Dhanush Bakthavatchalam is the Founder of xLogic Labs, a company building fully automated American factories for metal fabrication.gettyFor decades, American manufacturers have tried to compete with lower-cost regions by retrofitting existing factories with robots to make them more efficient. However, a production system designed around human adaptability can’t become autonomous one robot at a time. It remains constrained by scarce skilled labor and its original design.The production system and go-to-market strategy must be redesigned together around parts, processes and customers suited to fully automated production rather than forcing robots into workflows designed around human hands.We’ve seen this transition before. Computer numerical control (CNC) began as individual machines inside manual shops but eventually changed how parts were designed, programmed and produced. Manufacturers built around its capabilities, customers expected its economics and consistency, and CNC became the default for machining. Today, thousands of job shops worldwide operate fleets of CNC machines across nearly every major industry.The same transition can now happen across manufacturing. AI, robotics and software can turn high-mix production into a programmable process—but only if we design the factory, product and go-to-market strategy around autonomy from the beginning.Designing The Factory, The Robot And The Process TogetherConsider metal fabrication as an example. Fabricated metal parts make it possible for us to build bridges, buildings, ships, vehicles, trains, oil and gas rigs, data centers, energy infrastructure, industrial equipment and nearly every machine around us.Ideally, the cost of producing these parts should converge toward the cost of raw metal, energy and consumables. Today, it’s often many times higher. The difference includes recovering equipment investment along with setup, programming, handling, cutting, forming, welding, finishing, inspection and idle time between each step.Closing this gap requires designing the entire production system around autonomy. Unlike CNC machines, however, robotics-first manufacturing has no obvious form factor. The right system might be an arm, gantry, mobile platform, purpose-built machine or several coordinated robots. That makes deciding what to build harder, but it also creates the opportunity to design a production system uniquely optimized for one process, becoming extremely good at that one process and selling that capability to a customer. When it comes to manufacturing, one can specialize in so many areas. There’s so much room for everyone to win.Manufacturers serious about autonomy should follow four principles. Here’s an explanation considering robotic deburring as an example:1. Control the environment. Standardize how materials arrive, parts are positioned and quality is checked. In a robotic deburring cell, one robot might perform deburring while supporting systems load parts, change tools, inspect the finish and move parts downstream.2. Choose automation-compatible work. Select a process, part range and customers suited to autonomous production. A manufacturer doesn’t need to automate everything.3. Integrate around the process. Treat the robot, tooling, controls, vision, material handling and software as one system. The individual deburring robot is only one component. The complete autonomous cell is the factory, and the factory is the product.4. Build modularly. Once the process is reliable, encode what works, standardize the system and replicate it across factories. Reuse the same hardware and software across cells.This is how autonomous manufacturing scales—choose one process, learn what works exceptionally well, reproduce that advantage everywhere and sell the advantage.Specializing First, Then Selling CapacitySpecialization means designing the robotic system around one process and a defined range of materials, geometries and tolerances. Within that envelope, AI and software can enable high-mix production without turning every new part into another custom integration project. One doesn’t need to automate every process at once. One can start by making one process highly autonomous and then expand to the next.Many robotics startups I respect are already working on programming, perception, simulation, orchestration and process planning for automated manufacturing. Manufacturers don’t need to build the entire software stack themselves. The harder challenge may be unlearning traditional factory operations and redesigning products, workflows and customer relationships for autonomous production.Once a specialized robotic factory can handle enough variation within its process, its capacity becomes useful across multiple products and customers.GPUs offer a useful analogy. A GPU isn’t equally valuable for every workload, but it provides an enormous advantage for the right ones. Customers buy GPU-hours without owning or understanding the underlying hardware.A high-mix robotic factory can work the same way. A factory exceptional at autonomous deburring could sell deburring capability to customers whose parts fit its operating envelope. Not every customer will benefit equally, and that’s the point. The factory should focus on customers for whom its automation creates a decisive advantage.Pooling customers’ demand for one process increases utilization, spreads capital recovery across more productive hours and lowers the conversion cost of every part. The factory no longer merely sells parts or machines. It sells access to a specialized production capability that improves with every hour it operates.When Labor Matters Less, Proximity WinsAutomation won’t erase the manufacturing strength of China or other industrial economies. It can, however, shrink the importance of wage differences in the final cost of a product.As conversion costs fall, freight, inventory, lead time and coordination matter more. Raw sheet is dense and efficient to transport; finished assemblies are often bulky and expensive to move.If a robotic American factory can approach the conversion cost of an overseas factory, proximity wins.The Flywheel To Industrial AbundanceThe long-term opportunity is a compounding system: Automation creates more capacity. More capacity enables more production. More production generates more operating data. Better data improves the automation, making the next unit of capacity cheaper.This is the industrial abundance flywheel. Its success should be measured not by how many workers a robot replaces but by how dramatically it expands output per dollar of capital while improving quality and flexibility.America can manufacture competitively again by designing production around the advantages it has now and turning industrial capacity into abundant, programmable infrastructure.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. 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