______________________________________________________________ The Manufacturing Data Summit 2026
industrial information. Shared responsibility across IT, engineering, and operations can encourage collaboration, but it can also create uncertainty when nobody has explicit ownership of the results.
The discussions also highlighted the difficulties created by organizational silos. Skills and specialist expertise may already exist within a manufacturing business, but they are not always available to the teams or facilities that need them. Overcoming those boundaries can be as important as acquiring new technological capabilities.
Turning industrial data into operational value
The importance of data governance was another recurring theme, particularly as manufacturers begin introducing AI systems capable of interpreting information and recommending actions across increasingly complex operations.
Traditionally, governance has often been associated with compliance, documentation, and policies governing how information is stored or accessed. The discussions at MDS presented a more operational perspective, examining how unreliable information can create direct costs through slower decisions, duplicated effort, unnecessary inventory, and inconsistent planning.
When employees cannot trust the information available to them, they frequently resort to checking figures manually or maintaining separate spreadsheets. These workarounds may solve immediate problems, but they can also create competing versions of operational reality, making it harder to coordinate decisions across departments.
The arrival of agentic AI introduces additional questions. Systems that previously provided information for employees to interpret are increasingly being designed to perform tasks or initiate actions. This changes the significance of access permissions, data ownership, and the controls governing what software is authorized to do.
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