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1. Vision Report
Future-state narrative with guiding principles, target architecture/operating model, and north-star KPIs.
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3. Overview Report
Provides a summary and analysis or a certain aspect rather than deep technical or methodological detail.
4. Trends Report Report
Synthesis of market/technology shifts with evidence, signals, and implications for the next 12–24 months.
5. Predictions Report
Time-bound calls on what will happen and why, plus “so-what” actions to prepare.
6. Forecast Report
Quantitative projections (scenarios, assumptions, confidence ranges) for demand, adoption, or performance.
7. Benchmark Report
Comparative ranking against peers or standards; clear scoring model, criteria, and takeaway gaps.
8. Best Practices Report
Proven approaches with do/don’t checklists, prerequisites, and pitfalls to avoid.
9. Case Study Report
Real project story – challenge, approach, solution, and outcomes with metrics and lessons learned.
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Costs, benefits, risks, and options; ROI/SRIO/NPV/TEI calculations with sensitivity analysis.
11. Data Brief Report
Concise metrics snapshot with charts and commentary highlighting patterns and anomalies.
12. Survey Report
Methodology, sample, findings, and cross-tabs; includes key charts and practitioner insights.
13. Maturity Assessment Report
Diagnostic across dimensions; stage definitions, gap analysis, and prioritized recommendations.
14. Model/Framework Overview Report
Explains a model’s components and interactions, with usage scenarios and boundaries.
15. Methodology Guide Report
Step-by-step process, roles, deliverables, and artifacts; includes templates and acceptance criteria.
16. Roadmap Report
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17. Toolkit
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18. Template Report
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Our Featured Research
From Equity to Leadership
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).
VUCA and Artificial Intelligence: Navigating Complexity in the Age of Uncertainty
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.
Understanding Excellence Before Building its Framework
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).
The Strategic Dimension in the Structure of the Data Governance Maturity Index
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
| Overall Component | Total | Strategic Controls (Guidance) |
Executive Controls (Application) |
Analysis of Strategic Impact (Governance) |
| Total Controls | 77 Controls | 31 Controls (40%) | 46 Controls (60%) | The Strategic Dimension represents the cornerstone at 40%, ensuring that 40% of compliance requirements are linked to high-level policies and frameworks. |
| Total Specifications | 191 Specifications | 68 Specifications (36%) | 123 Specifications (64%) | Strategic specifications form nearly one-third (36%) of the general framework, setting the standards and principles that must be operationally translated. |
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
| Domain | Controls (Strategic %) | Specifications (Strategic %) | Summary of Strategic Role (Guidance and Control) |
| 1. Data Governance | 38% Strategic | 25% Strategic | Guiding Reference: Strategy sets the general frameworks and principles for governance, essential for directing long-term regulatory commitment. |
| 2. Metadata and Data Catalog | 50% Strategic | 35% Strategic | Governance Balance: Equal contribution in controls confirms that metadata creation requires a clear strategic vision before actual documentation. |
| 3. Data Quality | 25% Strategic | 23% Strategic | Foundational Planning: Execution dominates daily processes (approx. 75%), but Strategy determines target quality levels and long-term policies. |
| 4. Data Storage | 40% Strategic | 36% Strategic | Sustainability Definition: Strategic impact appears in setting sustainable strategies for infrastructure, with execution ensuring tangible application. |
| 5. Content and Document Management | 60% Strategic | 50% Strategic | Leading Strategic Role: Strategy is paramount in planning and establishing the content and document management system before operational execution. |
| 6. Data Architecture and Modeling | 57% Strategic | 69% Strategic | Highest Strategic Focus: Strategy represents the structure and design (long-term vision), the strongest factor, as execution depends entirely on the model's correctness. |
| 7. Reference and Master Data Management | 50% Strategic | 56% Strategic | Balance Tilted Toward Strategy: Requires strong strategic guidance to set policies/standards, with execution ensuring cross-system alignment. |
| 8. Business Intelligence and Analytics | 40% Strategic | 60% Strategic | Vision Strategy: Requires concentrated strategic planning (60% in specifications) to define long-term analytical goals and support high-level decision-making. |
| 9. Data Integration and Sharing | 25% Strategic | 25% Strategic | Coordination Guidance: Requires a strategic vision to guide data sharing and set unified integration strategies, with a greater focus on execution. |
| 10. Achieving Value from Data | 25% Strategic | 25% Strategic | Asset Building: Requires developing clear plans and strategies to transform data into valuable assets, with execution translating strategies into tangible results. |
| 11. Open Data | 40% Strategic | 20% Strategic | Transparency Planning: Strategic dimension is essential for setting publishing conditions and transparency plans, the foundation for execution to ensure accessibility. |
| 12. Freedom of Information | 50% Strategic | 22% Strategic | Framework Leadership: Strategic controls reflect responsibility for setting the general framework and access policies, even if execution dominates practices. |
| 13. Data Classification | 20% Strategic | 20% Strategic | Framework Leadership: Despite execution dominance (80%), Strategy is responsible for defining the classification framework and governing standards, the prerequisite for any practice. |
| 14. Personal Data Protection | 40% Strategic | 30% Strategic | Compliance Leadership: Requires setting guided plans and strategies for legal compliance, with execution focused on daily protection practices. |
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)
The So-Called “AI Washing”
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:
- Lack of universal standards defining what constitutes “real AI.”
- Limited regulatory oversight and auditing mechanisms.
- Low public and investor literacy in evaluating AI claims.
- 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).
The New “Khanfasharians” in the Age of Artificial Intelligence
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:
- A psychological need for visibility: A craving to appear knowledgeable without the effort of mastery.
- Profound intellectual emptiness: A lack of foundational understanding compensated by verbal showmanship.
- 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).
The Framework of Institutional Excellence
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.
Integrating Human Values at the Heart of Artificial Intelligence
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:
- Design: Incorporate ethical principles in project planning and model architecture.
- Development: Train AI professionals in ethics and data responsibility alongside coding and analytics.
- Testing and validation: Assess how systems uphold fairness, inclusivity, and respect in decision-making.
- 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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