Industrial artificial intelligence (AI) helps manufacturing companies identify and prevent problems at early stages. However, the effectiveness of such systems directly depends on engineers’ trust in AI recommendations and their ability to verify them.

Economic Potential and Implementation Conditions

By 2030, industrial AI is expected to generate an additional $70 billion in value. For these technologies to be successfully adopted, engineers must trust AI conclusions and be able to justify them. AI systems should be developed with engineering expertise and operational context in mind, while also ensuring transparency, traceability, and reliable data management.

Features of Industrial AI

Unlike corporate AI systems, industrial AI influences decisions related to physical processes. Each recommendation can impact personnel safety, product quality, and equipment performance, so thorough validation is required before acting on such advice.

Transparency and Explainability

To meet high standards, AI recommendations must be transparent, auditable, and based on operational data that engineers can understand and justify. Only then can AI support proactive decision-making, rather than being used solely for reactive investigations.

Historical Approach and Trust Challenges

Traditionally, manufacturers have relied on engineering methods and statistics because they are transparent, reproducible, and grounded in physical laws. Engineers trust these approaches as they can explain every conclusion. AI has not yet reached this level of trust: without proper management and an engineering foundation, it may seem opaque, providing results without explaining their origins. As a result, engineers often prefer to independently verify system performance or avoid using it altogether.

Requirements in Regulated Industries

In highly regulated sectors, such as pharmaceuticals, the manufacturing environment is strictly validated, and operational decisions are subject to regulatory review. If AI is involved in quality investigations or supports product release decisions, manufacturers must demonstrate how recommendations were formed based on reliable data. Without this level of explainability, AI cannot be used for critical decisions.

Integrating Engineering Expertise

Trust in industrial AI is built when operational expertise shapes how the system is developed and implemented. AI should reflect engineers’ understanding of equipment, processes, and constraints. To achieve this, systems are built on engineering principles, proven statistical methods, and the practical knowledge of operational technology specialists.

For example, an AI system managing a production process must account for operating ranges, equipment interconnections, process dependencies, and failure modes. Engineers define the operational context so the system can correctly interpret changing conditions.

Traceability and Data Linkage

Engineers need a clear connection between each AI recommendation and the operational data it is based on. Traceability allows teams to evaluate conclusions and decide on the appropriateness of actions. Recommendations should be directly linked to equipment signals, process variables, historical data, and maintenance records. If AI suggests a process adjustment, operators must be able to trace the recommendation back to specific parameters, such as temperature trends, pressure readings, or operating conditions.

The Importance of Transparency

Such transparency helps teams act faster and with greater confidence. As a result, AI can support earlier interventions and prevent problems from escalating into operational failures.

Data Management Organization

Implementing AI begins with integrating operational data. Maintenance systems, historical databases, and time-series platforms become more valuable when reliable APIs ensure accurate data flows into AI workflows. IT teams must create an environment where operational technology specialists have secure access to the data, systems, and tools needed to develop specialized AI solutions.

Practical Value of AI

The greatest impact is achieved when industrial AI accelerates decision-making and fits seamlessly into engineers’ established problem-solving processes. Eliminating doubts—not just increasing automation—turns AI into a source of operational value. When engineers can trace, verify, and justify AI recommendations, they confidently adopt these technologies and resolve emerging challenges more quickly.