To build trust in AI, manufacturers need models that are explainable, accountable and auditable. This begins with transparent AI models that reveal how and why predictions are made.
Without this, companies risk losing valuable control and insight into their operations, as XpertRule CEO Akeel Attar explains.
Is it enough for AI to simply deliver accurate results? For many in manufacturing, the answer seems obvious: yes. But after working in AI for over 40 years, I can tell you that accuracy alone isn’t enough. A model can be highly accurate but without understanding its reasoning, the results will lack depth and can lead to misguided decisions.
For AI to be a true asset, production teams need to understand the mechanisms and data behind its outputs. This transparency isn’t just a nice-to-have, it’s key to building trust, fostering continuous improvement and ensuring that AI enhances, rather than replaces, human expertise.
With a firm grasp of how an AI model reaches its conclusions, engineers can validate its decision-making and apply their own expertise to improve its performance. This powerful feedback system achieves two things:
- It creates a trust loop – understand, validate, apply – that aligns AI with operational needs;
- And it helps pinpoint exactly how and where in your operation AI can deliver value.
Why Explainability Matters
Machine learning (ML) is a proven technology that’s been delivering value in manufacturing for decades, particularly for tasks like condition monitoring and process optimization. However, a common misconception is that accuracy is its most crucial aspect. In reality, it’s the why behind a prediction that truly counts.
Imagine an ML model that detects impending equipment failures or process bottlenecks. Without insight into the underlying cause – be it vibration patterns, temperature spikes, friction or some other factor – engineers are left guessing, unable to take targeted action. To address the root cause, they need visibility into the specific variables and how they interact.
This context allows teams to make corrective adjustments, optimize processes and intervene proactively, shifting from reactive to predictive fault-finding and quality control. Without transparency and understanding cause-and-effect relationships within the data, even accurate models can lead engineers down a path of trial and error.
As well as driving root cause analysis, explainable AI also enables engineers to fine-tune models for their unique production environment. This makes systems more adaptable and resilient and opens up even greater opportunities for continuous improvement.
How Causation Drives Process Improvements
The choice between “this might fail” and “this might fail due to X, Y and Z” can mean the difference between fast corrective action and costly downtime. Yet, many overlook the fact that ML models typically only reveal associations between inputs and outputs, not causations.
This is where human expertise becomes essential. Explainable AI models allow engineers to apply their process knowledge to infer causation from the patterns AI reveals, enabling them to intervene before issues escalate.
For example, XpertRule helped a manufacturer successfully integrate an AI-powered predictive maintenance system to monitor airflow in grinding operations and detect anomalies. One day, the system flagged an airflow drop below a specified threshold. Armed with their deep understanding of the process and an explainable AI model, engineers identified the issue was due to a blocked air nozzle – despite the model not gathering that particular data point.
This example highlights the strength of human insight combined with explainable AI: engineers used AI-driven predictions to detect an issue, validate it and take preventative action. In the use-case above, engineers were able to avoid the cost and inconvenience of machine downtime by addressing the cause – a blocked nozzle.
When engineers can contextualize AI predictions with real-world causes, it creates a collaborative environment where human insights and machine intelligence work in harmony. Engineers can act on predictions with confidence, bridging the gap between AI’s output and real-world outcomes, creating a more efficient, robust operation.
The Pitfall of Overvaluing Accuracy
Relying on accuracy alone makes it easy for decision-makers to overlook a critical truth – if the data feeding your AI model is flawed, incomplete or low-quality, your results will be too. It’s the classic “garbage in, garbage out” scenario.
As AI becomes more advanced, manufacturers need to recognize that high accuracy on paper doesn’t guarantee reliability – especially if the model’s inner workings remain a mystery. For AI to drive genuine improvement in manufacturing, every step of the ML process must be transparent, controlled and explainable.
AI is often described as a “black box,” and this is particularly problematic in manufacturing, where traceability, regulatory compliance and safety are vital. If engineers can’t interpret or retrace how an algorithm arrived at a specific result, it introduces a significant risk factor.
Many regulatory bodies require companies to not only follow strict protocols but also demonstrate a clear understanding of why specific decisions were made; a task that becomes impossible without understanding how an AI model functions. When engineers comprehend the model’s workings, they’re better able to troubleshoot, validate and make informed decisions that meet compliance and keep operations on track.
Explainable AI Will Accelerate Adoption
Manufacturers face an important choice in how they integrate AI. Explainable AI models ensure that companies don’t just take the model’s output at face value. Instead, they allow human expertise to intersect with data-driven predictions, making AI a trusted partner rather than a mysterious black box.
By prioritizing understandability, AI becomes a force multiplier, helping engineers do what they do best – monitor, improve and innovate. Instead of relying on superficial insights, teams can use AI in combination with their own expertise to optimize operations, address root causes and drive continuous improvement, ensuring equipment and processes remain reliable and compliant.
Explainable AI models empower companies to not only harness the technology’s full capabilities, they also drive the development of AI systems based on trust and transparency. For manufacturers, this commitment lays the foundation for a responsible approach to AI deployment – one that emphasizes efficiency, compliance and preventing reputational and financial risk.
When engineers and decision-makers trust the outputs, AI will move from pilot projects to become an integral part of operations, driving greater efficiency and sustained competitive advantage.
This article is part of our new series, Reality Check: What AI Really Means for Manufacturing, designed to inform, inspire and help you implement AI in your manufacturing operation.
Look out for our upcoming article where XpertRule’s Akeel Attar and Iain Crosley will explain what Responsible AI is, why it needs to be more than a tick-box exercise and what companies can do today to prepare for global AI regulations.