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. 

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.

Noha Gad

Every product starts as an idea, but turning that idea into a profitable, sustainable offering requires far more than good design or effective marketing. Companies must coordinate research and development (R&D), manufacturing, supply chains, quality control, sales, customer support, and eventual retirement, often across multiple teams, markets, and systems. When this coordination breaks down, it can lead to delayed launches, inconsistent product information, rising costs, compliance risks, and lost customer trust.

This is where Product Lifecycle Management (PLM) becomes essential. PLM is a strategic business approach that manages a product throughout its entire life, from the initial concept and design stages through production, launch, growth, service, and end-of-life. Rather than treating each stage as a separate task handled by a different department, PLM creates a connected framework for managing product data, processes, decisions, and collaboration across the organization.

This approach is not important only for large manufacturers; it is also pivotal to consumer brands, technology companies, industrial firms, and even digital-first businesses that heavily rely on lifecycle management to bring products to market faster, maintain quality, control costs, and respond to changing customer expectations. 

Why does product lifecycle management matter?

At the core of PLM is the end-to-end integration of product data, processes, and systems along the entire value chain. Implementing PLM consistently can shorten decision paths, reduce errors cost, and bring innovations to market faster and more reliably. Increasing product complexity, shorter innovation cycles, and globally distributed value chains make structured PLM a decisive competitive factor that allows companies to manage product processes across departmental and site boundaries, while meeting regulatory, environmental, and economic requirements.

An integrated PLM approach creates the necessary transparency: development, production, quality assurance, sales, and aftersales all work from a shared data foundation. Therefore, decisions become faster, better informed, and more traceable. Also, a consistent, quality-assured product data landscape is a prerequisite for modern technologies such as AI, machine learning, and automation to deliver their full potential.

PLM approach also helps organizations keep up with the evolving complexity and engineering challenges of developing new products. It is one of the four cornerstones of a manufacturing corporation's information technology structure, alongside customer relationship management (CRM), supply chain management (SCM), and enterprise resource planning (ERP). 

For marketing, determining a product’s lifecycle stage guides the team’s strategy. For instance, a new product in the introduction stage needs to be explained, while a mature product needs to be differentiated.

Stages of PLM

PLM follows a structured sequence of phases that guide a product from its earliest idea to its eventual withdrawal from the market. Most PLM frameworks include the following core stages: 

• Concept stage. The concept stage involves initial ideas and planning for a new product, and includes market research, identifying customer needs, and determining the feasibility of the product. Often led by the R&D department, this stage kicks off the product lifecycle, as it is where the ideas are generated.
• Design stage. In this stage, the product is planned, developed, and tested. It involves creating product prototypes, refining the design, and ensuring that it meets all regulatory and safety requirements. 
• Production stage. This stage starts when the company feels confident in the new product and feels there is a market for it. This stage involves manufacturing the product, including sourcing raw materials, assembling components, and testing the final product. At this point, the company should have a fully completed product and should not be continually tweaking the design.
• Sales stage. After the product is made, it moves to the sales stage that involves promoting and selling the product to customers. This includes advertising, sales promotions, and pricing strategies. In many cases, the sales stage and production stage occur concurrently, as a company must try to forecast how many sales will occur.
• Support stage. The support stage involves providing ongoing support to customers after they have purchased the product. This includes customer service, warranties, and repairs. It may also include ongoing training or services provided to new owners to enhance user experience.
• Retirement Stage. Whether competitors have delivered a better product or the product is simply no longer demanded by the market, the product lifecycle ends with the retirement stage. This stage involves the end-of-life of the product, including disposal, recycling, or repurposing of the product.

Finally, product lifecycle management plays a pivotal role in providing companies with a structured approach to manage products from initial idea through design, production, sales, customer support, and retirement. By connecting teams, standardizing product data, and improving collaboration across the value chain, PLM helps businesses launch products faster, reduce errors and costs, maintain quality, and respond more effectively to market changes.

Ghada Ismail

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How can founders build companies that operate without them?

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

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

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

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

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

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

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

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

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

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

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

Ghada Ismail

When a company decides to enter a new market or expand its operations, one of the first questions it faces is whether to build from scratch or invest in an existing business or facility. This decision often comes down to two approaches: greenfield investment and brownfield investment.

Both strategies can help companies expand their presence, reach new customers, and increase their market share. However, they differ in how businesses establish operations, how much control they retain, and the costs and risks they take on.

Understanding these differences can help investors choose the approach that best fits their growth plans.

What Is Greenfield Investment?

Greenfield investment occurs when a company establishes a new operation from the ground up in a foreign or domestic market. Instead of purchasing an existing facility or business, the investor develops the project from scratch, which may include acquiring land, constructing buildings, installing equipment, and hiring employees.

One of the main advantages of this approach is control. Investors can design facilities, choose technologies, establish company culture, and structure operations according to their specific requirements.

Greenfield projects can also introduce new technology, create jobs, and support the development of local industries.

However, starting from scratch can be expensive and time-consuming. Investors may need to navigate licensing requirements, secure land, build infrastructure, and establish relationships with suppliers and customers before generating revenue. These challenges can become more significant when entering an unfamiliar market.

What Is Brownfield Investment?

Brownfield investment involves investing in an existing facility, business, or industrial site to expand operations or develop new activities. Depending on the project, investors may renovate an existing property, upgrade production equipment, acquire an operating company, or repurpose an underused industrial site.

This approach can offer a faster route into a market because essential infrastructure may already be available. Existing facilities may have access to utilities, transport networks, suppliers, and an established workforce.

Acquiring an operating business can also provide an existing customer base and established market relationships.

Nevertheless, brownfield investments come with their own challenges. Older facilities may require costly upgrades, while existing businesses can have operational inefficiencies or outdated technology. Investors may also face difficulties integrating employees, systems, and corporate cultures after an acquisition.

Greenfield vs. Brownfield Investment: Key Differences

The main difference lies in how the investment is established.

Initial costs: Greenfield projects typically require substantial upfront spending on land, construction, equipment, and staffing. Brownfield investments may reduce infrastructure costs, although acquisition prices and renovation expenses can be significant.

Time to market: Greenfield investments usually take longer because operations must be developed from the beginning. Brownfield projects can begin operating sooner when the existing infrastructure is suitable.

Control and flexibility: Greenfield investors have greater freedom to design operations and introduce new systems. Brownfield investors may need to adapt existing facilities, processes, or organizational structures.

Risk profile: Greenfield investments carry construction, permitting, and market-entry risks. Brownfield investments involve risks related to existing assets, legacy systems, environmental conditions, and potential hidden liabilities.

Local presence: Greenfield investors build their workforce and business relationships over time. Brownfield investors may benefit from established employees, suppliers, and customers.

Neither strategy is automatically cheaper or safer. The outcome depends on the market, the condition of available assets, and the investor's objectives.

Which Investment Strategy Should Companies Choose?

The right choice depends on what a company wants to achieve.

Greenfield investment may be more suitable for businesses that need specialized facilities, advanced technology, or complete control over their operations. It can also make sense when suitable existing assets are unavailable.

Brownfield investment may appeal to companies seeking faster market entry, existing infrastructure, or an established customer base. It can be particularly attractive when a business can improve an underperforming operation or modernize an outdated facility.

To Wrap Things Up…

Greenfield and brownfield investments offer different routes to business expansion. Greenfield projects provide greater flexibility and control but generally require more time and upfront investment. Brownfield projects can accelerate market entry by building on existing assets, although they may involve costly upgrades and inherited risks.

Ultimately, investors should evaluate total costs, expected returns, operational requirements, and market conditions before committing to either approach. The goal is not simply to build something new or acquire something existing, but to choose the investment structure that offers the strongest long-term value.

Noha Gad

When a company aspires to expand, acquire another business, or finance a major strategic project, traditional sources of funding may not provide the full amount required. Senior lenders may be unwilling to increase their exposure, particularly when the company already has significant debt or lacks sufficient collateral. At the same time, raising additional equity can reduce existing shareholders’ ownership and influence over the business.

Mezzanine financing addresses this gap, as it consists of a hybrid form of capital that combines features of debt and equity and occupies an intermediate position in a company’s capital structure. Companies often use this financing to obtain additional capital beyond the amount available from senior lenders, while limiting the need for a substantial equity raise.

What Is mezzanine financing?

Mezzanine financing represents a strategic financial tool that bridges the gap between senior debt and equity. This hybrid form of financing enables lenders to convert debt into equity, fostering flexibility and higher returns. Frequently employed in acquisitions and company expansions, mezzanine financing captures both opportunity and risk, offering companies crucial capital for growth. Although it is considered one of the highest-risk forms of debt, mezzanine financing offers some of the highest returns in debt investments.

Companies may choose mezzanine financing to fund specific growth projects or acquisitions with short- to medium-term time horizons. Often, these loans will be funded by the company’s long-term investors and existing funders of the company’s capital.

Main forms of mezzanine financing

Mezzanine financing is not a single standardized product. It can be structured in several ways, depending on the company’s financing needs, repayment capacity, capital structure, and the investor’s return requirements. The most common forms include:

• Subordinated debt. This form is a loan that ranks below senior debt in the repayment hierarchy but above equity. If the company defaults or enters liquidation, senior lenders are paid first, while subordinated lenders are repaid only after those obligations have been satisfied.
• Second-lien financing. It is secured by the same assets that support a senior lender’s first lien, but the mezzanine lender has a lower priority claim over those assets. If the borrower defaults, the first-lien lender generally has priority in enforcing its security and recovering the outstanding debt.
• Preferred equity. Preferred equity is legally a form of ownership, but it has features that make it resemble debt or other types of mezzanine capital. Preferred shareholders typically receive priority over common shareholders when dividends are paid, or assets are distributed during a liquidation.

Mezzanine financing provides companies with additional capital and more flexibility than traditional senior debt. Its characteristics make it useful for established businesses pursuing acquisitions, expansion projects, recapitalization, or ownership transitions. Key advantages are:

• Providing access to additional capital by filling the gap between the amount of senior debt a company can obtain and the total capital it needs.
• Helping existing owners retain a larger share of the business than they might retain after a conventional equity raise.
• Offering more flexible repayment structures than conventional bank debt.
• Supporting companies in completing transactions that require more funding than senior debt alone can provide.
• Offering lower cost than pure equity, particularly when the potential cost of giving up a large share of the company’s future value is considered.

Although mezzanine financing can provide flexible capital with less immediate ownership dilution than equity finance, it also exposes the company to higher costs, repayment pressure, and contractual restrictions. These disadvantages mean that it is generally suited to established businesses with predictable cash flows rather than to companies with unstable or insufficient earnings. 

Finally, mezzanine financing is a flexible hybrid capital that can help companies bridge the gap between senior debt and equity. By combining debt-like features with equity-linked characteristics, it enables established businesses to secure additional funding for acquisitions, expansion, recapitalization, and other strategic initiatives while potentially limiting immediate ownership dilution. However, it is generally more expensive than senior debt, may involve equity dilution through conversion rights or warrants, and can put significant pressure on a company’s cash flow because of interest payments and repayment obligations. 

Ghada Ismail

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

This is the idea behind a bolt-on acquisition.

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

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

How does a bolt-on acquisition work?

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

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

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

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

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

Why do companies choose bolt-on acquisitions?

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

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

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

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

How is it different from a major acquisition?

Not every acquisition is a bolt-on.

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

A bolt-on is usually more focused.

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

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

What is the challenging part here?

Smaller acquisitions are not automatically easy acquisitions.

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

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

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

To Wrap Things Up…

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

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

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

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

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

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

Not every startup wants to become the next billion-dollar company. Some founders are less interested in chasing huge valuations and more interested in building a business that can survive when things get tough.

This is where the idea of a cockroach startup comes in.

The name may sound unusual, but the idea behind it is fairly simple. A cockroach startup is built to be resilient. It aims to keep operating through difficult markets, limited funding, changing customer needs, and unexpected setbacks.

In other words, it is a startup that focuses on staying alive and growing steadily rather than expanding as quickly as possible.

Where Does the Term Come From?

The comparison comes from the insect itself. Cockroaches have a reputation for surviving harsh conditions, which is exactly the quality the term is meant to describe in a business.

A cockroach startup is usually careful with its money, keeps its operations relatively lean, and looks for ways to generate revenue instead of depending entirely on investors.

That does not mean these companies never raise funding. They can still attract venture capital and other forms of investment. The difference is that funding is treated as a tool for growth rather than the only thing keeping the company going.

A cockroach startup also takes a more cautious approach. Instead of asking, “How quickly can we grow?” its founders may be asking, “How can we grow without running out of money?”

That difference from other startups can affect almost every part of the business, from hiring and marketing to product development and expansion plans.

For example, a startup following the cockroach model may avoid hiring a large team before there is enough revenue to support it. It may also focus more heavily on keeping existing customers rather than spending heavily to acquire new ones.

What Makes a Startup a Cockroach?

There is no fixed formula, but a few characteristics tend to stand out.

The first is financial discipline. Founders pay close attention to expenses, cash flow, and how long their available capital can support the business.

Another is an early focus on revenue. A company does not necessarily have to be profitable from the beginning, but having paying customers can give it more room to operate when fundraising becomes difficult.

Then there is adaptability. Startups rarely follow their original plans exactly. Customer demand can change, competitors can appear, or an economic downturn can force founders to rethink their strategy. A resilient startup needs to respond rather than simply stick to the original plan.

A smaller, more focused team can help with this as well. When there are fewer layers of management, decisions can often be made faster, and resources can be directed toward what matters most.

Why Does the Model Matter?

The cockroach approach has become particularly relevant during periods when startup funding becomes harder to secure.

When investors are willing to put large amounts of money into startups, companies can afford to prioritize growth over profitability for a while. But when funding slows, businesses that have been spending heavily without generating enough revenue can quickly find themselves under pressure.

A more resilient company has a better chance of weathering that period.

It may not grow as quickly as a heavily funded competitor, but it can have more control over its future. It may also avoid having to raise money simply because it needs enough cash to keep the lights on.

Is a Cockroach Startup Better?

Not necessarily.

Some businesses genuinely need significant amounts of capital to grow. A technology company developing complex infrastructure, for example, may need substantial investment before it can generate meaningful revenue. In other markets, moving slowly can allow competitors to get ahead.

So the cockroach model is not a rule that every founder should follow.

Its real value is the mindset behind it: build a company that can survive before assuming it will always have access to more money.

A startup does not need a billion-dollar valuation to be successful. Sometimes, success simply means building a useful product, earning loyal customers, keeping the business financially healthy, and being able to make it through the next difficult period.

That may not be as flashy as a unicorn story, but for many founders, it can be a much more realistic definition of success.

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.