AI Explainability and Adoption in Manufacturing
7 min readIn brief
- AI algorithms can deliver exceptional accuracy and performance across many manufacturing tasks, but their inner workings are often hard for non-technical stakeholders to understand.
- This lack of transparency can make AI seem unreliable, slowing adoption and keeping companies reliant on traditional methods, like classical computer vision.
- By using explainable AI (XAI) techniques, technical teams can make models understandable, helping business stakeholders see their value and support adoption.
If you’ve ever tried to advocate for an AI project, you’ve probably run into the AI explainability problem. Many algorithms deliver impressive results, but for non-technical stakeholders, they feel like a black box. Decision-makers see the results but don’t understand how predictions are made, making it difficult to justify investment or approve deployment.
So, what is AI explainability? In simple terms, it refers to methods that make AI models transparent and interpretable. Explainable AI (XAI) translates complex model behavior into clear insights, helping teams understand why a model produces a certain outcome.
This clarity is especially critical in manufacturing, where companies are adopting Industry 4.0 practices, relying on data-driven systems to improve quality, reduce errors, and optimize production. Initiatives like predictive maintenance, automated defect detection, and adaptive quality control can only succeed when all stakeholders, not just engineers, trust the technology behind them. So how does XAI solve the explainability challenge in manufacturing? This article walks you through it.
classical methods vs. ai: understanding the tradeoffs
In manufacturing, companies often face a choice between classical algorithms and AI-based solutions. And this choice is at the heart of the explainability challenge.
Classical algorithms use fixed rules or statistical thresholds. Examples include:
- A computer vision system that flags defects by comparing each product to a predefined template.
- A statistical quality control model that identifies measurements outside established limits.
These approaches are easy to understand and reliable, so stakeholders can quickly see how decisions are made. However, their performance is limited: they struggle with complex products, subtle defects, or variable production environments.
AI algorithms, like deep learning models, learn patterns from large datasets. Examples include:
- A defect classification system that adapts to multiple product types without requiring custom rules.
- A predictive maintenance model that forecasts equipment failures using sensor data.
AI models often deliver higher accuracy and scalability, but their decision-making is less obvious. Without transparency, stakeholders may hesitate to approve AI projects, even when they outperform classical solutions.
THE EXPLAINABILITY GAP IN AI ADOPTION
Despite AI’s promise, many manufacturers face a significant explainability gap that slows down AI adoption in manufacturing. Managers, quality supervisors, and operations leads often worry about model reliability, hidden biases, or unpredictable behavior. They may question whether AI will generalize across production lines, handle rare cases, or introduce errors that are difficult to detect.
These concerns create real barriers. Projects may struggle to secure funding, deployment may be delayed, and confidence in AI initiatives can falter.
This is where AI explainability techniques play a critical role. By making models transparent and interpretable, engineers can clearly demonstrate why predictions are made. When stakeholders understand the logic behind AI outputs, confidence grows, approvals come faster, and projects can achieve their full potential in manufacturing environments.
CASE STUDY: HOW A BLACK BOX HINDERED AI ADOPTION
To illustrate, consider a real case from a large industrial company. The engineering team had long relied on classical algorithms to detect production defects. While these systems were reliable, their accuracy was limited. The team wanted to improve detection across multiple product types and turned to AI.
Their initial AI model improved classification accuracy by 12%, but stakeholders remained skeptical. The model performed well on test data but failed to generalize to new production datasets. Because the engineers could not explain how predictions were made, stakeholders blocked adoption.
This case highlights a key point: even highly accurate AI can be rejected if it’s perceived as a black box. Explainability is not just a technical feature; it is essential for securing trust, funding, and adoption in manufacturing.
MAKING AI TRANSPARENT: CORE TECHNIQUES FOR MANUFACTURING
To solve this, we applied several AI explainability techniques to make the model transparent and interpretable. These techniques allow engineers to “open the black box” and show exactly how decisions are made.
Class Activation Maps (CAM)
CAM generates heatmaps showing which parts of an image the AI model focuses on when making predictions. This helps verify whether the model is attending to the right features or being distracted by irrelevant details.
SHapley Additive exPlanations (SHAP) Values
SHAP provides a visual representation of the factors that most influence the AI algorithm’s decisions. By quantifying the contribution of each feature to a prediction, SHAP makes it easier to explain why the model made a particular choice.
Another popular technique in the XAI toolkit is LIME (Local Interpretable Model-Agnostic Explanations). LIME works by approximating complex models with simpler, interpretable ones locally, meaning it explains individual predictions rather than the model as a whole. While we didn’t use LIME in this project, it’s a valuable option for teams looking to make their models more understandable.
After applying CAM and SHAP, we discovered that the existing model had a critical flaw: it was focusing on the wrong parts of the images, learning from irrelevant features. With this insight, our teams were able to redesign the AI model to produce interpretable and correct results.
The improved model achieved a 10% increase in accuracy compared to classical methods, and, importantly, engineers could now explain its predictions across all datasets. With clear visibility into how the model worked, non-technical stakeholders felt confident in the results and approved the project, enabling the AI system to be fully adopted.
BREAKING DOWN BIAS WITH XAI
Explainable AI makes AI models transparent and understandable. Using visual tools and quantitative XAI metrics, engineers can clearly communicate how a model makes decisions and why its predictions are reliable.
This approach brings concrete benefits:
- Faster stakeholder approval: With clear insights, decision-makers can confidently greenlight projects without lengthy debates.
- Improved project ROI: Transparent models reduce costly errors and iterations, ensuring AI delivers measurable value.
- Scalable adoption: Once stakeholders see how explainable models work, AI can be more easily deployed across other production lines and industrial processes.
Scalable adoption: Once stakeholders see how explainable models work, AI can be more easily deployed across other production lines and industrial processes.
If you’re ready to implement XAI in your own processes, SoftServe can help. Our experts work with your teams to apply explainable AI in your industry, validate models, and ensure successful adoption across your operations.