Audience Opinion Piece

AI has become a transformative force across many industries, yet manufacturers seem to face unique challenges in adopting this technology. While the promise of artificial intelligence is well-known—streamlined operations, predictive maintenance, enhanced quality control—many manufacturers are hitting significant roadblocks. Why? It all comes down to data.

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The Core Challenge: Lack of Reliable, Contextual Data

AI, as powerful as it is, relies heavily on one thing: data. Not just any data but reliable, contextualized data from a multitude of sources. For AI to deliver actionable insights, it must be able to pull accurate, structured information from various systems such as:
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• Enterprise Resource Planning (ERP)

• Manufacturing Execution Systems (MES)

• Manufacturing Operations Management (MOM)

• Supply Chain Management (SCM)
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Unfortunately, many manufacturers struggle with siloed, inconsistent, or incomplete data across these platforms. Without a robust data foundation, AI simply cannot operate effectively. The insights derived from AI will always be limited, inaccurate, or unusable if the data it pulls from is unreliable.

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Laying the Foundation with Industrial Data Operations (Data Ops)

The solution to this challenge starts with a focus on Industrial Data Operations (Data Ops). This approach allows manufacturers to aggregate, standardize, and contextualize data from all the different systems across the business. With Data Ops, you can:
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• Consolidate data from ERP, MES, SCM, MOM, and other systems into a single, unified pipeline.

• Ensure that data is accurate, accessible, and available in real-time.

• Create a seamless data flow that AI models can work with to derive meaningful insights.
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Industrial Data Ops provides the structure needed to integrate and manage data effectively, allowing manufacturers to feed AI models with reliable information. This is the critical step manufacturers need to take to ensure AI can deliver on its potential.

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The Importance of Data Orchestration

Even with AI capabilities, without proper data orchestration, AI will not succeed. Data orchestration is the process of ensuring that the data AI uses is properly connected, contextualized, and continuously updated across the entire production environment.

Manufacturers need to ensure that their AI systems are pulling the right information at the right time. This involves managing the flow of data across systems, automating updates, and ensuring that any data input into AI models is accurate and reliable.

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AI Won’t Work Without Foundational Data Orchestration

Many manufacturers are eager to jump into AI without understanding that it all begins with laying the right foundation. Without organized, contextualized data, AI tools will fall short of expectations. Manufacturers must prioritize establishing this data foundation before they are able to adopt AI in a meaningful way.

Building this foundation through Industrial Data Ops and effective data orchestration will ensure that AI is properly equipped to provide real-time insights, predict maintenance needs, enhance product quality, and drive efficiency across the board.

The journey to AI-driven manufacturing is not just about adopting new technologies, it’s about preparing the infrastructure for those technologies to thrive. Reliable, contextualized data is the key to making AI work, and manufacturers who focus on building this foundation will see the greatest benefits from their AI investments.

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