When manufacturers discover their AI can’t reach the data it needs, the next word in the meeting is always the same: integration.

Connect the systems. Break the silos. Build the unified view.

The industry has been doing exactly this for twenty-five years. Five generations of integration technology — point-to-point custom interfaces, Enterprise Application Integration, Enterprise Service Bus, iPaaS, and modern API/event-streaming architectures — each better than the last. Each accompanied by billions of dollars of investment. And none of them has produced the record AI agents actually need: the item-specific, step-by-step production biography of a single unit.

The reason is structural, not tactical. Integration moves data between systems. The item-specific execution data was never stored in any of those systems in the first place. You cannot integrate your way to data that was never captured.

An event bus cannot transmit an event that was never fired. An API cannot expose a field that was never recorded. A data lake cannot aggregate records that were never generated in the shape they need to be in.

And the hidden cost compounds. Integration teams in mid-size and large plants routinely spend 60 to 80 percent of their capacity maintaining existing integrations — not building new capability. A $2 million integration fabric costs $6 to $8 million in maintenance over the next decade, before a single new connection is added. Over ten years, the original build is less than 20 percent of the total.

Integration has a legitimate role — as a boundary mechanism between systems that already hold what they are supposed to hold. It does not have a legitimate role as the answer to the item-specific visibility problem.

A different path is needed. Next post: what that path looks like. (Full argument in the anchor article — link in comments.)

When manufacturers discover their AI can’t reach the data it needs, the next word in the meeting is always the same: integration.

Connect the systems. Break the silos. Build the unified view.

The industry has been doing exactly this for twenty-five years. Five generations of integration technology — point-to-point custom interfaces, Enterprise Application Integration, Enterprise Service Bus, iPaaS, and modern API/event-streaming architectures — each better than the last. Each accompanied by billions of dollars of investment. And none of them has produced the record AI agents actually need: the item-specific, step-by-step production biography of a single unit.

The reason is structural, not tactical. Integration moves data between systems. The item-specific execution data was never stored in any of those systems in the first place. You cannot integrate your way to data that was never captured.

An event bus cannot transmit an event that was never fired. An API cannot expose a field that was never recorded. A data lake cannot aggregate records that were never generated in the shape they need to be in.

And the hidden cost compounds. Integration teams in mid-size and large plants routinely spend 60 to 80 percent of their capacity maintaining existing integrations — not building new capability. A $2 million integration fabric costs $6 to $8 million in maintenance over the next decade, before a single new connection is added. Over ten years, the original build is less than 20 percent of the total.

Integration has a legitimate role — as a boundary mechanism between systems that already hold what they are supposed to hold. It does not have a legitimate role as the answer to the item-specific visibility problem.

A different path is needed. Next post: what that path looks like. (Full argument in the anchor article — link in comments.)

#Manufacturing #Integration #DigitalThread #MES #DataArchitecture #IntelligentDigitalThread

Michael McClellan   www.productiondigitalthread.com