Is Your Operation’s Data Actually Ready for AI?
There is no shortage of AI, machine learning, and digital twin solutions on the market today. The capabilities are impressive, and in the right environment, the value can be significant.
I believe in the potential of these tools.
But most advanced analytics platforms assume something that many industrial operations do not yet have:
A strong data foundation.
AI models do not struggle with volume. They are designed to process massive streams of data. What they struggle with is poor quality, inconsistent structure, and lack of context.
And in many plants, the underlying reality still looks something like this:
- Incomplete or inconsistent datasets
- Faulty, drifting, or uncalibrated instrumentation
- Manually entered data stored in free-text comments
- Vague or generic reason codes that obscure root causes
When this type of data feeds an AI model, the output is not insight. It is often highly confident but misleading conclusions. Advanced algorithms can detect patterns—but they cannot distinguish between meaningful signal and poorly structured noise without proper foundations.
The Real Prerequisites for Advanced Analytics
Before advanced analytics can add meaningful value, the fundamentals must be in place.
1. Structured Data
Critical information cannot live exclusively in comment fields or operator notes. Key events, causes, and classifications must be structured in a consistent and queryable format.
2. Standardized Definitions
If different shifts or departments calculate Availability, downtime, or utilization differently, the model will inherit those inconsistencies. AI cannot reconcile conflicting definitions that humans have not aligned.
3. Contextualized Events
Knowing what happened is rarely enough. Effective analytics require understanding why it happened and how events relate to process boundaries, bottlenecks, and operating intent.
4. Trusted Instrumentation
Sensors must reflect physical reality. Poor calibration, intermittent signals, and unreliable tags introduce hidden bias into models that may go undetected.
AI Amplifies Systems — Good or Bad
Advanced tools are force multipliers.
If the underlying system is structured, disciplined, and trusted, AI can accelerate learning and surface insights faster than traditional analysis.
If the underlying system is fragmented or poorly defined, AI will amplify those weaknesses just as efficiently.
The uncomfortable truth is that many operations are not yet ready for AI-driven optimization—not because the technology is immature, but because the data ecosystem is.
Start with the Foundation
Before investing in advanced analytics initiatives, organizations should ask:
- Are our definitions aligned across the business?
- Do we trust our instrumentation?
- Is our data structured and standardized?
- Do we have clear ownership of the decisions this data is meant to support?
When those elements are in place, AI becomes a powerful enabler.
Until then, it risks becoming an expensive layer of complexity built on unstable ground.
Get the foundation right first.
Then let advanced tools do what they are actually good at.
