Industrial AI starts with trusted operational data, not with the AI
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3 mins
Artificial intelligence is moving rapidly into industrial operations. Production managers, engineers and operators are being asked how AI can improve efficiency, identify deviations and support better operational decisions.
But there is a practical problem that is often overlooked: AI can only work effectively with the operational information available to it. In many process plants, that information is fragmented across historians, ERP systems, LIMS, laboratory systems, spreadsheets and manual operator input.
Before asking what AI can do with your production data, it may therefore be more useful to ask:
Can we trust the production data we already have?
What can AI actually do in a process plant?
There are many potentially valuable applications.
AI can help engineers and operators identify unusual process behaviour, compare current production with similar historical periods, investigate possible causes of deviations, analyse energy or raw material consumption, and retrieve relevant operational information much faster than traditional reporting tools.
It can also provide a more intuitive interface to operational data.
Instead of navigating through multiple reports and systems, an engineer might ask:
Why has energy consumption per tonne increased during the last three shifts?
Or: When have we seen similar process conditions before, and what happened afterwards?
This is an important development. But answering these questions reliably requires more than connecting a language model to a historian.
Industrial data needs context
A process historian may contain millions of measurements. But a measurement alone is not necessarily useful information.
A value of 74.3°C needs context.
What does the signal represent? Which equipment does it belong to? What was being produced at the time? Was the measurement considered valid? Was it subsequently corrected? Was the plant operating normally? What other process conditions should be considered?
The same applies to information coming from SAP, laboratory systems and spreadsheets.
For AI to provide useful operational support, these different sources need to be brought into a consistent operational context.
Reporting is an important step towards AI
Many industrial companies have invested heavily in ERP systems, historians, automation and laboratory systems, yet still depend on spreadsheets for important production reporting.
This is not necessarily because the underlying systems are inadequate. It is often because operational reporting requires data from several systems to be combined, validated, calculated and interpreted.
That makes production reporting more important to an AI strategy than it may initially appear.
If an organisation still spends significant manual effort determining yesterday’s production figures, correcting data and reconciling different sources, adding AI will not automatically solve the underlying problem.
A reliable reporting process establishes something much more valuable: a trusted and structured representation of what actually happened in production.
That becomes the foundation for analytics, and eventually for AI-assisted decisions.
Traceability becomes more important, not less
As AI becomes involved in operational analysis, traceability becomes increasingly important.
Imagine an AI assistant telling a production manager:
Specific energy consumption increased by 6% compared with similar production periods. The largest changes coincide with higher pressure in stage X and longer operation in mode Y.
The next questions are obvious.
Which data was used? Were any values corrected? Which calculations produced the result? What time period was compared? Can an engineer reproduce the analysis?
In industrial operations, a plausible answer is not enough.
The underlying data, calculations and changes need to be traceable. For critical reporting and calculations, organisations may also need to preserve exactly what was known and calculated at a particular point in time.
AI does not remove this requirement. It makes it more important.
AI should support industrial expertise
The most useful role for AI in process industries is unlikely to be replacing operators or process engineers.
It is supporting them.
Experienced operators understand their plants in ways that are difficult to capture in any single system. Engineers understand process relationships, operating constraints and the consequences of changing conditions.
AI can complement that expertise by analysing more historical information, identifying patterns across large datasets and making operational knowledge easier to access.
The engineer remains responsible for understanding the process and deciding what action to take.
This combination, industrial expertise supported by trusted data and AI, is considerably more interesting than autonomous AI making operational decisions in isolation.
A practical path towards Industrial AI
For many process manufacturers, the journey towards useful Industrial AI is therefore evolutionary rather than revolutionary.
A practical sequence is: Trusted operational data → Real-time reporting → Operational analytics → AI-assisted decisions
Each stage creates value on its own.
Trusted data improves confidence in production information. Automated reporting reduces manual work and improves consistency. Operational analytics helps engineers understand performance and deviations. AI can then make that information easier to explore and use in daily decision-making.
The important point is that organisations do not need to wait for a large AI programme to begin.
They can start by improving the operational data foundation they already depend on.
And when AI is introduced, it has something reliable to work with.
About Mikon
Mikon provides industrial data platforms for production reporting, operational analytics and decision support in process industries. The platform integrates operational information from sources such as historians, ERP, laboratory systems and spreadsheets, creating a trusted operational data foundation for reporting, analytics and AI-assisted decisions.