AI Terms to Know: A Practical AI Glossary for CX Teams

Step-by-Step Guide to Launching Enterprise AI in <30 Days

As artificial intelligence and generative AI become increasingly central to how enterprises operate, it’s critical for customer-facing teams and leaders to understand the key concept shaping this transformation. But most of us in CX and GTM roles aren’t AI researchers and experts, and sometimes it’s hard to keep all of the AI terms and acronyms straight. That’s where an AI glossary comes in.

This glossary of AI terms provides concise, practical definitions of key AI terms, demystifying technical language and connecting it directly to the day-to-day work of Customer Support, Success, and Sales teams.

Whether you're evaluating AI tools, integrating new solutions, or just hoping to collaborate more effectively with technical teams across your company, this AI glossary will help you understand the terms that matter. It’s designed to help you speak the language of artificial intelligence with confidence—and more importantly, to figure out how to use it to better serve your customers.

AI Foundations: Your AI Glossary of Terms Starts Here

To work confidently with AI tools, it helps to understand the core technologies behind them. This section covers foundational AI terms to know—like machine learning, neural networks, and natural language processing—that explain how AI systems process data, learn patterns, and generate conversational responses. These technologies are the building blocks that power every application of AI across modern enterprises, from chatbots to sentiment analysis.

Term	Definition	Why it Matters
AI (Artificial Intelligence)	Technology that enables computers to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.	Helps customer teams automate repetitive tasks, improve efficiency, and scale without increasing headcount.
Machine Learning	A subset of AI that allows systems to learn from data and improve their performance over time without explicit programming.	Enables support and sales tools to continuously improve based on customer interactions and feedback, leading to better predictions, recommendations, and personalization.
Natural Language Processing (NLP)	Technology that enables computers to understand, interpret, and generate human language.	Powers tools that can understand customer questions in their own words, extract key information from conversations, and generate human-like responses. Also undergirds tools that summarize call transcripts, account activity, and more.
Large Language Model (LLM)	Advanced AI systems trained on vast amounts of text data that can understand and generate human-like text based on given prompts.	Forms the foundation of modern generative AI that can handle complex customer inquiries, draft responses, analyze text, and more.
Generative AI	AI systems that can create new content, including text, images, and audio, based on patterns learned from training data.	Enables customer-facing teams to quickly generate personalized emails, provide conversational self-service options, and other use cases that require creating new content.
ChatGPT	A conversational AI model developed by OpenAI that can engage in human-like dialogue and assist with various tasks.	ChatGPT is the most well-known LLM to the general public. By showing the world what generative AI can do, it paved the way for many AI implementations across enterprises. Its various models also undergird many tools and apps that customer-facing teams use each day.
Gemini	A family of multimodal AI models developed by Google DeepMind, capable of processing and reasoning across text, code, images, and more.	Offers advanced capabilities for processing text, images, and other data types together—making it well-suited for customer-facing use cases that require understanding screenshots, documents, or multi-step workflows. Gemini’s integration with Google Workspace and cloud tools also makes it easier for teams to embed AI into their existing environments.
Perplexity	An AI-powered search engine and assistant that combines large language models with real-time web results to answer user questions.	Gives customer-facing teams a glimpse of what search powered by generative AI looks like—fast, conversational, and backed by citations. While not purpose-built for enterprise, tools like Perplexity are shaping expectations for how AI should answer complex questions using trustworthy, up-to-date information.
Claude	An AI assistant developed by Anthropic, designed for helpfulness, harmlessness, and honesty in conversations.	Similar to the other LLMs listed above, Claude’s models power many AI tools used by sales, customer success, and support teams.
Transformer Models	A type of neural network architecture that processes entire text sequences at once, enabling deep understanding of context and meaning.	They power advanced AI tools that grasp customer intent and tone, making interactions feel more natural, responsive, and human.
Neural Networks	AI systems modeled after the human brain that learn patterns from data to make predictions or decisions.	They enable tools used by customer-facing teams to detect customer sentiment, anticipate customer needs, and personalize experiences—driving more effective and empathetic engagement.
Deep Learning	A subset of machine learning using neural networks with multiple layers to uncover complex patterns in large datasets.	Deep learning enables AI to interpret tone, context, and intent in customer conversations, spot behavioral trends, and tailor responses or recommendations—leading to faster resolutions, stronger relationships, and higher customer satisfaction.
Tokens	Units of text that language models process, typically representing parts of words or individual characters.	Knowing how tokens work helps teams control AI usage costs and craft prompts that yield clearer, more accurate responses.
Embeddings	Numeric vectors that represent the meaning of text or other data, enabling machines to understand relationships and context.	Helps AI to understand the true intent behind queries and match them with the most relevant information or responses.
Inference	The process of an AI model generating predictions or responses based on its training.	The speed and resource requirements of inference affect how quickly AI systems can respond to customers and how many concurrent interactions they can handle.
Semantic Similarity	A measure of how close two pieces of text are in meaning, rather than just keywords.	Powers more intelligent search and matching in customer support, connecting queries with relevant solutions even when terminology differs.

AI Training & Development

AI doesn’t work out of the box. Just like a new hire, AI has to be trained, tested, and continuously improved. This section of our AI glossary includes key terms and definitions that explain how AI is developed and improved over time, from data labeling to fine-tuning to evaluating performance. Understanding these AI glossary terms helps customer-facing teams grasp what’s behind the tools they use, and how to make them work better for real business needs.

Term	Definition	Why it Matters
Prompt Engineering	The skill of designing clear and strategic inputs to guide AI systems toward specific, useful outputs.	Good prompt engineering helps customer-facing teams get more accurate, relevant, and helpful responses from AI tools.
Fine-tuning	The process of further training an existing AI model on specific data to customize its behavior for particular tasks.	Allows organizations to tailor general AI models to understand company-specific terminology, products, and processes, providing more accurate outputs and performance.
Training Data	The initial dataset used to teach an AI model patterns, relationships, and how to perform specific tasks.	The quality and breadth of training data directly impacts how well AI tools understand industry-specific terminology and customer needs in different contexts. With poor training data, your AI will never perform well.
Feature Engineering	The process of selecting and transforming variables to improve the performance of machine learning models.	Influences how well AI systems understand customer needs and behaviors, affecting the quality of predictions and recommendations. For instance, a raw field like “last email date” might be transformed into a “days since last contact” field.
Data Annotation	The process of labeling data to train machine learning models effectively.	The quality of annotation directly impacts how well AI systems understand inputs and deliver appropriate responses.
LLM Training	The process of teaching language models to understand and generate human language.	Determines the capabilities and limitations of the AI systems that power customer interactions, affecting response quality and accuracy.
Few-shot Learning	The ability of AI models to make accurate predictions based on very limited examples.	Allows rapid adaptation of AI systems to new products, policies, or scenarios with minimal data, accelerating time-to-value.
Zero-shot Learning	The ability of AI models to make predictions for classes or tasks they haven't explicitly seen during training.	Enables customer support AI to handle novel questions or scenarios without requiring extensive retraining, improving adaptability.
Supervised Learning	A machine learning method where models are trained on labeled data (like support tickets) to learn patterns and make predictions.	Supervised learning powers classification tools that can automatically categorize customer inquiries, route them to appropriate departments, and suggest solutions based on past resolutions.
Unsupervised Learning	A machine learning approach where algorithms identify patterns in data without pre-existing labels (like chat transcripts).	Unsupervised learning helps uncover unexpected trends in customer behavior and identifies emerging issues before they become widespread problems.
Reinforcement Learning	A machine learning approach where algorithms learn optimal actions through trial and error.	Enables AI systems to continuously improve conversational strategies based on successful interactions and outcomes.
Bias in AI	Systematic errors in AI outputs caused by imbalanced training data or design flaws. For example, an AI chatbot trained on data from users in the USA might mishandle queries from international customers.	Understanding and mitigating bias helps ensure customer-facing AI treats all customers fairly and doesn't reinforce stereotypes or discriminatory practices.
AI Ethics	The field concerned with ensuring AI systems are designed and used in ways that are fair, transparent, and beneficial to humanity.	Helps customer teams ensure AI interactions maintain brand values, avoid bias, and treat all customers fairly and respectfully.
AI Alignment	The effort to ensure AI systems act in accordance with human goals, ethics, values, and intentions.	Customer-facing AI must align with company standards and service principles to maintain trust, brand integrity, and ethical interactions.
Model Drift	The degradation of AI model performance over time as real-world conditions change.	Requires regular monitoring and updating of customer-facing AI to ensure continued accuracy and relevance as products, policies, and customer needs evolve.
Prompt Templates	Pre-designed input patterns that help AI systems generate consistent, high-quality outputs.	Helps customer teams get reliable results from AI tools without requiring extensive prompt engineering expertise.
Data Cleansing	The process of identifying and correcting errors or inconsistencies in datasets used to train AI.	Improves the accuracy of AI systems by ensuring they learn from high-quality data, leading to better customer interactions.
AI Model Evaluation	The assessment of AI model performance against specific metrics and business objectives.	Ensures customer-facing AI systems deliver genuine value and continue to meet quality standards over time.
AI Explainability	The ability to explain how and why an AI system arrived at a particular output or decision.	Helps customer teams understand and trust AI recommendations, and enables them to provide transparent explanations to customers when needed.

AI in Practice

These important AI glossary terms are where AI technology connects to the ‘real world’ of customer-facing teams. This section includes AI terms that cover how AI shows up in your daily work: powering virtual assistants, automating ticket routing, surfacing product recommendations, and more.

If you’re using AI to support customers, streamline workflows, prevent churn, or drive sales, this section will help you understand the tools and techniques that make it possible.

Term	Definition	Why it Matters
RAG (Retrieval-Augmented Generation)	A technique that improves AI responses by retrieving relevant information from a knowledge base before generating an answer.	Ensures customer-facing AI systems provide accurate, up-to-date information about products, policies, and procedures (rather than hallucinating or providing outdated information).
Knowledge Base	A centralized repository of information that stores an organization's documentation, FAQs, and other resources.	Serves as the source of truth for customer-facing teams, ensuring consistent information delivery and reducing resolution times. AI systems can use knowledge bases that are customer-facing (like a help center), internal-only, or both.
Chatbot	An AI-powered software application designed to simulate conversation with human users, typically via text.	Provides immediate, 24/7 response to common customer inquiries, freeing human agents to handle more complex issues. Chatbots can also proactively engage website visitors, helping to drive new leads and improve conversion.
Sentiment Analysis	The use of natural language processing to identify and extract subjective information from text.	Helps teams monitor customer satisfaction in real-time, identify negative feedback, and find opportunities to recover from poor experiences.
Knowledge Graph	A structured representation of knowledge that shows relationships between entities like products, people, and concepts.	Enables customer support systems to understand complex relationships between products, issues, and solutions, providing more accurate and thorough answers and resolutions.
Vector Database	A specialized database optimized for storing and searching vector embeddings—numerical representations of text or other data.	Powers semantic search capabilities for knowledge bases, helping customers or customer-facing teams quickly find relevant information based on meaning (rather than just keywords).
API	A set of protocols that let different software applications communicate with each other.	APIs allow AI tools to plug into existing platforms used by customer-facing teams (like Salesforce or Gong), adding powerful features like automation and insights without disrupting current workflows.
Computer Vision	AI technology that enables computers to derive meaningful information from digital images and videos.	Powers visual customer support tools that can identify products from images, detect issues from screenshots, or verify identity through photo IDs.
Voice Recognition	Technology that converts spoken language into text or commands that computers can understand.	Enables voice-based customer systems that can understand and respond to spoken queries. Typically used in call centers and outbound sales.
Speech-to-Text	Technology that converts spoken language into written text.	Allows automatic transcription of customer calls, enabling better analysis, record-keeping, and agent assistance during conversations.
Text-to-Speech	Technology that converts written text into spoken words.	Powers voice assistants and automated calling systems that can deliver consistent information in a human-sounding voice.
Customer Intent Recognition	The ability of AI systems to identify what a customer is trying to accomplish through their interaction.	Helps customer support systems quickly understand customer needs and provide relevant solutions, reducing time-to-resolution and improving customer satisfaction.
Semantic Search	Search technology that understands the searcher's intent and the contextual meaning of terms to improve accuracy.	Helps customers and employees find relevant information faster, even when they don't use exact keywords that match documentation.
Contextual Understanding	The ability of AI systems to comprehend the broader context of a conversation or query.	Enables more natural, less repetitive conversations with customers as the AI can maintain context across multiple turns of dialogue.
AI Agent	An autonomous AI system that senses context, makes decisions, and takes action to achieve specific goals (e.g. updating a user’s shipping address).	Provides more sophisticated automation for customer interactions, including handling multi-step processes and adapting to unexpected customer responses.
Conversational AI	AI systems designed specifically for fluid, multi-turn dialogue with humans, understanding context and intent over the course of a conversation.	Powers more natural, engaging customer interactions across channels, reducing frustration and abandonment compared to rigid, rule-based systems.
Automation	The use of technology to perform tasks with minimal human intervention.	Reduces operational costs, speeds up routine processes, and allows customer-facing teams to handle higher volumes without sacrificing quality.
Workflow Automation	The automation of business processes where tasks, information, and documents are passed between participants according to defined rules.	treamlines customer onboarding, issue escalation, and follow-up processes, ensuring consistency and reducing human error.
Customer Journey Mapping	The process of visualizing the steps a customer takes when engaging with a company.	Helps identify touchpoints where AI can provide the most value, creating a seamless experience across the entire customer lifecycle.
Predictive Analytics	The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.	Helps customer success teams anticipate customer needs, identify at-risk accounts, and proactively take action to resolve issues and improve retention.
AI-powered CRM	A customer relationship management (CRM) system enhanced with AI to analyze data, automate tasks, and deliver predictive insights.	Helps sales and success teams prioritize leads, identify upsell opportunities, and predict customer needs based on behavioral patterns.
Customer Intelligence	The process of gathering and analyzing customer data to build deeper understanding and drive better business decisions.	Provides actionable insights into customer needs, preferences, and behavior patterns that can inform product development and service improvements.
Customer Self-service	AI-driven tools that empower customers to solve problems and find information without human assistance (like chatbots and knowledge bases).	Reduces support volume while improving customer satisfaction through 24/7 availability and immediate responses to common questions.
Response Generation	The process by which AI systems create human-like replies to queries or prompts.	Powers automated messaging that sounds natural and on-brand across all customer communication channels.
Multimodal AI	AI systems that can process and understand multiple types of input data, such as text, images, and audio.	Enables more flexible customer support channels where customers can communicate in their preferred format and receive consistent support.
Customer Support Automation	The use of AI to automate routine aspects of customer service.	Reduces response times and operational costs while allowing human agents to focus on complex or high-value customer interactions.
Ticket Routing	The automatic assignment of customer support requests to the most appropriate agent or department.	Improves first-contact resolution rates by ensuring inquiries reach the right expert without multiple transfers.
Automatic AI Summarization	AI-powered condensing of long texts into concise summaries retaining key information.	Helps customer-facing employees quickly understand customer and account history, even across lengthy email threads, multiple sales conversations, or different touchpoints.
Entity Recognition	The ability of AI to identify and categorize key information in text, such as names, products, or locations.	Enables automatic extraction of important information from customer communications, improving accuracy and speed of responses.
Intent Classification	The process of categorizing customer queries based on what they're trying to accomplish.	Helps route customer inquiries to the right department or knowledge base article, shortening resolution times.
Customer Data Platform	A system that unifies customer data from various sources to create comprehensive customer profiles.	Provides customer-facing teams with complete context for interactions, enabling more personalized and informed support.
Real-time Recommendations	AI-generated suggestions provided to customers or employees during live interactions.	Enables support agents to suggest relevant products or solutions based on the current conversation, increasing value and satisfaction.
AI-powered Analytics	The use of AI to analyze customer data and uncover actionable insights.	Helps identify trends, predict future behavior, and discover opportunities for improving customer experience that might not be apparent or possible through traditional manual analysis.
Personalization	Tailoring content, recommendations, or experiences to individual users based on their data and behavior.	Increases customer engagement, satisfaction, and loyalty by delivering relevant information and solutions based on individual preferences and history.
Sales Intelligence	AI-powered insights that help sales teams identify opportunities and optimize their approach.	Provides salespeople with actionable information about prospect needs and behaviors, increasing conversion rates and deal sizes.
Customer Churn Prediction	The use of AI to identify customers at risk of ending their relationship with a company.	Enables proactive intervention by customer success teams to address issues before customers decide to leave.
Customer Feedback Analysis	The use of AI to extract insights from customer reviews, surveys, and other feedback.	Helps identify recurring issues, emerging trends, and improvement opportunities from large volumes of customer comments.
Customer Segmentation	The division of customers into groups based on shared characteristics.	AI can identify more nuanced segments based on behavior patterns, enabling more personalized support, sales, and marketing strategies.
System Prompts	Instructions given to AI systems that guide their behavior across all interactions.	Shapes how AI represents your brand voice and values in customer interactions, ensuring consistency across all touchpoints.
Hybrid AI Systems	Solutions that combine AI automation with human oversight and intervention.	Balances efficiency with quality by automating routine tasks while maintaining human judgment for complex or sensitive customer situations.
AI Confidence Score	A numerical measure of how certain an AI system is about its prediction or response.	Helps determine when to automate responses versus escalating to human agents, balancing efficiency with accuracy.
Human-in-the-loop	An approach where AI systems escalate decisions to humans when they have low confidence or when dealing with sensitive issues.	Maintains service quality while maximizing automation by ensuring humans handle the most complex or nuanced customer situations.

AI Strategy & Governance

AI Glossary Terms related to AI Strategy and Governance

AI is a relatively new tool for most, and just like any new technology, adopting and implementing AI should be done responsibly and effectively (especially in large enterprises, where there’s significant brand and reputation risk to getting AI wrong).

This AI glossary section includes key terms related to AI implementation, ethics, compliance, and measurement. Whether you're evaluating AI ROI, navigating data privacy, or setting up the right guardrails, this part of the AI glossary will help you make smart, strategic decisions around how you implement AI into your organization.

Term	Definition	Why it Matters
AI ROI	A metric that evaluates the financial gains from AI compared to its implementation and operational costs.	Helps leaders justify AI investments by demonstrating tangible benefits like reduced handle times, improved conversion rates, or increased customer lifetime value.
AI Adoption	The process of implementing and integrating AI technologies into an organization's operations.	Successful AI adoption requires thoughtful change management to help customer-facing teams understand how AI will enhance their work.
Knowledge Management	The process of creating, sharing, using, and managing organizational knowledge and information.	Forms the foundation for effective AI implementations in customer-facing teams, ensuring systems have access to accurate, up-to-date information.
AI Implementation	The process of deploying AI solutions within an organization's existing systems and workflows.	Requires careful planning to ensure customer-facing teams can effectively leverage new AI tools without disrupting existing processes.
Custom AI Solutions	AI applications designed specifically for a particular organization's unique needs and use cases.	Provides competitive advantage by addressing specific customer pain points or operational challenges that out-of-the-box solutions cannot solve.
Enterprise AI	AI solutions designed specifically for large organizational use, offering features like enhanced security, scalability, and integration with common enterprise tools.	Provides the robust infrastructure needed to support AI across multiple customer-facing departments while maintaining security and compliance. Enterprise AI also enables the consolidation of an enterprise tech stack, often unlocking significant cost savings.
AI Integration	The process of connecting AI systems with existing business applications and databases.	Ensures customer data flows seamlessly between systems, providing a complete view of the customer journey and enabling consistent experiences.
AI Strategy	A comprehensive plan for implementing AI across an organization to achieve specific business objectives.	Aligns AI investments with customer experience and GTM goals, ensuring technology serves genuine customer and business needs and delivers meaningful AI ROI.
AI Governance	The framework of policies, procedures, and standards that guide the ethical use of AI within an organization.	Protects customers and the organization by ensuring AI systems operate within legal, ethical, and brand guidelines.
Customer Support KPIs	Key Performance Indicators used to measure the effectiveness of customer support operations.	Helps quantify the impact of AI on metrics like resolution time, first-contact resolution, and customer satisfaction.
Responsible AI	The practice of developing and using AI in ways that are ethical, transparent, and beneficial to users.	Builds customer trust by ensuring AI interactions are fair, respectful, and aligned with company values.
AI Guardrails	Policies, procedures, and technical safeguards that control what AI systems can and cannot do.	Protects brand reputation by preventing AI from providing inappropriate, inaccurate, or harmful responses to customers.
Data Privacy	The practice of protecting customer data from unauthorized access and ensuring compliance with privacy regulations.	Critical for maintaining customer trust and regulatory compliance when implementing AI systems that process customer information.
Training and Deployment	The process of preparing AI models and releasing them into production environments.	The quality of this process directly impacts how effectively AI systems perform in real customer interactions.

The native AI platform for customer-facing teams across B2B enterprises

Just finished the AI glossary? Well done. Getting familiar with core AI terms is a meaningful step—and it puts you in a strong position to navigate what’s next.

Of course, knowing the language is just the beginning. The real magic happens when you start applying AI in ways that actually move the needle for your team and your customers.

The good news? You don’t have to be an AI expert to take advantage of AI across your organization. You just need to find the right partner to help you intelligently implement AI, unlocking the massive ROI that AI can bring.

At Ask-AI, we’re building the world’s first AI native platform that’s purpose-built for B2B Sales, Customer Success, and Support teams. It’s a platform meant to focus customer-facing teams on the work that really matters, whether that’s troubleshooting complex customer issues or having more conversations with prospects.

If you’d like to see how enterprise-grade AI can make your teams more product and effective—while also keeping your customer and company data safe and secure—then we’d love to have a quick chat and show you what’s possible.

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