Introduction

The majority of AI is a LLM – Large language model, not a being and thinking model. Using a functional MRI we know that there is a distinct region of the brain focused on language that is different to the part of the brain that is focused on processing. Further than just neurology, it is the personal and interconnected sense of self (presence, cognition, maturation, etc) and relationship (social commons, disconnection, fragmentation, etc) that is at the centre of existential issue within and between human beings.

This means that tacit knowledge—the interior processing, the intuitive, experience-based understanding developed through years of practice is not captured accurately. This article explores how professionals using AI may significantly underrepresent their unique intelligence, rooted in subjective and intersubjective experiences, in AI creations. Over the 60,000 hours working and studying human beings I have noticed that many leaders are unable to clearly articulate the actual elements that make them successful in a particular domain. By looking into and integrating insights we can highlight actionable ways to foster cohesion between human wisdom and AI, ensuring technology amplifies rather than overshadows or displaces expertise and social cohesion.

Understanding Tacit Knowledge

Tacit knowledge, as defined by Michael Polanyi, encompasses intuitive, context-dependent insights that resist explicit articulation. In integral theory’s AQAL framework, it aligns with the first-person perspective—the “I” quadrant—representing individual subjective experiences, intentions, and consciousness. A psychotherapist’s ability to sense a client’s unspoken emotions (Transference and countertransference – a central skill in psychodynamic psychotherapy that takes years to master) or an engineer’s intuitive grasp of material dynamics exemplifies this knowledge, honed through lived experience. As Polanyi noted, “we know more than we can tell,” emphasising the personal, embodied nature of this wisdom.

The second-person perspective—the “We” quadrant—further enriches tacit knowledge through intersubjective understanding, shared cultural meanings, and relational presence. In fields like education or leadership, this perspective enables professionals to navigate complex human interactions, fostering empathy and collaboration. For instance, a teacher’s knack for managing classroom dynamics relies on shared cultural contexts and relational trust, which are inherently intersubjective.

From a transpersonal psychotherapy perspective, such as Stan Grof’s or Ken Wilber’s work, tacit knowledge reflects higher stages of consciousness, where intuitive wisdom transcends mechanistic thinking. Similarly, late-stage ego development models, like Suzanne Cook-Greuter’s, suggest that advanced professionals integrate complex, nuanced perspectives, enabling skillful action without conscious deliberation. These interior dimensions—subjective and intersubjective—are central to human expertise but elusive for AI.

Perspective	Quadrant	Description	Relevance to Tacit Knowledge
First-Person (“I”)	Upper-Left	Subjective, individual consciousness	Personal intuitions, experiential insights
Second-Person (“We”)	Lower-Left	Intersubjective, shared cultural meanings	Relational presence, empathy in interactions
Third-Person (“It”)	Upper-Right	Objective, observable behaviors	AI’s domain: data-driven analysis
Fourth-Person (“Its”)	Lower-Right	Interobjective, social systems	AI models systems externally, lacks interior understanding
Fifth-Person (Integrative)	All Quadrants	Holistic integration of all perspectives	Requires human consciousness, beyond AI’s reach

Why AI Struggles with Tacit Knowledge

AI excels in the third-person perspective—the “It” quadrant—processing objective data like medical images or statistical models with remarkable efficiency. However, tacit knowledge resides in the first-person (“I”) and second-person (“We”) quadrants, which AI cannot access. The “I” quadrant encompasses subjective experiences—intuitions, emotions, and personal judgments—that form the core of professional expertise. For example, a doctor’s present and intuitive sense of a patient’s condition, informed by years of practice, cannot be replicated by AI’s data-driven algorithms. We often talk about these things as The X Factor in great leaders, doctors, and other practitioners.

The second-person perspective, involving relational presence and shared meaning, is equally inaccessible. Because there is not First person self there, there is no authentic relationship. AI lacks the capacity for genuine relationships or cultural empathy, critical in fields like psychotherapy or education. AI cannot answer deep spiritual questions requiring subjective realization, as it operates solely in objective domains. This aligns with ontological perspectives which emphasize “being-in-the-world” as a lived, embodied experience AI cannot emulate. And indeed is the ground of fulfilment and joy, connection and love so critical in Maslow’s hierarchy of needs.

The fourth-person perspective (“Its”) involves interobjective systems, like societal structures, which AI can model externally but without the interior understanding humans bring. The fifth-person perspective, associated with integral consciousness (teal and beyond), integrates all quadrants holistically, requiring a level of awareness AI cannot achieve. Consequently, AI outputs may be technically proficient but lack the depth, creativity, and contextual nuance of human expertise.

Implications for Professionals

The gap between AI’s objective capabilities and human tacit knowledge has significant implications. AI-driven solutions risk being shallow if they exclude the subjective and intersubjective insights professionals bring. In healthcare, AI might recommend treatments based on data, but a physician’s tacit knowledge could tailor interventions to a patient’s unique needs. In design, AI optimizes explicit parameters, yet human creativity, rooted in the “I” quadrant, drives innovation.

Overreliance on AI may also erode professional skills, as routine tasks are automated, potentially diminishing critical thinking and intuition. This risks standardised solutions that fail to address diverse, dynamic and context-specific needs, contrary to integral theory’s emphasis on holistic integration.

Field	AI Capability	Human Tacit Knowledge	Impact of Gap
Healthcare	Data-driven diagnostics	Intuitive patient understanding	Risk of impersonal treatments
Education	Automated grading	Relational classroom dynamics	Less adaptive teaching methods
Engineering	Optimization algorithms	Creative problem-solving	Limited innovation in design

Bridging the Gap

Bridging this gap requires actionable strategies to integrate tacit knowledge with AI. Collaborative design, where professionals work with developers to embed domain-specific insights, can enhance AI’s effectiveness. Reflective practices, such as journaling or peer discussions, help externalize tacit knowledge, making it more accessible for AI integration. For example, medical simulations can articulate clinical reasoning for AI diagnostic tools.

Advanced AI techniques, like reinforcement learning, may approximate tacit knowledge by adapting to expert feedback, though they cannot fully embody it. From a systems theory perspective, fostering feedback loops between human expertise and AI can create more cohesive systems. MetaIntegral’s transdisciplinary approach suggests designing AI that aligns with human values, integrating embodiment and developmental theories to enhance presence and purpose.

Existentialist perspectives, like Werner Erhard’s, emphasize authentic action, encouraging professionals to prioritize their lived experience in AI collaborations. Late-stage ego development models advocate for reflective practices that cultivate higher consciousness, enabling professionals to articulate complex insights. By fostering cohesion across AQAL quadrants, professionals can create AI systems that amplify human capabilities, aligning with integral theory’s holistic vision.

Conclusion

AI’s transformative potential is undeniable, but its reliance on objective, third-person perspectives limits its ability to capture tacit knowledge rooted in subjective and intersubjective experiences. Integral theory highlights that the first-person (“I”) and second-person (“We”) quadrants, essential for human expertise, are inaccessible to AI, as are the integrative fourth and fifth perspectives. By recognizing this gap and employing collaborative and reflective practices, professionals can ensure AI complements rather than overshadows their unique intelligence. This approach, grounded in psychology, ontology, and integral theory, fosters a future where technology and human wisdom co-create solutions that honor the full spectrum of human experience.