AI in the factory is only as reliable as the data it receives
AI in the factory is never more reliable than the data it receives. This isn't a slogan: it's the concrete reason why most artificial intelligence projects in manufacturing stall before they even touch an algorithm.
Data exists, and in abundance. Modern production lines generate gigabytes of information every day: from sensor readings to CNC machine logs, from MES systems to ERPs, down to quality data and operational metrics. The problem isn't quantity. It's the quality, consistency, and accessibility of that data.
Data fragmented across different machines, collected at irregular intervals, locked in formats that don't talk to each other. On that foundation, even the best AI model stumbles.
When production data is instead collected continuously, normalized, and made accessible to any present and future application, even a relatively simple model produces results you can act on. AI doesn't create value out of nothing: it amplifies what it finds in the data. That's why building a solid data foundation isn't a secondary technical premise — it's the condition that determines whether AI in the factory actually works.
In this article we look at how this foundation is built, through two fundamental levers — data integration and multi-platform interoperability — and what additional elements are needed to make it fully AI-ready.
Why most AI projects get stuck before the algorithm
Ask anyone working in production why an AI project didn't deliver the expected results. The answer rarely concerns the model. The problem is almost always further upstream: the data wasn't ready.
What does "not ready" data mean?
- Unintegrated heterogeneous sources: PLCs, CNCs, sensors, SCADA, MES, ERP speak different protocols and have never been connected into a single coherent flow.
- Discontinuous collection: data is sampled at irregular intervals, with time gaps that make it impossible to build reliable historical series.
- Incompatible formats: every machine or system produces data in proprietary structures, without a shared semantic model.
- Vertical silos: every previous project created an ad hoc integration that can't be reused for new use cases.
- Lack of context: machine data exists, but isn't correlated with the process data (production orders, shifts, batches) that makes it interpretable.
The result is that data scientists spend most of their time collecting, cleaning, and rebuilding data instead of building models.
And when models are trained on inconsistent data, they produce incomplete predictions.
An AI model trained on dirty data doesn't fail visibly — it fails silently. It produces plausible but unreliable output, which erodes trust in the entire project.
The three pillars of an AI-ready data foundation in the factory
Building a data foundation that AI can genuinely work on requires acting on three distinct but interdependent levels: data integration, architectural interoperability, and continuous governance. Let's look at them one by one.
3.1. IoT Data Integration: collecting, normalizing, unifying
IoT Data Integration is the process of cleaning, combining, consolidating, and coordinating data from different sources and systems, with the goal of obtaining complete and contextual sets of information.
In the factory environment, this challenge has specific characteristics: a high number of sources, a variety of languages and data types, significant volumes, high update speed, and the need for correlation with process information.
Typical challenges of integration in OT environments
- Heterogeneous access: legacy machines with proprietary protocols, unintegrated OT and IT systems, difficulty scaling solutions beyond a single plant.
- Temporal consistency: without synchronization, data from different machines can't be reliably correlated — and this correlation is exactly what AI needs.
- The cost of fragmentation: incomplete and unsynchronized data costs data engineers and process engineers time, as they end up collecting data that will never be used.
IoT Data Integration best practices
An effective industrial Data Integration process includes:
- Source mapping: identifying all systems that generate relevant data — machinery, ERP, MES, quality control systems, supply chain management.
- Defining the target data model: deciding which data to integrate and in what semantic structure, before connecting any source.
- Choosing the platform: a platform capable of connecting to different sources, gathering information into a single repository, and offering powerful data normalization and cleaning capabilities.
- Continuous monitoring and maintenance: data consistency isn't a state to be reached once, but an ongoing process of verification, error correction, and updates.
Data pipelines for AI: the main use cases
Once data is integrated and normalized, it can feed specific pipelines for AI:
- Predictive maintenance: collection of sensor data and machine operational parameters, normalization, identification of anomalous patterns, prediction of the optimal moment for preventive maintenance. A simple model on clean data consistently outperforms a complex model on fragmented data.
- Predictive quality: correlation between process parameters (speed, temperature, tool wear) and quality measurements. AI can identify the first signs of degradation before the test gate generates scrap, reducing costs and maximizing yield.
- OEE optimization: real-time analysis of availability, performance, and quality data to identify causes of inefficiency and suggest targeted corrective actions.
3.2. Interoperable, multi-platform architecture: avoiding silos
Even when data integration is well executed, AI projects in the factory can fail for a second reason: the integrated data was built for a single application, in a closed architecture that can't be extended.
This happens when a "quick hit" approach is adopted: a fast solution is built for the most urgent use case, but it creates a vertical silo that then blocks any further evolution. The first integration — for example, production data into the ERP — becomes an obstacle when you want to add AI analytics, predictive models, or new data science tools.
Why interoperability is the real engine of digital industry
Interoperability is the ability to make heterogeneous systems "talk" to each other, exchanging data consistently and without loss of context or semantics.
An interoperable platform allows any present and future application to access machine data without requiring new ad hoc integrations.
The benefits are measurable — companies with high interoperability record:
- Revenue growth of up to 6× compared to companies with low interoperability
- OEE increase of up to +10% with open architectures
- Operational efficiency improved by up to +30%
- Savings of about 2 hours per employee per day, freed from manual data collection and reconciliation activities
Characteristics of an AI-ready architecture
To support AI projects in production, the data platform architecture must guarantee:
- Native multi-protocol connectivity: support for classic fieldbuses, legacy protocols, MQTT, REST APIs, direct integration with databases and cloud environments (AWS, Azure, Cumulocity, SAS Viya, Splunk).
- Standardized and adaptable data models: a coherent semantic model that guarantees the same structure as data moves between MES, quality systems, data science platforms, and AI models.
- Broadcast distribution: every piece of integrated data must be able to be distributed simultaneously to multiple applications, without pipeline duplication or customizations.
- Scalability and ease of deployment: the platform must enable PoCs to be built in days or weeks — not months — and scale without rewriting the architecture.
- Security and compliance: encryption, role-based access control, and adherence to industry standards are indispensable requirements, destined to become increasingly stringent as production data moves from the OT environment to the cloud.
«An open architecture significantly reduces time-to-market for new AI use cases: the data is already available, normalized, and accessible. All that's needed is to connect the new model.»
3.3. Continuous data governance: the often-forgotten factor
The third pillar is also the most often overlooked.
Integration and interoperability solve the problem of data collection and accessibility. Governance solves the problem of their reliability over time.
An AI model trained on good data today can silently degrade if the data changes — due to a replaced machine, a recalibrated sensor, an unrecorded process variation. Without continuous governance, the model drifts from reality without anyone noticing.
Industrial data governance means:
- Real-time data quality monitoring: automatic detection of anomalous values, missing data, process drifts that could invalidate models.
- Data model versioning: traceability of changes to the data structure, to understand when and why an AI model started behaving differently.
- Enriched and updated context: machine data must be continuously correlated with process information — production orders, shifts, batches, maintenance performed — to maintain its interpretive capacity.
- Feedback loop between model and data: the results of AI models must be able to feed back into the data collection process, signaling which variables prove most predictive and which can be eliminated.
A platform that implements intelligent edge computing contributes significantly to governance: by processing and filtering data close to the source, it reduces noise, ensures temporal consistency, and sends to the cloud only the information actually relevant to AI models.
Case study
From data to AI. A concrete example in production.
An industrial vehicle manufacturer began its AI journey in the factory not from an algorithm, but from a data problem.
The CNC machines, some decades old, each communicated differently. Process data existed, but wasn't correlated with quality data. The ERP operated in isolation from the shop floor.
SOLUTION:
Phase 1. Integration and normalization: the first intervention connected the legacy machines to the MES system through an IoT edge platform with over 5,000 ready-to-use machine drivers. Within a few weeks, OEE data, machine availability, cycle times, and scrap became accessible in a consistent, standardized format.
Phase 2. Interoperability for advanced analytics: several months later, the company decided to extend its analytical capabilities. Thanks to the open architecture already in place, connecting an advanced Business Intelligence platform took days, not months — with no redesigns or data migrations. Edge computing added real-time aggregation and correlation.
Phase 3. AI for predictive quality: with the consolidated data foundation, it became possible to train predictive quality models by correlating machine operating parameters (spindle speed, temperature, tool wear) with quality measurements. The model began flagging the first signs of degradation before the test gate generated scrap. Result: reduced scrap and more targeted maintenance.
The difference wasn't in the algorithm. It was in the quality and consistency of the data that algorithm could work on.
Alleantia: the platform that makes your data AI-ready
Alleantia Core is the platform that implements all three pillars described in this article integration, interoperability, and governance support — in a single operational framework.
Its key elements for factory data AI-readiness:
- +5,000 ready-to-use machine drivers: protocol translation and standardized data model for any industrial machinery, from legacy systems to the most modern machines. No custom code for each machine.
- Extended Machine Drivers and semantic models: beyond raw collection, extended drivers support standardized data models (including UMATI guidelines) and virtual variables with mathematical and logical operators to build datasets already ready for AI models.
- Real-time contextual correlation: integrated APIs allow machine data to be enriched with process information (orders, shifts, batches) in real time, without modifications to machine programs or PLCs.
- Broadcast distribution to +20 applications: integrated data can be distributed simultaneously to MES, ERP, BI platforms, data science tools, and AI models without duplicating pipelines or creating new silos.
- Certified cloud connectivity: certified connectors for AWS IoT, Azure IoT Hub, Cumulocity, Splunk, SAS Viya, and others to bring production data where AI models are trained and served.
- Intelligent edge computing: processing and filtering data at the source, to ensure temporal consistency, reduce noise, and send to the cloud only the information actually relevant.
Conclusion: AI starts from the data, not the model
Projects that work start from the foundation: continuous, normalized collection of machine data, an open architecture that avoids vertical silos, governance that keeps data reliable over time.
On this foundation, even relatively simple models produce results you can make decisions on.
Adopting an open, multi-platform platform today means investing in the ability to leverage any present and future AI technology, without having to rebuild the foundation every time.
In other words, freeing yourself from single-vendor lock-in, reducing obsolescence risks, and allowing AI to do what it's meant to do: amplify the value that's already in the data.