Vision Report

VUCA and Artificial Intelligence: Navigating Complexity in the Age of Uncertainty

Professor Ibrahim Al-Jarrah, Head of Artificial Intelligence Sector

15 January 2025

Summary

In recent times, there has been a growing sense of confusion and unease surrounding artificial intelligence (AI) across all sectors, from researchers, engineers, and physicians to educators, students, and business owners. Some fear AI silently; others approach it with excessive enthusiasm; while many stand by, watching cautiously, uncertain whether this wave will drown them or carry them forward. This collective uncertainty captures a global phenomenon aptly described by the concept of VUCA, an acronym for Volatility, Uncertainty, Complexity, and Ambiguity. The term was first introduced in a discussion with Dr. Saad Ibrahim AlKhalaf, Executive Vice President of Arrowad Group, who highlighted its relevance in understanding our relationship with artificial intelligence. Indeed, the world we inhabit today is volatile, complex, ambiguous, and filled with uncertainty, the defining traits of the AI era (U.S. Army Heritage and Education Center, 2018).

Living in a VUCA World

Under the influence of artificial intelligence, humanity now exists within a world governed by VUCA dynamics:

• Volatility: Rapid shifts that destabilize even experts.
• Uncertainty: Lack of predictability that disrupts decision-making.
• Complexity: Interconnected systems and intricate algorithms that blur cause and effect.
• Ambiguity: Widespread confusion that deepens hesitation and fear of the future.

The AI revolution is not merely a technological upheaval, it is a cognitive transformation that redefines how we think, decide, and perceive our surroundings. The real challenge is not AI itself, but the noise and misinformation surrounding it. Even specialists oscillate between extremes of over-enthusiasm and complete denial.

This raises critical questions:

• Where do we stand in this evolving landscape?
• Are we simply consumers of AI innovation?
• Can we still shape its trajectory?
• Or have we fallen too far behind, content merely to observe?

From Reaction to Understanding: Building Resilience through Awareness

The answer does not lie in chasing every emerging tool or trend, but in developing deep understanding. We do not need to master every AI system, but we must comprehend the essence of this paradigm shift.

Education must acknowledge the VUCA state we live in and prepare new generations to engage with ambiguity, uncertainty, and constant change. The goal should not be to prepare students for static jobs, but to equip them with adaptability, self-learning, and the ability to ask meaningful questions, the true survival skills of a VUCA-driven world.

At the individual level, it is time to move from reaction to action. We must cultivate technological awareness that helps us distinguish between hype and genuine progress. Human worth lies not in memorization or computation, but in wisdom, creativity, moral judgment, and an understanding of human and cosmic nature, qualities that remain far beyond the reach of machines.

From Strategy to Implementation: Institutional and Policy Implications

At the level of governments and institutions, adopting AI should not be reduced to technological display or branding exercises. Instead, it must be pursued as a strategic endeavor, beginning with education, passing through policy, and grounded in an understanding of our local and global context.

We need policies and frameworks that not only react to VUCA forces but adapt within them, turning volatility and complexity into engines for innovation and resilience. This means building systems that can learn, adjust, and evolve, not resist change but thrive within it.

Closing

We are living through the epicenter of a global earthquake whose aftershocks continue to reshape our world. Yet within this disruption lies an opportunity for renewal. Artificial intelligence will not define our future for us, we will, to the extent that we understand and embrace this volatile, uncertain, complex, and ambiguous reality.

AI is not merely a technical question; it is a philosophical and existential one. It asks whether humanity still possesses the capacity to understand itself, in a world that is increasingly unlike itself.

References

• U.S. Army Heritage and Education Center (2018) “Who first originated the term VUCA (Volatility, Uncertainty, Complexity and Ambiguity)?” USAHEC Ask Us a Question, The United States Army War College. Archived from the original on 2 June 2021. Retrieved 10 July 2018. Available here.

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Model/Framework Overview Report

Understanding Excellence Before Building its Framework

Dr. Khalid M. Aljarallah, Head of Research and Capacity Building Sector

26 September 2024

Summary

Many organizations aspire to achieve institutional excellence, yet few truly understand its essence before attempting to build its framework. Genuine excellence cannot emerge from complexity or abstraction; it grows when systems and concepts are presented simply, clearly, and in a way that everyone can understand and apply.

Understanding precedes application. The difference between success and struggle often lies in how clearly an organization communicates the goals, tools, and requirements of its excellence framework.

The Power of Simplification

A notable example is that of a person known for the ability to simplify complex scientific topics, transforming difficult material into engaging, accessible discussions that awaken curiosity and inspire learning.

The same principle applies to organizations pursuing excellence. The ability to simplify, clarify, and make systems accessible across all organizational levels is not trivial, it is central to success. When leaders present the full picture of an excellence framework, its objectives, requirements, and practical tools, employees can apply them more easily, from top management to frontline staff.

Simplification, then, is not a luxury, it is a strategy for sustainable excellence (Schein, 2010).

Shared Understanding Before Implementation

Excellence cannot be achieved through aspiration alone, nor through slogans, nor by assigning responsibility to a single person or department. It requires a collective understanding shared by all members of the organization.

Only when everyone comprehends the principles and tools of excellence can they take ownership of achieving it. This shared understanding fosters a sense of responsibility and belief in the value of the system itself (Kotter, 2012).

Without such understanding, organizations risk treating excellence as an external requirement rather than an internal culture.

Many organizations struggle with the requirements of excellence due to misunderstanding, misapplication, or perceived difficulty. In some cases, these challenges lead to disengagement, resistance, or submission of irrelevant data that do not serve the system’s true objectives.

To address this, leaders must provide clear guidance, expert consultation, and ongoing clarification to ensure that each requirement of excellence is well understood. As Deming (1986) noted, clarity of purpose is a prerequisite for quality and consistency.

When misunderstanding is left unaddressed, organizations risk undermining both morale and performance.

The Art of Communication: Speak in Their Language

Simplifying and clarifying the project, by clearly defining its objectives and roles, and communicating with stakeholders in a language they understand, is a successful formula and an effective approach. This includes addressing them in the languages they master as fluently as their native tongue.

The intent behind simplification here is not to be lenient in applying standards, to neglect the measurement of indicators, or to avoid the use of robust, proven systems necessary for closing the loop of continuous improvement. Rather, the goal is to make the excellence project easy to understand, practical to implement, linguistically clear, and harmoniously aligned with the capabilities of the individuals and units responsible for its execution.

These elements are like the teeth of a key that must align perfectly with the lock for the door to open smoothly and effortlessly. Indeed, addressing people according to their level of understanding is a noble prophetic principle.

Divine Example of Ease and Clarity

A profound reflection may be drawn from the Qur’anic verse: “And We have certainly made the Qur’an easy for remembrance, so is there any who will remember?” (Qur’an, 54:17)

Even the most powerful and eloquent form of divine guidance, the Book of God, is described as made easy. This illustrates a timeless truth: clarity and simplicity are not weaknesses but marks of strength, wisdom, and accessibility.

Closing

The journey toward institutional excellence begins not with systems or checklists, but with understanding. Simplification, communication, and clarity are not mere facilitative tools, they are the very foundation of sustainable excellence.

True excellence is achieved when organizations ensure that every member understands the purpose, tools, and value of their excellence framework, making it a shared mission rather than a management initiative.

Excellence is not built on complexity, it thrives on clarity, shared conviction, and simplicity in execution.

References

• Deming, W. E. (1986) Out of the crisis. Cambridge, MA: MIT Press.
• Kotter, J. P. (2012) Leading change. Boston: Harvard Business Review Press.
• Schein, E. H. (2010) Organizational culture and leadership. 4th edn. San Francisco: Jossey-Bass.
• The Holy Qur’an (n.d.) Surah Al-Qamar (54:17).

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Model/Framework Overview Report

The Strategic Dimension in the Structure of the Data Governance Maturity Index

Dr. Hisham Anani, Senior Consultant

17 November 2024

Summary

The National Data Governance and Maturity Index (NDI) measures how well entities in Saudi Arabia develop their data infrastructure and comply with national data standards. It is structured across 14 domains and two main dimensions: Strategic and Executive.

This report highlights the Strategic Dimension as the key driver that guides entities’ data management approach and aligns long-term visions with day-to-day operations. By setting this direction, it enables the Executive Dimension to effectively implement and comply with the required controls and specifications.

The Foundational Role and Strategic-Executive Relationship

The Strategic Dimension in the NDI represents the driving force and defining factor for the overall structure, making it the primary and most influential factor in the index's framework and in the successful achievement of compliance with controls and specifications issued by the National Data Management Office (NDMO).

The Strategic Dimension, with its long-term perspective, is the foundation upon which the Executive Dimension is entirely built. It contributes significantly and heavily to the maturity assessment (with percentages ranging from 20% to 75% across various domains, reaching 60% in Content Management and 57% in Data Modeling). This high proportion confirms that executive performance cannot achieve its tangible results unless it is consistent and guided by the approved strategic visions and directions. Consequently, this integrated relationship ensures that high-level plans are translated into effective daily practices, establishing sustainable compliance with the required national specifications.

Methodology and Defining the Governing Role of Strategy

The analysis of the index's structure and the contribution of the two dimensions relied on official detailed data for the distribution of controls and specifications within the National Index. This distribution is specifically based on the official documents issued by (NDMO, 2021) and (SADIA, 2021), which validates the quantitative basis for analyzing the Strategic Dimension's role as a fundamental guiding factor in the index's structure.

The analysis of the compliance index structure within the NDI shows that the Strategic Dimension is the guiding, governing, and foundational pillar of the index's structure. The definition of its key roles includes:

• Foundation/Pillar: It is the essential support element for the entire data management structure; without it, the index cannot be established or its objectives achieved.
• Guiding: It defines the direction and priorities, mapping the path for the Executive team to follow towards achieving the long-term vision, preventing arbitrary decisions.
• Governing: It sets the oversight framework and high-level controls (Governance Framework), determining "what must be done," "why it must be done," and "how it is measured," ensuring executive activities remain compliant with institutional policies.
• Foundational: It is responsible for establishing the initial organizational structure and policies, including founding committees and defining roles responsible for data management.

Structure and Guidance: The Command Relationship

The importance of the Strategic component lies in setting the long-term foundations, plans, and policies that organize the general framework for data management. The relationship between the two dimensions is built on the principle of Structure and Guidance:

• The Structure: The Strategic Dimension builds the framework within which the Executive Dimension operates (e.g., establishing a "Data Governance Committee").
• The Guidance: It sends directives and tasks to the Executive Dimension, which the Executive must translate into detailed operational procedures.

This relationship represents one of leadership and control: the Strategic Dimension establishes the framework (Structure) and sends the orders (Guidance), which the Executive Dimension must follow to transform them into practical reality.

Causality and Integration

This foundational premise establishes that the relationship between the two dimensions is built on the principle of causality and integration to ensure compliance:

• Strategic Dimension (The Primary Driver): It is the primary determinant and controller of the general framework, guaranteeing all efforts align with the institutional vision. Its contribution forms the basis of compliance controls and specifications (40% of controls and 36% of specifications overall). This distribution confirms that the soundness and effectiveness of executive procedures rely entirely on the correctness of the adopted strategic direction, with the Strategic Dimension serving as the prerequisite for success.
• Executive Dimension (The Operational Weight): This is the active tool responsible for translating strategies into daily practices and measurable outcomes. Although it carries the largest share of the actual compliance weight (contributing between 60% and 80% in some areas), its implementation efficiency remains contingent upon the guidance provided by the Strategic Dimension.

Ensuring effective integration and collaboration is the crucial key to applying strategic directions efficiently, leading to full compliance with the National Data Index.

Quantitative Analysis of Strategic and Executive Contributions

The quantitative analysis confirms that while effective execution carries the largest weight in achieving actual compliance, this execution is entirely constrained and guided by the strategic foundations, which ensure the integrity of the institutional direction.

Table 1: Overall Contribution of Strategic and Executive Dimensions

The analysis of the 14 domains shows that domains with a governance and planning nature (e.g., Modeling and Content Management) rely mainly on strategic direction, while domains with an operational and technical nature (e.g., Integration and Quality) focus on execution.

Table 2: Analysis of the Strategic Dimension's Contribution Across the 14 Data Management Domains

This analysis confirms a causal hierarchy between the two dimensions. In domains requiring planning and structuring (such as Content Management and Modeling), the Strategic Dimension rises to be the leader (over 50%). Conversely, in domains requiring daily operational effort (such as Integration and Quality), the strategic weight decreases, but the strategic direction remains the guarantor that the execution serves long-term objectives (Henderson and Venkatraman, 1993).

Closing

It is evident that the Strategic Dimension in the NDI is the fundamental and most influential pillar in the index's structure. It defines the long-term vision and directions for data management, establishing the governance framework and standards for compliance. This dimension acts as the primary mover and guide, ensuring that the Executive Dimension (concerned with applying policies and controls) is designed and implemented in a manner that serves the institution's higher objectives.

The core importance of the Strategic Dimension lies in ensuring Strategic Alignment between the organization's ambitions and its technical capabilities (Henderson and Venkatraman, 1993). While the Executive Dimension undertakes the task of translating these strategic visions into tangible results and actual compliance, their close integration is essential for achieving sustainable success and proving the institution's commitment to national and international standards.

The overall structure of the index is built on the principle of precise structural integration and balance, confirming it is not a mere collection of controls. The Strategic Dimension acts as a Guidance System, ensuring that the substantial execution efforts are effectively invested to achieve the National Data Vision.

References

• Henderson, J. C. and Venkatraman, N. (1993) ‘Strategic alignment: A model for organizational transformation’, Business Transformation Journal, 34(3), pp. 53–68.
• Office of National Data Management (NDMO) (2021) National Data Management and Governance Controls and Specifications, Version 1.5, January. Available at: https://www.ndmo.sa/ (Accessed: 17 November 2025).
• Saudi Data and Artificial Intelligence Authority (SDAIA) (2021) The National Data Index: Third Measurement Cycle. Available at: https://www.sdaia.gov.sa/ (Accessed: 17 November 2025)

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Vision Report

The So-Called “AI Washing”

Professor Ibrahim Al-Jarrah, Head of Artificial Intelligence Sector

07 March 2025

Summary

In the midst of the accelerating digital revolution, artificial intelligence (AI) has become synonymous with progress and innovation. Companies across industries are eager to associate themselves with this transformation—not only through genuine technological investment but also through aggressive marketing that positions them as “AI-driven.”

This has given rise to a widespread and controversial phenomenon known as AI washing, a term referring to the misrepresentation or exaggeration of AI capabilities in products or services. The practice raises significant concerns around credibility, ethics, and technological literacy among consumers and investors (TechTarget, 2024).

Understanding “AI Washing”: The Modern Equivalent of Greenwashing

AI washing mirrors the earlier concept of greenwashing, where organisations overstate their environmental efforts for marketing gain. In this newer context, companies claim to employ advanced AI systems to appear innovative or to attract investors, when in reality, their technologies may be limited to simple automation or manual workflows disguised by technical jargon.

Common Motivations for AI Washing:

• Attracting investors by appearing technologically advanced.
• Inflating valuations through the illusion of AI innovation.
• Enhancing reputation and brand credibility.
• Justifying higher prices for supposedly “intelligent” solutions.

Such exaggerations distort public understanding of AI’s true nature, creating a growing gap between expectation and reality.

The Consequences of Exaggeration: Eroding Trust and Misuse of AI

The greatest danger of AI washing lies in its erosion of public trust. When customers discover that a supposedly “AI-powered” system relies on rudimentary software—or even human labor—their confidence in the technology weakens.

This erosion of trust does not only harm deceptive companies but also undermines faith in legitimate AI applications. The risk becomes especially severe in critical sectors like education or healthcare, where misleading AI claims can lead to misguided decisions and serious outcomes. Once exposed, false claims can result in reputational collapse, terminated partnerships, and investor withdrawal.

Causes Behind the Phenomenon

Several structural and cultural factors contribute to the proliferation of AI washing:

1. Lack of universal standards defining what constitutes “real AI.”
2. Limited regulatory oversight and auditing mechanisms.
3. Low public and investor literacy in evaluating AI claims.
4. Media amplification that prioritizes hype over critical analysis.

Consequently, not every product labeled as “smart” or “AI-enabled” truly leverages artificial intelligence in any meaningful way.

Real-World Examples: When the Illusion Collapses

In recent years, several high-profile companies have raised large investments by promoting themselves as AI platforms that empower users to build applications autonomously. Investigations later revealed that many of these systems relied heavily on manual human intervention, misleading users into believing the technology was fully intelligent.

The fallout from these revelations included terminated contracts, investor losses, and widespread skepticism toward AI startups. These cases illustrate that AI washing is far from a harmless exaggeration, it carries serious financial, ethical, and societal implications.

Building Transparency and Accountability

To combat AI washing, both regulatory reform and cultural awareness are essential. Organisations must:

• Disclose the technical foundations of their AI systems.
• Undergo third-party validation of claimed AI capabilities.
• Avoid ambiguous marketing that confuses automation with intelligence.

The media should also adopt an analytical role, focusing on verifying technical claims rather than amplifying promotional narratives. Moreover, universities and research institutions must train graduates to think critically, equipping them with the skills to differentiate authentic AI systems from superficial marketing.

Closing

AI washing represents a credibility crisis at the heart of the technological revolution. The goal is not to limit AI’s expansion, but to protect it from dilution and deception. Genuine AI does not need exaggerated claims—its impact is self-evident. False promises, however, inevitably collapse under scrutiny.

Building a culture of trust, transparency, and technical literacy is therefore the foundation for sustaining AI innovation and ensuring it remains a transformative force for good.

References

• TechTarget (2024) AI washing explained: Everything you need to know. 29 February. Available here (Accessed: 5 June 2024).

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Overview Report

The New “Khanfasharians” in the Age of Artificial Intelligence

Professor Ibrahim Al-Jarrah, Head of Artificial Intelligence Sector

10 November 2025

Summary

Throughout history, every era has known its “Khanfashari”, the person who wears the illusion of knowledge, presenting themselves as a scholar without ever truly approaching scholarship. In today’s age of artificial intelligence (AI), such figures have multiplied in modern forms, dressed in the cloak of “experts,” wielding dazzling terminology that captivates the public, misleads newcomers, and clouds the work of genuine researchers. This modern phenomenon mirrors the ancient story of “Al-Khanfashar,” recounted in Nafḥ al-Ṭīb and other Arabic sources. The tale tells of a man who claimed knowledge in every field. Six companions, doubting his pretensions, invented a fictitious word, Khanfashar, and asked him its meaning. Without hesitation, he fabricated an elaborate answer: he claimed it was a fragrant plant in Yemen that curdled camel milk, quoted a fabricated verse, and even attributed a false reference to Dāwūd al-Anṭākī before falsely linking it to the Prophet. When confronted, he was exposed as a fraud, earning the title “Al-Khanfashari”, a lasting symbol of pretension and intellectual deceit (Wikipedia, n.d.).

The Modern Khanfashari: Digital Pretenders in the Age of AI

Today, the Khanfashari of artificial intelligence no longer invents meaningless words but skillfully recycles real ones, without grasping their true essence or application. They speak with authority about machine learning, sentiment analysis, or decision-making algorithms, promising revolutionary platforms and innovations, yet offering neither published research nor functional prototypes.

These individuals thrive on borrowed vocabulary, using complexity as camouflage. They mesmerize audiences with buzzwords while avoiding the rigor that defines authentic expertise. Their confidence, not competence, becomes their credential.

The Social and Institutional Consequences

The danger of this trend extends beyond personal deceit, it has collective consequences. Organizations are often seduced by these pretenders, allocating budgets to projects promised to “transform the future,” only to end up with beautiful interfaces and nonfunctional algorithms.

As a result, AI, an exact and demanding science grounded in mathematics, statistics, and engineering, is distorted into a spectacle of illusion. It becomes, in the hands of pseudo-experts, a marketing weapon, a symbol of status, or even a tool of deception.

The Roots of the Phenomenon

At the heart of this intellectual epidemic lie three main causes:

1. A psychological need for visibility: A craving to appear knowledgeable without the effort of mastery.
2. Profound intellectual emptiness: A lack of foundational understanding compensated by verbal showmanship.
3. A cultural environment that glorifies rhetoric over truth: Where eloquence often triumphs over evidence, and applause replaces critical questioning.

Such individuals flourish in societies that do not value verification, where audiences rarely question sources, and where the distinction between the scholar and the imitator has become blurred.

Restoring Intellectual Integrity

To escape the grip of this phenomenon, societies must empower critical thinking and foster a culture of verification and accountability. Genuine knowledge connects words to action, a true expert does not merely speak but builds, tests, and presents measurable results.

The authentic scholar simplifies complexity, communicates with humility, and recognizes the limits of their own understanding. Meanwhile, the impostor thrives on obscurity, complexity, and applause.

AI is not magic nor mysticism, it is a discipline rooted in mathematics, statistics, algorithms, and experimentation. Those entitled to speak on it are those who have built, tested, and contributed measurable work to their communities. The new Khanfasharians, however, belong not in scientific circles but in literature, as cautionary tales of vanity, falsehood, and inevitable downfall.

Closing

The age of artificial intelligence has not only expanded human potential but also magnified human pretense. The challenge before us is to protect knowledge from distortion and science from vanity.

We must guard our collective awareness against this new intellectual epidemic. True progress begins not with loud claims but with truth, humility, and perseverance. For what is built on Khanfashar cannot stand, and what is built on knowledge will endure.

References

• Wikipedia (n.d.) [“Khanfashar”]. Available here (Accessed: 6 November 2025).

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Model/Framework Overview Report

The Framework of Institutional Excellence

Dr. Khalid M. Aljarallah, Head of Research and Capacity Building Sector

26 September 2024

Summary

Why do some organizations succeed on their journey toward institutional excellence while others falter? Despite the similarities among global excellence frameworks, such as the King Abdulaziz Quality Award (KAQA) and the European Foundation for Quality Management (EFQM) model, organizational success depends not only on the application of standards and indicators, but also on deeper factors related to organizational culture and leadership.

This report presents seven foundational pillars that underpin the success and sustainability of institutional excellence systems: Shared Vision, Clear Communication, Credibility and Integrity of Purpose, Institutionalization, Leveraging Technology, Change Management,  and Motivation and the Spirit of Excellence.

Shared Vision: One Team, One Goal

The leader represents both the mind and the heartbeat of the organization. Leadership excellence manifests when the leader’s enthusiasm aligns with that of employees across all levels, fostering conviction in the importance of building an excellence system.

A shared goal, in which every individual understands their role, feels responsible, and recognizes mutual benefit, nurtures a unified team spirit. Active participation in decision-making strengthens this cohesion, while differing aspirations or weak commitment can disperse efforts and jeopardize continuity (Kotter, 2012).

Clear Communication: Speak to People at Their Level of Understanding

The Irish philosopher Edmund Burke once said, “When you fear something, learn about it as much as you can; knowledge conquers fear.” Misunderstanding breeds resistance, while clarity builds trust and motivation.

Therefore, the clearer and simpler the requirements are, expressed in language that the average person can easily understand, the more likely they are reassured, motivate stakeholders and encourage their positive engagement. Simplifying the overall picture of the excellence framework and clarifying its tools and concepts fosters collective understanding and effective participation, facilitating smoother and more efficient implementation (Schein, 2010).

Credibility and Integrity of Purpose

Credibility and sustainable excellence are inseparable. True success does not arise from performative compliance, but from sincere intention, ethical behavior, and transparency.

In early 2023, Harvard University’s Faculty of Medicine withdrew from the U.S. News & World Report global university rankings after concerns emerged about the credibility of the ranking criteria, despite Harvard’s long-standing top position (Harvard Gazette, 2023).

This decision reflects a vital principle: genuinely excellent institutions prioritize integrity and reputation over appearances and awards. Moreover, transparency and accountability are essential elements that reinforce trust and drive continuous improvement (Deming, 1986).

Institutionalization of Work

Building a system of excellence should be a strategic institutional endeavor, not an individual initiative tied to specific people or temporary efforts.

True sustainability occurs when excellence activities are integrated into the organization’s governance and daily operations, becoming part of routine practice rather than a separate or short-term project.

Institutional excellence is built on structured systems, documented procedures, and accountability mechanisms, for chaos never produces success (Deming, 1986).

Leveraging Technology

Technology is both the language and the arena of the modern era. Beyond enhancing speed and precision, it also promotes transparency and credibility.

Studies indicate that applying modern technologies can reduce process timelines by up to 60% and decrease errors by 50%, while the use of intelligent chatbots (AI Chatbots) has improved customer response times by 70% (McKinsey & Company, 2023).

With rapid advances in artificial intelligence (AI) and machine learning (ML), organizations now have greater opportunities to enhance excellence management through predictive analytics and data-driven decision-making.

Change Management

Resistance to change is inevitable, but wise application of change management methodologies makes all the difference.

Effective implementation of structured change models can reduce waste by up to 50% and increase productivity by 60% (Prosci, 2021).

Notable frameworks include the ADKAR model (Hiatt, 2006) and Kotter’s Eight-Step Change Model (Kotter, 2012).

As an organization grows in size and complexity, the need for a structured yet flexible approach to change becomes greater, balancing both the human and structural dimensions to ensure adaptability and sustainability.

Motivation and the Spirit of Excellence

Positive competition is a creative catalyst that breathes life into any organization.

Proven motivational practices include assigning shared performance indicators across departments to encourage collaboration and healthy competition, followed by recognition for outstanding performance.

For instance, one ministry established an annual institutional excellence award honoring outstanding individuals and departments in a ceremony attended by the ministry’s top leadership.

Ultimately, the role of leadership in fostering and recognizing excellence remains the most critical motivational factor. Effective leaders must possess the skill and influence to inspire conviction and enthusiasm for excellence, making the journey both meaningful and enjoyable.

As Dr. Ghazi Al-Gosaibi observed: “A subject cannot be useful unless it is engaging; it cannot be engaging unless it is simple; and it cannot be useful, engaging, and simple unless the teacher exerts far more effort than the student.” (Al-Gosaibi, 2005, p. 47)

Closing

All seven pillars converge on a single truth: leadership is the cornerstone of institutional excellence.

Sustainable success is not achieved through tools and standards alone, but through a culture grounded in shared purpose, integrity, and adaptive intelligence.

Excellence, therefore, is not a final destination, it is a mindset and a leadership philosophy built on clarity, honesty, and collective aspiration.

References

• Al-Gosaibi, G. (2005) The Life of an Educator. Riyadh: Dar Al-Obeikan.
• Deming, W. E. (1986) Out of the Crisis. Cambridge, MA: MIT Press.
• Harvard Gazette (2023) Harvard Medical School withdraws from U.S. News rankings. Available at: https://news.harvard.edu/gazette  (Accessed: 3 November 2025).
• Hiatt, J. (2006) ADKAR: A Model for Change in Business, Government and Our Community. Loveland, CO: Prosci Research.
• Kotter, J. P. (2012) Leading Change. Boston: Harvard Business Review Press.
• McKinsey & Company (2023) The State of Digital Transformation 2023. Available at: https://www.mckinsey.com/business-functions/digital  (Accessed: 3 November 2025).
• Prosci (2021) Best Practices in Change Management. Loveland, CO: Prosci.

Schein, E. H. (2010) Organizational Culture and Leadership. 4th edn. San Francisco: Jossey-Bass.

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Vision Report

Integrating Human Values at the Heart of Artificial Intelligence

By Professor Ibrahim Al-Jarrah, Head of Artificial Intelligence Sector

20 August 2024

Summary

In today’s world, humanity is experiencing profound transformations driven by innovations in artificial intelligence (AI). These technologies have come to touch every aspect of daily life, from healthcare and education to agriculture and food security. Yet, as societies and organizations race toward a technologically advanced future, a fundamental question arises: How can we ensure that AI development progresses hand in hand with human values, without being swept away by the waves of technological advancement?

The question extends beyond individuals, it is an organizational and societal imperative. Machines must not only learn how to process data but also how to embody moral concepts such as justice, empathy, and fairness, values that vary across cultural and individual contexts (OECD, 2019). What one society considers fair may differ dramatically from another’s perspective, requiring AI systems to be developed with cultural awareness and inclusivity (UNESCO, 2021).

Defining Core Human Values in Organizational AI Strategy

From Arrowad Group’s perspective, the first and most essential step toward responsible AI is to define the human values that form its foundation. Concepts such as justice, transparency, respect, and equality must not remain abstract ideals, they should become measurable criteria guiding AI development (European Commission, 2020).

Organizational Actions to Define and Embed Values:

• Ethical governance alignment: Integrate human values into corporate vision, mission, and AI governance frameworks.
• Stakeholder inclusion: Engage experts and stakeholders from diverse disciplines, engineers, ethicists, policymakers, and community representatives, to define shared ethical priorities.
• Cultural adaptability: Ensure that organizational values reflect inclusiveness and account for cultural variation in perceptions of fairness and morality.

By doing so, organizations ensure that AI becomes a mirror reflecting humanity’s ethical diversity and not merely a product of technological efficiency.

Embedding Values in the AI Development Lifecycle

Once human values are identified, they must be embedded across all stages of AI development, from initial design to real-world deployment (European Commission, 2020). Developers and organizations should focus not only on technical precision but also on moral responsibility.

Integration Phases:

1. Design: Incorporate ethical principles in project planning and model architecture.
2. Development: Train AI professionals in ethics and data responsibility alongside coding and analytics.
3. Testing and validation: Assess how systems uphold fairness, inclusivity, and respect in decision-making.
4. Deployment: Implement transparency protocols ensuring that AI-driven processes are explainable and accountable.

This holistic integration ensures that every element of an AI system, its data, logic, and interface, reflects respect for human dignity and organizational responsibility (OECD, 2019).

Evaluation and Continuous Accountability

Responsible AI requires systematic evaluation tools to measure how deeply human values are embedded within AI systems. Organizations must develop clear, objective standards and conduct regular ethical reviews (UNESCO, 2021).

Essential Components:

• Ethical assessment metrics: Quantitative and qualitative indicators for transparency, inclusivity, and fairness.
• Accountability structures: Governance bodies to oversee the ethical performance of AI projects.
• Transparency to stakeholders: Clear communication about how AI systems make decisions, handle data, and impact individuals.

Such evaluation not only safeguards compliance but also builds stakeholder confidence and public trust, both crucial for sustainable AI adoption.

Collaboration and Public Engagement

The path toward responsible AI is inherently collaborative and interdisciplinary. Scientists, developers, policymakers, and the public must work together to ensure that AI technologies serve humanity collectively, not selectively.

Key Collaboration Principles:

• Legal and ethical frameworks: Establish global and national guidelines for responsible AI use (OECD, 2019).
• Public inclusion: Empower communities through education and dialogue to understand and shape AI’s societal role.
• Institutional partnerships: Encourage collaboration between private enterprises, academia, and civil society to promote shared accountability.

Public participation, through awareness programs, training, and forums, enhances transparency and allows citizens to meaningfully engage in shaping AI’s ethical trajectory.

The Arrowad Group Model: A Practical Example of Value Integration

Within this framework, Arrowad Group demonstrates a tangible model for aligning innovation with ethics. Through its subsidiaries, including Arrowad for Values Building, the organization strives to balance the integration of AI in its products and services with a steadfast commitment to shared human values.

This approach reflects Arrowad’s vision of achieving an ideal equilibrium between technological advancement and moral integrity. The Group’s efforts represent a practical application of responsible AI principles, promoting human dignity and contributing to a more inclusive and just society.

Closing

The journey toward responsible AI is not a purely technical pursuit, it is a moral, organizational, and societal endeavor. It requires global collaboration among governments, companies, academic institutions, and civil society to ensure that AI serves humanity as a whole, not just a privileged few.

Arrowad Group emphasize that the true success of AI lies not only in achieving technological superiority but also in reflecting the highest respect for human dignity and values. The future of responsible AI depends on organizations that view ethics as a strategic foundation for innovation, not an afterthought.

References

• European Commission (2020) Ethics guidelines for trustworthy AI. Available here (Accessed: 6 November 2025).
• OECD (2019) OECD principles on artificial intelligence. Available here (Accessed: 6 November 2025).
• UNESCO (2021) Recommendation on the ethics of artificial intelligence. Available here (Accessed: 6 November 2025).

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Vision Report

Don’t Study Artificial Intelligence—Unless…!

Professor Ibrahim Al-Jarrah, Head of Artificial Intelligence Sector

09 September 2025

Summary

The title may sound surprising, but it reflects an important truth about today’s world. Artificial intelligence (AI) dominates global conversations, universities are racing to launch new programs, companies are competing to recruit AI talent, and the public repeatedly hears that AI is the future of every industry, job, and economy.

Each year, after the release of high school results and during the university application period, experts receive numerous calls and messages from parents asking: “What should our children study?” With growing enthusiasm around AI, many students and families now view it as the ideal choice for their future. Yet, experience shows that this decision requires careful reflection. Success in AI is not determined merely by job market trends, it depends on genuine readiness for the intellectual and emotional journey the field demands.

Understanding the Nature of the Field

AI is not for everyone. Entering it simply because it is popular or because “everyone is studying it” can lead to disappointment and burnout. As the saying goes, “Choosing the wrong path might keep you walking for a long time, but you will never arrive.”

This discipline requires true passion for technology, mathematics, and statistics, along with curiosity about how things work from the inside. If you do not enjoy solving problems, creating solutions, and approaching challenges with originality, you may find yourself lost among complex codes, equations, and mathematical concepts.

As Einstein wisely stated, “It is not enough to know, you must understand.” Success in AI depends not on memorization but on comprehension, exploration, and creative thinking.

Continuous Learning: The Lifelong Commitment

Learning AI is a continuous and lifelong process. What is new today may become obsolete within months, or even weeks. The field evolves at an incredible pace, with new tools, techniques, and concepts emerging almost daily.

Experts in the field often emphasize:

“You must read research papers and articles regularly, and practice on platforms such as Kaggle, Hugging Face, and GitHub.”

These platforms should become part of your daily routine, just like social media, visited frequently to explore ideas, solve challenges, and share projects. In AI, true learning comes not from listening but from consistent, practical application.

Embracing Uncertainty and Experimentation

AI projects are often full of surprises. Datasets may be incomplete or contain errors, models may fail to perform as expected, and results can contradict initial assumptions. Yet, these challenges are not signs of failure, they are part of the process.

To succeed in AI, one must develop patience, adaptability, and resilience, along with the willingness to experiment repeatedly until achieving the right solution. Each challenge teaches valuable lessons, reinforcing the mindset that progress is built through persistence and curiosity.

The Right Mindset for AI Students

If you are self-motivated, enjoy solving problems, view every challenge as an opportunity for growth, and find joy in discovering new things each day, then AI could indeed be your gateway to a rewarding and globally relevant career. The field opens doors to international opportunities and allows individuals to make a meaningful impact on people and societies.

• However, before committing to this path, test your interest and aptitude:
• Attend free online lectures or courses.
• Try writing simple code and experimenting with AI tools.
• Explore real-world AI projects to understand the field’s practical demands.

These experiences will help you decide whether you see yourself thriving in AI over the next decade.

Checklist: Don’t Study AI Unless You…

Before you choose AI as your major, make sure these statements describe you:

• Have a strong desire for lifelong learning and adaptability to fast technological change.
• Enjoy problem-solving and take pleasure in approaching problems differently.
• Are interested in mathematics, statistics, logic, and programming.
• Can persevere through a long learning journey, developing skills before reaching your dream career.
• Are comfortable working with imperfect data or unexpected results.
• Enjoy experimentation, iterative testing, and learning from trial and error.
• See learning as a continuous journey that extends far beyond lectures and textbooks.
• If these describe you, AI could be one of the best academic and career decisions you ever make, opening global opportunities and allowing you to contribute to real-world transformation.

If, however, you prefer a stable field where information changes slowly and routines remain predictable, AI might not be the right fit.

Closing

Artificial intelligence is an exciting and transformative field, but it demands curiosity, dedication, and the ability to keep learning. Those who view education as a lifelong pursuit and embrace challenges with creativity will find fulfillment and success.

Don’t study artificial intelligence unless you are ready to grow with it. The field rewards not those who follow trends, but those who combine technical skill with passion, patience, and an enduring love of discovery.

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