In our rapidly automated & VUCA & BANI world, artificial intelligence (AI) has emerged as a revolutionary force promising unprecedented efficiency, productivity and economic benefits. AI can solve problems swiftly (while sometimes creating new ones along the way), handle repetitive tasks flawlessly (more often than not), and free human beings from (apparently) mundane obligations. However, beneath this compelling promise lies an essential philosophical and ethical question: Should every task, decision or experience be delegated to AI merely because it’s more efficient or feasible?
To answer this question once and for all, I’ve come up with a comprehensive decision-making model, that helps clarifying when and why we should maybe deliberately preserve authentic human experiences, even if AI provides faster or seemingly better solutions.
The necessity of a Human-Centered Decision Model
Modern society is dominated by efficiency, productivity and economic optimization, often to the loss of deeper, more nuanced human values. While technological advancements typically favor outcomes, they frequently disregard the intrinsic value of human experiences involved in achieving those outcomes. Consequently, processes rich in emotional depth, cultural significance or social responsibility can be unintentionally compromised by automated systems.
Without a clear decision-making framework, we risk prioritizing productivity over personal growth, cultural preservation, democratic integrity and ethical responsibility. Therefore, it’s vital to develop thoughtful criteria to find out when and where human engagement is irreplaceable.
This decision-making model is structured around a fundamental distinction: determining whether a goal or task primarily serves private / personal (individual) interests or public (societal) interests. Each of these dimensions has clearly articulated questions based on profound philosophical and theoretical foundations.
The individual decision path
Tasks of personal or private significance are evaluated with the following four philosophical questions:
1. Emotional, Identity-Forming, or Meaningful Value
This question addresses one of the most fundamental dimensions of human life: the capacity to feel, relate and get meaning through embodied, subjective experience. Drawing from the philosophical tradition of phenomenology, particularly the work of Edmund Husserl and Maurice Merleau-Ponty, this criterion highlights the irreplaceable nature of first-person experience.
For Husserl, consciousness is always intentional, it’s directed at something and this directedness is inseparable from the meaning we attribute to the world. Our emotions, memories and bodily sensations are not just private data points, they are the very medium through which the world becomes meaningful. Merleau-Ponty deepens this idea by showing how experience is always mediated through our lived, sensing body, something no machine can emulate.
These insights have profound implications for how we think about human vs. machine roles. AI may be able to recognize emotional expressions, replicate speech patterns or even analyze mood data, but it cannot feel sadness, love, grief, awe or longing. It cannot suffer loss or be happy in triumph. Processes that are deeply emotional, such as mourning a loved one, experiencing intimacy, giving birth or confronting mortality, are not just about outcomes. They are constitutive of what it means to be human.
Similarly, identity formation is not a static state but a dynamic process shaped by the emotions we live through, the relationships we build and the narratives we tell ourselves. Tasks like journaling, creating art or having difficult conversations often shape and reveal who we are becoming. These are not merely expressive, they are formative.
This question therefore serves as a protective boundary: If a task or process contributes to emotional depth, identity development or existential insight, it should remain the domain of human experience. Delegating it to AI would not just be inadequate, it would be a denial of the very human work of becoming who we are.
2. Personal Growth and Deeper Self-Understanding
Human development is not only a matter of gaining knowledge but of deepening self-awareness and evolving our capacity for judgment, empathy and autonomy. Developmental theorists like Abraham Maslow, Erik Erikson and Robert Kegan provide powerful insights into why this question matters.
Maslow’s hierarchy of needs culminates in self-actualization, the full realization of one’s potential, which can only emerge through actively engaged human experience. Erikson’s psychosocial model shows that identity and maturity unfold in stages that require negotiation with real-world challenges and relationships and Robert Kegan extends this by framing adult development as the capacity to shift from being shaped by one’s beliefs to critically shaping them. All of these frameworks undelrine that personal growth arises from engagement with complexity, ambiguity and transformation. These are processes in which AI cannot participate on our behalf.
Therefore, we must ask: does this task or process support my own developmental trajectory as a human being? If so, it’s worth engaging with directly, even if AI offers a shortcut.
3. Journey vs Outcome
In modern society, outcomes tend to dominate our assessments of value. We celebrate results, reward end-products and often overlook the processes through which they were achieved. Yet from an existential and humanistic standpoint, it’s precisely the journey, the effort, struggle, failure, reflection and eventual insight, that shapes meaning and growth.
Existentialist philosophers such as Albert Camus and Viktor Frankl emphasize that human beings are not simply defined by what they achieve, but by how they respond to the conditions of life. Frankl, in particular, argued that meaning emerges not despite suffering and challenge, but through them. In Man’s Search for Meaning, he writes that the way a person bears suffering can become the highest expression of human dignity. Camus, likewise, saw the absurd condition of human existence as a call to active engagement with life, not a reason for resignation.
This principle applies directly to many human tasks. Writing a book, learning a new skill, enduring a difficult relationship conversation, climbing a mountain, these are not only worthy because of their end results, but because of who we become along the way. The very difficulty of the process becomes the crucible of meaning.
AI, by contrast, is designed to minimize or eliminate such difficulty. It provides shortcuts, removes friction and optimizes results. While this is often valuable, it becomes problematic when it deprives humans of the kinds of challenges that contribute to psychological depth, character development or existential insight.
This question in the decision tree thus asks: is this a process where the doing itself is intrinsically valuable, not just the outcome? If the answer is yes, then it should remain in the domain of human experience. To automate such a process would not only miss the point, it would erode the value embedded in the journey itself.
4. Standardized, Objective Tasks
At first glance, this question might appear to be the logical inverse of the previous one, if the journey has no intrinsic meaning, then surely the task is standardized. But this distinction is not always straightforward and the fourth question serves a distinct and necessary function.
While Question 3 deals with the subjective value of a process (whether the experience itself offers personal meaning or transformation), Question 4 addresses the structural nature of the task, whether it’s repetitive, emotionally neutral and/or governed by fixed rules. A task may lack personal meaning but still involve ambiguity, ethical nuance or creative variation that makes it unsuitable for complete automation.
For example, writing a product description may not be emotionally fulfilling or identity-shaping, but it might still require subtle rhetorical skill, cultural sensitivity or ethical framing that precludes pure standardization. On the other hand, tasks like basic data entry or calculating tax deductions are both emotionally neutral and procedurally fixed and therefore ideal candidates for AI.
This question, therefore, clarifies when AI can genuinely replace human labor without sacrificing complexity or human judgment. It ensures that the decision to delegate a task is not just based on whether it feels meaningful, but also on whether it’s structurally appropriate for automation.
Societal Decision Path
For public or collective concerns, four crucial questions rooted in social philosophy guide the decision-making:
1. Social Inclusion and/or Exclusion
The question of inclusion is central to the ethical evaluation of AI in public contexts. Social philosophers such as Nancy Fraser and Amartya Sen emphasize that justice is not only about fair distribution of resources but also about equal participation in social, cultural, and political life. Technological systems, particularly those powered by AI, can unintentionally reinforce social inequalities by replicating existing biases, excluding marginalized voices, or creating digital divides.
Fraser argues for a "parity of participation," where all members of society must be able to interact as peers in social life. Sen's capabilities approach further stresses that individuals must be enabled to achieve valuable states of being and doing. These theoretical insights highlight that inclusion isn't just about access to tools or platforms, it’s about ensuring that people have meaningful opportunities to contribute, shape and be affected by social processes.
AI-driven systems, such as automated hiring tools, predictive policing algorithms or public service bots etc., can marginalize people when not carefully designed and overseen. They may exclude those with limited digital literacy, reinforce systemic racism or prioritize efficiency over equity. Human engagement is essential to notice, correct, and resist these distortions.
Therefore, this question serves as a gatekeeper: Does this process risk excluding certain populations or perspectives? If so, automation must be approached with caution and supplemented with human judgment, empathy and responsibility.
2. Public Dialogue and Democratic Deliberation
Jürgen Habermas’ discourse ethics provides a foundational framework for understanding the necessity of human participation in democratic processes. According to Habermas, legitimacy in a democratic society emerges not merely from outcomes or efficiency, but from the process through which consensus is reached: an open, rational, and inclusive discourse among free and equal participants.
In this view, communicative action, a dialogue if you will, that aims at mutual understanding rather than strategic success, is central. AI, no matter how advanced, cannot authentically participate in such discourse. It may simulate argumentation or facilitate debate, but it cannot possess intention, moral accountability or the capacity to be persuaded or changed by reason.
Tasks like political debates, civic consultations or community decision-making thus require genuine human presence. Delegating them to automated systems risks undermining democratic legitimacy, reducing complex social negotiations to mere data operations and eroding the public’s trust in democratic institutions.
This question ensures that wherever deliberate legitimacy is required, human beings remain central to the process, affirming the ethical and procedural foundations of democratic life.
3. Cultural Transmission and Preservation
Cultural knowledge is not just a set of facts, data or historical records, it’s a living system of practices, narratives, rituals, values and symbols that shape collective identity and intergenerational meaning. Anthropologist Clifford Geertz describes culture as a "web of significance" spun by humans themselves. Similarly, philosopher Hans-Georg Gadamer emphasizes the interpretive, dialogical and embodied dimensions of cultural understanding.
AI may be capable of storing, organizing and retrieving vast amounts of information, but it cannot grasp or convey the lived texture of culture. Culture is not only known, it’s practiced, negotiated and renewed through human interaction. Language, art, religious practice, traditional crafts, and storytelling are modes of knowledge that require human presence and performativity to remain vibrant and meaningful.
Automation risks flattening this richness. For example, an AI can catalog indigenous artifacts but cannot carry forward the living tradition of a community’s ancestral knowledge or rituals. Likewise, AI-generated art may mimic stylistic features but lacks the intentionality, embeddedness and situated meaning that characterize authentic cultural expression.
Therefore, this question ensures that when processes involve the creation, transmission or transformation of cultural memory and meaning, human agency remains central. Without human involvement, culture risks being reduced to aesthetic form without function, memory without embodiment or knowledge without living continuity.
4. Social Justice, Power Dynamics, and Ethical Responsibility
Questions of justice, power and ethical accountability are central to any societal deployment of AI. Philosopher Michel Foucault’s analyses of knowledge and power structures reveal that systems we assume to be neutral often reinforce existing hierarchies and social inequalities. AI, as a product of human societies, inevitably reflects the values, blind spots and biases embedded in its data and design processes.
Ethical challenges are most visible in domains such as criminal justice, education, social services and access to healthcare, where AI tools might be used to e.g inform decisions about parole, resource distribution or even eligibility assessments. When these tools operate intranspararent or without adequate human oversight, they can reproduce historical injustices under the guise of impartiality.
Moreover, AI systems rarely offer opportunities for redress or contestation. If a person is harmed by an algorithmic decision, who is responsible and can that person appeal? Ethical responsibility, in the fullest sense, requires human beings who are capable of understanding the real consequences of a decision and morally evaluating its impact. Hannah Arendt’s concept of "the banality of evil" warns us of systems where individuals fail to uphold personal moral responsibility in favor of procedural correctness, something that becomes all too easy when decision-making is handed over to machines.
This question is thus indispensable: If the process has the potential to shape people’s rights, dignity or life chances, then human responsibility must remain present and visible. Ethical accountability cannot be outsourced to systems that do not / cannot comprehend the stakes of their own outputs.
Real-World Applications
Writing a Book
Book writing represents an individual journey of emotional depth, intellectual exploration and identity formation. While AI can streamline editing or grammar checks, authentic creativity and profound expression rely fundamentally on human experience.
Legal Contract Review
Routine tasks such as reviewing contracts for compliance and accuracy are objective, standardized and emotionally neutral. These processes are ideal for automation, allowing humans to focus their expertise on interpreting complex nuances and ethical implications.
Moderating Political Debates
Democratic debates require real-time empathy, nuanced understanding and sensitive handling of spontaneous, emotionally charged interactions. Automation here would jeopardize democratic legitimacy and undermine public trust, reinforcing the necessity of human moderation.
Social Media Moderation
This benefits mostly from hybrid solutions: AI handling routine and standardized tasks, with human oversight ensuring sensitivity, fairness and responsiveness to complex ethical issues such as misinformation, hate speech and cultural in/sensitivities.
Practicing a Musical Instrument
Learning an instrument is not just about hitting the correct notes, it's about developing intuition, emotional expression and a sense of timing and flow that emerges through live practice. The discipline and patience cultivated in the learning process are themselves developmental rewards. AI can assist with technical corrections, but it cannot internalize or transmit the experiential joy and frustration that are part of the journey.
Philosophical and Ethical Implications
This decision-making framework does not merely offer practical guidance for individual or organizational choices, it opens a window into the fundamental philosophical and ethical questions that define our relationship with technology. It challenges us to confront what it means to live a human life in an age of intelligent machines and invites us to carefully consider where we draw the line between delegation and embodiment, between automation and moral agency.
The Nature of Human Experience
Philosophically, the model rests on a deeply humanist conviction: that there are aspects of life which are not reducible to computation or efficiency. Following the phenomenological tradition (Husserl, Merleau-Ponty), the model emphasizes that lived experience, the way we feel, suffer, hope and grow, is the core of what constitutes human meaning. AI cannot inhabit a body, cannot know joy or grief and cannot struggle towards understanding. To surrender deeply human processes to AI is to misrecognize their very nature.
Autonomy, Responsibility, and the Moral Subject
Ethically, this framework aligns with traditions that prioritize human autonomy, moral agency and the irreducibility of ethical responsibility. From Kantian ethics to modern virtue theory, human beings are understood as moral subjects, not simply processors of outcomes, but evaluators of meaning and consequence. The model draws attention to the fact that AI, as a tool lacking subjectivity and moral consciousness, cannot bear responsibility. Delegating ethically charged decisions to such systems not only raises issues of accountability but threatens the foundations of justice itself.
Democracy and the Conditions of Legitimate Power
In the societal domain, the model highlights the normative importance of public discourse and shared values. Building on Habermas’ discourse ethics, it stresses that democratic legitimacy depends not just on results, but on processes that are transparent, inclusive and rooted in mutual recognition. Machines can optimize, but they cannot deliberate. They can recommend, but they cannot justify. In an era of algorithmic governance, the decision to preserve human deliberation is not nostalgic, it’s democratic.
Cultural Continuity and the Fragility of Meaning
Culturally, the model responds to a growing concern that AI may strip processes of their depth by turning cultural practices into data points. Inspired by thinkers such as Geertz and Gadamer, the framework affirms that culture is enacted and interpreted in living dialogue, not stored or simulated. Preserving human involvement in ritual, tradition and narrative is a form of resistance against the flattening effect of computation.
A Call for Philosophical Vigilance
Ultimately, the model is a call for vigilance, not against technology, but against unthoughtful adoption. It asks us to distinguish between what we do because we can, and what we do because we must. It encourages a sober view of AI, not as enemy or savior, but as a powerful tool that must be carefully situated within a moral, cultural and existential landscape that remains irreducibly human.
In this way, the model is not only a framework for decision-making, it’s a philosophical stance. It reasserts the centrality of human dignity, complexity and responsibility in an age increasingly shaped by systems that, while intelligent, are not and cannot be alive.
As we advance further into an AI-driven world, adopting this comprehensive model can significantly influence how we approach technological integration into our lives. It supports a future where technology serves humanity, not the other way around and where decisions prioritize authenticity, community values, cultural heritage, democratic integrity and ethical responsibility over mere efficiency.
By consciously adopting this structured, philosophical approach, we protect and nurture the profound richness of human experience, ensuring our technological future enhances rather than diminishes our shared humanity.
By mario
