What CNC Taught Us About Adopting AI in Manufacturing

Back in the late 1970s and early ’80s, the decision by Modern Machine Shop’s editorial staff to shift much of its coverage to CNC was not without risk. Even though numerical control had been developed in the 1950s, much of the industry — particularly small- and mid-sized shops — still relied on manual mills and analog methods decades later. As explained to me by former MMS editor-in-chief Tom Beard, turning our editorial focus away from manual and analog processes toward CNC was viewed by some as a misguided or even radical idea. Why spend so much time reporting on technologies that had not been widely adopted by American job shops?

Today, it feels like we are standing at a similar crossroads with artificial intelligence (AI). But the stakes feel different today, and they feel higher. Back in the ’70s and ’80s, the full impact of offshoring production had yet to be felt. Our manufacturing workforce was more stable, and China had yet to emerge as a dominant player. It was a different industrial landscape back then, pure and simple.

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While the rise of CNC and the rise of AI may have some parallels on the ground level — namely the high initial costs/limited access and resistance from skilled labor — they are fundamentally different technologies emerging in fundamentally different times. Where CNC replaced handwheel motions with coded instructions, AI is much more of a black box. Where CNC is task-specific and tied to hardware, AI functions on layers of abstract thinking. It requires clean data to learn from patterns and make suggestions or decisions. Crucially, it asks us to trust a system we can’t really see — a major hurdle to overcome for a technology still in its infancy.

Still, we are faced with the same question today as back in the early 1980s: Why is Modern Machine Shop devoting so much coverage to a technology that has not been widely adopted by its core audience? It’s a fair question that deserves a response.

Why We’re Covering AI Now

Modern’s focus will continue to be on core machining technologies, tools and applications. But it’s also true that we’ve written extensively about AI in recent months, from CAM programming assistants, to robotic surface finishing systems to AI-powered ERP integrations. Just as we did with CNC several decades ago, we’re following emerging AI technologies closely. But our approach must be different, not just because AI is different from CNC but also because it doesn’t simply change how parts are made; it challenges how decisions are made, and by whom.

By the early 1980s, CNC was already showing tangible results in cycle time reduction, precision, repeatability and labor efficiency. But even at that time, many shops and some OEMs still struggled with training, the initial high investment costs and the “quiet rebellion” among some journeymen machinists against the technology. Eventually, CNC was democratized through conversational controls like ProtoTRAK and Mazatrol, and we kept our reporting focused on that trajectory as CNC matured into a stable and trusted technology.

By its nature, CNC is deterministic — it makes machining programmable via coded instructions. As long as the inputs and setups remain the same, machinists can trust that it will produce the same motions and results every time. AI operates differently. Instead of executing fixed commands, AI models learn from data, analyze patterns and sometimes produce outcomes that even AI developers can’t fully explain. Its responses to variation are harder to determine. For job shops conditioned by CNC systems, AI’s non-deterministic behavior can feel like jumping into the void.

Our current coverage reflects this tension but also highlights real breakthroughs happening today. In this issue, Senior Associate Editor Evan Doran explores how aerospace suppliers are linking AI tools directly to ERP systems to predict delays and optimize workflows. And for this month’s cover feature, I spoke with GrayMatter Robotics’ co-founder Dr. Satyandra Kumar Gupta to discuss how the company applies physics-informed AI-powered force control and computer vision to robotic grinders, allowing them to adapt to variation in complex parts. The technologies at the center of these stories are not gimmicks. They are working systems in the early stages of development. But while they already are producing measurable gains, they still require scrutiny.

Interrogating AI on the Shop Floor

Because applied AI in machining is still seeking widespread adoption, we’re engaging directly with its architects. At the upcoming Automated Shop Conference (TASC), we’ll do exactly that in a panel discussion titled “Artificial Intelligence in CNC Machining: Real-World Strategies & Results.” The panel brings together leaders from four companies pushing AI beyond the conceptual stage and into shops, including Tanmay Aggarwal of Lambda Function, whose adaptive optimization software fine-tunes cutting parameters in real time using live machine data; Bill Bither of MachineMetrics, whose AI models detect performance anomalies and predict equipment failure; Theo Saville of CloudNC, whose CAM Assist tool uses generative algorithms to automate toolpath strategy selection; and Al Whatmough of Toolpath Labs, whose software learns from past cutting conditions to provide proven machining steps. Each of these companies is embedded in real production environments today.

The goal of the session will not only be to showcase AI’s potential but also to interrogate its real-time performance: Where does AI offer repeatable value? Where does it still fall short? And what should shops be doing now to prepare for what’s coming next? This is the framing that readers can expect from us. Because where CNC largely solved mechanical challenges, AI is operating in a murkier landscape — it promises to augment human decision-making while also obscuring it.

It is not our job to predict AI’s future in machining. We need to investigate it in real conditions under real constraints found on today’s shop floors. That needs to be our approach going forward.