Industry example: Shifting role of technicians and supervisors Industry example: Elevating planner roles with AI-supported decision makingBOX 2 BOX 3A global wheel manufacturer has experienced a shift in the role of their technicians and supervisors with the introduction of a prescriptive AI solution for process parameter adjustment developed by a Cape Town-based AI solution provider. Instead of managing process details, technicians now focus on identifying root causes and driving continuous improvement by optimizing the plan-do-check-act (PDCA) cycle. Supervisors, in turn, are evolving into AI users, interpreting AI-driven insights and guiding operators towards more efficient problem- solving. This transition enables both operators and supervisors to concentrate on long-term, systemic improvements rather than routine, reactive tasks. A Fortune 500 technology manufacturer elevated the role of its planners from executors to architects of its supply chain decision-making process. Previously relying solely on humans, the company struggled with delayed decision-making, resulting in large inventories and long lead times. By harnessing an AI agent solution from a US-based decision intelligence company, they automated routine decisions in inventory management while routing exceptions to human experts with contextual data, analysis and recommendations. The platform optimized stock levels and ensured supply was matched to regional demand. As a result, 77% of agent recommendations were automatically executed and 90% were accepted without change. This evolution will require manufacturers to anticipate a transition in workforce skills and cultural identity, making early engagement of operators in the transformation journey critical for success.In a future with largely self-controlling systems, humans may partner with machines, harnessing collaborative intelligence to focus on higher-value tasks, such as: –Strategic decision-making involves using AI-driven recommendations to make business- critical decisions. For instance, in an automotive plant, AI may recommend adjustments to production schedules or shift planning. A human planner may weigh these recommendations against factors such as projected customer demand or current labour availability. –Performance supervision involves monitoring and adjusting autonomous systems as needed. For instance, in a semiconductor plant, operators may monitor autonomous systems handling wafer fabrication. If performance metrics show yield deviations that systems cannot resolve, humans can step in to address the issue. –Continuous improvement involves solving complex problems and optimizing processes. For instance, in a chemical processing plant, engineers may use AI to identify inefficiencies in mixing or reaction processes. They can then redesign workflows or machine configurations to optimize output and reduce waste. –Creativity and innovation involve developing new production processes and rethinking factory layouts. For instance, in a consumer electronics plant, a maintenance worker might introduce creative ideas to streamline tool changes by mounting additional supports that have been employed in other industries. In a future with largely self-controlling systems, humans may partner with machines, harnessing collaborative intelligence to focus on higher- value tasks. Frontier Technologies in Industrial Operations 9

Frontier Technologies in Industrial Operations 2025