Mid-size manufacturers evaluating AI adoption tend to start with a tool. A vision system for the line, a sensor package for predictive maintenance, a forecasting platform for scheduling. The tool gets selected, budgeted, and installed, and only then does anyone discover that the plant’s data doesn’t support it the way the sales deck assumed.
AI adoption in manufacturing is a data and infrastructure project first, and an AI project second. That order matters more at 50 to 200 employees than it does at any other scale, because mid-size plants carry the same fragmented systems as larger manufacturers without the integration budget to paper over it.
The work starts with data and infrastructure, not with the tool.
The bottleneck sits in the data layer
Recent research from Kaufman Rossin found that only 27% of manufacturing companies surveyed maintain a data warehouse or data lake, and mid-sized manufacturers run at roughly half the data-readiness rate of comparable non-industrial mid-market companies. Every manufacturer in that study remained somewhere in the experimentation phase. None had moved AI fully into production.
This is a problem because manufacturing data typically lives scattered across systems that were never built to feed each other. Machine data sits in a historian system that maintenance checks and few others ever open. Inventory counts live in the ERP, updated on a lag. Quality records live in a spreadsheet that gets emailed around and edited in three different ways by three different people. Each system works fine on its own, but they weren’t built to talk to each other, and nothing forced that connection until AI needed a single, consistent feed of clean data.
A tool fed inconsistent inputs from three disconnected systems produces exactly what inconsistent inputs produce: false alarms, missed signals, and a pilot that gets shelved after two quarters.
Quality and uptime get the attention
The use cases mid-size manufacturers reach for first are the visible ones. Vision systems catching defects before packaging. Sensors flagging a bearing trending toward failure. Scheduling tools tightening production plans around actual demand instead of a planner’s memory.
These use cases earn attention because the return is easy to point to: fewer scrapped units, fewer emergency repairs, less overtime. That visibility is exactly why they get funded first, and exactly why the harder work sits one layer down, in whether the systems feeding these tools were designed to feed anything at all.
A vision system that flags defects reliably depends on consistent lighting, consistent camera placement, and a quality database it can actually write to. A predictive maintenance sensor is only useful if someone connects its output to the maintenance scheduling system instead of a dashboard that goes unchecked. The use case is straightforward. The plumbing underneath it rarely is.
Integration work outlasts the pilot
Shearer’s Foods, a snack manufacturer running 16 plants across North America, offers a useful example of what this looks like in practice. The company had operated on the same ERP system for more than two decades before bringing in an implementation partner and an integration layer to modernize how its plants connect to each other and to newer tools. The shift wasn’t framed as an AI initiative. It was framed as making the underlying systems flexible enough to support whatever came next, AI included.
That sequencing, integration and data architecture handled as the primary project, with AI use cases arriving as what follows, is the pattern worth noticing at Shearer’s Foods and in the plants that move past pilot stage.
Skipping ahead to tool selection means the integration work surfaces mid-pilot, usually after the vendor demo is already approved and the budget already spent.
Mid-size manufacturers don’t have the luxury of that discovery happening late. There’s no dedicated data engineering team to absorb the rework, and no enterprise budget to fund a second attempt after the first one stalls.
What the sorting work involves
This starts without a data science team or a platform migration on day one. It starts with an honest inventory of where information lives, how it moves, and where it breaks down between systems.
That inventory usually surfaces the same handful of issues. Machine data trapped on a local historian with no network path to anywhere else. A quality system that exports to spreadsheet instead of connecting through an API. Network segmentation that was never designed with IoT sensors in mind, leaving new devices bolted onto infrastructure that wasn’t built to carry their traffic securely.
Fixing these issues isn’t glamorous, and it doesn’t produce a demo anyone can show the board. It’s also the difference between a pilot that scales past one line and one that stops getting used.
What to expect from a technology partner here
A provider walking into this conversation should be assessing infrastructure and data flow before recommending a single AI tool. That includes network segmentation between IT and OT systems, whether machine and quality data can move between platforms without manual export, and whether cloud infrastructure exists to host and process what a sensor network or forecasting model would actually need.
The right sequence starts with one process worth fixing: a single line, a single data source, a single use case proven end to end before expanding to the next one. Providers proposing a full-plant AI transformation before addressing basic data connectivity are proposing the same purchase-before-plan mistake that stalls most pilots in the first place.
Getting the order right
AI adoption in manufacturing rewards plants that treat infrastructure as the first deliverable in the project, ahead of any tool selection. Real progress depends on whether the data underneath the pilot can actually support it, not on how many pilots are running at once.
At Syntech Group, that groundwork, network readiness, data integration between existing systems, and infrastructure built to carry what AI tools actually need, is where the conversation starts before any tool selection happens. For manufacturers weighing where AI adoption should begin, an honest look at what the current systems can and can’t support is the more useful first step than a vendor demo.
