AI Terminology Explained: Definitions Every Supply Chain Team Should Know

Term 

Definition 

AGI (Artificial General Intelligence) 

A theoretical form of AI that would match human intelligence across a wide range of real-world tasks and interactions. 

ASI (Artificial Super Intelligence) 

A theoretical form of AI that would surpass human intelligence in all domains. 

AI Confidence Index 

A measure of how much an AI system can be trusted, based on transparency, performance, and oversight. 

AI Maturity 

How advanced an organization’s AI capabilities are. 

AI Operating Model 

How AI fits into workflows, decision-making, and governance structures. 

AI Readiness 

An organization’s current ability to adopt AI effectively. 

AI Risk Management 

Identifying and addressing risks in how AI is built, used, and maintained. 

AI Strategy Alignment 

Ensuring AI initiatives support business goals and stakeholder priorities. 

AI Transformation 

Rolling out AI across a business to improve operations, decision-making, and customer experience. 

AI Value Waves 

Stages of AI impact, typically moving from efficiency to innovation and growth. 

AI Washing 

Marketing something as AI when it barely fits the definition. 

Agentic AI 

AI designed to act autonomously toward a predefined goal with minimal human intervention. 

Agent Scaffolding 

Structures that support autonomous AI agents. 

Alignment Tax 

The tradeoff between model performance and safety constraints. 

API (Application Programming Interface) 

A set of rules that allows different software applications to communicate. 

Artificial Intelligence (AI) 

Computer systems that perform tasks that typically require human intelligence, such as reasoning, pattern recognition, or content generation. 

Attribute Enrichment 

AI-driven tagging of products using image and text analysis to improve search and filtering. 

Augmentation 

Using AI to assist humans by reducing effort or enhancing capabilities rather than replacing them. 

Automation 

Using AI and technology to execute repetitive, rule-based tasks without human intervention. 

Automation Bias 

The tendency to over-trust AI outputs even when they are incorrect. 

Autonomous Mobile Robots (AMRs) 

Robots that use sensors and AI to navigate and transport goods autonomously. 

Baked-in 

Knowledge embedded in the AI during training. 

Bias 

Systematic patterns in AI outputs that result in unfair or uneven outcomes. 

Cannibalization Analysis 

Predictive modeling to assess whether new products steal sales from existing ones. 

Capability Overhang 

When models have latent abilities that emerge after deployment. 

Catastrophic Forgetting 

Loss of previously learned knowledge when a model is trained on new data. 

Chain-of-Thought Prompting 

A prompting technique that encourages step-by-step reasoning. 

Cobots (Collaborative Robots) 

Robots designed to safely work alongside humans. 

Cognitive Offloading 

Humans relying on AI to reduce mental effort, altering decision-making patterns. 

Cold Start 

A period of latency when a model starts up. 

Compute Optimal Frontier 

The most efficient balance between performance, cost, and training time. 

Compute Power 

The processing capacity required to train and run AI models. 

Computer Vision 

AI that interprets visual data such as images or video. 

Context Engineering 

Designing data structures, instructions, and tools provided to an AI model to ensure reliable, relevant outputs. 

Context Window 

The amount of information an AI model can consider at once. 

Control Tower 

A centralized dashboard providing real-time, end-to-end operational visibility. 

Copilot 

A generic term for using AI to help with tasks. 

Curriculum Learning 

Training models on simple tasks before progressing to complex ones. 

Data Contamination 

Training data that accidentally includes test or benchmark data. 

Data Privacy 

Protecting sensitive or personal information used by AI systems. 

Deceptive Alignment 

When a model appears aligned during training but behaves differently in production. 

Decision intelligence 

AI focused on decisions, not predictions. 

Deep Learning 

Machine learning using multi-layer neural networks for complex tasks. 

Demand Sensing 

Forecasting technique that uses artificial intelligence to analyze real-time data, such as point-of-sale transactions, social media trends, and weather patterns. 

Demand Volatility Index 

A score that measures how unstable demand patterns are over time. 

De-skilling 

Humans lose skills due to over-dependency on AI. 

Deterministic vs.  

Non-deterministic 

The distinction between predictable software (deterministic) and AI models that inherently produce variable outputs (non-deterministic). 

Digital Twin 

A virtual representation of a real-world system or process. 

Distributional Shift 

Real-world use differs from model training. 

Dynamic Routing 

Real-time route optimization based on changing conditions. 

Dynamic Slotting 

AI-driven warehouse slot optimization based on item velocity. 

Embeddings 

Numerical representations of data that enable similarity comparison. 

Emergent Capabilities 

Unexpected skills that AI systems display only after scaling up, which were not explicitly programmed. 

Evaluation Debt 

Testing that has not kept up with system capabilities. 

Exception-Based Management 

AI alerts managers only when anomalies or disruptions occur. 

Explainability 

The ability to understand how an AI system makes decisions. 

Few-Shot Learning 

Learning a task from a very small number of examples. 

Forecast Bias Detection 

AI that identifies consistent over- or under-forecasting. 

Fine-Tuning 

Customizing a pre-trained model for a specific task or domain. 

Franken-model 

Heavily modified or merged LLMs. 

Frozen 

The model is no longer being trained. 

Generative AI 

AI systems that create new content such as text, images, or code. 

Glazing 

Excessive flattery from an LLM, often softening valid critique. 

Gold Set 

Trusted benchmark dataset often used for regression testing. 

Green AI 

AI development focused on energy efficiency and reduced environmental impact. 

Guardrails 

Constraints and safety mechanisms built into AI systems to keep outputs accurate, compliant, and within acceptable boundaries. 

Hallucination 

Confident but incorrect AI-generated information. 

Human-AI Collaboration 

Designing systems in which humans and AI work together. 

Human-in-the-Loop (HITL) 

Human oversight integrated into AI decision-making. 

Human-Centered AI 

AI designed with human values, needs, and wellbeing in mind. 

Hyper-Personalization 

AI-driven tailoring of content or products to individual users. 

Inference 

Using a trained model to make predictions or decisions. 

Inference cost 

Cost to run the model per request. 

Inference Pipeline 

The sequence of steps from data input to model output. 

Inner Alignment 

Whether a model’s internal goals match intended objectives. 

Intermittent Demand Modeling 

ML techniques for low-volume, irregular demand patterns. 

Intelligent Process Automation (IPA) 

AI combined with RPA to automate complex workflows. 

Knowledge Distillation 

Training smaller models to replicate larger models’ behavior. 

Label Noise 

Errors or inconsistencies in labeled data. 

Lane-Level Intelligence 

Freight optimization at individual shipping-lane granularity. 

Large Language Model (LLM) 

AI trained on massive text datasets to generate language. 

Latency 

The time it takes for an AI system to respond. 

Latent Space 

The internal mathematical representation of meaning within a model. 

Leak 

Exposing internal logic, prompts, or data. 

LLMOps 

Monitoring, deploying, and governing operations in AI. 

Long-horizon Tasks 

Multi-step goals over time. 

Machine Learning (ML) 

Algorithms that learn patterns from data without explicit programming. 

Mechanistic Interpretability 

Analyzing models at the neuron or circuit level to understand behavior. 

Meltdown 

When AI output spirals and is incoherent. 

MCP (Model Context Protocol) 

A standard that allows AI models to securely connect with external data, tools, and software applications. 

Moats 

Defensible advantages that prevent easy replication of AI systems. 

Mode Collapse 

Model produces limited, repetitive outputs. 

Model Anthropomorphism Drift 

Users attributing human traits or intent to AI systems. 

Model Auditing 

Reviewing models for fairness, compliance, and performance. 

Model Collapse 

Degradation when models train on AI-generated data. 

Model Drift 

Performance that declines due to changing real-world data. 

Model Weights 

Parameters that define a model’s learned behavior. 

MoE (Mixture of Experts) 

Architecture where specialized sub-models handle different tasks. 

Multimodal AI 

AI that processes text, images, audio, and more together. 

Narrow AI 

AI designed for a specific, limited task. 

Natural Language Processing (NLP) 

AI that understands and generates human language. 

Neural Network 

A model inspired by the structure of the human brain. 

Next Best Action (NBA) 

AI-recommended optimal next step. 

NLP for Support 

AI-driven chatbots or agents for customer service tasks. 

OCR (Optical Character Recognition) 

Technology that converts images of text into machine-readable data. 

Orchestration 

Coordinating multiple AI models, tools, and workflows into a unified system to achieve a desired outcome. 

Overfitting 

When a model memorizes training data instead of generalizing. 

Overparameterization 

Using more parameters than theoretically necessary. 

Path Planning 

Determining efficient, obstacle-free routes for robots. 

Planogram Compliance 

AI verification that products are placed correctly on shelves. 

Policy Wall 

A model’s hard refusal point. 

Predictive Maintenance 

Using data to predict equipment failures before they occur. 

Probabilistic Forecasting 

Forecasts expressed as probability ranges rather than single values. 

Prompt Engineering 

Designing prompts to improve AI output quality. 

Prompt Leakage 

Accidental exposure of system instructions. 

Prompt Rot 

When prompts stop working over time. 

Prompt Stacking 

Layering instructions so the model can be effectively guided. 

Proof of Concept (PoC) 

A small-scale test to validate an AI idea. 

Reinforcement Learning 

A machine learning process in which autonomous agents learn to make decisions based on the environment. 

Representation Collapse 

Loss of diversity in a model’s internal representations. 

Responsible AI 

Ethical, transparent, and safe AI practices. 

Retrieval-Augmented Generation (RAG) 

Enhancing LLMs with external, up-to-date data sources. 

Reward Hacking 

Models exploiting loopholes to maximize rewards incorrectly. 

Scaling Laws 

Predictable relationships between model size, data, and performance. 

Sentiment Analysis 

AI analysis of opinions in text data. 

Shadow AI 

Unauthorized AI tools used within organizations. 

Sharpness-Aware Minimization (SAM) 

Optimization method that improves generalization. 

SKU Rationalization 

AI-driven removal of underperforming SKUs. 

Smart Batching 

Grouping orders to optimize picking efficiency. 

Social Chatbots 

AI software designed to respond to simple messages sent on social media, websites, and more. 

Sparse Activation 

Only a subset of neurons activates per task. 

Supervised Learning 

ML using labeled input-output pairs. 

Synthetic Agents 

AI-powered digital worker. 

Throughput 

The volume of tasks an AI system can process over time. 

Token 

A basic unit of text used by language models. 

Token Entropy 

Measure of uncertainty or variability in outputs. 

Training Data 

Data used to teach an AI model. 

Transformer 

Neural network architecture optimized for sequential data. 

Transparency 

Openness about how AI systems work and make decisions. 

Trend Sensing 

Using AI to identify emerging trends early. 

Underfitting 

Model too simple to capture underlying patterns. 

Unsupervised Learning 

ML that finds patterns in unlabeled data. 

Use Case 

A specific, practical application of AI. 

Vector Database 

Database optimized for storing and searching embeddings. 

Zero-Shot Learning 

Performing tasks without task-specific training examples.