Before the AI Magic: Why Manufacturing’s Biggest GenAI Problem Is Still Data

Before the AI Magic: Why Manufacturing’s Biggest GenAI Problem Is Still Data

Generative AI promises a revolution on the shop floor—faster decision-making, adaptive production, smarter quality control. But ask any manufacturing leader what’s actually slowing things down, and you won’t hear about algorithms. You’ll hear about data.

At IIoT World’s Manufacturing Day, the conversation around GenAI quickly turned from excitement to infrastructure. The verdict? Manufacturers aren’t short on ambition. They’re short on accessible, structured, contextualized data.

Data Scarcity in a Sea of Machines

Despite being surrounded by sensors, machines, and control systems, most manufacturers don’t have the kind of clean, labeled, or connected data required for GenAI to do its job. In many plants, critical process data is trapped in legacy systems, spreadsheets, paper logs—or not captured at all. Without integration, AI can’t deliver anything meaningful.

Industrial-Grade AI Starts with Lineage

In high-stakes environments, explainability matters. A black-box model that can’t trace its recommendation back to source data is dead on arrival. That’s why vendors and manufacturers alike are focusing on lineage and governance: knowing where data comes from, how it’s transformed, and whether it can be trusted.

The Real AI Bottleneck: Format and Context

Not all data is created equal. Even when it’s available, manufacturing data often lacks context. A sensor might detect a vibration spike—but without knowing what was running, who was operating, or what material was in use, it’s just noise. Successful GenAI use cases rely on stitching together multiple sources—ERP, MES, IoT, manuals—into a coherent, usable narrative.

Collaboration Beats Proprietary Silos

If data is the new oil, many companies are still hiding barrels in different sheds. A key takeaway: manufacturers must shift from a “protect at all costs” mindset to a “collaborate for value” one. This doesn’t mean open-sourcing IP. It means building internal data platforms and trusted partnerships that allow secure sharing and model training on proprietary but siloed information.

Start with the Plumbing, Not the Promises

The best GenAI strategy in manufacturing doesn’t start with pilots. It starts with plumbing: data accessibility, standardization, and secure infrastructure. The companies winning with AI are the ones that treat data readiness as core infrastructure—not a side project.

Written based on insights from the session “Generative AI in Manufacturing: From Design to Production Optimization,” part of IIoT World Manufacturing Day 2025.

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