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◀ Employees have an essential human-in-the-loop role in embodied AI deployments
To make these kinds of multi-AI use cases successful and scalable, manufacturers need to have a few foundational elements in place. That includes solid, trusted and fresh data from internal sources across all operations and from external sources such as suppliers, as well as a platform that has standard, userfriendly interfaces for running autonomous workflows, with the flexibility to design and deploy customer-specific requirements and workflows. Governance is also critical. The workings and output of AI models must be transparent, easily validated and readily auditable by human beings. While the goal with AI is to let it handle certain tasks autonomously so people can focus on higher-value pursuits, human beings must be able to verify AI is working as intended and hold ultimate responsibility for confirming it’ s making on-target business decisions. Training or upskilling employees to execute these human-in-the-loop responsibilities is also essential to any AI deployment.
With each successful proof of concept, the business case for pursuing multi-AI use cases strengthens. The race is on to scale these successes across the manufacturing enterprise, so they become a true competitive difference-maker. ■
Boris Dmitriev | Daniel Krampe www. sap. com / industries / industrial-manufacturing. html
Boris Dmitriev is a global industry advisor for Industrial Manufacturing at SAP. Connect with him on LinkedIn at www. linkedin. com / in / borisdmitriev. Daniel Krampe is an industrial manufacturing solution expert at SAP. Connect with him on LinkedIn at www. linkedin. com / in / danielkra. As a global leader in enterprise applications and business AI, SAP stands at the nexus of business and technology. For over 50 years, organizations have trusted SAP to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain and customer experience.
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