Artificial intelligence is set to transform the manufacturing industry. Predictive maintenance, production optimization, AI assistants for shop floor operators, and advanced quality control systems are just a few examples of applications and use cases that are already being adopted in modern factories.
In this context, many manufacturing companies are focusing their efforts on identifying which AI solution to use, which model to implement, or which technology partner can best support their digital transformation journey.
However, there is a fundamental challenge that determines the success of any industrial AI initiative before any algorithm is deployed: data quality.
Artificial intelligence can analyze millions of records in seconds, but it cannot determine on its own whether that data is complete, consistent, accurate, or truly reflects what is happening on the production floor.
The issue, therefore, is not simply having access to large volumes of manufacturing data. The real challenge is building a reliable, contextualized, and connected data foundation that accurately represents day-to-day factory operations.
In this article, we explore why the success of many industrial AI projects depends far less on the algorithm itself and far more on the quality of the manufacturing data it relies on. We also examine the critical role that Manufacturing Execution Systems (MES) play in collecting, contextualizing, and preparing production data for successful AI implementation.
Why do some industrial AI projects fail?
When manufacturers begin exploring the potential of artificial intelligence in industrial environments, their first focus is often on the technology itself: which AI model to use, which platform to adopt, or which solution will deliver the greatest business value.
However, in many cases, the biggest obstacle is not the AI technology—it’s the quality of the data it receives.
Artificial intelligence learns by identifying patterns. To detect anomalies, predict equipment failures, or recommend operational decisions, AI systems must analyze historical data and uncover relationships between multiple production variables.
The problem arises when that data does not accurately reflect what is happening on the shop floor.
A manufacturing plant may generate thousands of production records every day, but if some of that information is stored in spreadsheets, some resides in disconnected systems, and some depends on manual entries made by different operators, the resulting data picture will be fragmented and incomplete.
Among the most common challenges are:
- Data scattered across multiple disconnected systems.
- Different versions of the same KPI depending on the department accessing it.
- Manual data entry errors and incomplete records.
- Machine data without operational context.
- Lack of end-to-end process traceability.
- Limited workforce readiness, including insufficient training, engagement, or AI adoption skills.
Under these conditions, artificial intelligence can process data, but it cannot necessarily generate actionable insights.
AI does not replace effective data management. On the contrary, it exposes weaknesses in how an organization captures, structures, manages, and uses its manufacturing data.
What data does artificial intelligence need?
One of the most common misconceptions about industrial AI is the assumption that a manufacturing company is ready for AI simply because it generates large volumes of data.
However, data volume and data quality are not the same thing.
A machine may produce millions of data points every year. A production system may store thousands of work orders. An ERP system may contain decades of historical business data.
All of this information has value, but it does not automatically mean it is suitable for artificial intelligence applications.
The real difference lies in whether that data provides the context needed to understand what was actually happening on the shop floor when it was generated.
| Many factories have… | AI actually needs… |
|---|---|
| Millions of records | Reliable data |
| Data scattered across multiple systems | Connected information |
| Isolated machine variables | Operational context |
| Historical information | Traceable data |
| Spreadsheets and manual records | Structured data collection |
| Different KPIs across departments | Standardized criteria |
An isolated data point—such as a temperature reading, cycle time, or production output—may appear useful. However, on its own, it does not explain what is actually happening in the manufacturing process.
A machine temperature reading only becomes meaningful when it is linked to the broader production context: which product was being manufactured, which raw materials were used, which machine settings were applied, which operators were involved, and what the final production outcome was.
The difference between raw data and valuable information lies in the context that surrounds it.
Artificial intelligence does not learn from numbers alone—it learns by identifying relationships between events, processes, and operational conditions.
Context is the key to turning AI into industrial intelligence
In a manufacturing environment, data is valuable not only because of what it represents, but because of the connections it enables across the production process.
An AI system may detect that a machine is behaving differently than usual. However, to determine whether that change indicates a real issue, it must correlate that information with other operational data.
It needs to understand what happened before the event, what happened afterward, and which factors may have influenced the outcome.
For this reason, truly valuable manufacturing data should be connected to information such as:
- The active production order.
- The product being manufactured.
- The raw materials or components used.
- The process parameters and machine settings.
- Quality inspection results and outcomes.
- Recorded production events, deviations, or incidents.
This interconnected data is what enables manufacturers to move beyond simply collecting information and toward a true understanding of the production process.
What role does an MES play in preparing a factory for AI?
A Manufacturing Execution System such as Mapex is not an artificial intelligence platform. Its primary role is to manage, integrate, and connect data across the manufacturing process.
However, this capability makes an MES one of the most important building blocks for organizations looking to adopt AI and move toward a smarter factory.
AI requires reliable, high-quality data—but that data does not become useful on its own. It must be collected, structured, connected, and enriched with the operational context needed for meaningful analysis.
An MES creates this foundation by linking:
- Production orders.
- Raw materials and components.
- Machines and equipment.
- Process parameters and machine settings.
- Quality inspections and test results.
- Actual production times and performance data.
By connecting these data sources, manufacturers move from isolated datasets to a complete, real-time view of shop floor operations.
This connected data foundation enables advanced AI applications, from predictive analytics and process optimization to intelligent manufacturing assistants like Maik that can answer questions using real production data and operational context.
Rather than replacing artificial intelligence, an MES provides one of the essential ingredients AI depends on: a reliable, contextualized, and trusted source of manufacturing data.
To learn more, download our guide on the potential of AI in manufacturing and discover how to invest in AI technologies that deliver measurable business impact.
What questions should you ask before starting an industrial AI project?
Before launching an industrial AI initiative, manufacturers should first evaluate whether their data foundation is ready to support it.
Some key questions include:
- Can we trust the data we use every day to make operational decisions?
- Is the entire organization working from a single, consistent source of truth?
- Can we easily reconstruct everything that happened during a production order?
- Is our manufacturing data contextualized and connected across systems?
- Do we receive information in time to take corrective action?
Answering these questions requires a careful assessment of how data is captured, managed, integrated, and used across the production environment.
For many manufacturers, this evaluation highlights the need for systems that can structure and connect production data. An MES provides exactly that foundation by integrating information from machines, production processes, materials, and manufacturing orders into a unified source of operational knowledge.
Learn more about the role of data in industrial AI
Artificial intelligence has the potential to transform manufacturing, but every successful AI initiative starts with the same requirement: reliable, high-quality data that accurately reflects what is happening on the shop floor.
In this video podcast, Rick Franzosa, Vice President of Research for Manufacturing at Tech-Clarity and former Gartner analyst, discusses why data quality is the foundation of every industrial AI strategy, how operational context impacts AI performance, and the critical role that Manufacturing Execution Systems play in preparing production data for AI-driven manufacturing.
FAQs about industrial AI and data quality
Can a manufacturer implement AI without an MES?
Yes. It is possible to develop industrial AI applications without an MES, particularly if the organization already has well-structured, integrated, and reliable data sources.
However, as manufacturing operations become more complex, having an MES that centralizes production data, ensures end-to-end traceability, and provides operational context makes it significantly easier to build the data foundation required for successful AI implementation.
Why does manufacturing data need context?
Because a standalone data point does not explain what is happening in a production environment. For artificial intelligence to identify meaningful patterns, it must connect process variables with operational information such as production orders, materials, machines, process parameters, and quality outcomes.
Without this context, AI can analyze data but cannot reliably generate actionable manufacturing insights.
How can a manufacturing company prepare for AI adoption?
The first step is to assess the quality of existing manufacturing data by evaluating where it is generated, how it is collected, and whether it accurately reflects real shop floor operations.
From there, manufacturers should establish a more structured approach to data management by connecting processes, equipment, systems, and people. Building this unified data foundation is essential for deploying artificial intelligence that delivers reliable, measurable business value.
