AI as a Core Competency: A Practical Framework for Upskilling Industrial & Systems Engineers

Presentation Summary

AI and Machine Learning have moved from experimental technologies to core “Industry Optimization Tools” that influence forecasting, scheduling, quality, and operational decision-making. As these tools become embedded in daily workflows, Industrial and Systems Engineers must build new competencies that enable them to work effectively with advanced analytics and agentic AI—AI systems capable of monitoring processes, identifying exceptions, and recommending or initiating actions within defined boundaries.

This presentation outlines a practical framework for integrating AI and agentic skills into an ISE team’s professional development strategy. The session begins with a structured approach for identifying the competencies most relevant to ISE roles, including predictive modeling, simulation, optimization, anomaly detection, and the supervision of agentic workflows that provide decision-ready insights.

A streamlined learning model will be presented, emphasizing core AI literacy, applied problem-solving, and tool-specific proficiency. The focus is on building confidence in interpreting model outputs, understanding uncertainty, validating agent recommendations, and integrating AI insights into existing engineering routines.

The session also provides guidance on designing workflows that support adoption—establishing human-in-the-loop review steps, encouraging transparent decision-making, and creating feedback loops that strengthen both the engineer’s skills and the performance of AI agents.

Finally, attendees will learn practical methods for assessing capability growth and linking new skills to improvements in decision quality, operational efficiency, and team readiness. This presentation offers a concise, actionable roadmap for developing a future-ready ISE workforce equipped to collaborate with AI and agentic systems.