Beyond Explicit Knowledge: The Rise of AI for Expert Tacit Sensing

      Current Artificial Intelligence (AI) systems have revolutionized how we process explicit knowledge. They excel at tasks like information retrieval, document summarization, generating explanations, and streamlining codified workflows. Yet, the true depth of human expertise often lies beyond easily articulated facts. Human experts frequently rely on what is known as "tacit sensing" – an intuitive ability to perceive faint indicators, recognize developing issues, sense deterioration, and foresee instability long before concrete data confirms it. This subtle, unquantifiable aspect of expert cognition, often difficult to formalize, is becoming increasingly crucial in the evolving landscape of AI.

      This new vision suggests that for AI to genuinely augment high-level professional work, it must evolve beyond merely processing explicit data. It needs to develop capabilities for longitudinally modeling expert tacit sensing. This shift represents a profound re-evaluation of AI's role, moving it from a mere tool for task execution to an integral part of a persistent cognitive environment where human and machine intelligences are deeply interdependent. The challenge lies in capturing, structuring, modeling, and interpreting these elusive "tacit signals" over time, enabling AI to anticipate and provide insights that mimic expert intuition (Source: Tacit Signal Infrastructure: Towards AI Systems that Model Expert Sensing Over Time).

The Spectrum of Knowledge: Explicit vs. Tacit

      Explicit knowledge encompasses information that can be readily documented, shared, and codified – think of manuals, reports, checklists, and data tables. Current generative AI systems are exceptionally good at handling this type of knowledge. They can parse vast datasets, identify patterns, and generate coherent responses or summaries, proving invaluable for tasks that are well-defined and rely on accessible information.

      However, professional expertise extends far beyond this. Tacit knowledge refers to the unstated, intuitive understanding that experts develop through years of experience. It includes the ability to "read the room," sense shifts in team dynamics, or instinctively know when a system is about to fail based on subtle cues. These are often called "weak signals"—faint indications that might be dismissed by a novice but recognized as critical by a seasoned professional. Examples include a slight change in machine hum indicating an impending fault, a subtle shift in a patient’s demeanor signaling a deeper issue, or a barely perceptible tension in a negotiation that could lead to a breakthrough or breakdown. Such signals are rarely captured in traditional databases or explicit performance metrics, yet they profoundly influence expert judgment in complex, real-world scenarios.

Introducing Tacit Signal Infrastructure

      To bridge the gap between AI's explicit knowledge processing and the nuanced world of expert intuition, the concept of Tacit Signal Infrastructure emerges. This is envisioned as a dedicated layer within cognitive infrastructure designed to capture, structure, model, and interpret expert tacit signals over time. The goal is not to automate human judgment completely, but to create systems that make these subtle, weak signals more visible, analyzable, and actionable for human experts and next-generation AI alike.

      Such an infrastructure would systematically gather "cognitive traces" from human experts—not just what they say or type, but potentially patterns in their actions, their attention shifts, their physiological responses, or even the subtle ways they interact with information systems. By observing these patterns longitudinally, AI could begin to identify correlations and precursors that mimic human expert weak-signal perception. For instance, in an industrial setting, while an existing AI Video Analytics system might detect a worker without a hard hat, a Tacit Signal Infrastructure could potentially learn to identify subtle, early behavioral patterns that often precede safety violations, based on expert observations over time.

Long-term Cognitive Operations: Governing AI for Intuition

      Modeling expert tacit sensing requires a new operational paradigm called Long-term Cognitive Operations. This framework focuses on the continuous practices necessary to maintain, validate, and govern AI systems that move beyond explicit knowledge. Key components include:

• Memory Curation: Systematically collecting and organizing diverse cognitive traces.
• Semantic Organization: Structuring collected data in ways that allow AI to understand context and relationships.
• Tacit Signal Modeling: Developing AI algorithms specifically designed to identify and interpret weak, subtle patterns that mirror human intuition.
• Reasoning Calibration: Continuously validating AI interpretations against human expert feedback.
• Cognitive Governance: Establishing ethical guidelines and oversight for AI systems that model human cognitive processes.

      This new paradigm also suggests the emergence of a Cognitive Operations Manager role. This AI-native professional would be responsible for coordinating the capture and modeling of tacit signals, semantic modeling, AI system calibration, expert validation, and ethical governance. Their work would ensure that AI systems accurately learn from and augment human intuition without overstepping ethical boundaries. For instance, ARSA's AI Box Series, which processes video streams at the edge, could be an foundational component for distributed tacit signal capture, providing localized data processing without cloud dependency for enhanced privacy and security, as part of a larger Tacit Signal Infrastructure initiative.

Practical Applications and Business Impact

      The shift towards AI systems that model expert sensing carries significant practical implications across various industries. By recognizing emerging tensions, coherence degradation, or anticipatory risk, enterprises can achieve:

• Proactive Risk Management: Detect subtle indicators of system failure, security threats, or market shifts before they escalate into crises. This can prevent costly downtime, security breaches, or significant financial losses.
• Enhanced Decision-Making: Provide decision-makers with AI-augmented intuition, offering insights into nuanced situations that explicit data alone might miss. This leads to more informed and timely strategic decisions.
• Optimized Operations: Improve efficiency and safety in environments like manufacturing or logistics by predicting human error or equipment malfunction based on weak behavioral and environmental signals.
• Deeper Customer Understanding: Anticipate customer needs, satisfaction shifts, or emerging preferences by modeling subtle interactions and feedback beyond explicit surveys.

      Implementing such sophisticated AI systems demands a robust and flexible approach. Companies seeking to develop AI capabilities that delve into tacit sensing often require highly specialized solutions tailored to their unique operational contexts. ARSA Technology, with its expertise in custom AI and IoT solutions, can partner with enterprises to design and deploy systems capable of gathering and processing diverse data streams that lay the groundwork for a Tacit Signal Infrastructure. Our custom AI solutions are engineered to meet the demands of mission-critical operations, providing the precision, scalability, and measurable ROI needed for such advanced cognitive systems. ARSA has been experienced since 2018 in building and deploying practical AI that delivers measurable impact.

Conclusion

      The journey towards AI systems that model expert tacit sensing marks the next frontier in artificial intelligence, moving beyond simple task automation to genuine cognitive augmentation. By establishing a Tacit Signal Infrastructure and embracing Long-term Cognitive Operations, organizations can harness the subtle power of human intuition, making it more visible, interpretable, and actionable through AI. This evolution will not replace human experts but empower them with a sophisticated AI partner, fostering a future where AI and human cognition are seamlessly integrated to solve the most complex challenges.

      To explore how advanced AI and IoT solutions can transform your operations and to discuss building the foundational infrastructure for expert sensing in your organization, contact ARSA for a free consultation.

      Source: Yuan, Annie. “Tacit Signal Infrastructure: Towards AI Systems that Model Expert Sensing Over Time.” arXiv preprint arXiv:2605.24332 (2026).