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This paper summarizes the findings of an industry panel study evaluating how new Autonomous Intelligence technologies, such as artificial intelligence and machine learning, impact the system and operational architecture of supply chain control tower (CT) implementations that serve the pharmaceutical industry. Such technologies can shift CTs to a model in which real-time information gathering, analysis, and decision making are possible. This can be achieved by leveraging these technologies to better manage decision complexity and execute decisions at levels that cannot otherwise be managed easily by humans. Some of the key points identified are in the areas of the fundamental capabilities that need to be supported and the improved level of decision visibility that they provide. We also consider some the challenges in achieving this, which include data quality and integrity, collaboration and data sharing across supply chain tiers, cross-system interoperability, decision-validation and organizational impacts, among others.

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