Artificial intelligence has long been driven by the quest to develop systems capable of comprehending and navigating the intricacies of the real world. A fundamental aspect of this pursuit is the concept of “situational awareness”—the ability of an intelligent agent, whether human or machine, to perceive elements in its environment, interpret their significance, and anticipate their future state. The prevailing paradigm in AI research and development has adopted a fundamentally realist perspective. This paradigm assumes that situations are objective, pre-existing contexts that can be discovered, analyzed, and modeled with sufficient data and computational resources. From this viewpoint, the primary objective of an intelligent system is detection and classification: to accurately sense the world, identify the prevailing “ground truth” of a situation, and act accordingly. This post challenges that assumption.

Situations don’t precede human interpretation—they emerge through the act of judgment itself. We present a framework built on three interconnected elements: Judgment (the interpretive act that creates meaning), Status (the external expression of that judgment), and Situation (the emergent context formed by multiple interacting judgments). This perspective reveals why contemporary artificial intelligence, despite its computational sophistication, lacks true situational awareness. We introduce the concept of episodic intelligence—the capacity to remember, learn from, and reason about complete situational episodes—as a critical capability for next-generation intelligent systems. The post concludes with practical design principles for building systems that can meaningfully participate in situation formation rather than merely process data about pre-existing conditions.

This isn’t a refinement of prior models, but a profound ontological shift. It redefines the problem of intelligence from passive perception and accurate representation to active participation and meaningful construction.

The Core Problem in System Design

Consider a site reliability engineer monitoring system dashboards during a service disruption. Multiple screens display cascading alerts: CPU utilization spikes, API latency increases, and database throughput declines. These individual metrics lack meaning or a coherent narrative. The engineer identifies a pattern, perhaps correlating payment processing errors with database performance degradation. This observation leads them to conclude a “database bottleneck.” Traditionally this is viewed as an objective condition discovered by the engineer.

This perspective misinterprets the process. The engineer didn’t find an existing situation. Instead, they created a coherent interpretation that transforms disparate signals into a meaningful situation requiring specific responses. Interpretation is the foundation of intelligence, not a secondary analytical step.

Understanding Situational Intelligence

A triad is proposed to elucidate the dynamic interplay between interpretation, communication, and emergent context. These elements engage in continuous recursion. Judgments generate status indicators that aggregate into situations, which subsequently provide novel context that triggers further judgments. This recursive process constitutes the fundamental mechanism through which organizations navigate complexity and uncertainty.

The triad offers a formalized vocabulary and framework for comprehending the process of organizational sensemaking in real-world, high-stakes settings such as technology incident response.

The Nature of Situational Experience

Situations are constructed rather than discovered, as per pragmatist philosophy. Problematic situations arise when established patterns of action are inadequate. Judgment reconfigures perception and response into coherent episodes that facilitate effective action. Situations have distinctive structural characteristics that differentiate them from mere events or data points. They establish temporal boundaries that segment experience into meaningful episodes with clear beginnings, developmental phases, and resolution points.

Situations focus organizational attention by highlighting relevant information while filtering out peripheral noise. They establish role assignments that distribute responsibility and coordinate collective action.

A production outage serves as a concrete example of these dynamics. The outage operates as a holon, a concept from systems theory that describes a temporary organizational entity that transcends the sum of its constituent parts. It influences behavior patterns, directs communication channels, and coordinates response activities across technical and business functions. Upon resolution, this entity disintegrates, releasing its components to facilitate the formation of new organizational configurations.

In complex, ambiguous environments, achieving a perfectly accurate and complete picture of reality is often impossible. What’s necessary is a story that’s reasonable, coherent, and sufficient to provide a springboard into action. A plausible account allows people to move from anxiety and confusion to coordinated activity.

Memory and Situation Formation

Episodic memory and situation formation are interdependent processes that can’t be understood separately. Situations act as narrative boundaries that organize and make experiences retrievable in memory. Without situational structure, memory degenerates into undifferentiated streams of disconnected events. It’s this cognitive architecture that underpins the human capacity for situated experience, and its absence in machines explains their inability to truly participate in the construction of situations.

Without memory, situations lack continuity and evolutionary development. This relationship operates through several mechanisms. Situations create temporal segmentation, breaking down continuous experiences into discrete, memorable episodes. They establish causal scaffolding, linking actions to their consequences within coherent narrative frameworks. Additionally, they provide social anchoring, reflecting shared perspectives and conflicts within organizational contexts. Comprehending this relationship is crucial for designing systems that can meaningfully engage in organizational sensemaking, rather than merely processing isolated data points.

Episodic Intelligence as a Target

Artificial intelligence, despite advancements in pattern recognition and data processing, primarily relies on semantic recall and statistical classification. These systems lack essential capabilities for genuine situational participation, such as recognizing novel situations, constructing narratives, storing complete episodes as integrated memories, and applying situational memory analogously to new circumstances.

Episodic intelligence is the missing capability that’d enable artificial systems to function as genuine partners in organizational sensemaking. Such systems would detect early indicators of situational emergence, frame situations appropriately, accumulate judgments from multiple perspectives, synthesize them into coherent assessments, and encode complete situational narratives that preserve interpretive context and retrieve relevant historical episodes. They wouldn’t merely process information more efficiently; they’d meaningfully participate in constructing organizational reality through situated interpretation and contextual reasoning.

A situation constructed through sensemaking is the very thing that must be encoded into a system possessing episodic intelligence. Here, sensemaking is the process of creating episodes, and episodic memory is the architecture for storing and reusing them.

Designing Situational Systems

Building systems that support rather than replace human judgment requires fundamental shifts in design philosophy and implementation approach. These systems must evolve beyond static dashboards and rule-based automation to become active partners in organizational sensemaking.

Visualizing Situational Dynamics: Traditional systems provide isolated metrics that need human interpretation to be meaningful. Situational systems should visualize relationships, dependencies, and evolving patterns across time. These visualizations should reveal judgment clusters, role transitions, and status escalations.

Multi-Perspective Analysis: Organizational situations rarely have a single interpretation. Different stakeholders have unique perspectives based on their roles, expertise, and interests. Effective systems must accommodate this complexity by providing tools that allow conflicting judgments to coexist, be compared, and resolved through organizational processes, not algorithms.

Tracking Judgmental Evolution: Organizations should treat judgments as valuable telemetry. Systems should monitor judgment shifts in specific domains, track consensus and divergence across stakeholders, and assess interpretive framework stability. This information provides insight into learning and adaptation.

Enabling Situational Replay: Learning from experience requires revisiting complete situational episodes, not just data logs or timeline reconstructions. Systems should preserve factual information, interpretive context, stakeholder perspectives, and decision-making processes that shaped an organizations’ response. This enables analogical reasoning and supports learning from both successful and problematic episodes.

Taken together, these four principles form a coherent design philosophy that represents a fundamental shift in the human-computer relationship. Traditional Human-Computer Interaction (HCI) centers around designing tools that enable humans to interact with external objects, such as data or systems. However, the principles proposed here emphasize designing a symbiotic system. In this new paradigm, AI transcends its role as a mere interface to data; it actively participates in collaborative conversations about it. AI functions as a medium for shared cognition, facilitates social sensemaking, and serves as a repository for collective episodic memory. This transformation goes beyond enhancing user interfaces; it signifies a deeper integration of technology into the cognitive and social fabric of organizations.

Conclusion

This post rejects the myth that situations are objective facts waiting to be discovered through better sensing or analysis. Situations emerge through judgments in motion, and every intelligent organizational act represents a collaborative wager on constructing shared meaning from complex circumstances. Developing systems that recognize and support this reality is both an engineering challenge and a philosophical commitment. It requires treating intelligence as meaningful participation in constructing workable organizational realities, not control over them. Organizations that embrace this understanding will develop more adaptive, resilient, and ultimately more intelligent approaches to navigating contemporary business challenges.

Appendix A – Notes

A crucial aspect of situations-as-subjects is that intelligent systems are active participants in their environment. This aligns with second-order cybernetics, which posits that the observer (or system) becomes an integral part of the system being observed. In practical terms, this framework emphasizes that an intelligent platform, such as a digital twin, isn’t a passive observer but rather an active participant whose statements (statuses) influence how human operators perceive and interact with the environment. By adopting this perspective, systems can become more effective. Designers can incorporate these feedback effects into their systems, leading to adaptive systems that learn from how their alerts and predictions impact real-world operations. This potential can be harnessed positively, for instance, by using feedback to continuously enhance predictions, if the system is designed with a clear understanding of its role in shaping situations.

The framework calls for adaptability to recursive dynamics – essentially, a form of self-awareness in the system. Developing AI or algorithms that recognize their own influence on the environment is an open challenge (related to areas like reinforcement learning with feedback loops or causal analysis). Some organizations might see this as a daunting requirement, potentially slowing adoption of such advanced digital twins. While the situations-as-subjects concept is insightful, it demands high vigilance in design: ensuring the system’s judgments are well-calibrated, transparent, and continuously evaluated to prevent self-inflicted errors.

In industrial IoT environments, digital twins serve as monitoring systems that predict equipment failures. By applying this framework, a digital twin not only detects a fault but also declares a “maintenance situation.” This underscores the significance of thresholds for alarms (vibration, temperature, etc.) in determining when the situation of “machine requires maintenance” transitions into a “real” condition. Companies can leverage this concept to modify their digital twin’s judgment criteria in alignment with their business strategy. For instance, a plant may tolerate minor deviations as normal to mitigate alert fatigue, while conversely adopting stricter criteria to prioritize preventive maintenance.

A supply chain digital twin can simulate scenarios such as “supply disruption risk” or “demand surge,” which then become the actual situations that managers respond to. For instance, the twin may detect a “supply chain disruption” due to a delayed shipment and automatically reorder stock or reroute deliveries. This action can prevent a crisis, or if the model was overly pessimistic, it might incur additional costs (ordering unnecessary inventory). By treating these situations as constructed, companies can implement oversight measures such as requiring human review of high-impact situation declarations or conducting simulations (“what if” analyses) before treating the situation as fully real. Over time, by tracking the frequency of the twin’s declared situations matching actual events (was there indeed a disruption or did supplies arrive as expected?), the judgment criteria can be refined.

Autonomous vehicles or robots also interpret situations and take actions based on those interpretations, effectively making those interpretations a reality in terms of consequences. For instance, a self-driving car continuously evaluates situations, such as whether it’s a “pedestrian crossing” or a “clear road,” and these evaluations determine its actions, such as stopping or going. If it mistakenly constructs a situation like “obstacle ahead” due to a sensor glitch, it might abruptly brake, causing a hazard. Conversely, if it fails to recognize a real situation, it might not brake at all. Designers of such systems are already addressing this issue, but the proposed framework would encourage them to incorporate redundancy and explainability. Essentially, it’d treat the AV’s internal classification as a hypothesis that may require confirmation (through sensor fusion or even communication with other cars or infrastructure). Additionally, it emphasizes the importance of having these systems signal confidence levels or request human confirmation in ambiguous cases, rather than making unequivocal decisions based on potentially faulty judgments.