For more than two decades, I have worked with service organizations, learning from and helping them move from reactive troubleshooting to structured, guided troubleshooting using causal reasoning. Today, as AI systems enter the field, the same lesson applies at a new scale, as reasoning matters more than prediction, which is how generative AI works.
Across the service industry, we hear service leaders learning a hard truth: AI that sounds confident isn’t necessarily correct. Conversational, generative, and agentic AI systems can handle everyday questions with impressive fluency, but when technicians rely on them to fix critical equipment, “close enough” can mean downtime, safety issues, or compliance failures.
We have seen this across multiple industries, where technicians test conversational AI using many years of service data and product documentation, and report that some suggestions are incorrect or even dangerous.
The problem isn’t a lack of data or ambition, but a lack of proper reasoning.
Generative AI, built on large language models and pattern recognition, is designed to anticipate what sounds right rather than determine what is right. It performs well for information retrieval, but struggles in complex technical environments where decisions depend on cause-and-effect reasoning, incomplete data, and the real-world cost of each troubleshooting step.
That’s why the future of service AI depends on more than linguistic intelligence. It requires causal intelligence that can reason before responding.
Here is why RAG, Copilot systems, and domain-specific LLMs struggle with real-world troubleshooting and why a casual approach to troubleshooting is superior.
When AI guesswork becomes a risk
In high-stakes industries such as medical devices, energy, and transportation, AI-driven guidance must follow approved and validated procedures. A predictive or “guess-based” model may suggest a plausible but unverified step, unintentionally creating compliance or safety risks.
The real danger isn’t the obvious errors, it’s the plausible ones. In complex systems, a generated step can look credible enough to pass inspection but still deviate subtly from the correct procedure. These small deviations compound over time, introducing hidden warranty, safety, or regulatory risks that surface only after damage has been done.
Many teams assume that human oversight will catch such issues, but in fast-paced environments, technicians often trust the AI’s confident tone, especially under time pressure.
Causal approach:
Causal AI enables intelligent reasoning by combining knowledge, experience, and real-world context to evaluate large, complex systems – much like a seasoned expert would. Rather than simply identifying patterns, it understands cause and effect. That distinction matters.
Causal AI closely mirrors human reasoning because it doesn’t just calculate probabilities; it evaluates relationships between events and determines what truly drives an outcome. This allows it to handle uncertainty in a structured and transparent way, even when data is incomplete.
In complex service environments, perfect information rarely exists. Machines fail in unexpected ways. Technicians face time pressure. Causal AI is designed for exactly these conditions as it continuously updates its reasoning as new evidence becomes available, narrowing down root causes and identifying the most efficient path to resolution.
Inconsistency undermines trust
One of the fastest ways to erode confidence in any AI system is inconsistency. Two technicians can describe the same issue differently, and a generative model might produce two different answers or even contradict itself if the phrasing changes. For field teams, that inconsistency creates hesitation, and if the guidance isn’t repeatable, how can it be relied upon in high-pressure situations?
The issue runs deeper than language. Pattern-based systems depend heavily on context windows and prompt interpretation. Small differences in wording can lead to entirely different answering chains, meaning the same problem may appear to have multiple “right” solutions. This not only slows resolution but also undermines the technician’s trust in the AI’s reliability.
Causal approach:
Causal reasoning delivers consistency by design. Instead of predicting likely responses from text patterns, it evaluates the relationships between symptoms, causes, and effects in the same way an experienced human expert would, just better, as it guarantees the most optimal step every time. Each diagnostic path is grounded in probabilities derived from historical data and validated expert input, not linguistic nuance.
Because causal models reason from structure rather than syntax, they ensure that the same problem always leads to the same optimal resolution path, regardless of skills or location. The result is a dependable, repeatable decision framework that technicians can trust, managers can audit, and organizations can scale with confidence.
Knowledge gaps lead to fabricated answers
When data is incomplete or documentation is inconsistent, conversational AI often compensates by generating information that sounds correct but isn’t. These aren’t random errors; they are plausible fictions produced because the LLM model’s goal is linguistic confidence and not factual certainty.
A technician might receive an instruction to check a non-existent component or apply a configuration step that’s slightly wrong for a specific model revision. The AI is not lying; it is just predicting what is likely to exist based on probabilities. But in regulated or mission-critical environments, such improvisation can lead to warranty violations, equipment damage, or safety incidents. The bigger the data gap, the more confident the AI often sounds.
Causal approach:
Causal reasoning systems are built to acknowledge uncertainty rather than mask it. Utilizes the advantages of, e.g., Bayesian network technology to optimize the troubleshooting process. It guides the user through the most efficient sequence of questions and steps for solving the problem, whether it is due to hardware, software, environmental conditions, or a host of other sources of trouble. It takes into consideration various factors: the most likely cause of the problem, the “cost” of each troubleshooting step (time, difficulty, risk, money, etc.), how much information can be gathered from each step, etc. The causal troubleshooting system adjusts its internal variables and, therefore, its advice at each step, depending on the answers to the questions and the results of each troubleshooting step.
Complex procedures need logical structure
Troubleshooting and repair are rarely one-step tasks. They involve sequences of interdependent actions in which each step determines what happens next. Conversational copilots and retrieval-based systems often treat each query as a standalone question. The AI might provide a correct step in isolation, but lose awareness of where that step fits within the full process.
For example, a technician might ask how to recalibrate a sensor and receive a plausible response, but the AI doesn’t know whether prerequisites, such as voltage checks or system resets, have already been performed. This fragmentation breaks logical flow and can lead to costly rework or equipment failure.
Causal approach:
Causal reasoning systems maintain procedural context and dependencies throughout the troubleshooting process. They don’t just answer questions, they evaluate the entire state of the problem and update probabilities dynamically as new information becomes available.
This approach mirrors how seasoned experts think: when they test hypotheses, update beliefs, and adjust actions based on outcomes. In effect, causal AI serves as a reasoning engine that guides technicians through a coherent decision path rather than a list of possible actions. The result is a safer, faster, and more reliable resolution.
Humans still matter, but their workload can change
Even the most advanced AI cannot replicate the judgment and intuition of experienced service professionals. Yet, as senior technicians retire and documentation struggles to keep up, much of an organization’s expertise risks being lost. At the same time, expecting experts to manually document and maintain troubleshooting logic is unrealistic. Much of the tribal knowledge remains locked in case logs and service notes accessible only through human memory or AI ingestion and search systems.
Causal approach:
Rather than replacing experts, the new role of generative AI is to extend their reach. Generative models can do the heavy lifting of extracting insights from unstructured sources such as manuals, case histories, reports, and organize that information into a structured form. Then, causal reasoning can transform this raw input into a logical troubleshooting framework that reflects expert understanding.
This hybrid model creates a partnership between AI and human expertise. Subject matter experts retain control by reviewing, validating, and refining AI-generated guides, but their role shifts from content creation to quality assurance and optimization. Over time, this accelerates knowledge capture and builds a living system of intelligence that continuously improves with every service interaction.
Retrieval isn’t reasoning
Much of today’s enterprise AI success comes from information retrieval and not decision-making. Systems built on large language models or retrieval-augmented generation excel at finding and summarizing information, but retrieving knowledge is not the same as applying it.
When technicians diagnose a fault, they need more than facts. They need to connect those facts in a meaningful sequence and weigh probabilities, test hypotheses, and choose actions that minimize time and cost. Generative AI lacks a native concept of causality, and it cannot distinguish between correlation and causation. That’s where most AI-driven service failures occur.
Causal approach:
Causal reasoning introduces a new layer of intelligence that focuses not on what has been written, but on why things happen. It explains the relationships among symptoms, root causes, and outcomes, updating its beliefs as new evidence emerges. The result is actionable, auditable guidance that improves over time.
Retrieval gives access to knowledge; reasoning gives direction. Together, they form a complete system: generative AI to organize the world’s information, and causal AI to turn it into structured, explainable troubleshooting.
From conversation to the optimal path
AI adoption in service operations is accelerating, but expectations are evolving just as fast. Service organizations expect to load all their historical data into conversational AI and support technicians through chatbots or AI agents, but that will only take them so far on the journey towards field service excellence. For complex repairs on critical equipment, they need AI that can diagnose faults, guide repairs, and optimize the system simultaneously. Many current systems fall short because they were designed for conversation and not causation.
The future of service AI isn’t about choosing one technology over another, but about combining them intelligently. Generative models can read, ingest, and organize data from manuals and case histories, while causal reasoning determines why something is wrong, what to check next, and how to resolve it in the most efficient, lowest-cost way.
This is a shift from conversation to precision and from generating responses to reasoning through solutions.
P.S. At Dezide, we’ve built guided troubleshooting systems on these causal principles for more than 25 years. We embrace new AI technologies as they evolve, and we continue to believe that one truth remains: service excellence depends on reasoning that can be explained, audited, and trusted.
About Dezide
We have 25 years of experience helping businesses of all sizes capture, organize, and optimize expert knowledge using Causal AI. Our clients range from the world’s largest enterprises in the wind industry, mining sector, and air compressors to consumer printing and telecom.
Get in touch and see why they trust Dezide to build brilliant knowledge bases powering the world’s best service organizations.
