In theory, AI should have transformed manufacturing by now. From predictive maintenance and fatigue detection to real-time quality control, the promise has always been smarter, faster, and safer operations. But in practice, the factory floor is still a place where AI ambitions often run into real-world limitations.
That’s a huge problem, especially because the size and weight of this industry are hard to ignore. U.S. manufacturing alone contributes $2.9 trillion to the economy, accounting for over 10% of total output and supporting nearly 13 million workers, according to the National Association of Manufacturers. Globally, manufacturing represents 16% of world GDP and a total market value well over $16 trillion, per a new report from Cargoson.
Now, as AI advances even further and policymakers push for reindustrialization in the U.S.—aiming to restore domestic production capacity, regain supply chain control, and modernize strategic infrastructure—the spotlight is back on factories. There’s momentum and money behind the movement, but without restructuring the fragmented digital systems that dominate most production floors, that momentum may stall. An estimate by MarketsandMarkets projects the global AI in manufacturing market would grow to $155 billion by 2030, up from $34 billion in 2025 — but that growth will remain theoretical unless companies solve the bottlenecks slowing down adoption.
