What you'll learn:
- Explain key AI technologies for operations (ML, NLP, vision, robotics, RPA) and match them to the right operational problems.
- Use data-driven frameworks to move from reactive decisions to predictive, measurable operational improvements.
- Apply AI use cases across forecasting, inventory, routing, maintenance, quality, and production scheduling.
- Define AI project success metrics, prioritize use cases (value vs feasibility), and design pilots that scale.
- Describe the AI lifecycle and MLOps basics: deployment, monitoring, drift, retraining, and model governance.
- Implement change management and ethical guardrails (privacy, bias, transparency) for real-world adoption.
Did you know…
US retailers wrote off $12B+ in excess inventory in 2023—often driven by poor forecasting and slow data signals
IDC estimates unplanned downtime costs manufacturers ~$260,000 per minute
Gartner reports ~70% of AI pilots stall before delivering measurable value
McKinsey finds ~55% of AI projects never reach production
Operations is where plans meet reality—inventory, schedules, logistics, quality, customer support, and staffing. When the data gets messy and the pace gets fast, traditional tools (spreadsheets, static dashboards, fixed rules) can’t keep up.
That’s why AI for Operations Management is becoming a core capability. AI helps you predict disruptions before they happen, optimize decisions in real time, automate repetitive work, and continuously improve performance—without relying on guesswork.
In this course, you’ll learn how AI actually works in operations (in plain language) and how leading organizations apply it across the end-to-end operating system—from supply chain planning to the factory floor to service operations.
You’ll learn how to:
Understand the AI toolbox for operations: machine learning, NLP, computer vision, robotics, RPA, IoT, digital twins, and generative AI
Build data-driven decision habits that shift your team from reactive “firefighting” to predictive control
Apply AI to demand forecasting, inventory optimization, and supply chain routing/risk management
Use AI for predictive maintenance, quality inspection, and production scheduling optimization
Improve service operations with AI chatbots, email/case triage, call-assist tools, and process automation (RPA)
Optimize workforce planning with AI scheduling, coverage forecasting, and safety/fatigue monitoring concepts
Create an AI strategy that ties directly to business metrics (cost, service levels, uptime, lead time, productivity)
Run AI projects end-to-end: scoping, pilots, deployment, monitoring, and continuous improvement with MLOps
Lead adoption with practical change management so AI tools get used (and trusted)
Evaluate emerging trends (autonomous planning, digital twins, genAI assistants) and manage ethics: bias, privacy, transparency
You’ll also walk through a real case study on John Deere’s AI transformation (computer vision “see & spray”) to see what it takes to make AI work in harsh, real-world operating conditions.
By the end, you’ll be able to identify high-value AI opportunities, speak confidently with technical teams and vendors, and design a practical roadmap to implement AI in your operations—starting small, proving ROI, and scaling responsibly.
