🚀 Industrial AI: How to Build Trust?

[Erkan Teskancan]

Industrial artificial intelligence (AI) enables us to detect quality deviations and equipment problems in manufacturing more quickly. According to Bain & Company, this field will create $70 billion in new value by 2030. However, to fully utilize this potential, engineers need to trust the data provided by AI and act before problems escalate.

🤔 Why is Trust So Important?​

Unlike enterprise AI, industrial AI drives decisions that affect physical processes. Every recommendation can impact worker safety, product quality, and equipment performance. Therefore, it must undergo a much stricter review before any action is taken.

To meet this standard, AI recommendations must be transparent, auditable, and based on operational evidence that engineers can understand and defend. Only then can AI support proactive decisions; otherwise, it will be nothing more than another tool for reactive investigations.

⚙️ Industrial AI Must First Earn the Trust of Engineers​

In the past, manufacturers relied on first-principle engineering and statistical methods because they were transparent, repeatable, and physics-based. Engineers trust these methods because they can explain how each result was reached, rather than simply accepting the answer.

AI has struggled to earn the same trust. Without proper governance and an engineering foundation, AI can appear opaque; it can produce an answer without showing how it arrived at it. And because engineers are ultimately responsible for the outcome, they may prefer to verify the work themselves or not use the technology at all.

In regulated industries like pharmaceutical manufacturing, the stakes are even higher. Production environments are strictly validated, and operational decisions are subject to regulatory scrutiny. If AI helps investigate a quality deviation or supports a batch release decision, manufacturers must demonstrate exactly how that recommendation was generated using reliable operational data. Without this level of explainability, AI remains outside critical decision-making processes.

Therefore, AI governance is not just a compliance exercise. It is what enables adoption. Basing AI on trustworthy analytical techniques and validated operational data makes recommendations understandable and defensible, transforming AI from another source of uncertainty into a practical operational tool.

✅ 3 Steps to Building AI That Supports Proactive Operations​

Trustworthy industrial AI starts with giving engineers the confidence to use it. This trust develops when operational expertise guides how AI is built and deployed.

1. Build AI in an Engineering Context

Industrial AI must reflect how engineers understand equipment, processes, and operational constraints. Manufacturers can do this by grounding AI in first-principle engineering, established statistical techniques, and the practical knowledge of OT subject matter experts.

For example, an AI system monitoring a manufacturing process should account for known operating ranges, equipment relationships, process dependencies, and failure modes. Engineers should help define the operational context so that the system can interpret changing conditions in a way that aligns with how the facility operates.

When engineering expertise shapes the system from the outset, AI fits more naturally into existing workflows and generates insights relevant to operational decisions.

2. Make Every Recommendation Traceable

Engineers need a clear line of sight from every AI recommendation back to the operational data behind it. Traceability gives teams the information they need to evaluate an insight and decide whether action is necessary.

Recommendations should link directly to relevant equipment signals, process variables, historical data, and maintenance records. For example, if AI suggests adjusting a production process, operators should be able to trace the recommendation back to specific temperature trends, pressure readings, flow rates, or historical operating conditions. Engineers can then verify the analysis themselves without having to recreate it.

This transparency helps teams act earlier and with greater confidence. As a result, AI can support earlier intervention and prevent emerging issues from escalating into operational disruptions.

3. Shift AI Governance from Control to Empowerment

Many manufacturers still approach AI governance with the question of what software OT teams should be allowed to use. A more useful question is how AI can empower OT teams to solve operational problems.

This starts with connected operational data. Maintenance systems, historical data, control systems, and time-series platforms become more valuable when robust APIs allow reliable data to flow into AI workflows. Instead of being gatekeepers, IT teams should focus on creating an environment where OT teams have secure access to the data, systems, and tools they need to build purpose-built AI workflows.

🌟 Trust Creates AI Value​

The greatest gains occur when industrial AI accelerates decision-making while naturally fitting into how engineers already investigate problems.

It's not just about increasing automation, but about eliminating hesitation, which transforms AI into operational value. If engineers can trace, verify, and defend AI-driven recommendations, they can confidently implement AI and solve problems earlier.

 

[BuinsoftTech]

A very pertinent topic. Building trust in AI systems in industrial environments is truly critical. In our experience (we integrate AI for B2B processes on buinsoft.com), the biggest obstacle is the "black box" perception. Clearly showing what goes into and what comes out of the system, i.e., explainability, significantly increases the adoption rate. Additionally, starting with small, repeatable, and measurable business processes at the beginning builds a trust environment much faster. The major turning point is usually the first 3-month pilot period.