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The distinction between allowing an AI system to examine information and permitting it to act upon that information is particularly important in manufacturing, where incorrect decisions could affect production equipment, material availability, or operational continuity.
Participants also considered whether manufacturers should collect and centralize every available piece of factory data. Although greater connectivity can improve visibility, moving large volumes of highfrequency equipment information into centralized systems can increase costs and introduce additional security considerations without necessarily improving decisions.
A more selective approach, based on clearly defined operational requirements, offers an alternative. Data can remain close to production equipment where immediate processing is necessary, while information required for broader analysis can be made available to enterprise systems.
Many manufacturing assets were installed before modern connectivity and cybersecurity requirements became established, making legacy equipment particularly difficult to integrate with contemporary data platforms without additional interfaces, gateways, and protective measures.
These challenges reinforce the importance of designing digital infrastructure around specific business outcomes rather than pursuing connectivity as an objective. The summit also explored what becomes possible once organizations establish more reliable and accessible information.
One operational AI case study examined how a growing consumer products business had moved beyond spreadsheetbased inventory planning by combining information from enterprise applications within a common data environment.
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