The unseen bottleneck holding back American manufacturing is not machines—it is know-how.

(SeaPRwire) –   The most defining technology narrative of this century is how software has reshaped the way we work. We now communicate, exchange information, and run companies in a completely different way than those who came before us in 2000.

But one of the world’s largest industries has largely skipped the software revolution, and still operates, to a striking degree, on human know-how. In manufacturing, the actual bottleneck is rarely the machine on the factory floor: it is the worker running it, who holds years of hard-earned knowledge in their head, gained one job at a time… and whose expertise is nearly impossible to scale up.

In the current U.S. context, where tariffs are once again at the center of industrial policy, this issue matters a great deal. In Washington, almost everyone is suddenly calling for expanded U.S. industrial capacity. But while policy can shift incentives, it cannot build new capability on its own.

This is the key point that too many conversations about reshoring still miss: a factory’s limits are not just physical. They are cognitive.

On top of buildings, machines, and customers, you need experts who know how to actually run operations: price new jobs, program equipment, avoid wasted material, and work around the unique quirks of the factory’s machines (and the people who run them). Too much of this workflow still only exists in one person’s head.

This has always been the core misstep in manufacturing. Factory leaders almost always choose to invest in new machines over software. But this gets the priority backwards. Machines are just overhead; the software that captures knowledge and connects all team members is what determines how well the business performs.

I once watched the CTO of Ocado, the UK online grocery and technology company, approach a terminal in a warehouse that gave him exact step-by-step instructions for his task. He was able to work effectively almost immediately, even though he had never been in that part of the facility before. All of the operational intelligence for the job was already captured by the system.

Today, this is nothing like what happens in manufacturing, where every worker at every factory has their own way of doing things, and all that knowledge is locked in silos. But if we want to meet society’s demand for the goods we need, this has to change.

The solution to this problem is AI. But for manufacturing, we don’t need a tool that just helps us communicate better or create more content; AI has to become a foundational building block that supports the entire industry.

An AI Tailored for Purpose

Factories need systems built around the real constraints of tools, machines, materials, tolerances, and physics. In manufacturing, wrong decisions have physical consequences that break costly machines and stop production entirely.

So, an AI that is actually useful in this context will be domain-specific, reliable, and built into existing real-world workflows. At my company, CloudNC, we have built an AI that thinks like an experienced machinist, and it already speeds up CNC machine programming at hundreds of factories across the U.S. This was not an overnight success — it took 10 years of development (so far) and may never be perfect or fully finished — but it proves that expert human judgment can be turned into software.

Once this happens, the results are not just theoretical. The hidden drag on manufacturing today is waiting for the one person who knows how to do the job, create a toolpath, or make a key decision. AI can free senior programmers from repetitive work so they can oversee more work, or give less experienced workers a strong starting point instead of a blank screen. When done correctly, using domain-specific AI makes best practices become more consistent, and the scarcity of expert knowledge stops being the bottleneck.

This is why AI matters far more to reshoring and defense than most people realize. If the U.S. wants a stronger industrial base, it will take more than tariffs, subsidies, or new factory buildings. It will need factories that can handle more complexity and produce more output with the skilled workers they already have. This is especially true for defense, where the conversation has shifted decisively toward manufacturability, affordability, and speed.

This transformative shift does not stop at CAM, or at any single workflow. The same pattern will play out across quoting, process planning, scheduling, setup, inspection, and the dozens of operational decisions that still live in unwritten tribal knowledge. Over time, AI becomes not just a tool inside the factory, but the core framework for how the factory is run.

That is when manufacturing starts to fundamentally change. Equipment uptime rises, lead times shrink, costs fall, and more production moves closer to where it is needed. And the pace of hardware innovation finally starts to creep closer to the pace of innovation in software.

Every industrial revolution starts the same way: first as a competitive advantage, then as a mandatory requirement. AI in manufacturing will follow the same path. It is currently an enabling technology, but it will quickly become a required one.

The factories of the future will still need skilled machinists, engineers, and operators. But they will no longer be limited by how much critical knowledge can fit inside the heads of just a few people. And once this shift takes hold, AI will stop looking like an optional tool for manufacturing: it will look like core infrastructure.