AI (Artificial Intelligence)
The field of computer science focused on building systems that can perform tasks requiring human-like intelligence, such as reasoning, learning, and problem-solving.
AI Assistant
A software agent or application that helps users perform tasks using natural language or automation, e.g., ChatGPT, Siri.
AI Orchestration
The process of coordinating multiple AI models, automations, and workflows to deliver end-to-end business outcomes.
AI Search
Search engines or tools powered by AI to deliver more context-aware, relevant results than traditional keyword-based search.
Agentic AI
AI systems capable of autonomous decision-making or taking actions on behalf of a user within defined constraints.
AGI (Artificial General Intelligence)
Hypothetical AI that can perform any intellectual task a human can, not limited to specific tasks.
AI Copilot
An AI-powered assistant that helps users complete tasks by providing suggestions, automation, or guidance within applications.
AI Hallucination
When a model generates information that sounds correct but is factually wrong or irrelevant.
AI-Powered Guidance
Context-aware help delivered inside products and applications to reduce friction and improve user adoption.
Autonomous Support
AI systems that proactively solve user issues without requiring manual intervention.
BOT
A software application that automates tasks, often using AI. Examples include chatbots and process bots.
Business Process Automation (BPA)
Using AI and automation to streamline complex business processes across departments.
Business Value Realization
The measurable impact an AI solution delivers, such as reduced costs or faster resolution times.
Chatbot
An AI program that simulates conversation with humans, often used in customer support.
Cognitive Automation
AI systems that combine automation with reasoning, judgment, and learning.
Contextual AI
AI that adapts outputs based on context, such as user behavior or workflow stage.
Data Governance
Policies and practices for managing the availability, usability, integrity, and security of data.
Data Privacy
Protecting personal and sensitive data in compliance with regulations like GDPR or CCPA.
Digital Adoption Platform (DAP)
Tools that guide users through software applications to improve adoption and productivity.
Enterprise AI
AI designed specifically for large-scale, business-critical use cases.
Explainable AI (XAI)
AI systems designed to provide transparency into how they make decisions.
Employee Experience Automation (EXA)
AI-driven automation to enhance employee productivity and reduce manual effort.
Generative AI
AI that creates new content (text, images, audio, video) based on learned patterns.
Generalization
The ability of an AI model to perform well on unseen data.
Hallucination (AI)
Incorrect or fabricated responses generated by AI models.
Heuristics
Rules of thumb or shortcuts used in AI decision-making.
Human-in-the-Loop (HITL)
Involving humans to review, validate, or guide AI decision-making.
Information Retrieval (IR)
AI techniques to search and extract relevant information from large datasets.
Intent Recognition
AI identifying the goal or purpose behind a user’s input.
Intelligent Automation
The combination of AI and automation to streamline business workflows.
Job Automation
Using AI and bots to complete repetitive or rules-based tasks.
Journey Analytics
Analyzing user or customer journeys across multiple touchpoints using AI.
Knowledge Base
A centralized repository of information used by AI systems to answer questions.
Large Language Model (LLM)
AI trained on massive text datasets capable of generating and understanding natural language.
wer questions.
Low-Code / No-Code AI
Platforms that allow users to build and deploy AI solutions with minimal coding.
Multi-Modal AI
AI systems that process multiple data types (text, images, audio, etc.) simultaneously.
Natural Language Processing (NLP)
The branch of AI that enables machines to understand and generate human language.
Neural Network
A set of algorithms inspired by the human brain that recognize patterns in data.
Orchestration Layer
A unifying system (like Beacon.li) that coordinates AI, automations, and workflows.
Pre-Training
Initial training of AI models on large, general datasets.
Proactive Support
AI identifying and solving problems before users report them.
PoC (Proof of Concept)
A pilot implementation to demonstrate AI solution feasibility.
Query
A request for information from a database or AI system.
Retrieval-Augmented Generation (RAG)
Combining external knowledge retrieval with LLM outputs.
Robotic Process Automation (RPA)
Automation of repetitive digital tasks with software robots.
Self-Healing Workflows
Automated workflows that detect and fix errors without human intervention.
Semantic Search
AI-enhanced search that understands intent and meaning, not just keywords.
Support Ticket Deflection
AI-driven automation that resolves user queries before they become tickets.
Time to Value
The speed at which users realize benefits from an AI solution.
Transformer Model
The deep learning architecture behind LLMs like GPT.
Trustworthy AI
AI systems that are reliable, ethical, and transparent.
User Adoption
How quickly and effectively users embrace a new AI-powered system.
User Experience (UX)
The overall usability and satisfaction users have when interacting with software.
Virtual Agent
An AI system that interacts with users conversationally to provide support.
Trustworthy AI
AI systems that are reliable, ethical, and transparent.
Workflow Automation
AI and automation streamlining multi-step business processes.
What-If Analysis
Scenario analysis using AI to predict outcomes of different decisions.
XAI (Explainable AI)
AI designed to make decision-making transparent and understandable.
Zero-Shot Learning
AI performing tasks without any training examples.