________________________________________________________________________________________________________________________
investment should be directed, what separates a successful pilot from an initiative that never progresses beyond experimentation, and how organizations can establish a credible business case for scaling new capabilities.
An important distinction running through the discussions was between demonstrating what technology can achieve and establishing the organizational conditions required to sustain that achievement. Industrial AI depends on access to information from equipment, production systems, and wider business applications. However, simply collecting that information does not make it suitable for analysis. Manufacturers must also establish consistent definitions, understand the context in which data is generated, and determine who is responsible for maintaining its accuracy.
These requirements become more demanding when organizations operate multiple facilities with different systems, production processes, and management structures. A solution developed for one factory may not transfer easily to another unless the underlying architecture, governance arrangements, and operational requirements have been considered from the outset.
The importance of organizational alignment became particularly apparent during discussions about the relationship between information technology and operational technology. While IT departments typically prioritize network security, architecture, and enterprise standards, operational teams must ensure that production equipment remains safe, reliable and available.
Neither perspective can be treated as secondary. Successful convergence requires manufacturers to establish shared objectives while recognizing that responsibility for technical infrastructure and accountability for operational outcomes are not necessarily the same thing.
A recurring theme was the importance of assigning clear accountability to the business functions that ultimately depend on
36