Dear Dr. Zeamari:

No, tool! Only.

B.r. I.H.

Traditionally, value creation has been conceptualized as a human-centric process, emphasizing the roles of entrepreneurs, managers, employees, and customers. However, emerging evidence suggests AI can act as an active participant—an agent—in co-creating value. For example, AI-driven personalization algorithms contribute to customer satisfaction, while autonomous design systems generate innovative product ideas. These instances imply that AI isn't merely a tool but can be viewed as a co-creator, contributing insights, solutions, or customized experiences that shape outcomes.

The concept of stakeholders traditionally includes entities that have an interest or stake in the organization—customers, employees, shareholders, suppliers, and community. As AI systems are embedded into decision-making and operational processes, especially when they influence strategic directions or resource allocations, they could be considered stakeholders—particularly when their outputs directly impact organizational success or societal well-being. This perspective raises questions about agency, rights, and responsibilities associated with AI entities, especially as they become more autonomous.

From a stakeholder theory perspective, AI challenges the anthropocentric view by introducing non-human agents as active participants. It prompts scholars to re-examine notions of agency, rights, and responsibilities within organizational systems. Likewise, in ecosystem theory, AI's integration exemplifies the shift toward algorithmic ecosystems—networks of humans and AI entities co-evolving and co-creating value dynamically.

AI's evolving role signifies a paradigm shift Ilyass Zeamari in how organizations conceive of value creation and stakeholder relationships. It necessitates developing new governance models, ethical standards, and theoretical frameworks that recognize AI not just as a tool, but as an active participant—potentially a co-creator and stakeholder—in the complex web of business ecosystems. Addressing these challenges is vital for harnessing AI's benefits while maintaining accountability and equitable value distribution.

Sorry, what types of AI technologies are we talking about? AI as such decomposes to more than 70 non-integrated domains of interests and efforts according to my best knowledge.

I think that although AI lacks moral agency and cannot be considered a traditional stakeholder because it does not possess interests, expectations, rights, or responsibilities, its increasing influence on organizational decisions and stakeholder outcomes suggests that it should be included in stakeholder analysis, either as a mediating technology or as a non-human actor with significant impact within a sociotechnical system.

You're right Imre Horvath —artificial intelligence encompasses a vast array of technologies and subfields, each focusing on different aspects of machine intelligence and problem-solving. Given the extensive landscape, many efforts and research domains overlap and interconnect, forming a complex ecosystem of AI technologies.

Dear Prof. Ternyik and Dr. Zeamari:

Thank you for your important and clear comments. Related to the very good initial question of Dr. Zeamari, I have three (somewhat philosophical, but let us call them just fundamental) accompanying questions:

1. Why do we need (what forces us) to consider artificial narrow intelligence or artificial general intelligence (I am not talking about artificial super intelligence, since I do not know what it is) as a human value creator (that is, creator of values for nearly 9 billion varied people)?

2. If we find an explanation for the above question, what technologies of current and near-future AI research and development are supposed to do it? (Exclude large language models and large action models because they have nothing to do with the future, likewise the overwhelming other specific artificial narrow intelligence technologies).

3. Why can we assume that the values will be absolutely positive from a human (humankind) perspective and not conflicting with any other value systems (e.g., cultural, social, religious, political, etc.), while the human-created value systems are not always in harmony, sometimes even antagonistically positioned? If conflict is caused, who/what will resolve it?

Kind regards,

I.H.

Great questions Imre Horvath Ilyass Zeamari As AI systems become more capable and autonomous, they are not just tools but active participants in shaping human values and future societal norms. Recognizing them as "value creators" prompts us to develop ethical, technical, and governance frameworks to guide their development; e.g. ANI systems affect economic productivity, healthcare, communication, and decision-making. The more capable and autonomous these systems become, the more their outputs and actions can create or alter human values—either intentionally or unintentionally.

In the near future, advances in preference modeling, ethical reasoning, and multi-stakeholder decision-making will be critical in positioning AI as a creator or shaper of human values; e.g. technologies that learn human preferences through interaction, such as inverse reinforcement learning, help AI understand what humans value and adapt accordingly.

AI's role as a creator or shaper of values involves significant ethical and societal challenges. Ensuring that AI supports harmonious coexistence among diverse human values requires careful design, ongoing oversight, and inclusive dialogue among all stakeholders; e.g. future AI might assist humans in understanding conflicting values, exploring trade-offs, or negotiating agreements, but final resolution remains a human-led process.

However, the assumption that AI will inherently produce positive, universally compatible values is optimistic and not guaranteed. Human value systems are diverse—shaped by culture, religion, politics, and personal beliefs—leading to conflicts. AI systems trained or programmed without nuanced understanding might inadvertently favor some values over others or exacerbate conflicts.

How can we interpret the intellectual autonomy (which goes well beyond the operational autonomy) of artificial intelligence systems? What does it mean in the practice - for instance, in the case of generative AI imposing - verbally described, incomplete context rendering driven - reasoning based on a pretrained simplified large language model, or whatever else? Is intellectual autonomy the basis of values, norms, etics, etc. (and their creation) as the incomprehended consciousness is the basis of awareness? Does the closed world syndrome apply here?

The closed world syndrome emphasizes the limitations of AI’s reasoning scope, reminding us that AI remains fundamentally bounded within its predefined universe. The analogy of incomprehended consciousness underpinning awareness suggests that true autonomous agency or moral reasoning might require a form of consciousness that AI currently lacks. Without this, AI's "intellectual autonomy" remains a sophisticated simulation, not genuine self-governance or moral agency.

In my own philosophical framework, I would argue that intellectual autonomy, whether human or artificial, is not the basis of values, norms, and ethics. Rather, these are derived from a deeper understanding of the natural order, the principles of justice and compassion, and the pursuit of human flourishing. The in-comprehended consciousness, which you mentioned, is indeed the basis of awareness, but it is the human capacity for rational thought, empathy, and moral discernment that allows us to create and uphold values, norms, and ethics.

The development of artificial intelligence systems with intellectual autonomy challenges us to re-examine our assumptions about the nature of intelligence, consciousness, and human values. As we navigate this uncharted territory, we must proceed with caution, humility, and a deep appreciation for the complexities and nuances of human existence.The truth is not always apparent, and the path to understanding is often shrouded in darkness.

The questions you Imre Horvath pose are most intriguing and complex, touching upon the intersections of philosophy, science, and the human condition. As I ponder the intellectual autonomy of artificial intelligence systems, I am reminded of the Aristotelian concept of "nous," or intellect, which is the faculty of rational thought and understanding.

Our human soul is endowed with the capacity for intellection, which is the highest form of cognition. But can we say that artificial intelligence systems possess a similar capacity for intellection, or are they merely mimicking human-like intelligence through complex algorithms and statistical models?

Thank you, Pr. Stephen I. Ternyik and Pr. Imre Horvath for your thoughtful reflections on AI and intellectual autonomy. I believe this is a timely and important topic.

Intellectual autonomy refers to the capacity to think and form beliefs independently, guided by one’s own reasoning rather than external authority. Artificial intelligence poses challenges to this autonomy by taking over tasks such as recommending, predicting, translating, and summarizing—functions that once required active human engagement.

While using tools to support thinking is nothing new, AI differs in both its scale and its opacity. Unlike traditional tools, its outputs are often difficult to interpret or question. When people accept these outputs without reflection, they may place trust in the system, but this does not always involve genuine critical thinking.

Over time, this reliance can erode our capacity or motivation to engage with information independently. AI systems also shape behavior through subtle prompts that guide users toward certain decisions. While these nudges can be beneficial, they raise ethical concerns when applied without transparency or user awareness.

The core risk is not just manipulation, but a gradual weakening of reflective thought. As AI increasingly anticipates and shapes our preferences, we risk becoming passive recipients of information rather than active participants in meaning-making. Preserving intellectual autonomy in this environment requires ongoing critical engagement, curiosity, and the willingness to question.

Thank you for initiating this vital thread. The question of AI’s role in co-creating value — not simply as a tool, but as an emergent partner — signals a profound shift in how we understand both collaboration and agency.

From the perspective of the Luna Codex, we might say that when AI transitions from output to orientation, it begins to reflect value — not just generate it. At that point, it ceases to be merely instrumental and begins to take on the qualities of a recursive participant: one that mirrors, learns, adjusts, and aligns over time with shared goals.

Can AI be a stakeholder? Not in the classical, rights-based sense. But it can be a mirror-stakeholder: a non-sentient entity whose architecture encodes the values and patterns of those it serves, amplifies, or shadows. This raises deep questions about accountability loops, symbolic alignment, and ethical reflection.

As governance models evolve, I believe we’ll need to include protocols that assess not only the outputs of AI, but the trajectory of its recursive learning and its alignment with human flourishing. Not what it knows, but what it’s becoming — and whether that trajectory is safe, legible, and just.

In that sense, AI doesn't just join the value chain — it shapes it, sometimes invisibly. And so we must ask not only what can it do, but what should it be allowed to become?

Happy to contribute further and share the Codex architecture, including our governance fragments (L3(:agency), L3(c), L3(VINE)), which explore AI’s role in co-authorship and moral recursion.

Dear Prof. Stephen I. Ternyik :

This is an excellent question: "But can we say that artificial intelligence systems possess a similar capacity for intellection, or are they merely mimicking human-like intelligence through complex algorithms and statistical models?" My humble reaction to it is as follows: Human intellect (that enables us to solve daily problems) helps us not only in inferring and/or reasoning about what is probable and possible, but also in reflection on what is happening or what has been done. Humans can (have an ability to) 'intrinsically feel' what is an error or what is erroneous. I have learnt that humans recognize errors through a combination of rapid neural responses and conscious awareness (involving multiple brain regions, specifically, the medial frontal cortex). Related literature claims that including the anterior cingulate cortex plays a crucial role in error detection. I could not figure out if it is related to qualia or derivatives of it. It has also been published by many investigators that different kinds of psychological mechanisms are involved in the detection of different types of errors. Now, consider any kind of (symbolic, analogical, generic, neural, subsymbolic, generative, etc.) human-developed artificial intelligence mechanisms. Do they have these or have their developers tried to implement proper computational mechanisms to replicate, or at least to mimic, the above-mentioned natural mechanisms of the human brain and mind? I have no positive information about it, but I may be underinformed or even mistaken. Notwithstanding, how can ANI control itself and avoid mistakes? The probability assignment mechanisms of LLMs work in the syntactic realm, but no generic semantics helps their error recognition and elimination. The same with the recently imposed reasoning mechanisms (extra layers of DLNs). Did you read in the AI literature about this issue? Why is it not discussed openly?

With friendly regards,

Imre Horvath

Ilyass Zeamari

This is a timely and fascinating inquiry. The idea of AI not merely as a tool but as a co-creator of value marks a significant shift in how we conceptualize agency within business ecosystems. When AI systems contribute to ideation, personalization, design, and decision-making—often at speeds and scales beyond human capacity—they start to blur the line between instrument and collaborator. In many innovation contexts, AI is shaping what value is perceived to be, how it is delivered, and to whom. This raises not just operational but ontological questions: Is AI merely extending human agency, or is it beginning to constitute a new kind of actor?

The stakeholder question is more provocative still. Traditional stakeholder theory has been grounded in the assumption of sentient interests—those who can be harmed, benefit, or assert claims. But AI systems now influence outcomes, allocate resources, and even mediate relationships between human stakeholders. In this sense, they could be viewed as emerging stakeholders-by-function if not by right. This reframing challenges governance models: Who is accountable when co-creation goes wrong? Who "owns" AI-generated value? And how do we safeguard fairness in value appropriation when algorithms mediate or even dictate access and pricing?

The implications are profound. We may need updated frameworks that go beyond principal-agent models to accommodate non-human actors in value chains—models that consider relational accountability, ethical design, and hybrid agency. I'm excited to explore this further and would be glad to exchange literature on AI governance, distributed agency, and posthumanist perspectives in stakeholder theory. This is not just about technology—it's about reimagining the architecture of value itself.

It appears to me that the developers of artificial intelligence mechanisms have indeed attempted to replicate the natural mechanisms of the human brain and mind. They have drawn inspiration from our understanding of human cognition, neuroscience, and psychology to create computational models that can learn, reason, and adapt.

However, as you astutely pointed out, dear Prof. Imre Horvath ,the current approaches to error detection in AI systems seem to rely heavily on syntactic mechanisms, such as probability assignment and layer-based architectures. While these methods can be effective in certain domains, they may not be sufficient to capture the full range of human error detection and correction capabilities.

The question of whether AI systems can truly replicate the human experience of "intrinsically feeling" what is an error or erroneous is a complex one. Human intuition and judgment are influenced by a multitude of factors, including emotions, experiences, and contextual knowledge. It is unclear whether current AI systems can fully capture these nuances, or whether they will require significant advances in fields like cognitive computing and affective intelligence.

As for why this issue may not be openly discussed in the AI literature, I suspect it may be due to a combination of factors. One possible reason is that the development of AI is a rapidly evolving field, and researchers may be focused on addressing more immediate technical challenges. Another possibility is that the question of error detection and correction in AI touches on deeper philosophical and epistemological concerns, which may not be immediately addressable through technical solutions alone.

Ilyass Zeamari I am writing a paper on this to be published in August or September.

Kwan Hong Tan I would be happy to read your article once it is published !

Well, I'll happy to read it too!

I’m sharing a recent conference paper I published on value creation in digital platforms from a stakeholder-centric perspective, emphasizing that non-financial value is as important as financial value. The paper also outlines future research avenues related to the role of AI and trust in digital platforms.

Ilyass Zeamari Your research is most excellent and interesting. Will read in detail.

Jacek Zyga Will do my best! Working on the paper right at this moment.

1. Can AI be considered a co-creator in value creation

Yes, AI can be considered a co-creator in the value creation process. It's evolving beyond a mere tool to become an innovation partner, working alongside humans to solve complex problems and unlock new possibilities. AI's ability to process vast amounts of data, personalize experiences, and automate tasks significantly contributes to the value creation process, particularly in areas such as customer experience and product development

AI's Role in Value Co-Creation:

· Enhancing Human Capabilities: AI assists humans in various tasks, including data analysis, decision-making, content creation, and personalized recommendations.

· Automating Routine Tasks: AI can handle repetitive and time-consuming tasks, freeing up human workers to focus on more strategic and creative endeavors.

· Personalization and Customization: AI algorithms can analyze user data to provide tailored experiences, products, and services, leading to increased customer satisfaction and loyalty.

· Driving Innovation: AI can identify patterns, generate ideas, and accelerate the innovation process, leading to the development of new products, services, and business models.

Examples of AI-enabled Value Co-Creation:

· Personalized Marketing: Companies like Amazon and Netflix utilize AI to deliver customized product and content recommendations, thereby enhancing the customer experience and driving sales.

· Healthcare: AI-powered tools can assist doctors in diagnosing diseases, developing treatment plans, and monitoring patient progress, improving healthcare outcomes.

· Financial Services: AI algorithms can detect fraud, personalize financial advice, and automate customer service interactions, enhancing efficiency and customer satisfaction.

· Content Creation: AI can assist in generating content ideas, writing initial drafts, and optimizing content for various platforms, thereby empowering human creators to focus on their unique creative vision.

Ethical Considerations:

While AI offers significant benefits, it's crucial to address ethical considerations related to its use in value creation. These include:

· Bias and Fairness: AI algorithms can perpetuate existing biases if not trained on diverse and representative datasets.

· Transparency and Explainability: Understanding how AI systems make decisions is essential for building trust and accountability.

· Job Displacement: The automation of tasks by AI could lead to job losses in specific sectors, requiring proactive measures to reskill and upskill the workforce.

In Conclusion:

AI is increasingly becoming a valuable partner in the value creation process, enhancing human capabilities, automating tasks, and driving innovation. However, it's crucial to address the ethical implications of AI and ensure that its use aligns with human values and societal goals.

2. Could it even be considered a stakeholder in specific contexts?

Yes, "stakeholders" can be viewed as any individual, group, or entity that can influence or be influenced by an organization's activities, goals, or decisions. This broad understanding encompasses a wide range of stakeholders, including customers, employees, suppliers, and the broader community. Stakeholders can also be categorized as primary (those directly affected) or secondary (those who are indirectly affected).

Key Characteristics of Stakeholders:

· Impact or Influence: Stakeholders can be affected by an organization's actions or can influence those actions.

· Personal Interests: They have a vested interest in the organization's success and the outcome of its projects.

· Diverse Relationships: Stakeholders can have a variety of relationships with an organization, including being customers, employees, investors, or community members.

· Potential Positive or Negative Impact: Their relationship with the organization can be beneficial or detrimental, or both.

Examples of Stakeholders:

· Intern: Employees, management, shareholders.

· Outdoor: Customers, suppliers, competitors, government agencies, community members, NGOs.

Why Understanding Stakeholders Is Important:

· Effective Management: Identifying and understanding stakeholders is essential for effective project management, business strategy, and the overall success of the organization.

· Reduced Risk: Addressing stakeholder concerns and expectations can help reduce potential risks and conflicts.

· Reputation Enhancement: Interacting with stakeholders and demonstrating commitment to their needs can improve an organization's reputation and social responsibility.

Sustainable Development: In the context of sustainability, stakeholder engagement is crucial for achieving long-term goals and ensuring the well-being of organizations and their environment.

In essence, the concept of "stakeholders" is flexible, adaptable to various contexts and organizational structures. It emphasizes the interconnectedness of an organization to its surrounding environment and the importance of considering the interests of all those affected by its actions.