Noha Gad

Have you ever dealt with the manager who asks you to Cc them on every email? The one who rewrites your work not because it is wrong, but because it is not exactly how they would have done it. They think they are protecting quality, meeting deadlines, and ensuring good results by focusing on minor details, demanding approval for small decisions, and leaving employees with little freedom to make their own choices.

A micromanager excessively supervises employees, focusing on controlling every task detail. Although this management style might yield short-term results, it often undermines morale and hampers team productivity in the long run. Instead of directing employees on how and when tasks can be accomplished, micromanagers watch employees’ actions closely, providing frequent criticism.

Key features of micromanagers

Micromanagers share several common behaviors. Although every situation is different, these signs usually appear:

• Lack of trust and delegation. These managers refuse to delegate tasks or take over work because they believe no one else can accomplish it right.
• Continuous supervision. Micromanagers demand frequent status updates, require approval for minor decisions, and track every step of a project.
• Excessive focus on minor details: They obsess over formatting, wording, or trivial errors instead of looking at overall progress. They may criticize a report because of its font or structure, even when the content is strong and the client is satisfied.
• CC'ing on everything: They insist on being copied on all emails and sitting in on routine communications. This makes employees feel unable to act without asking permission first.

Negative impact on the workplace

Micromanagement may seem like a way to maintain control and quality, but it often creates serious problems for employees and the wider business, such as: 

• Reducing employee morale. When employees feel constantly observed and corrected, they start losing enthusiasm for their work. They may feel that their manager does not trust their skills or respect their judgment.
• Increasing stress. Frequent check-ins, repeated revisions, and constant approval requirements can make employees feel pressured and insecure. They may worry about making mistakes or being criticized for small details.
• Lowering confidence. When a manager rewrites employees’ work, questions their choices, or takes over tasks, they may begin to doubt their own abilities.
• Discouraging creativity and new ideas. Micromanagers often prefer their own method, leaving little room for different approaches. This makes employees less likely to share ideas when they believe every suggestion will be rejected or changed. 
• Slowing down the work cycle. When every small decision needs approval, projects take longer to be done, as employees may spend more time waiting for feedback than doing actual work.
• Reducing productivity.  Although micromanagers often believe they are improving performance, their behavior can have the opposite effect. Employees may lose motivation to work efficiently because they know their manager will likely change the work anyway.

Micromanagers can take concrete steps to change their management. First, they should establish a few key metrics that define success for any project, ignoring every other detail that falls outside those parameters. They should delegate specific needs to be done, not control the way team members do it. An open-door policy should be in place so team members can seek coaching or further guidance whenever they feel they need it. Finally, managers should set a deadline for each stage of an assigned project.

To sum up, micromanagement may seem to be a commitment to quality, but it often creates stress, lowers morale, and reduces productivity. When employees feel constantly monitored and are given little freedom to make decisions, they may lose confidence, avoid sharing ideas, and become less engaged in their work. Successful leaders focus on clear goals, trust their teams, delegate responsibility, and provide guidance when needed.

Ghada Ismail

As artificial intelligence continues to reshape education, edtech startups are exploring new ways to personalize learning, improve student performance, and support teachers with data-driven insights. AILA is among the companies working to transform the learning experience through AI-powered solutions tailored to students’ individual needs and the Saudi education system. In this interview with Sharikat Mubasher, AILA discusses its approach to personalized learning, the challenges of developing effective AI-driven educational tools, and the opportunities for edtech startups as Saudi Arabia advances its education and digital transformation goals.

AILA has recently secured a $3 million funding round. What does this investment enable the company to do that it could not do before?

Our earlier funding helped us build the product and reach product-market fit. We now have schools using AILA every day and results we can stand behind. This round is about scaling what already works. 

First, it lets us go deeper on our adaptive engine and our research. We want to keep proving, with rigorous evidence, that AILA improves how students learn, building on the work we have done with King Fahd University of Petroleum & Minerals. 

Second, it lets us scale our commercial and customer success teams so we can serve many more schools across the Kingdom, and support each one properly after launch. 

Third, it lets us expand beyond the Saudi curriculum. Today we are strongest around Qudurat, Tahsili and NAFES. By adding other curricula, we can take the same engine to new markets and serve many more students. 

Do you expect this round to take AILA to profitability, or is the priority still aggressive growth and market expansion? 

Our priority is growth, but disciplined growth. We have built a business with strong fundamentals, and our product runs at a gross margin of around 67%, which gives us a lot of room to grow efficiently. 

Reaching profitability is a clear goal for this round. We want to scale to more schools and new markets while becoming profitable, so that we go into our Series A from a position of strength. We are not buying growth at any cost. Every school we sign needs to make sense on its own. 

AILA describes its approach as personalized, AI-powered learning. What makes your personalization technology different from the growing number of AI tutoring and edtech platforms entering the market? 

Many new AI platforms are essentially content generators: you ask a question, you get an answer. That is useful, but it is not the same as teaching. AILA is built around diagnosis. We work out exactly where each student is, what they have mastered and what is holding them back, and then we guide them on the shortest path to improvement. 

Three things set us apart. First, we are built for the Saudi context, mapped to national learning outcomes and assessments such as Qudurat, Tahsili and NAFES, and available in both Arabic and English. Second, we work inside schools, so the insight does not stay with the student. Teachers, administrators and principals see the same picture and can act on it. Third, we measure results. Our benchmark shows students learning about 2.3 times more efficiently, and our pilot results have been validated through a research partnership with King Fahd University of Petroleum & Minerals. 

How does AILA use student data and learning behavior to determine what a student should learn next? 

Every interaction on the platform tells us something: whether the answer was right, how long it took, how many attempts were needed, and what kind of mistake was made. We map all of that to a structure of skills and learning outcomes, so we can estimate each student's level of mastery skill by skill. 

From there, the engine decides what comes next. It looks at the gaps that matter most, the prerequisite skills that need to be secured first, and how heavily each skill counts in the assessment the student is preparing for. It also brings back earlier material at the right moment, so learning sticks rather than fading. 

The same data flows to teachers and school leaders as clear, actionable reports, so they know which students need help and on what. 

What are the biggest technical challenges in building AI systems that can genuinely improve learning outcomes rather than simply generate educational content? 

The hardest part is measuring learning rather than activity. It is easy to build something students click on a lot. It is much harder to prove they actually know more afterwards. That is why we invest in validation and research, not just in features. 

The second challenge is quality and alignment. Content must be accurate, pedagogically sound and aligned with the Saudi curriculum, in both Arabic and English. Generative AI can produce a lot very quickly, but in education a confident wrong answer does real damage, so we keep strong quality control around everything the system shows a student. 

The third is getting the recommendation right from limited data, especially when a student is new to the platform, and keeping it right as they progress. 

And finally, the system has to fit how schools actually work. AI does not replace the teacher. It has to give teachers and school leaders insight they can act on in the time they have. 

What gaps in the Saudi education system did you identify when you first built AILA, and how have those needs evolved since the company launched? 

When we started, the biggest gap was that one classroom holds students at very different levels, yet they are largely taught the same way at the same pace. Teachers knew some students were falling behind, but they did not have the time or the granular data to know exactly why. At the same time, national assessments carry high stakes for students, and much of the targeted preparation was happening outside school. 

We built AILA to bring that personalized, data-driven support inside the school, where every student can access it. 

At the same time, AI itself has advanced enormously since we launched. Models today understand language, reasoning and student errors far better than they did a few years ago, in Arabic as well as English. That has let us do much more than we originally planned: sharper diagnosis, richer and more personal feedback, and a far better experience for students and teachers. Our offering today is stronger than what we could have built at the start. 

The need has also matured. Schools are no longer only asking how to prepare students for an exam. They want continuous visibility on learning outcomes across the year, aligned with national standards, so leaders can make decisions based on evidence. That shift, driven by Vision 2030 and the Human Capability Development Program, has shaped how our platform has grown. 

Saudi Arabia is investing heavily in education technology as part of its broader digital transformation. Where do you see the biggest untapped opportunities for Saudi edtech startups? 

I see three big opportunities. The first is assessment and learning analytics. The Kingdom has clear national learning outcomes, and there is huge value in tools that show schools, in real time, where students stand against them. 

The second is scale in the public sector. Private schools have moved fast, but the largest impact will come from solutions that work reliably across government schools, at national scale. 

The third is high-quality AI built for our own context. Global tools are not designed around Saudi curricula, assessments, and classroom realities. Startups that build for this market from the ground up, in both Arabic and English, have a real advantage. 

More broadly, anything that connects learning in school to what comes next, university and the job market, is still underserved. 

Looking three years ahead, what would success look like for AILA in terms of users, markets, and impact? 

Today we work with around 100 schools and roughly 65,000 students. In three years, success means having a proven adaptive engine, backed by published research, used by 300,000 students. 

On markets, Saudi Arabia will remain our home and our largest market, and we plan to extend across the GCC and the wider MENA region, supported by the new curricula we are adding. 

But the number that matters most is impact. We want to show, with independent evidence, that students on AILA achieve measurably better learning outcomes, and that schools using AILA make better decisions because of the data we give them. If we can prove that at scale, the growth will follow. 

Ghada Ismail

Finding the right investment opportunity is a crucial step toward achieving strong returns. Before investors evaluate valuations, conduct due diligence, or negotiate terms, they must identify businesses and assets that fit their objectives. This process, known as deal sourcing, plays a central role in private equity, venture capital, and mergers and acquisitions (M&A).

What Is Deal Sourcing?

Deal sourcing is the process of identifying and establishing contact with potential investment opportunities. These may include startups seeking funding, established businesses looking for investors, or companies available for acquisition.

Investors use market research, professional networks, referrals, direct outreach, and financial intermediaries to build a pipeline of potential deals. Effective sourcing focuses not only on finding opportunities but also on identifying businesses that match an investor’s sector preferences, budget, risk tolerance, and expected returns.

Two common approaches are proprietary and intermediated deal sourcing.

What Is Proprietary Deal Sourcing?

Proprietary deal sourcing involves identifying opportunities through an investor’s own research, relationships, and direct outreach rather than relying primarily on formal intermediaries or widely marketed transactions.

For example, a private equity firm may approach a business owner directly to explore an acquisition before the company is formally put up for sale. Investors can develop such opportunities through industry events, founder networks, referrals, and targeted research.

The main advantage is the potential for less competition, giving investors more time to build relationships and negotiate terms. Early engagement can also help investors understand a business before a formal transaction begins.

However, proprietary sourcing requires considerable time, research, and relationship-building. It does not guarantee exclusivity, lower valuations, or a successful transaction.

What Is Intermediated Deal Sourcing?

Intermediated deal sourcing involves accessing investment opportunities through third parties, such as investment banks, M&A advisers, business brokers, and other professional networks.

For example, a company planning to sell may appoint an investment bank to identify potential buyers and coordinate the bidding process. Interested investors receive information about the business and can assess whether it fits their investment strategy.

This approach can provide access to a broader range of opportunities and streamline the initial screening process. However, competitive bidding may push valuations higher, while intermediary fees can increase transaction costs. Investors may also face tighter timelines when evaluating deals.

Proprietary vs. Intermediated Deal Sourcing

The key difference lies in how investors access opportunities and the level of competition involved.

Proprietary sourcing emphasizes direct relationships and targeted outreach. It may provide earlier access and less competition but requires sustained effort to build a reliable pipeline.

Intermediated sourcing uses established professional channels to connect investors with potential transactions. It can improve access to available deals and simplify the sourcing process, although competition and fees may be higher.

Neither approach guarantees better investment outcomes. Success ultimately depends on the quality of the opportunity, due diligence, valuation, and negotiated terms.

Which Strategy Is Better?

For many investors, combining both approaches offers the greatest flexibility. Proprietary sourcing helps uncover opportunities and establish long-term relationships, while intermediated sourcing provides access to businesses already seeking investment or considering a sale.

The right balance depends on the investor’s objectives, resources, target market, and investment stage. Ultimately, successful deal sourcing is not about finding the most opportunities, but about identifying the right investments and building relationships that lead to sustainable value creation.

Noha Gad 

Artificial intelligence (AI) is no longer a tool for improving products and services; it has become a strategic asset that shapes economic competitiveness, national security, and digital independence. As governments and enterprises increasingly rely on AI for critical decision-making, questions are emerging about who controls the data, infrastructure, models, and rules behind these systems.

As AI deployment is accelerating worldwide, countries have concerns about their ability to build and run AI systems independently without the need for foreign technology providers, while maintaining the highest levels of security and privacy with regard to data, technology infrastructure, operations, and legal structures. This has brought sovereign AI to the center of global technology and policy debates.

What is meant by sovereign AI?

Sovereign AI refers to AI systems developed, deployed, and governed within a nation's legal and operational boundaries to ensure data and jurisdictional control. It helps governments and organizations address issues related to control, governance, and dependence. 

The difference between sovereign AI and global AI models lies in where the data resides and who holds the legal authority over the system. For Jurisdictional control, sovereign AI ensures that all data and operations remain under the legal authority of a specific country or region, perhaps within a sovereign cloud. Meanwhile, global AI models often operate on international cloud platforms where data may move across borders 

For data movement and privacy, sovereign AI restricts data movement, ensuring that sensitive information never leaves the local jurisdiction, unlike global AI models that use users' data to improve a centralized, multi-tenant model governed by a provider’s terms of service.

For alignment and autonomy, sovereign AI helps create models that are natively aligned with local dialects, legal codes, and social norms. In contrast, global models may reflect the cultural or linguistic biases of the region where they were developed. 

Types of sovereign AI deployment models

Sovereign AI is not a single architecture, but rather a spectrum of control based on data sensitivity, regulatory requirements, cost, scalability, and the need for operational control.

• Full sovereignty: This model hosts data, models, infrastructure, and personnel all within national borders, utilizing a domestic supply chain for hardware and software wherever possible.
• Hybrid sovereignty: Sensitive workloads remain on domestic, sovereign infrastructure, while non-sensitive tasks are offloaded to global platforms to take advantage of scale.
• Regional or bloc sovereignty: Sovereignty is defined at the level of a political alliance, such as the European Union, allowing for cross-border data flows within the member states while preserving regional legal control.
• Sector-specific sovereignty: Sovereignty requirements are applied only to critical industries like defense, healthcare, or finance, while other sectors continue to use global AI services.

Pros and Cons

Implementing a sovereign AI strategy provides the regulatory assurance and strategic control necessary for high-stakes environments. Other advantages include:

• Enhanced regulatory compliance: Systems adhere to national data protection laws from the point of deployment, reducing the risk of legal penalties.
• Greater data control: Sensitive government, financial, healthcare, and business data remain under local jurisdiction, with clear rules for storage, access, processing, and deletion.
• Data and intellectual property protection: Sensitive data and proprietary models are stored in isolated, controlled environments to prevent data leaks and model theft.
• Operational resilience: Local, hybrid, or disconnected deployment can reduce reliance on a single external provider and support continuity for critical services.
• Operational continuity: Localized infrastructure ensures that critical national and corporate services remain online regardless of international geopolitical events or trade restrictions.

Building and operating sovereign AI capabilities also involves high costs, talent constraints, and continued dependence on global supply chains. Key disadvantages are:

• High infrastructure and legal costs: Building local compute clusters and navigating complex, often conflicting regional regulations requires massive upfront capital investment.
• Talent shortage: There is a global shortage of AI engineers, data scientists, cybersecurity specialists, and infrastructure professionals required to build and maintain complex sovereign environments.
• Hardware dependence: Even with local infrastructure, many countries remain dependent on global supply chains for advanced chips, GPUs, and specialized components.
• Data lineage and provenance: Maintaining a verifiable, auditable record of where training data originated is essential for proving compliance with local privacy and copyright laws.

Noha Gad

AI adoption is accelerating rapidly across Saudi Arabia, driven by Vision 2030’s ambitions and supported by significant national investment and strong executive commitment. As organizations move from pilots toward enterprise-wide deployment, attention is turning to whether the operational foundations are in place to deliver results at scale.

As the global process context platform for enterprise AI deployment, ARIS works with organizations across Saudi Arabia and the broader Middle East to build the process context and governance that AI success depends on. 

Sharikat Mubasher held an interview with Ayham Alzaaim, senior vice president for Asia Pacific and Middle East at ARIS, on the sidelines of LEAP 2026 to learn more about how the company helps organizations deploy AI responsibly at scale.

In this interview, Alzaaim discussed the importance of process context in achieving progress, how organizations can move from traditional process management to real-time operational intelligence, the common pitfalls they face when scaling AI and automation, and why execution discipline will define AI leadership in the Kingdom and beyond.

ARIS believes that progress starts with process. In practical terms, how does process context directly translate into operational resilience and productivity gains for organizations?

Process context gives organisations a clear, trusted picture of how their operations actually work - who owns what, how decisions are made, where risks exist, and where inefficiencies are hiding. That visibility is the foundation of resilience. You cannot protect what you cannot see, and you cannot improve what you have not measured.

In practical terms, organisations that have built strong process context are able to respond to disruption faster, identify bottlenecks before they become crises, and deploy technology, including AI, with far greater confidence. The productivity gains come not from adding more tools but from finally understanding the operations those tools are meant to serve. For organisations across the Kingdom, that understanding is what turns Vision 2030's digital ambitions into measurable day-to-day outcomes.

Based on your experience across the Middle East, what does the journey from traditional process management to real-time operational intelligence look like for leading organizations—and what can the GCC learn from it?

The journey has three distinct stages. First, organisations map and govern their processes, creating a single trusted view of how the business actually operates. Second, they connect that foundation to real execution data so they can see how work flows in real time, not just how it was designed to flow. Third, they use that dynamic intelligence to drive continuous improvement and, increasingly, to deploy AI agents on a governed foundation.

What the GCC can take from leading organisations globally is that the sequence matters enormously. The organisations that try to skip to the third stage without completing the first two are the ones struggling to show AI returns. Those that invest in the foundation first move faster, govern better, and scale with confidence.

AI adoption is accelerating rapidly across the Middle East. What are the most common pitfalls organizations across the region face when attempting to scale AI and automation, and how can they avoid them?

The most common pitfall is deploying AI before the operational foundation is ready. Organisations invest in the technology, run a successful pilot, and then find that when they try to scale it across the enterprise, it breaks down. The reason is almost always the same: the AI does not have the process context it needs to operate reliably in a complex, live environment.

The way to avoid it is to treat process context as a prerequisite, not an afterthought. Before deploying AI at scale, organisations need to know how their processes actually work, who owns them, what rules govern them, and how performance is measured. When that foundation is in place, AI can be deployed with confidence and scaled without losing control. That is the execution discipline Vision 2030 demands, and the organisations that build it now will be the ones defining AI leadership in the Kingdom

LEAP 2026 is one of the region’s most significant technology platforms. From your perspective, what role do events like LEAP play in accelerating digital transformation across Saudi Arabia and the broader Middle East?

LEAP brings together government decision-makers, enterprise leaders, technology innovators, and investors under one roof in the heart of the Kingdom. That convergence accelerates transformation in ways that individual conversations cannot.

For ARIS, LEAP is where we have some of our most important conversations with organisations across the region. It is where strategy becomes concrete, where leaders who are thinking about AI transformation move into planning and action. The energy and ambition in the room reflect exactly where Saudi Arabia and the broader GCC are in their transformation journey right now.

Saudi Vision 2030 aims to position the Kingdom as a global AI hub. What is driving that momentum, and what should organizations across the Kingdom prioritize to accelerate that vision?

What is driving it is a combination of factors that very few countries can bring together simultaneously: clear national direction, significant investment, world-class infrastructure, and genuine executive decisiveness at both government and enterprise levels. Saudi Arabia is not just talking about AI leadership; it is building for it.

What organisations should prioritise now is execution discipline. The investment and the ambition are both firmly in place. The next phase is to ensure AI initiatives deliver measurable outcomes rather than impressive announcements. That means building the operational and governance foundations that allow AI to be deployed at scale, responsibly, and with control. Organisations that get that right now will not just contribute to Vision 2030; they will help define what AI leadership looks like globally.