불확실성 시대 인간 AI 공생 지능과 조직 회복력
📝 Abstract
Organizations increasingly operate in environments characterized by volatility, uncertainty, complexity, and ambiguity (VUCA), where early indicators of change often emerge as weak, fragmented signals. Although artificial intelligence (AI) is widely used to support managerial decision-making, most AI-based systems remain optimized for prediction and resolution, leading to premature interpretive closure under conditions of high ambiguity. This creates a gap in management science regarding how human-AI systems can responsibly manage ambiguity before it crystallizes into error or crisis. This study addresses this gap by presenting a proof of concept (PoC) of the LAIZA human-AI augmented symbiotic intelligence system and its patented process: Systems and Methods for Quantum-Inspired Rogue Variable Modeling (QRVM), Human-in-the-Loop Decoherence, and Collective Cognitive Inference. The mechanism operationalizes ambiguity as a non-collapsed cognitive state, detects persistent interpretive breakdowns (rogue variables), and activates structured human-in-the-loop clarification when autonomous inference becomes unreliable. Empirically, the article draws on a three-month case study conducted in 2025 within the AI development, involving prolonged ambiguity surrounding employee intentions and intellectual property boundaries. The findings show that preserving interpretive plurality enabled early scenario-based preparation, including proactive patent protection, allowing decisive and disruption-free action once ambiguity collapsed. The study contributes to management theory by reframing ambiguity as a first-class construct and demonstrates the practical value of human-AI symbiosis for organizational resilience in VUCA environments.
💡 Analysis
Organizations increasingly operate in environments characterized by volatility, uncertainty, complexity, and ambiguity (VUCA), where early indicators of change often emerge as weak, fragmented signals. Although artificial intelligence (AI) is widely used to support managerial decision-making, most AI-based systems remain optimized for prediction and resolution, leading to premature interpretive closure under conditions of high ambiguity. This creates a gap in management science regarding how human-AI systems can responsibly manage ambiguity before it crystallizes into error or crisis. This study addresses this gap by presenting a proof of concept (PoC) of the LAIZA human-AI augmented symbiotic intelligence system and its patented process: Systems and Methods for Quantum-Inspired Rogue Variable Modeling (QRVM), Human-in-the-Loop Decoherence, and Collective Cognitive Inference. The mechanism operationalizes ambiguity as a non-collapsed cognitive state, detects persistent interpretive breakdowns (rogue variables), and activates structured human-in-the-loop clarification when autonomous inference becomes unreliable. Empirically, the article draws on a three-month case study conducted in 2025 within the AI development, involving prolonged ambiguity surrounding employee intentions and intellectual property boundaries. The findings show that preserving interpretive plurality enabled early scenario-based preparation, including proactive patent protection, allowing decisive and disruption-free action once ambiguity collapsed. The study contributes to management theory by reframing ambiguity as a first-class construct and demonstrates the practical value of human-AI symbiosis for organizational resilience in VUCA environments.
📄 Content
Modern organizations increasingly operate in environments (both internal and external) characterized by volatility, uncertainty, complexity, and ambiguity (VUCA) (Johansen and Euchner 2013). Originally developed in military and strategic research, and subsequently adopted in management studies, the VUCA framework reflects conditions in which traditional mechanisms of planning, forecasting, and control within an organization lose their effectiveness. In such environments, decision-makers are faced not only with a lack of information relevant to the decisions they make (despite its abundance in general terms, which may be a consequence of, for example, an inability to detect weak signals), but rather with its high volatility and, at times, its ambiguity, including rapidly shifting, conflicting, and weakly structured signals that resist straightforward interpretation. The term “signals” should be understood as time-varying events, words, messages, stimuli, emotions, behaviors, as well as preferences or intentions that carry information, enabling communication and understanding of the state of reality, and thus influencing decisions made in the organization, not only of a strategic nature, but also tactical or operational. In the decision-making process, those signals need to be (1) identified, to be (2) interpreted, and based on it (3) used for the final decision making. One of the specific categories of signals important for the functioning of an organization are weak signals, which are evidence-based, early, imprecise, and therefore difficult to perceive, as well as surprising, uncertain, irrational, or even unreliable signs of inevitably approaching significant events and their consequences (very strong phenomena) (Ansoff 1975). Weak signals are not only difficult to detect and pick out from the “information noise,” but also cause interpretation problems due to, among other things, their imprecision and uncertainty. It should be emphasized, however, that it is not only weak signals that can be characterized by uncertainty or ambiguity.
At the same time, organizations increasingly rely on hybrid decision systems in which humans and artificial intelligence (AI) jointly shape actions, forecasts, and strategic choices. These systems are currently present in strategic management, leadership support, human resource decisions, risk assessment, and organizational analysis. Although AI-assisted systems are excellent at identifying factors that influence the functioning of an organization and processing large amounts of data (including even those that may be weak signals) and identifying stable and universal patterns, even assuming dynamic variability within them, this is not sufficient for modern organizations. AI remains optimized for broad and rapid access to knowledge bases, which is indeed the basis for forecasting and problem solving, as it enables rapid detection of even weak signals, especially those coming from the organization’s environment, but it does not fully secure the decision-making process. Admittedly, progress in this area is dynamic, access to knowledge is increasingly broad and error-free, but unfortunately even this is not sufficient for decision-making (especially in managing the external and internal VUCA environment), because, for example, not all AI systems are equipped with the ability to match the information provided by AI to the changing needs and context relevant to the user (P-AI fit, dynamic P-AI fit (Bieńkowska et al. 2025). So, while signals from the environment are within the reach of AI systems, data from within the organization are not. What’s more, unfortunately, AI systems remain vulnerable to a fundamental limitation: ambiguity in VUCA conditions. Situation, in which ambiguity of signal arises occurs when signals relevant to decisionmaking are incomplete, contradictory, unstable, or transitional, such that no single interpretation can be confidently selected (March 1978;Weick 1995;Kail 2011). The ambiguity of signals makes interpretation difficult (or even impossible) in this context, as it is directly related to assigning meaning to these signals in a specific context. Contextual meaning-making should be understood as a dynamic process of interpretation, in particular psychological and social process of attributing meaning to the aforementioned signals, which shapes the perception of reality and influences decisions, often through the prism of personal experiences, values, and context. In this context, it should be emphasized that assigning meaning to signals depends on the person who assigns that meaning (the observer) and the situation in which they find themselves.
In VUCA environments (both internal and external), ambiguity is not an exception but a persistent state, frequently preceding critical organizational events such as strategic inflection points, leadership stress and cognitive overload, ethical dilemmas, employee disengagement or burnout, and cascading coordina
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