Overview

This session provides a practical, business focused introduction to how AI can be applied across core operations, from back-office functions to frontline work, to drive measurable efficiency and better decision making. Instead of leading with tools or trends, the session starts with the problems organizations are trying to solve and examines where AI is appropriate, where it is not, and how Lean principles help ensure real outcomes.

Participants will explore real world use cases, common failure points, and the foundational elements required for successful AI adoption, including solving the right problem, data readiness, risk management, and change management. The session blends foundational AI knowledge with Lean thinking to help teams develop a clear, actionable approach to digital transformation that works in the real world.

What You Will Learn

At the conclusion of the course, you will be able to:

• Clearly explain what AI is and isn't, and when it makes sense to apply it
• Identify common business and operational pain points where AI can deliver real value, focusing on where AI can improve efficiency, reduce waste, and support better decisions across functions
• Recognize common misconceptions and risks that derail AI initiatives, while also introducing how Lean principles can help derisk these factors
• Assess data readiness and organizational constraints before pursuing AI solutions
• Define practical next steps toward an AI roadmap aligned with business goals

Key Topics Covered

Key topics covered in this interactive short course include:

• AI Foundations: Plain language overview of core AI concepts, including machine learning and generative AI, and where they are most effective
• Problem First Thinking: Framing AI initiatives around business problems rather than technology adoption
• Cross Functional Use Cases: Examples across operations, finance, customer service, supply chain, and knowledge repositories
• AI and Lean Six Sigma Integration: Using Lean principles to prioritize the right problems, avoid waste, and drive practical implementation
• Risks, Limitations, and Misconceptions: Why AI initiatives fail, including data issues, trust, bias, and adoption challenges; How to overcome these risks with Lean principles
• Data Readiness: What data is required, how to evaluate quality and accessibility, and common pitfalls
• From Ideas to Roadmap: Translating opportunities into a prioritized, phased roadmap with clear success measures

Prerequisites

None