AI Automation Glossary
Plain-English definitions of every AI and automation term your business needs to know. No jargon, no fluff.
Agentic AI
AI systems that independently plan, reason, and take actions to accomplish goals with minimal human intervention.
AI Agent
Software that uses AI to autonomously perceive, decide, and act to complete business tasks end to end.
API Integration
How different software tools connect and share data automatically through APIs.
Artificial Intelligence (AI)
Technology that enables computers to learn from data and perform tasks requiring human-like thinking.
Automation
Using technology to perform repetitive business tasks with minimal human involvement.
Chatbot
AI-powered tools that handle customer conversations automatically via text or voice.
Computer Vision
AI that enables machines to interpret and analyze visual information from images and video.
Conversational AI
Technology enabling natural dialogue between humans and machines through chatbots and voice assistants.
CRM (Customer Relationship Management)
Software that centralizes customer data, tracks interactions, and automates sales and marketing workflows.
Data Labeling
The process of tagging data with categories so AI models can learn from labeled examples.
Data Pipeline
Automated systems that move and transform data between your business tools seamlessly.
Decision Tree
A transparent AI model that makes predictions by following a series of yes/no questions like a flowchart.
Deep Learning
AI that uses layered neural networks to learn complex patterns, powering chatbots, image recognition, and more.
Edge AI
AI that processes data locally on devices instead of the cloud, for faster results and better privacy.
ETL (Extract, Transform, Load)
The three-step process of pulling data from sources, cleaning it, and loading it into destination systems.
Few-Shot Learning
AI that learns to perform new tasks from just a handful of examples rather than thousands.
Fine-Tuning
Customizing a pre-trained AI model with your own data to improve performance for your specific use case.
Generative AI
AI that creates new content including text, images, code, and audio from prompts.
GPT (Generative Pre-trained Transformer)
The AI architecture behind ChatGPT and many business AI tools that process and generate text.
Hallucination
When AI generates confident but incorrect or fabricated information that sounds plausible.
Hyperautomation
Combining multiple AI and automation technologies to automate complex end-to-end business processes.
Intelligent Document Processing
AI that extracts, classifies, and processes data from documents of any type automatically.
Knowledge Base
A centralized repository of information used by humans and AI to answer questions accurately.
Large Language Model (LLM)
AI systems like ChatGPT and Claude that understand and generate human language for business tasks.
Lead Scoring
A system that ranks prospects by their likelihood to become customers so sales calls the right people first.
Low-Code
Platforms that let you build applications and automations with minimal coding through visual interfaces.
Machine Learning
AI that learns from data and improves over time without being manually reprogrammed.
Model Training
The process of teaching an AI system to perform tasks by exposing it to relevant data and examples.
Multimodal AI
AI that processes and generates multiple data types: text, images, audio, and video in one system.
Natural Language Generation
AI that produces human-readable text from data, powering automated reports, emails, and content.
Neural Network
Computing systems inspired by the brain that learn patterns from data, the foundation of modern AI.
NLP (Natural Language Processing)
AI that understands, interprets, and generates human language for chatbots, email, and content.
No-Code Automation
Building automated workflows using visual drag-and-drop tools with zero programming required.
OCR (Optical Character Recognition)
Technology that converts images of text into machine-readable, editable, and searchable text.
Predictive Analytics
Using AI and statistics to forecast future business outcomes from your historical data.
Prompt Engineering
The practice of crafting specific instructions for AI systems to produce accurate, useful outputs.
RAG (Retrieval Augmented Generation)
AI architecture that combines text generation with real-time data retrieval for accurate, grounded responses.
RPA (Robotic Process Automation)
Software robots that mimic human actions in digital systems to automate screen-based tasks.
SaaS (Software as a Service)
Cloud-based software you access online and pay for monthly, the delivery model for all modern AI tools.
Sentiment Analysis
AI that detects emotions and opinions in text from customer reviews, tickets, and social media.
Speech-to-Text
AI that converts spoken language into written text for meeting transcription and voice data entry.
Supervised Learning
Training AI models using labeled examples to make accurate predictions on new data.
Text-to-Speech
AI that converts written text into natural-sounding spoken audio for phone systems and voice agents.
Token
The basic unit of text AI models process, roughly 3/4 of a word. Determines pricing and capacity.
Training Data
The information used to teach AI models, the single most important factor in AI project success.
Transfer Learning
Reusing knowledge from pre-trained AI models for new tasks, making AI affordable for small businesses.
Vector Database
Specialized database enabling AI-powered semantic search that understands meaning, not just keywords.
Voice AI
AI that understands and generates speech for virtual phone agents, assistants, and IVR systems.
Workflow Automation
Connecting multiple tools into end-to-end automated processes from a single trigger.
Zero-Shot Learning
AI that performs tasks it was never specifically trained on by leveraging broad knowledge and reasoning.