they drop off?

Beta testing can be closed, meaning access is by invitation only, or open, where anyone can sign up. Early-stage startups often prefer closed beta programs to manage feedback and maintain control.

This phase generates critical data. It reveals whether assumptions about customer behavior were correct. It also surfaces issues that internal teams may overlook, especially around user expectations and workflows.

The Strategic Role of Testing in Startup Growth

For startups operating with limited capital and time, alpha and beta tests are not optional. They are part of a disciplined launch strategy.

Testing reduces uncertainty. Instead of betting everything on a full-scale launch, founders collect evidence first. They identify weak points before marketing budgets are deployed. They adjust pricing models before scaling sales teams.

In lean startup environments, alpha and beta testing align closely with the build-measure-learn cycle. Each testing round provides measurable insight that informs product decisions.

Importantly, testing is not just about fixing problems. It is also about discovering opportunity. Many successful startups refine their value proposition during beta testing. Users may gravitate toward a feature that was initially secondary. Pricing strategies may evolve. Target segments may shift.

This flexibility is critical in early growth stages.

Common Mistakes to Avoid

Despite their importance, alpha and beta tests are often mismanaged.

One common mistake is rushing through testing to meet artificial launch deadlines. Compressed testing phases reduce feedback quality and increase post-launch risk.

Another mistake is ignoring negative feedback. Founders can become attached to product assumptions. Testing only works if feedback drives change.

A third issue is testing with the wrong audience. If beta users do not reflect the intended customer base, insights may be misleading.

From Testing to Launch

Alpha and beta testing are bridges between development and commercialization. They transform a product from a concept into a market-ready solution.

For investors, thorough testing signals discipline. For customers, it improves reliability. For founders, it provides clarity.

In competitive markets, speed matters. But controlled speed matters more. Startups that treat alpha and beta testing as strategic milestones, rather than procedural checkboxes, increase their odds of building products that not only launch successfully, but scale sustainably.

In the end, alpha and beta tests are about learning before scaling. And for startups, learning early is one of the few true competitive advantages.