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Artificial intelligence is rapidly entering industrial environments—from predictive maintenance and operational optimization to intelligent decision support.Across industries such as energy, manufacturing, and infrastructure,organizations are exploring how industrial AI can improve operational performance and unlock value from growing volumes of industrial data.
However,many companies quickly encounter the same challenge.
The issue is not the AI models.
The issue is the data.
For most industrial organizations, the information required for AI already exists—but itis not AI-ready. Critical operational knowledge is often scatteredacross engineering documents, technical reports, equipment manuals, andmaintenance records. While these documents contain valuable information, the data within them is frequently unstructured industrial data that AI systems cannot easily interpret.
AI does notwork effectively on documents alone. It requires structured, reliable, and connected industrial data.
Accordingto McKinsey, 88% of organizations report using AI in at least one business function, yet only approximately one-third have successfully scaled AI acrosstheir operations (McKinsey, State of AI 2025). One of the primary barriers isthe lack of high-quality industrial data required to support AI models.
For industrial companies, enabling AI therefore starts with building a reliable industrial data foundation.
AI-readyindustrial data refers to information that is structured, connected, and reliable enough for AI systems to interpret and analyze.
In industrial environments, this means that data must clearly describe:
When theserelationships exist, AI systems can identify patterns, generate insights, and support operational decisions.
Without this structure, even advanced AI models struggle to interpret industrialinformation.
Researchsuggests that 85% of AI projects fail due to poor data quality or insufficientrelevant data (Gartner). Even highly advanced models cannot produce reliable outcomes when the underlying data lacks consistency, context, orgovernance.
In other words, AI performance depends directly on the quality and structure of the underlying industrial data.
Many industrial companies already possess the information needed to support AI.
The challenge is where that information resides.
Critical knowledge about assets, equipment, and operations is often stored in:
In large industrial projects, thousands of documents may describe equipmentspecifications, system relationships, or operational procedures. While this information is essential for operations and maintenance, it is rarelystructured in a way that AI systems can easily analyze.
For example, equipment identifiers, system relationships, and technical specifications may be embedded within documents rather than stored asstructured data.
This meansthe knowledge exists—but it is difficult to use for analytics, automation, or AI.
As a result, organizations attempting to deploy industrial AI often discover that their data environment is not yet ready to support it.
Sharecat addresses this challenge by helping industrial companies transformdocument-based information into structured industrial data.
Industrial organizations manage vast amounts of information across engineering systems,maintenance platforms, procurement tools, and document management systems. Yetmuch of the most important operational knowledge remains locked insidetechnical documents rather than structured data models.
TheSharecat platform helps companies extract and structure the information contained within these documents, creating a single source of truth for industrial asset data.
This enables organizations to:
Rather than focusing only on AI algorithms, Sharecat focuses on the data layer required for industrial AI to work reliably.
With thisstructured industrial data foundation in place, AI systems can access reliable information and generate insights that support maintenance, operations, andasset management.
Most traditional systems will not deliver industrial data in the structure AI needs.Before deploying AI, industrial organizations should ensure their dataenvironment meets these requirements:
✅ Structured asset data – Assets and equipment described with consistent attributes and identifiers such as tag numbers and functional locations
✅ Connected documentation – Technical documents linked to the correct assets and equipment
✅ Single source of truth – One governed platform where all asset data and documentation is managed
✅ Complete and validated data – Data checked for completeness, accuracy, and consistency before use
✅ Traceable relationships – Clear links between assets, documents, systems, and operational data
Sharecat is built to meet all of these requirements—giving industrial organizations the data foundation they need before AI can deliver real value.
Artificial intelligence has significant potential to transform industrial operations.However, unlocking this value requires more than deploying advanced AI models.
It requires AI-ready industrial data.
For many organizations, the information needed already exists—but it must be structured,connected, and made accessible before AI can use it effectively.
By transforming document-based knowledge into structured industrial data,companies can build the reliable data foundation required for industrial AI.
And withthat foundation in place, AI can move from isolated experimentation to meaningful impact across industrial operations.
