Summary

In recent years, the balance of power in the world has no longer been measured solely by military or economic strength. Instead, it is increasingly shaped, more subtly and profoundly, by artificial intelligence and social media. We are witnessing a qualitative shift in the structure of geopolitical power: a shift managed not from military operations rooms, but from data centres, recommendation algorithms, and artificial reasoning models.

This transformation reflects the growing recognition that control over digital infrastructures and algorithmic systems has strategic implications for states, societies, and cultural identities (Zuboff, 2019; Kwet, 2020). As algorithmic systems increasingly shape information flows and collective perception, technological power becomes inseparable from political and cultural influence.

Social Media and Algorithmic Influence on Collective Consciousness

Social media platforms that initially appeared as tools for communication and expression have evolved into highly influential—and potentially dangerous—spaces if approached without critical awareness. They are no longer neutral platforms; rather, they operate as instruments of soft power that shape awareness, reorder priorities, and influence values and behaviours.

Today, these platforms affect:

• public mood and social discourse,
• value systems and behavioural patterns,
• political perception and decision-making.

More critically, these platforms are deeply intertwined with artificial intelligence systems that learn from user interactions and then reshape users’ experiences according to algorithmic logic. Scholars have described this phenomenon as algorithmic mediation, where automated systems increasingly determine what individuals see, think about, and discuss (Beer, 2017; Gillespie, 2018).

The Cultural Implications of Relying on a Single AI Ecosystem

Reflecting more deeply, it becomes problematic to rely on a single artificial intelligence system to answer questions that extend beyond educational or professional domains into intellectual, ethical, and spiritual dimensions.

Human societies do not share identical histories, cultures, or philosophical traditions. When minds are consistently fed answers framed within a purely Anglo-Saxon intellectual context, societies are not merely consuming knowledge—they are importing a worldview, a system of values, and a model of human life that may not reflect their own cultural identities.

This concern aligns with the growing discussion around digital colonialism, where technological platforms developed in dominant economies influence knowledge production and cultural narratives globally (Kwet, 2020).

The Responsibilities of States, Institutions, and Society

The responsibility for addressing these challenges does not lie with individuals alone. It extends across multiple layers of society.

Key stakeholders and their roles

Key stakeholders and their roles

The goal should not merely be to become smart users of artificial intelligence, but to become creators, theorists, and competitors in the global AI ecosystem.

Governments, in particular, must recognise that AI is no longer a technological luxury; it has become part of national security in its broadest sense. AI capabilities influence economic resilience, cultural autonomy, and sovereign decision-making (Bostrom, 2014; OECD, 2019).

Technological Sovereignty and the Chinese Example

In this context, the Chinese experience provides a notable case study. China has not limited itself to using foreign technological tools; instead, it has developed its own artificial intelligence capabilities and social media platforms—such as TikTok and DeepSeek—in alignment with its cultural, political, and developmental priorities.

Whether one agrees or disagrees with the Chinese model, it demonstrates a clear awareness of the risks of digital dependency and a deliberate effort to build technological sovereignty.

This approach illustrates how national AI ecosystems can serve as instruments of strategic autonomy and long-term geopolitical influence (Lee, 2018).

The Future Battlefield: Minds and Algorithms

The author concludes that future conflicts will not be decided solely on land or in the air. Instead, they will be determined in the realm of human cognition and the algorithms that shape it.

Those who fail to recognise this transformation risk becoming consumers of others’ visions rather than architects of their own futures. Artificial intelligence is therefore not an inevitable destiny but a strategic choice.

The real challenge lies not in learning prompt engineering alone but in designing the algorithms themselves:

• Who defines their logic?
• Who determines their boundaries?
• Who shapes their biases?
• Who decides the limits of the questions before answers are generated?

These questions highlight the deeper issue of algorithmic governance, which determines how technological systems influence societies and political decision-making (Gillespie, 2018).

Closing

This report highlights a fundamental shift in the nature of sovereignty and global competition. In the age of algorithms, technological capability is no longer merely a driver of economic development; it is a central pillar of cultural autonomy, political independence, and national security.

Societies that rely exclusively on external algorithmic systems risk importing not only technological tools but also foreign epistemologies and value frameworks. Building indigenous AI capabilities, fostering critical digital literacy, and designing culturally aware algorithmic systems are therefore strategic imperatives.

Ultimately, the future will belong not simply to those who use artificial intelligence, but to those who design its logic and shape its boundaries.

References

• Beer, D. (2017) The social power of algorithms. Information, Communication & Society, 20(1), pp. 1–13. https://doi.org/10.1080/1369118X.2016.1216147
• Bostrom, N. (2014) Superintelligence: paths, dangers, strategies. Oxford: Oxford University Press.
• Gillespie, T. (2018) Custodians of the internet: platforms, content moderation, and the hidden decisions that shape social media. New Haven: Yale University Press.
• Kwet, M. (2020) ‘Digital colonialism: US empire and the new imperialism in the global south’, Race & Class, 60(4), pp. 3–26. https://doi.org/10.1177/0306396818823172
• Lee, K.-F. (2018) AI superpowers: China, Silicon Valley, and the new world order. Boston: Houghton Mifflin Harcourt.
• OECD (2019) OECD principles on artificial intelligence. Available at: https://oecd.ai/en/ai-principles (Accessed: 15 March 2026).
• Zuboff, S. (2019) The age of surveillance capitalism: the fight for a human future at the new frontier of power. New York: PublicAffairs.

Summary

Psychological safety unlocks the full capacity of human intellect. It transforms honesty into knowledge, knowledge into improvement, and improvement into sustainable institutional excellence.

In safe environments, trust flourishes, learning accelerates, and organisational impact deepens. Every institution’s “ceiling” rises in proportion to the level of safety its people experience. When safety becomes an embedded culture, minds work with clarity and organisations move steadily toward leadership and distinction.

era (U.S. Army Heritage and Education Center, 2018).

The Centrality of Safety in Human Life

Safety is not merely a workplace concept—it is deeply rooted in human existence and relationships.

Over twelve centuries ago, the Arab poet Abu Tammam emphasised that security begins with people before buildings. Neighbours create reassurance through mutual respect and everyday goodwill (Al-Saeedi, 2005). He wrote:

“I built the neighbour before the home.”

Here, safety is framed as relational: walls may protect the body, but a good neighbour protects the soul from loneliness and fear. The Prophet Muhammad (peace be upon him) also sought refuge from the harm of a bad neighbour, highlighting the moral weight of social safety (Al-Nasa’i, 2001).

A Bedouin poetic anecdote further illustrates how, in moments of hardship, essentials outweigh luxuries. When asked what meal he desired while trembling from cold, he replied:

“Cook me a cloak and a shirt.”

This rhetorical device (mushākalah) reveals a simple truth: when need intensifies, comfort becomes more urgent than indulgence (Al-Hashimi, n.d.).

Why Safety Is Necessary in the Workplace

The same principle applies directly to organisational life.

Employees cannot innovate or contribute fully when preoccupied with self-protection—measuring every word, anticipating misinterpretation, or fearing blame.

Many brilliant ideas remain unborn simply because no safe space existed for them to be spoken. Often, institutional excellence begins at the moment a person feels their voice truly matters.

In daily meetings and interactions, a workplace climate forms that determines whether minds will operate at full capacity or merely “show up” to avoid accountability.

Modern business demands have expanded our understanding of the relationship between performance and psychological wellbeing. Workplace quality is now measured not only by productivity indicators, but also by its ability to protect mental balance and professional identity.

This recognition has led organisations to treat mental health as an investment in human capital rather than a peripheral issue (Public Health Authority, 2022).

Psychological Safety as a Learning Climate

In this context, safety emerges as a shared belief that:

• speaking honestly
• asking questions
• discussing mistakes
• challenging assumptions

are behaviours that strengthen teams rather than weaken them.

When such a climate prevails, knowledge flows, trust grows, and dialogue becomes a continuous engine of learning.

Research confirms that psychologically safe teams learn faster, decide better, and innovate more effectively (Landry, 2021; Edmondson, 1999).

Why Safety Changes the Rules of Performance

Organisational studies identify psychological safety as one of the strongest predictors of collective performance

Safe teams handle sensitive information transparently, detect risks early, and transform failures into learning opportunities.

The cycle becomes:

Error → Knowledge → Improvement → Excellence

Such teams discuss setbacks objectively and build cumulative capability over time (Landry, 2021; Frazier et al., 2017; Newman et al., 2017).

The Employee Journey: From Belonging to Impact

Psychological safety develops through sequential stages of maturity. The following framework is widely recognised (Clark, 2020):

Psychological Safety as a Competitive Advantage

Leading organisations treat safety as a strategic asset. Its impact appears in:

• Higher-quality strategic decisions
• Faster organisational learning
• Sustainable innovation
• Stronger talent retention
• Improved organisational health

Conscious leadership creates this climate through:

• intellectual humility
• encouraging feedback
• rewarding initiative
• designing inclusive conversations where everyone has a voice

Note on Consolidation

Some near-identical sentences in the Arabic source were consolidated in translation to improve readability while preserving intent, in accordance with the instructions.

Closing

Psychological safety is the soil in which institutions grow human brilliance. It ensures that ideas are spoken before they disappear, differences are managed as strengths, and learning becomes continuous.

Ultimately, safety is not a “soft” organisational value—it is a decisive leadership choice that elevates both people and performance together (Edmondson, 2018; Public Health Authority, 2022).

References

• Al-Attal, H.F.S. (2020) The role of intellectual capital in achieving organisational ambidexterity in health organisations: A case study of the Palestinian Red Crescent Society [Unpublished Master’s thesis]. Al-Quds University.
• Al-Hashimi, A.I. (n.d.) Jawahir al-balaghah fi al-ma‘ani wa al-bayan wa al-badi‘. Edited by Y. Al-Sumaili. Beirut: Al-Maktabah Al-Asriyyah.
• Al-Nasa’i, A.B.S. (2001) Al-sunan al-sughra (al-mujtaba). 2nd edn. Edited by A. Abu Ghuddah. Beirut: Islamic Publications Office. (Original work published 303 AH).
• Al-Saeedi, A.A. (2005) Bughyat al-idah li-talkhis al-miftah fi ‘ulum al-balaghah. 17th edn. Cairo: Maktabat al-Adab. (Original work published 1391 AH).
• Boushlagham, Z. (2018) Social interaction in virtual groups: The role of social presence [Unpublished doctoral dissertation]. University of Algiers 3.
• Clark, T.R. (2020) The 4 stages of psychological safety. Oakland, CA: Berrett-Koehler.
• Edmondson, A. (1999) ‘Psychological safety and learning behavior in work teams’, Administrative Science Quarterly.
• Edmondson, A. (2018) The fearless organization. Hoboken, NJ: Wiley.
• Frazier, M.L. et al. (2017) ‘Psychological safety: A meta-analytic review’, Personnel Psychology.
• Haber, J. (2022) Critical thinking. Translated by Hindawi Foundation. Cairo: Hindawi. (Original work published 2020).
• Landry, S. (2021) ‘Psychological safety in the workplace: Why it’s important’, HBS Online, 1 July. Available at: https://online.hbs.edu/blog/post/psychological-safety-in-the-workplace (Accessed: 17 February 2026).
• Newman, A. et al. (2017) ‘Psychological safety: A systematic review’, Human Resource Management Review.
• Public Health Authority (2022) Guideline for mental health in workplace environments (First edition). Riyadh: Public Health Authority.
• Omar, H. (2014) ‘Psychological security and its relationship to motivation for learning: A field study in the secondary schools of Berriane’, Journal of Humanities and Social Sciences, (16), pp. 191–210.

Summary

In the modern digital economy, data should be treated as a tangible organisational asset rather than a purely technical by-product. It is no longer a secondary output of IT systems; it is a strategic resource that requires governance and management extending beyond software code and infrastructure (DAMA International, 2017).

Despite this shift, many organisations struggle to realise value from establishing Data Management Offices (DMOs). A persistent challenge is functional disconnection: the DMO operates as a conceptual or policy unit, IT as an execution unit, and business units as data consumers. This fragmentation dissipates the expected value of data and weakens enterprise-wide governance.

The Problem: Functional Disconnection and Data Silos

Drawing on professional experience in establishing and operating DMOs at Kaizen Consulting, the author observes a “silent gap” threatening even the largest data governance initiatives. In many entities, the DMO becomes an isolated island that produces paper-based policies, while technical and operational realities follow a completely different trajectory.

This structural challenge is widely recognised in academic and professional literature as data silos, the separation of governance from execution and the lack of institutional integration between DMOs, IT, and operational units (Khatri and Brown, 2010). The result is a governance model that exists in theory but not in practice.

Structural Tensions Between DMO, IT, and Business Units

1. DMO–IT structural conflict

IT departments often perceive the DMO as an administrative burden or a compliance body that slows delivery. This perception leads to:

• Paper-based governance: data policies defined by the DMO that cannot be technically implemented.
• Standards duplication: IT teams designing databases independently, without reference to approved data modelling standards.

Such misalignment undermines the integration of governance into the system development lifecycle and reinforces siloed practices (Otto, 2011).

2. Knowledge gap between DMO and business units

Operational departments typically treat data from a short-term, immediate-need perspective, while DMOs approach data from a sustainability and governance perspective. This gap results in:

• Absence of data ownership: business units disclaim responsibility for data quality, shifting blame to IT or the DMO.
• Loss of business value: the DMO’s limited understanding of business context leads to governed data that lacks operational relevance.

The literature highlights this issue as a failure to embed governance roles within business processes, rather than positioning them as external controls (Weber, Otto and Österle, 2009).

Academic Analysis of Root Causes

From an academic standpoint, three root causes explain this persistent disconnection:

1. Flawed operating model: The DMO is often designed as a standalone department rather than a cross-functional function. Best practice positions data governance as an enterprise capability embedded across organisational boundaries (DAMA International, 2017).
2. Conflicting performance indicators (KPIs): When IT is measured by speed, the DMO by compliance, and business units by profitability, without a shared KPI for data-driven value, internal conflict becomes inevitable. This misalignment erodes collaboration and shared accountability (Khatri and Brown, 2010).
3. Weak data-driven culture: Data management is frequently perceived as an additional task, not a core responsibility. The absence of a data culture prevents employees from integrating data quality and stewardship into daily work (Earley, 2016).

A Roadmap for Integration and Value Realisation

To address this fragmentation, organisations must shift from a model of isolated islands to a strategic partnership model. This transition involves coordinated action across governance, operations, technology, and organisational structure.

Governance: from control to enablement

Data governance should evolve from a compliance-focused model to an enabling one. The DMO should move from saying “no” due to non-compliance to providing practical tools, such as data dictionaries and automated data quality mechanisms, that facilitate technical and operational performance (Weber, Otto and Österle, 2009).

Operations: activating data owners and stewards

Each business unit should formally appoint Data Owners and Data Stewards to act as active interfaces with the DMO. Their responsibilities go beyond nominal titles:

• Defining business-driven data quality rules.
• Translating these rules into requirements for IT implementation.
• Linking incentives and rewards to the accuracy and maturity of their unit’s data.

This approach embeds accountability directly within business operations (Khatri and Brown, 2010).

Technology: procedural integration with the SDLC

Data standards review must become a mandatory step within the Systems Development Life Cycle (SDLC). No system should be launched without formal DMO approval of its data architecture from the earliest design phase. This ensures that governance is “coded into” systems rather than retrofitted later (Otto, 2011).

Organisation: establishing a steering committee

A cross-functional steering committee, led by senior management and comprising IT, operations, and the DMO, is essential. Its role is not to debate technical details, but to ensure that:

• The data strategy serves business objectives.
• Technology infrastructure effectively supports operational needs.

Such governance bodies are widely recognised as critical for aligning strategy, governance, and execution (DAMA International, 2017).

Conclusion: Data as the Organisational Nervous System

Data can only generate maximum value when treated as the nervous system of the organization, connecting the brain (leadership) to the limbs (operations) through technological pathways. The success of a DMO should not be measured by the number of policies produced, but by the extent to which those policies are embedded in IT code and reflected in everyday operational decisions.

The central question remains: in your organisation, is the Data Management Office an enabler of business value, or merely a regulatory body?

Closing

This report highlights that the failure of many DMOs is not technical but structural and cultural. Bridging the gap between governance, technology, and operations requires redesigning operating models, aligning incentives, embedding stewardship roles, and integrating governance into system development. When these elements converge, data governance shifts from theory to practice, enabling sustainable, data-driven value creation across the enterprise.

References

• DAMA International (2017) DAMA-DMBOK: data management body of knowledge. 2nd edn. Basking Ridge, NJ: Technics Publications.
• Earley, S. (2016) The data lake: a foundation for enterprise agility. Boston, MA: O’Reilly Media.
• Khatri, V. and Brown, C.V. (2010) ‘Designing data governance’, Communications of the ACM, 53(1), pp. 148–152. https://doi.org/10.1145/1629175.1629210
• Otto, B. (2011) ‘Organizing data governance: findings from the telecommunications industry and consequences for large service providers’, Communications of the Association for Information Systems, 29(1), pp. 45–66.
• Weber, K., Otto, B. and Österle, H. (2009) ‘One size does not fit all - a contingency approach to data governance’, Journal of Data and Information Quality, 1(1), pp. 1–27. https://doi.org/10.1145/1515693.1515696

Summary

Many governments and technology developers seek to embed digital inclusion as a core enabler within their services, in order to strengthen social participation and promote sustainable development. According to the International Telecommunication Union (ITU), digital inclusion refers to ensuring that everyone, regardless of geographic location, gender, age, or differences in abilities, can access and benefit from digital products and services in a fair and equitable manner (ITU, n.d.).

Digital inclusion encompasses several groups of technology users, including people with disabilities of different types (such as physical, hearing, visual, and cognitive disabilities), senior citizens, and residents of remote areas. In this context, a report issued by the Saudi Digital Government Authority in July 2025 indicates that there are more than 1.3 million persons with disabilities in the Kingdom of Saudi Arabia, while older persons account for approximately 5% of the total population (Digital Government Authority, July 2025).

Strategic Value of Digital Inclusion

Beyond ensuring equitable access to digital services, digital inclusion contributes to achieving a range of strategic objectives for governments and organizations. It plays a key role in enhancing transparency and trust in institutional services, supporting economic growth by encouraging broader social participation, and improving quality of life for all segments of society through easy and convenient access to services.

However, embedding inclusion within digital services depends on several interrelated dimensions. These dimensions collectively shape the extent to which digital services are accessible, usable, and beneficial to diverse user groups, and they form the foundation for sustainable digital transformation (ITU, n.d.).

Key Dimensions of Digital Inclusion

Digital inclusion rests on multiple, mutually reinforcing dimensions. Each dimension addresses a different barrier to equitable participation in the digital environment and requires coordinated policy and operational responses.

Regulatory and Legislative Dimension

Effective legal and regulatory frameworks are essential to guarantee access to digital services as a fundamental right. Such frameworks obligate public and private sector entities to incorporate inclusive features into their digital services, ensuring that accessibility and usability are not optional but mandated components of service design.

Technical Infrastructure Dimension

Another critical dimension is the availability of adequate technical infrastructure, including telecommunications networks and internet connectivity. This infrastructure enables individuals across different geographic areas, particularly remote and underserved regions, to access digital services reliably and efficiently.

Cultural and Educational Dimension

The cultural and educational dimension focuses on building the skills required to interact with technology. This includes training and capacity-building initiatives, especially for groups with greater needs, such as elderly, to ensure that access to digital services contributes into effective and meaningful use.

Saudi Arabia’s National Efforts in Digital Inclusion

The Kingdom of Saudi Arabia places particular emphasis on digital inclusion, as evidenced by the launch of national initiatives and the adoption of relevant legislation. Digital transformation efforts under the National Transformation Program include initiatives aimed at enhancing digital inclusion across various sectors, thereby contributing to the reduction of social gaps and the expansion of access to resources and services.

A prominent manifestation of these efforts is the ecosystem of integrated government digital platforms, such as Madrasati, Sehhaty, Qiwa, and Balady, among others. These platforms provide comprehensive digital services designed to serve a broad spectrum of users.

In addition, the Kingdom has established an authority dedicated to the care of individuals with disabilities, serving as an umbrella body responsible for addressing their needs, including the development and enhancement of services provided to them. Furthermore, the Digital Government Authority has launched the “Digital Inclusion Program”, which primarily aims to increase the use of digital platforms by elderly and people with disabilities (Digital Government Authority, 2025).

Digital Inclusion as a Measure of E-Government Maturity

The importance of digital inclusion is not merely limited to enabling equitable social participation or improving service quality, but it has become one of the key criterion for assessing countries’ progress, the maturity of their e-government services, and their compliance with relevant international standards.

One of the most prominent international references in this regard is the United Nations E-Government Development Index (EGDI), in which the Kingdom ranked fourth out of 193 countries in 2024. This index is based on three dimensions that are closely linked to digital inclusion enablers (United Nations, 2024).

Saudi Arabia’s commitment to inclusive and high-quality digital services has also resulted in a significant recent achievement: attaining second place globally out of 197 countries in the Digital Government Maturity Index issued by the World Bank Group (World Bank, 2024).

At the national level, international indicators related to digital inclusion are complemented by local measurement frameworks. The Digital Government Authority has introduced an annual index to measure the maturity of the digital experience, with a strong focus on promoting inclusive access to, and effective use of, digital services by all user groups. Together, these indicators and initiatives reflect the growing importance of digital inclusion in achieving digital equity and reinforcing governments’ leadership positions.

Closing

The article emphasizes that digital inclusion has evolved from a marginal concern into an important pillar of modern digital government. By addressing regulatory, infrastructural, and cultural dimensions, Saudi Arabia has embedded digital inclusion as both a core social equity priority and a key indicator of e-government maturity. National and International indicators demonstrate that inclusive digital services are essential to enhancing transparency, trust, and global competitiveness, thereby reinforcing the Kingdom’s progression from ensuring digital equity to achieving digital leadership.

References

• Digital Government Authority (2025) Digital inclusion report. Riyadh: Digital Government Authority. Available at: https://www.dga.gov.sa (Accessed: 28 December 2025).
• International Telecommunication Union (ITU) (n.d.) Digital inclusion. Available at: https://www.itu.int (Accessed: 28 December 2025).
• United Nations (2024) United Nations e-government survey 2024: accelerating digital transformation for sustainable development. New York: United Nations Department of Economic and Social Affairs. Available at: https://publicadministration.un.org (Accessed: 28 December 2025).
• World Bank (2024) GovTech maturity index: benchmarking digital government transformation. Washington, DC: World Bank Group. Available at: https://www.worldbank.org (Accessed: 28 December 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.

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).

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)

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).

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).

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.

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).

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.

Summary

In today’s world, data and technology are indispensable for effective decision-making and achieving institutional excellence. The critical question is: How do we align business strategy with data and information technology strategies? This article presents two pioneering models used to achieve this alignment: the Strategic Alignment Model (SAM) and the Amsterdam Information Model (AIM).

The Strategic Alignment Model (SAM)

The Strategic Alignment Model (SAM), developed by Henderson and Venkatraman, is designed to create a coherent relationship between business strategies and information technology (IT) strategies. Its purpose is to ensure that data and technology are utilized in a way that supports the long-term goals of the organization.

Components of the SAM Model

1. Business Strategy: Defines the organization’s overarching goals and business direction, including elements such as growth, expansion, and competitive excellence.
2. IT Strategy: Focuses on how technology is leveraged to support and achieve the goals of the business strategy. It includes the development of technological systems, infrastructure, and applications that enhance efficiency and reduce costs.
3. Organizational Infrastructure: Involves the organizational structure and internal processes that support both the business and IT strategies. This includes institutional culture, team structures, and work policies.
4. IT Infrastructure: Refers to the core IT systems that ensure the continuity of operations, including servers, networks, and databases that support daily technological usage within the organization.

How the SAM Model Works

The SAM model emphasizes that there must be alignment between these components for success. For instance, the IT strategy should be designed to support the business strategy, not just as standalone systems. Additionally, the organizational infrastructure must be capable of facilitating coordination between business and IT teams.

The Amsterdam Information Model (AIM)

The Amsterdam Information Model (AIM), developed by Abcouwer, Maes, and Truijens, is another framework designed to connect business strategy with IT in an organization. AIM addresses how to manage information within organizations, considering the organizational structure, institutional culture, and tactical approach to enhance the strategic use of data.

Components of the AIM Model

AIM consists of a matrix with nine interconnected cells, divided into three main levels: strategy, tactics, and operations. The model places significant emphasis on data governance and quality in the context of business operations.

1. Level 1 – Strategy:
   • Business Strategy and Governance: Defines the strategic goals of the organization.
   • DATA Strategy and Governance: Directs the information strategies to support decision-making.
   • IT Strategy and Governance: Utilizes technology to achieve business strategy objectives.
2. Level 2 – Tactics:
   • Organizational Structure and Processes: Ensures integration between departments to implement strategies.
   • Information Engineering and Planning: Ensures information is stored and organized efficiently for accessibility.
   • IT Infrastructure and Planning: Plans the use of IT infrastructure to support the organization’s strategies.
3. Level 3 – Operations:
   • Business Execution: Translates strategies into actionable tasks and processes.
   • Information Management and Usage: Ensures data is managed effectively to support decision-making.
   • IT Services: Provides IT services that support business operations and quality.

Relationships Between the Components

The AIM model outlines several interrelated relationships for managing data and IT:

• Horizontal Perspective: Links business strategy with IT strategy.
• Vertical Perspective: Connects business strategy with daily operational activities.
• Data Governance and Quality: Ensures efficient management of data across all organizational levels.

How the AIM Model Works

AIM fosters communication between technical and business teams, ensuring that data strategies align with operational needs. It also strengthens data governance and quality, encouraging improvements that enhance the effectiveness of data use across the organization.

Closing

As organizations face rapid changes, those that successfully align their business strategies with data and technology will be able to lead the market, make smarter decisions, and fully capitalize on digital transformation. Does your organization apply one of these models?

References

• Abcouwer, A., Maes, P. and Truijens, J. (n.d.) The Amsterdam Information Model: Aligning Business Strategy with IT. Available here (Accessed: 14 May 2025)
• Henderson, J.C. and Venkatraman, N. (1993) ‘Strategic alignment: Leveraging information technology for transforming organizations’, IBM Systems Journal, 32(1), pp. 4–16.
• DAMA International (2024) The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK2R). 2nd ed, revised. Sedona, AZ: Technics Publications, LLC.

Summary

The launch of the National Artificial Intelligence Index (NAII) in Saudi Arabia represents a strategic milestone reflecting the maturity of the national vision and the government’s determination to accelerate the transition toward a knowledge- and innovation-driven economy. Led by the Saudi Data and Artificial Intelligence Authority (SDAIA), the initiative is more than a measurement tool; it is a national compass that organises, directs, and aligns government efforts to adopt AI technologies in line with the ambitions of Vision 2030 (SDAIA, 2025).

Framework and Structure of the Index

Core Dimensions

At its core, the index is built on a comprehensive and unified framework for assessing governmental readiness and maturity in adopting AI solutions. It moves beyond superficial evaluation to diagnose institutional capabilities through a robust structure composed of three main pillars:

• Orientations – covering strategy and governance.
• Enablers – including data, infrastructure, and human capabilities.
• Outcomes – measuring actual applications and tangible impact.

These pillars branch into seven main themes and 23 sub-domains, assessed through 26 precise questions supported by over 480 evidential indicators. This design ensures depth and inclusiveness, offering not only a measurement of each entity’s digital maturity but also practical recommendations and tailored development plans to strengthen performance and close improvement gaps (SDAIA, 2025).

Strategic Significance and Economic Impact

The NAII is closely tied to the objectives of Vision 2030, with 66 out of 96 goals directly or indirectly linked to data and AI. This underscores AI’s centrality to the Kingdom’s future development. According to PricewaterhouseCoopers (PwC, 2019), AI is projected to contribute around $135.2 billion to Saudi Arabia’s GDP by 2030, approximately 12.4 per cent of its economy.

The Index acts as both a catalyst and regulator for this growth, guiding public entities to adopt innovative AI solutions that improve operational efficiency, enhance service quality, boost productivity, and create a sustainable competitive advantage. Moreover, it establishes a clear governance framework balancing innovation with ethical responsibility, reinforcing trust in emerging technologies and supporting the Kingdom’s aspiration to become a global AI leader (PwC, 2019; Vision 2030, n.d.).

Measurement Mechanism and Maturity Levels

The Index applies a three-stage assessment process:

• Awareness workshops for government entities.
• Comprehensive questionnaires supported by documentary evidence.
• Data analysis and validation.

Based on results, entities are classified across six maturity levels:

• 0 – Absence of capabilities
• 1 – Building
• 2 – Activation
• 3 – Competence
• 4 – Excellence

This classification is not a final judgment but a starting point for continuous development, providing each entity with a precise understanding of its current state and a roadmap for advancing capabilities (SDAIA, 2025).

Analytical Insights: AI as a Strategic Necessity

Kaizen AI is no longer optional; it is a strategic necessity. The Index calls for aligning plans and initiatives with national directions, transitioning from limited experimental projects to a systematic adoption of AI within operations and services.

The key challenges include:

• Data quality and governance,
• Skills gaps, and
• Robust, flexible infrastructure.

By measuring these aspects, the Index offers a scientific basis for investment prioritisation, capacity-building programs, and regulatory policy design that accelerates innovation while ensuring responsible and secure AI use (OECD, 2023).

Governance, Ethics, and Organisational Integration

The Index highlights that data quality determines AI quality. It recommends investing in advanced data infrastructure covering collection, cleaning, standardisation, and secure, accessible storage.

A mature data culture should treat information as a strategic asset, governed by rigorous standards of accuracy, completeness, and consistency. It must include source documentation, usage rights, and data-sharing mechanisms across entities to break silos and foster integrated, high-impact solutions.

Enhancing advanced analytics and machine learning capabilities is also essential for extracting actionable insights that support decision-making and improve services (World Bank, 2024).

Technological Infrastructure and Operational Resilience

The Index sets benchmarks for computing, network, and storage capabilities necessary to support AI applications, especially generative and agentic AI models. Entities are encouraged to:

• Invest in high-performance servers with GPUs, or
• Utilise flexible cloud computing aligned with national platforms and standards.

It also mandates provisions for operational resilience, including service continuity, backup and disaster recovery, and proactive monitoring systems that ensure annual availability exceeding 99.5 per cent (ISO, 2023).

Human Capital: The Cornerstone of AI Maturity

stresses building specialised capabilities through integrated strategies that:

• Attract talent via competitive employment programs.
• Foster partnerships with universities and institutes to develop national competencies.
• Invest in advanced professional development aligned with the National Qualifications Framework.
• Create innovation-friendly workplaces that encourage experimentation and career growth.

These measures aim to ensure stability, reduce talent attrition, and embed innovation within institutional culture (World Economic Forum, 2022).

Benchmarking and Continuous Improvement

The Index also provides a benchmarking mechanism, enabling entities to compare their standing nationally and internationally. This fosters mutual learning, accelerates collective progress, and enhances the overall maturity of the national AI ecosystem.

Through periodic measurement cycles, the Index supports progress tracking, evaluates initiative effectiveness, and cultivates a results-based improvement culture. Thus, it becomes a comprehensive enabler accompanying entities throughout their digital maturity journey, from foundational readiness to advanced integration and innovation leadership (SDAIA, 2025).

Transparency, Accountability, and Policy Alignment

By setting unified performance standards linked to institutional KPIs, the Index reinforces transparency and accountability in government performance. Publishing results gives policymakers, researchers, and the public a realistic understanding of AI adoption levels, supporting data-driven decision-making (Transparency International, 2024).

Closing

The National Artificial Intelligence Index acts as a beacon illuminating Saudi Arabia’s digital future. It guides entities toward excellence, warns of pitfalls, and highlights optimal pathways. However, its effectiveness depends on leadership will, sustained investment, and the courage to innovate responsibly.

Entities that engage seriously and invest in capability development will deliver better services, enhanced efficiency, and contribute to a diversified, knowledge-based economy. Those who treat the Index superficially risk falling behind.

Ultimately, the Index transcends evaluation; it embodies an integrated strategic philosophy for building a promising digital future for the Kingdom, providing a common language and unified framework to accelerate AI adoption and transform ambition into measurable achievement.

References

• ISO (2023) ISO/IEC 27001:2022 – Information security, cybersecurity and privacy protection. Geneva: International Organization for Standardization.
• OECD (2023) Artificial Intelligence Policy Observatory: AI governance and national strategies. Paris: OECD Publishing. Available at: https://oecd.ai (Accessed: 12 Nov 2025).
• PricewaterhouseCoopers (PwC) (2019) The macroeconomic impact of artificial intelligence on the Middle East. Available at: https://www.pwc.com/me/aiimpact (Accessed: 12 Nov 2025).
• Saudi Data and Artificial Intelligence Authority (SDAIA) (2025) National Artificial Intelligence Index Framework. Riyadh: SDAIA.
• Transparency International (2024). AI accountability and transparency in public governance. Berlin: Transparency International.
• UNESCO (2021) Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO. https://doi.org/10.54675/unesco.ai.ethics.2021
• Vision 2030 (n.d.) Saudi Vision 2030. Available at: https://www.vision2030.gov.sa (Accessed: 12 Nov 2025).
• World Bank (2024). Data governance for digital transformation: Global practices and lessons learned. Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-XXXX-X
• World Economic Forum (2022). The future of jobs report 2022. Geneva: WEF. Available at: https://www.weforum.org/reports/the-future-of-jobs-2022 (Accessed: 12 Nov 2025).