Two-thirds of manufacturing COOs report their AI programs are still in exploration or pilot mode, and only 2% say AI is fully embedded across operations. The culprit is rarely the model — it's the industrial data architecture beneath it, which was built for operational control rather than multi-year historical replay, cross-domain analytics, or continuous machine learning. This playbook identifies what separates manufacturers that scale AI from those that stall. It covers the architecture required to unify OT and IT data streams, the storage requirements for high-frequency sensor telemetry and vision inspection data, and how an S3-native object storage layer unlocks the analytics and AI capabilities that factory-floor systems were never designed to deliver. Real-world use cases illustrate the transition from isolated pilots to enterprise-wide AI deployment.
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Two-thirds of manufacturing COOs still have AI stuck in pilot mode — the core constraint is legacy industrial data architecture, not model quality or compute capacity.
Industrial data architectures designed for operational control cannot support the multi-year historical replay and cross-domain analytics that enterprise AI deployments require.
A modern S3-native object storage foundation unifies OT and IT data streams, enabling continuous machine learning and the scale-out AI capabilities that move manufacturing beyond the pilot stage.
Manufacturing technology leaders, OT/IT integration architects, and plant operations teams working to operationalize industrial AI and analytics at production scale.
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