AI Terms to Know: A Practical AI Glossary for CX Teams
A practical glossary of AI terms—built for Customer Support, Success, and Sales teams navigating AI in their day-to-day work.
Key takeaways
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.
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.
See how Ask-AI helps enterprise CX teams reduce tickets by up to 40%. Book a demo.
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