The Essential AI Glossary for Customer Service

The Essential AI Glossary for Customer Service

Table of contents

AI in customer service

AI has revolutionized contact centers, enhancing both customer interactions and operational effectiveness. Nonetheless, it’s crucial to acknowledge that AI is a broad category covering diverse subfields and methods. In this article, we will explore the significant influence of AI on contact centers and provide a comprehensive list of fundamental AI terms commonly employed in the industry.

Your essential Artificial Intelligence glossary

Agent Assist: 

AI-driven tools offering real-time guidance and suggestions to agents, enhancing productivity and customer interactions.


Automated Speech Recognition (ASR): 

AI technology designed to accurately and efficiently convert spoken language into text.


Bayesian network

Enter the world of probability with the Bayesian network, an intelligent model estimating the likelihood of events unfolding using AI’s speed to analyze vast datasets.



Explore advanced natural language processing with BERT, a deep-learning model by Google designed for tasks like answering questions, sentiment analysis, and translation.


Call Analytics:

AI-driven analysis of call recordings and data to extract insights, identify trends, and enhance agent performance.



An AI-powered virtual assistant engaging in text-based conversations with customers, providing instant responses and assistance.



A conversational AI running on GPT, a language model using natural language processing to understand text prompts and generate content.


Data privacy:

Upholding data privacy and ethical principles in handling customer data and interactions in today’s AI-driven landscape.


Emotive AI:

AI technology interpreting human emotions through facial expressions, tone of voice, and other cues for personalized interactions.


Feature engineering:

Selecting specific features from raw data for system learning during training.


Feature extraction:

Breaking input into specific features for classification and understanding.


Generative AI:

Processing prompts by identifying patterns to produce an output aligned with initial learning.


Intelligent routing:

AI-based call routing directing customers to the most suitable agent.



Essential words in a user’s utterance describing what the user wants the IVA to do, usually a combination of a verb and a noun.


Intent Recognition:

The process by which the NL engine analyzes the structure of a user’s command to identify each word’s meaning and correctly match the user’s intent.


Image recognition:

Machine learning process using algorithms to identify objects, people, or places in images and videos.


Knowledge base:

A centralized repository of information for AI-powered systems to provide accurate responses.


Large Language Models:

Trained on large historical datasets, using knowledge to complete specific tasks.


Machine Learning:

AI approach enabling systems to learn from data without explicit programming.


Natural Language Processing (NLP):

AI technology enabling machines to understand and interpret human language for customer interactions.


Natural Language Generation (NLG):

Processing language to accurately complete tasks based on understanding.


Natural Language Query (NLQ):

A written input appearing as if said aloud, without special characters or syntax.


Neutral networks:

In AI, a computerized replication of a human brain’s neural network for system development.



A customer-centric approach integrating various communication channels for seamless interactions.


Predictive Analytics:

AI-driven data analysis using historical data to predict future outcomes and customer behavior.


Quality Monitoring:

AI-driven evaluation of agent interactions for performance, compliance, and customer satisfaction.


Robotic Process Automation (RPA):

Use of AI-powered bots to automate repetitive tasks in contact center operations.


Real-time Decision Support:

AI tools assisting agents during customer interactions with relevant information and suggestions.


Sentiment Analysis:

Use of AI to determine the emotional tone of customer interactions for gauging satisfaction.


Speech Recognition:

AI technology converting spoken language into text for voice interactions and transcription.


Training data:

Data given to a machine for learning future tasks.


Transfer learning:

A machine learning technique using a pre-trained model as the starting point for a new task.


Unsupervised learning:

System finding patterns and drawing conclusions from data without human input.


Virtual Assistant:

An AI-powered application interacting with customers in a human-like manner, providing assistance and answering queries.


Virtual Queue Management:

AI-driven system optimizing customer wait times and agent availability for efficient call routing.

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AI serves as the cornerstone for achieving excellence in contact centers, enhancing customer experiences, and streamlining operations. At Born Digital, we are dedicated to leveraging AI’s capabilities to deliver groundbreaking CX solutions, ensuring each customer interaction is genuinely outstanding.

Find out how you can leverage Born Digital's Generative and Conversational AI solutions to drive business results.

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