The failure patterns explored in Part 1 of this series reveal why traditional machine learning approaches struggle in industrial environments. The 80% failure rate of industrial AI projects isn't simply a matter of poor execution; it reflects fundamental misalignment between conventional AI technologies and the unique requirements of mission-critical industrial operations. However, a new generation of AI architecture is emerging that addresses these challenges through sophisticated hybrid approaches that combine multiple AI technologies in ways specifically designed for industrial applications.
Beyond Limits, emerged from NASA's autonomous space operations technology, where we developed what is termed a "knowledge-oriented approach" to industrial AI that represents a fundamental shift from traditional machine learning models. This approach combines symbolic reasoning, machine learning, and large language models in hybrid architecture designed to operate effectively with incomplete data while incorporating human expertise and domain knowledge.
The hybrid AI approach addresses the core challenges that cause most industrial AI projects to fail, which are data scarcity and quality issues, the need to incorporate domain expertise, requirements for explainable decision-making, and the necessity of operating reliably in safety-critical environments. By understanding how advanced architecture works and why it’s better suited to industrial applications, organizations can make more informed decisions about AI implementation strategies that have higher probabilities of success.
The concept of the "operational breakpoint" emerged from the briefing discussion as a critical insight into why traditional AI approaches fail in industrial settings. As Jose Larazres explained, organizations must recognize that "because we don't have all of the data, simply trying to use a purely numeric machine learning model driven approach will in most cases not[RM1.1] lead to the outcomes we need."
This recognition leads to a fundamentally different approach that prioritizes knowledge integration over pure data analysis. Traditional machine learning models require large amounts of clean, labeled data to achieve acceptable performance levels. In industrial settings, such data is often unavailable due to the complexity of operational environments, the uniqueness of specific equipment configurations, and the relatively infrequent occurrence of the failure modes and operational scenarios that AI systems need to understand.
The operational breakpoint concept acknowledges that industrial operations involve complex decision-making processes that cannot be fully captured through historical data analysis alone. Instead, these systems must incorporate the physics-informed models that organizations have developed over decades, integrate symbolic and advanced reasoning constructs, and embed the domain expertise that experienced operators use to make critical decisions under uncertain conditions.
This approach is particularly important given the statistics highlighted in the industry briefing that 42% of organizational knowledge exists uniquely in employees' minds, with an additional significant portion contained in documents, procedures, and institutional knowledge that traditional machine learning systems cannot easily access or interpret. The knowledge-oriented approach provides frameworks for capturing, codifying, and operationalizing this expertise in ways that complement rather than replace human decision-making.
This also reflects the reality that industrial operations must continue functioning while AI systems are being developed and deployed. Unlike consumer applications where users can tolerate occasional errors or system downtime, industrial operations require continuous, highly reliable performance that maintains safety, environmental compliance, and operational efficiency. This requirement means that AI systems must be designed to work alongside existing operational systems and human expertise rather than attempting to replace them entirely, at least in the early days of AI deployment.
Understanding this broader challenge helps explain why many AI projects that show promise in laboratory or pilot environments fail when deployed in actual operational settings. The controlled conditions and clean data sets used in development environments don't reflect the complexity, uncertainty, and time pressure that characterize real industrial operations.
The concept of "agentic AI" has emerged as a critical evolution in industrial AI, offering a path beyond the limitations of traditional systems. While conventional AI typically delivers isolated insights or recommendations, agentic AI is built to manage and coordinate complex, multi-step workflows, spanning multiple decision points, teams, and operational systems.
As Jose Larazres described it, agentic AI represents "orchestration and rethinking [RM3.1][KM3.2]automation" through "orchestrable flows that depend on a system of agents and tools that allow you to shift from advising to actionable intelligence." This approach enables organizations to move beyond simple advisory systems toward autonomous end-to-end orchestration that can interact with existing operational systems while maintaining human oversight and control.
The agentic approach is especially valuable in industrial environments, where operations involve multi-step planning and real-time decision-making. Instead of offering isolated recommendations, agentic AI can manage entire workflows, coordinate across supply chains down to process units, integrate with control systems, and adapt to changing conditions.
These systems rely on intelligent agents that operate autonomously and collaborate to meet broader operational goals. Each agent can specialize in areas like process optimization, equipment monitoring, maintenance, quality control, or safety, working together through orchestration frameworks to ensure cohesive execution.
Orchestration is essential in industrial settings, where interdependence means that a change in one area can ripple across the system. Traditional AI often focuses on isolated optimizations, missing these connections and producing solutions that may help locally but harm overall performance and not produce the global optimum.
Beyond Limits’ agentic AI approach builds on technology originally developed for NASA’s autonomous space missions, where systems must operate independently in uncertain environments with limited communication. This heritage guides the creation of AI systems that can be trusted to perform and explain their decisions when it matters most.
Agentic AI also tackles scalability, a common barrier in industrial deployments. Instead of building separate AI systems for each function, this architecture allows new agents to be added or workflows modified, enabling modular growth without sacrificing performance or reliability.
A key differentiator in hybrid AI approaches is the integration of symbolic reasoning capabilities that complement traditional machine learning and large language model technologies. Symbolic reasoning systems provide the logical framework necessary for making decisions under uncertainty while maintaining the explainability and reliability that industrial applications require.
Symbolic reasoning can incorporate the rules, constraints, and logical relationships that govern industrial operations in ways that pure machine learning models cannot. They can represent the cause-and-effect relationships that experienced operators understand intuitively, encode safety constraints that must never be violated, and provide the logical framework for explaining why specific recommendations are made under circumstances.
The integration of symbolic reasoning with machine learning and large language models creates what Jose described as "explainable AI that can be understood and how decisions are being made." This explainability is crucial for building trust with experienced operators and engineers who need to understand not just what the system recommends, but why those recommendations make sense given current operational conditions.
Symbolic reasoning systems excel at handling the kind of logical inference and constraint satisfaction problems that are common in industrial operations. They can reason about complex relationships between operational variables, evaluate multiple scenarios simultaneously, and ensure that recommendations comply with safety, environmental, and operational constraints that may not be explicitly represented in historical data.
Advanced reasoning enables AI to handle multi-step planning and complex problem-solving, much like expert human decision-making. These systems go beyond pattern matching to reason through scenarios, weigh multiple variables, and generate solutions that address both immediate needs and long-term goals.
Symbolic reasoning, a key part of hybrid AI, is especially useful when dealing with uncertainty and incomplete information, common in industrial environments. It allows AI to make sound decisions even with partial data, where statistical models alone may fall short.
Beyond Limits’ symbolic reasoning is rooted in decades of work on autonomous systems for space missions. Designed to function without real-time human input and under high-risk conditions, these systems are built for reliability, transparency, and trust, even in the most uncertain environments.
The knowledge-based layer of hybrid AI tackles a major hurdle in industrial AI: capturing and applying the domain expertise operators rely on to make complex decisions. This is vital, especially given that 42% of organizational knowledge exists only in employees’ minds, with much of the rest buried in documents and procedures that conventional AI struggles to process.
Knowledge-based systems provide structured ways to represent and apply different types of expertise, factual (e.g., equipment specs), procedural (e.g., task execution), and heuristic (e.g., best practices built over years). These frameworks go beyond simple rules, using semantic networks, ontologies, and case-based reasoning to handle complex relationships and uncertainty.
One of the strengths of this approach is its ability to function even when quality historical data is lacking. By encoding how systems should behave, AI can make sound decisions in rare or novel scenarios where data-driven models fall short. This methodology also addresses the context-specific nature of industrial operations. While machine learning often struggles to generalize across environments, knowledge-based systems adapt principles to specific operational conditions.
Beyond Limits’ implementation includes advanced tools for knowledge acquisition, allowing subject matter experts to contribute directly without needing deep technical skills. This ensures AI systems can evolve with operational changes. Moreover, because the knowledge is explicitly structured, it supports clear validation and auditability, essential for building trust and accountability in high-stakes environments.
Integrating large language models (LLMs) into hybrid AI marks a significant advance in how AI systems interact with humans and fit into industrial workflows. Rather than acting as standalone decision-makers, LLMs serve as orchestration layers, connecting different AI components while enabling natural language communication.
Because LLMs excel at processing language, they make it easier for operators to engage with AI using familiar communication styles. This eliminates the need for complex interfaces by allowing users to ask questions, receive clear explanations, and generate readable summaries and reports. In industrial settings, LLMs add value by coordinating multiple AI systems while preserving context. For example, an LLM can manage interaction between a predictive model that flags equipment issues, a reasoning engine that assesses maintenance options, and a knowledge base that offers procedural guidance.
They are also effective at handling unstructured information like maintenance records, incident reports, and operator notes. By including this data, hybrid AI systems can draw from a broader base of operational knowledge than traditional approaches allow. LLMs simplify knowledge capture as well. Experts can contribute insights into plain language without needing technical training in formal representation methods, making it easier to keep systems aligned with real-world procedures.
However, concerns around hallucinations and reliability make it risky to rely on LLMs for core decision-making in critical environments. Hybrid systems manage this by using LLMs primarily for communication and coordination, while decisions remain grounded in symbolic and knowledge-based components that ensure accuracy and compliance.
This approach also helps manage complexity. Operators interact with one simplified interface, while the system handles the underlying coordination, unlocking the benefits of hybrid AI without requiring deep technical understanding.
Beyond Limits looked at a three-tiered framework for implementing hybrid AI in industrial environments. This phased approach supports a gradual shift from semi-autonomous to fully autonomous systems, addressing technical hurdles and organizational change while building trust at each stage.
Tier One focuses on integrating current technologies, rules, graphs, and machine learning, to support operators with actionable guidance. The AI system acts as an advanced advisor, enhancing visibility, identifying risks early, and suggesting actions based on data and expert knowledge. Human operators remain in full control, using AI-generated insights to inform decisions. This stage helps teams gain familiarity with AI while delivering immediate operational value.
Tier Two introduces agentic capabilities, causal reasoning, and physics-informed models to enable autonomous task execution within set boundaries. The AI system begins to act independently for specific functions, such as adjusting parameters, performing routine maintenance, or optimizing setpoints in real time. Human oversight is still present, with operators able to intervene as needed. This phase demonstrates autonomous performance while reinforcing confidence in system reliability.
Tier Three moves to full autonomous orchestration. AI now manages entire multi-step operational processes, integrating deeply with control systems, safety protocols, and explainability tools. Human roles shift from hands-on control to strategic oversight, exception management, and performance tuning. Operators supervise rather than execute, focusing on higher-level optimization and continuous improvement.
This structured progression is essential. Successful AI deployment in industry depends not just on technical sophistication, but on organizational readiness and workforce adaptation. Skipping steps to reach full autonomy too quickly often leads to failure. Building capabilities, trust, and value through each tier makes long-term success far more likely.
The theoretical frameworks of hybrid AI take concrete form in practical applications that demonstrate how advanced architectures address real operational challenges. Beyond Limits has implemented hybrid AI solutions across various industrial domains, providing valuable insights into how these technologies perform in actual operational environments.
In well health management applications, hybrid AI systems address sand management challenges that represent leading indicators of formation destruction and potential well rework requirements. The system integrates acoustic sensors and other downhole monitoring technologies with physics-informed AI models to provide risk-based predictions that enable operators to make informed decisions about acceptable risk levels for individual wells.
The hybrid approach combines real-time sensor data with geological formation models and historical performance data to create comprehensive well health assessments. Machine learning components analyze sensor patterns to detect early signs of sand production, while symbolic reasoning systems evaluate the implications of different sand management strategies based on operational constraints and business objectives. Knowledge-based components provide procedural guidance for implementing recommended actions, while LLM orchestration layers enable natural language interaction with the system.
Drilling optimization applications demonstrate how physics models can be integrated into AI systems to understand field-wide impacts of drilling decisions. Rather than optimizing individual wells in isolation, the hybrid system considers the effects of new drilling activities on the entire field’s performance, enabling more strategic decision-making that maximizes overall production while minimizing operational risks.
The drilling optimization system combines geological modeling capabilities with machine learning algorithms that analyze historical drilling performance and symbolic reasoning systems that evaluate different drilling strategies based on operational constraints and business objectives. The hybrid approach enables the system to reason about complex trade-offs between short-term drilling costs and long-term production benefits while accounting for uncertainty in geological conditions and equipment performance.
In lubricant formulation applications, hybrid AI has accelerated research and development processes by combining historical data with knowledge-based guardrails from senior researchers. What previously required months of iterative testing and refinement can now be accomplished in hours through AI-guided optimization that considers both desired performance characteristics and practical constraints around available raw materials and manufacturing processes.
The lubricant formulation system demonstrates how hybrid AI can augment human expertise rather than replacing it. Machine learning components analyze historical formulation data to identify patterns and relationships between raw material properties and performance characteristics. Symbolic reasoning systems ensure that new formulations comply with regulatory requirements and manufacturing constraints. Knowledge-based components incorporate expert knowledge about formulation principles and best practices. The LLM orchestration layer enables researchers to interact with the system using natural language queries and receive explanations of recommended formulations in terms that facilitate understanding and validation.
These applications demonstrate that successful industrial AI implementation requires more than advanced algorithm, it demands deep understanding of operational contexts, integration with existing systems and processes, and careful attention to the human factors that influence technological adoption in industrial environments.
Organizations that successfully implement hybrid AI systems gain significant competitive advantages that extend beyond simple operational efficiency improvements. The comprehensive nature of hybrid approaches enables new capabilities and business models that are not feasible with traditional AI implementations or purely human-operated systems. The explainability capabilities of hybrid AI systems provide important advantages in regulated industries where organizations must demonstrate compliance with safety, environmental, and operational requirements. The ability to provide clear explanations of AI decision-making processes enables organizations to satisfy regulatory requirements while benefiting from AI-powered optimization and automation.
The knowledge-based components of hybrid systems provide important advantages for organizations facing workforce transitions and knowledge transfer challenges. By capturing and codifying expert knowledge in AI systems, organizations can preserve institutional knowledge while enabling new employees to benefit from the experience of retiring experts.
The agentic orchestration capabilities of hybrid systems enable organizations to implement more sophisticated automation strategies that coordinate across multiple operational domains. This comprehensive approach to automation can deliver greater operational benefits than isolated AI applications while maintaining the flexibility to adapt to changing conditions and requirements. The modular architecture of hybrid AI systems also provides important advantages for organizations that need to expand AI capabilities incrementally. Rather than requiring complete system replacements, hybrid architecture can be extended and enhanced through the addition of new agents and the modification of orchestration workflows.
Perhaps most importantly, hybrid AI approaches provide a sustainable path toward autonomous operations that build organizational capabilities and trust gradually rather than attempting disruptive transformations that often fail. Organizations that master hybrid AI implementation are better positioned to capitalize on future advances in AI technology while maintaining operational excellence throughout the transition process.
The next article in this series will explore how organizations can move from understanding hybrid AI concepts to implementing practical systems that deliver operational value, examining the specific strategies and methodologies that enable successful deployment of these advanced AI architectures.
This is Part 2 of a 4-part series on AI in industrial operations. Part 3 will explore "From Pilot to Production: A Practical Roadmap for Industrial AI Success," examining implementation strategies and real-world deployment approaches.
This article is based on insights from Beyond Limits' expert online industry briefing featuring Don Howren (COO), Jose Lazares (Chief Product Officer), Richard Martin (Global Energy Expert), and Pandurang Kulkarni (Senior AI Product Manager). > link to on demand video here
