Advanced manufacturers are facing a turning point in their workforce. Skilled trades require years to master, yet experienced workers are retiring faster than they can be replaced. The proportion of manufacturing employees over age 55 has more than doubled in the past two decades, accelerating the loss of deep institutional expertise.
The result is more than a labor shortage. It is a knowledge gap.
As veteran employees leave, critical “tribal knowledge”, the unwritten expertise that keeps operations running smoothly, often leaves with them. Manufacturers are increasingly turning to AI-enabled knowledge management tools to address this challenge. Industry analysts project that more than half of manufacturers will adopt AI-driven tools by 2027 to help reskill workers and preserve expertise.
But this raises a deeper question:
What is the most effective way to capture and operationalize tribal knowledge, structured rule-based expert systems, or emerging agentic AI models?
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Understanding the Differences: Generative AI, Agentic AI, and Rule-Based Expert Systems
Generative AI excels at content creation. It produces text, images, code, and summaries in response to prompts. While powerful for communication and analysis, it is not inherently designed to preserve structured operational expertise or enforce decision logic on the plant floor.
Agentic AI focuses on execution. It represents a class of autonomous systems designed to act independently to pursue defined goals. Rather than simply generating responses, agentic AI can plan, sequence, and carry out multi-step tasks across connected systems and workflows. It evaluates conditions, determines appropriate actions, and executes them with minimal human intervention, shifting AI from passive assistance to active operational participation.
A rule-based expert system formalizes tribal knowledge by translating expert reasoning into executable if–then–else logic embedded directly within the control and monitoring environment. Rather than depending on individual memory or informal knowledge transfer, proven best practices are codified into structured rules that evaluate real-time data, trigger guided actions, and ensure consistent, repeatable responses across operations.
Because the logic is transparent and deterministic, it can be tested, audited, and refined over time. The expertise of senior personnel becomes embedded in the system itself, scalable across machines, lines, and facilities, ensuring operational continuity even as the workforce changes.
The strategic question for manufacturers is not about content creation. It is about operational reliability: should tribal knowledge be captured through deterministic rule-based frameworks, adaptive agentic systems, or a thoughtful combination of both?
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The Advantages of Rule-Based Expert Systems
A rule-based expert system captures human expertise by translating domain knowledge into structured, executable logic, typically in the form of conditional rules such as if–then-else statements. Instead of relying on informal mentoring or individual memory, expert reasoning becomes embedded directly into the system itself.
When applied to the challenge of preserving tribal knowledge, rule-based expert systems offer several distinct advantages:
Deterministic and Reliable
Rule-based systems produce the same output for the same input every time. This determinism makes them highly reliable in industrial environments where safety, compliance, and operational consistency are critical.
Transparency and Explainability
Every decision can be traced back to a specific rule. The system can clearly show why a recommendation or action was triggered, which supports auditing, validation, and regulatory requirements.
Operational Stability and Predictability
Because the logic is predefined, outcomes follow a traceable sequence of states. There is no ambiguity in how conclusions are reached, reducing uncertainty in high-stakes environments.
No Runtime Learning Required
The expertise is already codified. The system does not need to “learn” or reinterpret data during execution, making performance stable and predictable.
Consistency Across Teams and Facilities
The same rules are applied uniformly across machines, lines, and sites, eliminating human variability and ensuring standardized procedures.
Targeted Scope and Rapid Response
For well-defined problems, rule-based systems operate efficiently without processing massive datasets.
Ease of Maintenance
Individual rules can be updated, refined, or expanded without redesigning the entire system. As processes evolve, the knowledge base can evolve with them.
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The Challenges of Rule-Based Expert Systems
While rule-based expert systems offer stability and transparency, they are not without limitations. Understanding these constraints is essential when determining where they fit within a broader industrial intelligence strategy.
Rigidity and Limited Adaptability
Rule-based expert systems operate strictly within the boundaries of their predefined logic. If a situation falls outside programmed conditions, the system cannot adapt or infer a solution. This makes them highly reliable within defined parameters but less flexible in novel or rapidly evolving scenarios.
Knowledge Acquisition Requires Upfront Effort
Capturing tribal knowledge is not automatic. It requires deliberate collaboration with subject matter experts to translate real-world decision-making into structured, executable rules. This upfront effort can be time-intensive, especially when critical expertise is undocumented or distributed across multiple individuals.
However, tools matter. In ADISRA SmartView, the intuitive graphical tree-structure design (shown below) simplifies rule creation and organization. This structured visual approach makes entering, editing, and managing rules straightforward, reducing complexity while preserving the integrity of expert knowledge.
Manual Rule Creation
Unlike machine learning systems that derive patterns from large datasets, rule-based expert systems rely on explicit rule authoring. Developing and validating those rules requires deliberate engineering and domain expertise.
Scalability and Rule Complexity
As rule bases grow, they can become increasingly complex. Without careful design and governance, systems may experience “rule sprawl,” where overlapping, redundant, or conflicting rules accumulate. Over time, this can create maintenance challenges and technical debt if not properly managed.
Brittleness in Unstructured Environments
Because intelligence is limited to what has been explicitly encoded, rule-based expert systems can struggle with abstract patterns, ambiguous inputs, or unforeseen edge cases. They excel at well-defined operational problems but are less suited to environments that require interpreting highly unstructured data.
Governance and Lifecycle Management
Maintaining large rule sets requires structured oversight. Version control, validation processes, and centralized governance are critical to preventing uncontrolled growth and preserving system integrity.
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Agentic AI: The Next Evolution in Autonomous Intelligence
As manufacturers explore solutions beyond deterministic logic, attention is shifting toward agentic AI, an emerging class of systems designed to operate with greater autonomy.
Agentic AI represents an evolution beyond traditional generative AI models. While generative AI focuses primarily on producing content in response to prompts, agentic systems are designed to perceive conditions, reason through options, and act toward defined goals. These systems can operate semi-autonomously or fully autonomously, integrating with software platforms, data sources, and workflows to complete multi-step tasks with limited human intervention.
Unlike chat-based tools that respond reactively, agentic AI is proactive. It can monitor environments, evaluate changing conditions, make decisions based on objectives, and trigger actions across connected systems.
For manufacturers, this introduces a new dimension of intelligence; one that moves beyond codified logic and toward adaptive, goal-driven execution.
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The Advantages of Agentic AI
Agentic AI introduces a different type of capability; one centered on autonomy, adaptability, and continuous learning. Where rule-based systems rely on predefined logic, agentic systems are designed to observe, reason, and act within dynamic environments.
When applied to the challenge of capturing tribal knowledge, agentic AI offers several compelling advantages:
Active, Real-Time Observation
Rather than relying solely on documented procedures, agentic systems can observe workflows in real time. By analyzing multimodal inputs, such as voice interactions, screen activity, logs, and operational data, they can extract expertise directly from how experienced personnel perform their work.
Continuous Operation
Unlike human experts, AI agents can operate 24/7. They can monitor systems, document workflows, and analyze patterns continuously without interruption, expanding the window for knowledge capture.
Handling Ambiguity and Novelty
Where fixed rule sets perform best in well-defined conditions, agentic AI can evaluate multiple signals, logs, and variables to assess what is most likely occurring, even in situations that were not explicitly preprogrammed.
Automatic Knowledge Codification
Agentic AI can transform informal, unwritten expertise into structured digital work instructions or playbooks. As processes evolve, these digital artifacts can be updated dynamically rather than remaining static.
Self-Refining Intelligence
Because these systems learn from ongoing interactions and feedback, they can improve over time, building a continuously evolving knowledge base that becomes more robust with use.
Adaptability to Change
If procedures are modified, systems updated, or new operational constraints introduced, an agentic model can adjust guidance and notify relevant teams, keeping institutional knowledge aligned with current realities.
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The Challenges of Agentic AI
While agentic AI introduces powerful capabilities in autonomy and adaptability, it also presents meaningful risks, particularly in industrial environments where safety, compliance, and reliability are non-negotiable.
Difficulty Capturing Tacit and Intuitive Knowledge
Tribal knowledge is often subtle, situational, and deeply contextual. It lives in conversations, instincts, and years of lived experience. While agentic systems can observe workflows and analyze data, they may struggle to fully capture the reasoning behind decisions, the “why” rather than just the “what.” Nuance can be lost in translation.
Non-Deterministic Behavior
Unlike rule-based expert systems, agentic AI is probabilistic. The same prompt or situation may produce different outcomes unless tightly constrained. This variability can make auditing, validation, and regulatory compliance more complex.
Risk of Hallucinations and Data Distortion
Agentic systems can generate plausible but incorrect outputs, sometimes referred to as hallucinations. In operational contexts, this could mean recommending inappropriate action or incorrectly confirming that a task was completed. When tacit knowledge is externalized into structured outputs, important nuances can also be distorted.
Reliability Concerns in Complex Environments
In large-scale or safety-critical deployments, accuracy and consistency become paramount. Without strong validation layers, outputs may require human review, limiting full autonomy.
Data Quality and “Knowledge Contamination”
If agents learn from incomplete, outdated, or incorrect data sources, the resulting guidance may embed those flaws. Poor data hygiene can propagate bias or misinformation across workflows.
Maintenance and Lifecycle Management
Agentic systems require ongoing supervision. Models must be updated, permissions reviewed, and workflows tested regularly to ensure alignment with evolving operational realities.
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A Hybrid Model: Deterministic Guardrails with Adaptive Intelligence
Rather than treating rule-based expert systems and agentic AI as competing technologies, manufacturers can unlock greater value by integrating them within a structured architecture.
Agentic systems introduce autonomy and adaptability, but they must be deployed responsibly. Effective implementation requires defined permissions, approval workflows, action thresholds, logging, validation procedures, and continuous oversight. Without disciplined governance, even minor configuration changes can cascade across interconnected systems, creating unintended operational risk.
This is where a rule-based expert system becomes foundational. It establishes the guardrails. By encoding validated operational knowledge into deterministic logic, it ensures safety, compliance, and procedural consistency. It clearly defines what must always occur, what must never occur, and how approved responses should be executed under known conditions. In safety-critical industrial environments, this structured and predictable framework is not optional; it is essential.
Agentic AI, layered above or alongside that framework, can provide adaptive capabilities. It can observe workflows, analyze patterns, detect anomalies, and recommend actions in scenarios that extend beyond predefined rule boundaries. It can assist with data gathering, workflow orchestration, and contextual interpretation across systems.
In this hybrid architecture:
- The rule-based system governs execution.
- The AI layer augments insight.
- Deterministic logic enforces operational integrity.
- Adaptive models surface emerging patterns and recommendations.
Machine learning models can contribute predictive and analytical intelligence to this structure. These models can identify trends, detect anomalies, and forecast outcomes based on historical data. However, their outputs should be evaluated within a governed framework. When integrated properly, predictive models inform decisions, while rule-based logic ensures those decisions remain aligned with operational standards.
This layered approach transforms tribal knowledge into structured, enforceable logic, while still allowing the system to evolve as new data, patterns, and operational realities emerge.
The result is not rigid automation. It is controlled intelligence.
Conclusion: From Knowledge Loss to Knowledge Architecture
Manufacturing is at a crossroads.
As experienced workers retire, the industry faces more than a staffing challenge; it risks losing decades of institutional expertise. Tribal knowledge, once passed through mentorship and repetition, must now be captured intentionally and embedded into digital systems.
Generative AI may assist with documentation.
Agentic AI may enhance adaptability and orchestration.
But rule-based expert systems remain uniquely suited to institutionalizing validated operational logic in a transparent, auditable, and repeatable way.
The most resilient strategy is not choosing between them. It is deliberately architecting intelligence; combining deterministic guardrails with adaptive insight.
Manufacturers who treat knowledge capture as a structured design decision, not a reactive technology experiment, will be best positioned to maintain productivity, protect safety, and accelerate innovation in the years ahead.
If you are exploring how to embed tribal knowledge into your HMI/SCADA architecture as you prepare for AI-driven evolution, now is the time to evaluate your approach.
Download ADISRA SmartView here or request a personalized demonstration to see how structured expert systems and evolving AI capabilities can work together inside a modern industrial automation platform here.
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