Walk into almost any operations and supply chain meeting today and you’ll hear it:
“We should use AI for this.”
“Can we plug in an LLM?”
“Let’s add a copilot.”
Machine Learning Machine learning uses historical data to detect patterns, improve predictions and support decisions. In real-world operations, that includes:
- Demand forecasting
- Inventory optimization
- Predictive maintenance
- Quality and anomaly detection
LLM (Large Language Model) LLM refers to systems that can read and generate human-like text. It processes and generates language based on patterns learned from large datasets. It shows up:
- Summarizing supplier emails or RFQs
- Drafting customer responses
- Translating ERP data into plain language
LLMs don’t “know” a business unless connected to the firm’s data. Without that context, they can sound confident—but be wrong.
Copilots “Copilot” is one of the most overused—and misunderstood—terms. They are a layer that sits on top of a business system (ERP, CRM, email) to assist users in real time. It is useful for:
- Suggesting responses inside email
- Helping navigate ERP workflows
- Recommending next steps
A copilot doesn’t replace a system—it improves how people interact with it.
Agents Agents move from assisting to acting. They are systems that can take a goal and execute steps to achieve it.
Examples:
- Monitoring inventory
- Detecting shortages
- Reaching out to suppliers
- Proposing or initiating reorders
Most agent-based systems are still early. They require strong guardrails and tight integration to work reliably in production environments.
Embeddings (The Quiet Connectors) Embeddings convert a company’s data into a format AI systems can understand and search. That’s what allows AI to:
- Reference ERP data
- Search internal documents
- Provide context-aware responses
For operations students and faculty, the goal is not to become AI experts. It’s to understand the language well enough to ask better questions and identify where these tools can create real advantage.