The Practical Path To AI For SMB Manufacturers

If you listen to the consultants, it’s easy to dismiss AI as a "big company" luxury reserved for massive budgets and robot arms. But the reality is simpler: AI isn't sci-fi; it’s an operational tool, no different than a new CNC controller. Practical AI gives the talented people you already have better digital tools so they can focus on production, not filing paperwork. 

Our 2026 AI Readiness Report surveyed over 500 business leaders to cut through the noise. What we found is a mix of caution and curiosity. 

While 70% of the business leaders surveyed have a positive view of AI, nearly 40% of those using it say they haven’t seen measurable results yet.  

The disconnect? Most shops are stuck at the start, using AI chatbots for simple tasks, like emails, but failing to connect to the actual data that runs the business.  

Meanwhile, pressure mounts as 30% of business leaders now view AI as "critical," believing shops without a digital strategy will get left behind. Leading manufacturers aren't looking at AI as a fad that will pass; they’re looking to it as the fix for tedious, repetitive manual work with a possibility of full scope “dark factories” in the future, running 24/7 with no manual intervention.  

For now, let’s start with the basics of AI implementation 

Success on the shop floor is a game of continuous improvement, and implementing AI is the next phase. It is part of an ongoing investment aimed at moving from recording what’s already happened to real-time decision-making. 

Let’s break down AI maturity into four levels: 

Level 1: The way we’ve always done it. 

At this level, businesses have likely kept the same processes since the shop doors first opened. Schedules are outlined on whiteboards; jobs are documented on paper; and any digitization lives across several tools. Inventory counts and job status rely on word-of-mouth reporting at the end of a shift. It’s effective, but not necessarily efficient. There is no single source to tell what is working and what’s not. 

Level 2: The data-driven shop  

Here, a shop has invested in centralized technology, likely an enterprise resource planning (ERP) system. There is a single source of truth that’s visible at all levels of the organization. Decisions are made from data rather than gut feel or “the way we’ve always done it.” Inventory signals when parts are low, so operators always know when it’s time to restock. Job status is actively recorded, providing visibility from order to production to shipping. Everyone is working off the same data found in the same place. 

Level 3: AI as your consultant 

With an ERP at the center of data collection, AI now has a repository to draw conclusions from. It becomes like a business consultant, helping to make quick decisions that reduce lead times and get product to customers faster. It may suggest efficiencies in resource allocation or machine capacity. It can help build better schedules based on employee and machine performance data. AI can also be responsible for the manual, repetitive processes, keeping a human in the loop to act as a data auditor rather than a data enterer. At this level, AI is all about speeding up processes. 

Level 4: Full ERP intelligence 

The next generation of ERP won’t just store data, it will react to it. AI will respond to a problem before it completely derails production. If a machine signal indicates a slowdown, AI can proactively reschedule in your ERP before you miss a deadline. It won’t just automate tasks; it will make fully formed decisions based on data collected from your ERP by the machines and people around your shop.  

If you’re ready to apply AI in your shop, here’s a practical path to upleveling: 

From level 1 to level 2:  

The first step in AI implementation is collecting and centralizing the data that will eventually feed the AI. An industry-specific ERP, like Deacom, JobBOSS² or M1, is designed to easily fit your shop’s workflow. This means no overhaul, no workarounds, just software that plugs in to the processes your team knows. Your ERP should be cloud-native and API-first; this will make future AI integrations much simpler. 

Once you’ve selected an ERP, take the time to really get to know the software and ensure everyone understands their part in connecting data to the solution. 

From level 2 to level 3: 

It is important to establish your company’s AI policy before going any further in implementation. A good policy defines where employees can use AI to be more productive without accidentally exposing sensitive company blueprints to the public web. Once policy is established, you can start automating low-value manual tasks. 

It is recommended to pick just one workflow to introduce AI automation, like BOM building. AI BOM Builder eliminates the manual work associated with keying in paper BOMs. It takes BOM information from PDFs, Excel spreadsheets, or images and automatically populates it in your ERP. This feature is embedded directly in your JobBOSS² ERP, eliminating any chance of your data becoming public knowledge. 

From level 3 to level 4: 

Your AI lives with your data, enabling it to provide data-driven insights. While your tech is advancing rapidly, this is the most important step for a human-in-the-loop approach. At its current capabilities, AI still has a tendency to pull misinformation. AI should handle the heavy lift of the first draft, but human experts need to make the final call. The AI should point you to the exact drawing or historical record it used to make a choice. With this approach, quality and safety standards are never compromised. 

At the end of the day, you need to look at AI adoption as a logical next step, not a giant leap. The shops seeing results are the ones that start with a solid data foundation and begin applying AI automation to a single workflow. Each level isn’t a finish line; it’s a layer that makes the next step possible. The future of manufacturing won’t be machines versus humans, it will be what happens when you put them to work together 

AI is not just for large enterprises. For small and mid-sized manufacturers, it offers a pathway to greater efficiency and innovation. By understanding the levels of AI maturity and emphasizing human oversight, shops can begin to see real benefits.