98% of manufacturers are exploring AI-driven automation. Only 20% feel prepared to deploy it at scale.
That’s not a technology problem. That’s an implementation problem. And it’s the biggest opportunity in industrial AI right now.
The global AI-in-manufacturing market hit $34.18 billion in 2025 and is growing at 35.3% CAGR. LLM adoption in manufacturing more than doubled to 35% in the past year alone. Seven in ten manufacturers have automated less than half their core operations.
But here’s what the reports don’t say: most of that AI spend is going to production-floor automation — robots, vision systems, predictive maintenance. The engineering and compliance workflows that eat 40% of a process engineer’s time? Still manual. Still Excel. Still three-ring binders.
PHA documentation. RAGAGEP gap analysis. Engineering standards cross-referencing. MOC tracking. These aren’t glamorous AI use cases. They don’t make the keynote at CES. But they’re where the hours are — and where AI delivers measurable ROI in weeks, not years.
The 78% of manufacturers who aren’t ready for AI at scale aren’t blocked by technology. They’re blocked by the gap between a general-purpose AI platform and a tool that actually understands their regulatory environment, their standards library, and their audit cycle.
Domain-specific AI for heavy industry isn’t a niche. It’s the gap between 20% and 98%.
What’s the most time-consuming manual process in your engineering workflow that hasn’t been touched by automation yet?
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