The essential glossary of AI terms for B2B professionals
As AI continues to evolve and drive innovation across industries, understanding key AI terms and concepts is essential for business leaders looking to leverage its power.
Whether you’re evaluating AI solutions, integrating AI into your operations, or simply keeping up with industry trends, this AI glossary serves as a practical reference for making informed decisions.
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Core AI concepts
Artificial intelligence (AI)
Artificial intelligence (AI) is the development of computer systems that can simulate human intelligence to perform tasks such as understanding language, recognizing patterns, making decisions, and solving problems.
AI encompasses a range of technologies, including machine learning, deep learning, and natural language processing, that enable systems to learn from data and improve over time.
For B2B companies, AI is a powerful tool for automating workflows, enhancing customer interactions, driving predictive analytics, and optimizing business operations—ultimately improving efficiency and decision-making at scale.
Artificial general intelligence (AGI)
Artificial general intelligence, also known as strong AI, is a theoretical form of AI that possesses human-like cognitive abilities, enabling it to perform any intellectual task that a human can.
Unlike today’s AI systems, which are designed for narrow, specific applications, AGI would be capable of reasoning, learning, and adapting across multiple domains without needing explicit programming.
While AGI remains a long-term goal of AI research, its potential implications for businesses include automation of high-level decision-making, autonomous problem-solving, and significant advancements in innovation and productivity.
Learn more: What is AGI? A guide to artificial general intelligence
Artificial narrow intelligence (ANI)
Artificial narrow intelligence, also known as weak AI, refers to AI systems designed for specific tasks rather than general reasoning.
ANI powers most AI applications in business today, including chatbots, recommendation engines, and predictive analytics.
These systems excel at performing predefined functions, such as image recognition, speech translation, or fraud detection, but lack the adaptability and cognitive flexibility of human intelligence.
Artificial superintelligence (ASI)
Artificial superintelligence is a hypothetical form of AI that surpasses human intelligence in all aspects, including creativity, reasoning, and problem-solving. While AGI aims to match human cognitive abilities, ASI would exceed them.
Deep learning
Deep learning is a subset of machine learning that uses neural networks with multiple layers (deep neural networks) to process and interpret complex data. It enables AI systems to recognize patterns in images, speech, and text.
For example, deep learning powers AI-driven chatbots that can understand customer queries, detect sentiment, and provide accurate, context-aware responses.
Generative AI
Generative AI refers to AI models capable of creating new content, such as text, images, audio, and video, by learning patterns from training data. Businesses use generative AI for tasks such as marketing content creation, product design automation, and customer engagement through AI-driven chatbots.
Machine learning (ML)
Machine learning is a subset of AI that enables systems to learn from patterns in data, make predictions, and improve decision-making processes without explicit programming.
Machine learning models can be categorized into supervised, unsupervised, and reinforcement learning, each suited for different types of tasks.
Supervised learning relies on labeled data to train models for classification and regression tasks, while unsupervised learning identifies hidden patterns in unlabeled data. Reinforcement learning enables AI systems to learn optimal actions through trial and error based on rewards.
Neural network
A neural network is a computational model inspired by the human brain, consisting of layers of interconnected nodes that process data. Neural networks are the foundation of deep learning and enable AI to recognize patterns, classify data, and perform complex tasks.
Natural language processing (NLP)
Natural language processing enables AI systems to understand, interpret, and generate human language. NLP is used in applications such as chatbots, voice assistants, sentiment analysis, and automated text summarization to enhance human-computer interactions.
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AI agents and autonomous systems
Agentic AI
Agentic AI refers to artificial intelligence systems that operate autonomously, proactively making decisions and taking actions to achieve defined objectives.
Unlike traditional AI models that passively respond to inputs, agentic AI dynamically adapts to real-world contexts, using tools, APIs, and learned strategies to solve complex problems without human intervention.
This form of AI is particularly valuable in environments where rapid responses and adaptability drive competitive advantage, such as retail, finance, and supply chain management.
AI agent
An AI agent is an autonomous system designed to perform tasks, make decisions, and interact with users or other systems using artificial intelligence. AI agents can also adapt and improve through continuous learning, making them valuable for dynamic business environments.
For example, an AI agent in customer support can handle inquiries, process refunds, and escalate complex issues to human agents when necessary.
AI agent builder
An AI agent builder is a platform or tool that allows businesses to design, configure, and deploy AI agents for specific use cases.
These builders provide a no-code or low-code interface, enabling non-technical users to create AI agents that automate workflows, handle customer inquiries, or integrate with enterprise systems.
AI sales assistant
An AI sales assistant is an AI-driven tool that supports sales teams by automating repetitive tasks like lead qualification and scheduling follow-ups.
AI sales assistants help businesses improve sales efficiency, personalize outreach, and optimize customer relationship management strategies.
AI workforce
The AI workforce refers to a collection of AI-driven agents and automation systems working together to perform tasks traditionally handled by human employees.
By integrating AI workforces into business operations, companies can scale workflows, improve decision-making, and streamline repetitive tasks.
Autonomous AI
Autonomous AI refers to AI systems that can operate without human intervention. They make independent decisions based on real-time data and predefined objectives.
Autonomous agent
An autonomous agent is an AI-driven system that operates independently while continuously learning and adapting to its surroundings.
For example, an autonomous customer service AI agent can personalize interactions by analyzing customer behavior, predicting preferences, and adjusting recommendations in real time to enhance engagement and satisfaction.
Customer service AI agent
A customer service AI agent is an AI-driven assistant designed to handle customer service inquiries, automate responses, and resolve issues across multiple channels.
Unlike basic chatbots that can only provide scripted responses, AI customer service agents leverage advanced AI models to understand intent, maintain conversation context, and execute actions, such as processing refunds.
Enterprise AI agent
An enterprise AI agent is an advanced AI-powered system that supports large-scale business operations. These agents integrate with enterprise software, such as CRM, ERP, and supply chain management tools, to enhance efficiency and decision-making.
Generative AI agent
A generative AI agent leverages advanced deep learning models to dynamically generate original content, responses, or solutions.
For example, a B2B marketing team can use a generative AI agent to create personalized email campaigns, draft engaging social media content, and generate dynamic ad copy tailored to different audience segments.
Graph-based AI agent
A graph-based AI agent leverages knowledge graphs to structure, retrieve, and analyze information efficiently. These agents are useful in recommendation systems, fraud detection, and enterprise knowledge management, where relationships between data points enhance decision-making.
For example, a graph-based AI agent in finance can detect fraudulent transactions by analyzing relationships between accounts, identifying unusual patterns, and flagging suspicious activities in real time.
Hybrid AI agent
A hybrid AI agent blends different AI techniques—such as machine learning, generative AI, rule-based systems, and reinforcement learning—to leverage their strengths while mitigating weaknesses.
For example, in customer service, a hybrid AI agent might use rule-based logic to answer common questions, machine learning to detect customer sentiment, generative AI to provide conversational responses, and reinforcement learning to refine its approach based on user interactions.
Intelligent agent
An intelligent agent is an AI-driven system that perceives its environment, processes information, and takes action to achieve specific objectives.
For example, in e-commerce, an intelligent agent can analyze a customer’s browsing history, detect patterns in purchasing behavior, and proactively offer personalized product recommendations.
LLM-powered agent
An LLM-powered agent is an AI system that leverages a large language model (LLM) to understand, process, and generate human-like responses in natural language.
For example, an LLM-powered agent in customer support can interpret complex inquiries, retrieve relevant information from a knowledge base, and offer personalized troubleshooting steps.
Multi-agent system (MAS)
A multi-agent system is a network of autonomous AI agents that interact to solve complex problems. Each agent in a MAS has specific capabilities and objectives, but they work together to achieve a common goal.
For example, in sales, a multi-agent system can coordinate between lead qualification agents, pricing optimization agents, and customer support agents to streamline the sales process, provide real-time personalized offers, and enhance customer engagement for higher conversion rates.
Omnichannel AI agent
Omnichannel AI agents allow businesses to deliver a consistent and personalized customer experience across web, mobile, social media, email, and in-store touchpoints. Omnichannel AI ensures that customer interactions remain seamless as they move between different channels.
For example, a customer can start an inquiry on a website chat, continue the conversation via email, and receive follow-up support through a mobile app without losing context. However, it does not actively learn from interactions. It relies on predefined workflows to maintain continuity.
Omnipresent AI agent
Omnipresent AI allows businesses to ensure seamless customer service across all touch points by integrating real-time data and conversational memory. Omnipresent AI agents continuously learn from customer interactions, retain context across engagements, and proactively offer assistance based on past behaviors.
For example, an ecommerce company using omnipresent AI can provide consistent, real-time support across its website, mobile app, and social media channels. Customers could start a conversation on one platform and continue it seamlessly on another without repeating information.
Personalized AI agent
Personalized AI agents use machine learning and data-driven insights to tailor interactions and services to individual users. They can analyze past behavior, preferences, and real-time inputs to deliver customized recommendations, automated responses, and proactive engagement.
Retrieval-based AI agent
A retrieval-based AI agent retrieves pre-existing responses or solutions from a database rather than generating new ones. These agents are commonly used in customer support to provide consistent, reliable responses while minimizing hallucination risks.
For example, a retrieval-based AI agent in customer support can quickly pull relevant answers from a company’s knowledge base to respond to frequently asked questions.
Self-learning AI agents
Self-learning AI agents continuously improve their performance by analyzing data, refining their decision-making processes, and adapting to new inputs without human intervention.
Companies can use self-learning AI agents to improve efficiency and effectiveness by performing tasks such as predictive maintenance, fraud detection, and personalized marketing campaigns.
Stateful AI agent
A stateful AI agent retains memory across interactions, allowing it to provide context-aware and personalized responses over time. These agents use past conversations and user history to improve continuity and tailor recommendations.
Stateful AI agents can improve customer support by allowing the AI to remember past interactions, eliminating the need for customers to repeat information.
Stateless AI agent
A stateless AI agent treats each interaction independently without retaining previous conversation history. This design prioritizes efficiency and privacy while ensuring that each interaction starts fresh.
Stateless AI agents are commonly used in transactional support, simple task automation, and cases where preserving user data is unnecessary or poses security concerns.
Vertical AI agent
A vertical AI agent is an AI-powered system designed specifically for a particular industry or business function, such as healthcare, finance, or retail.
Unlike general AI agents, vertical AI agents are trained on domain-specific data and workflows, allowing them to provide more accurate, relevant, and actionable insights.
Businesses can leverage vertical AI agents to streamline industry-specific processes, improve compliance, and enhance operational efficiency.
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AI functionality and capabilities
Agent handoff
Agent handoff refers to the transition of customer interactions from AI agents to human representatives. This process ensures that AI handles routine inquiries while directing more complex or sensitive cases to human agents.
AI knowledge base
An AI knowledge base refers to a centralized repository of structured and unstructured data that AI systems use to retrieve, process, and generate accurate responses. It enables AI agents to access and apply relevant information in real time.
AI reasoning
AI reasoning refers to the ability of AI systems to process information logically, draw conclusions, and solve complex problems based on available data. It enables AI systems to analyze patterns, interpret data, and make informed decisions.
AI ticketing
AI ticketing refers to using artificial intelligence to automate ticket management in customer support. AI-powered ticketing systems can categorize, prioritize, and route support tickets to the appropriate team members or resolve issues autonomously.
AI workflow orchestration
AI workflow orchestration refers to the intelligent coordination of multiple AI models, tools, and processes to ensure seamless automation. Businesses use AI workflow orchestration to integrate AI capabilities across departments, enhance collaboration, and optimize end-to-end workflows for maximum efficiency.
Conversational AI
Conversational AI refers to AI-powered technology that enables machines to interact with humans through natural language. It includes chatbots, virtual assistants, and voice AI, which leverage natural language processing (NLP) and machine learning.
Few-shot learning
Few-shot learning is a machine learning technique that enables AI models to recognize patterns and make predictions with minimal labeled data. This capability makes few-shot learning valuable when training examples are limited or costly to obtain.
Hallucination
Hallucination in AI refers to instances where a model generates information that is factually incorrect or nonsensical. These inaccuracies arise from model limitations, biases in training data, or misinterpretations of context.
Multi-turn AI conversations
Multi-turn AI conversations involve AI agents that can sustain context across multiple interactions, remember past inputs, adjust responses accordingly, and provide personalized assistance.
For example, in technical support, a multi-turn AI agent can guide a user through troubleshooting steps, adjust instructions based on the user’s progress, and pick up where the conversation left off if the user returns later.
Predictive analytics
Predictive analytics uses machine learning to analyze historical data and forecast future trends. Businesses can leverage predictive analytics to make data-driven decisions, optimize resource allocation, and improve strategic planning.
Prompt engineering
A prompt is the input provided to an AI model to generate a response, guiding the AI’s output. Prompt engineering is the process of designing and optimizing inputs to improve the quality and relevance of AI-generated responses.
Task automation
Task automation involves using AI to perform repetitive, rule-based tasks with minimal human intervention. Businesses implement AI-driven task automation to improve operational efficiency, reduce errors, and streamline workflows.
Transfer learning
Transfer learning is a machine learning technique that allows AI models to apply knowledge from one domain to another, reducing the amount of training data required for new tasks.
Zero-shot learning
Zero-shot learning is a machine learning technique that allows models to perform tasks without having seen specific examples during training. Instead, the model generalizes knowledge from related domains to infer solutions.
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AI models and architectures
Chain of thought (CoT)
Chain of thought (CoT) is a prompting technique used in AI systems to improve reasoning. It breaks down complex problems into intermediate steps, enhancing problem-solving capabilities.
For example, in financial analysis, a CoT approach enables an AI model to break down a complex investment decision by evaluating market trends, risk factors, and historical data before providing a well-reasoned recommendation.
Model distillation
Model distillation is a compression technique for transferring knowledge from a large, complex AI model to a smaller, more efficient model while maintaining performance and accuracy.
Fine-tuned model
A fine-tuned model is a pre-trained AI model that has been further trained on a specialized dataset to improve its performance on a specific task or domain. Fine-tuning allows businesses to adapt large-scale models like LLMs to industry-specific use cases.
Foundation model
A foundation model is a large-scale AI model trained on vast datasets and designed to serve as a base for various downstream tasks. These models, like GPT, provide generalized capabilities that can be fine-tuned for specific applications.
Large language model (LLM)
A large language model (LLM) is an AI model trained on massive datasets to understand and generate human-like text. LLMs power chatbots, content generation tools, and AI-powered assistants.
Retrieval augmented generation (RAG)
Retrieval augmented generation (RAG) is an AI framework that enhances generative models by incorporating retrieved data from a knowledge base. This approach improves accuracy, reduces hallucinations, and ensures that responses are grounded in verified information. For example, RAG enables AI chatbots to retrieve a company’s up-to-date policy documents and product manuals.
Mixture of experts (MoE)
A mixture of experts (MoE) is a machine learning architecture where multiple specialized models, or “experts,” contribute to decision-making. For example, in a customer service AI system, an MoE model can assign different AI experts to handle inquiries related to technical support, billing issues, and product recommendations.
Small language model (SLM)
A small language model is a more compact version of an LLM, optimized for efficiency, lower computational costs, and faster response times. SLMs are well-suited for enterprise AI applications that need efficient natural language processing without the high computational demands of larger models.
Transformer model
A transformer model is a neural network architecture that uses self-attention mechanisms to process and generate sequential data. This model underpins modern generative AI applications, enabling sophisticated language understanding and generation.
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AI data management and optimization
Chunking
Chunking refers to breaking down large datasets, documents, or text inputs into smaller, more manageable parts. In AI, chunking improves processing efficiency by ensuring models handle data within their context window limits.
Embedding
Embedding is a method of representing words, phrases, or entire datasets as numerical vectors. This technique helps machine learning models understand relationships between concepts.
Indexing
Indexing is the process of organizing and structuring data to enable faster search and retrieval by AI systems. It ensures efficient access to large datasets and improves the performance of search engines, knowledge bases, and AI-powered applications.
Inference
Inference is the process of using a trained AI model to make predictions or generate outputs based on new data. It is a key component of AI applications because it allows models to apply learned knowledge in real-world scenarios.
LLM grounding
Grounding large language models (LLMs) involves anchoring AI-generated outputs in verifiable, real-world knowledge. It helps prevent hallucinations and ensures AI-driven responses align with factual information.
Token
A token is the smallest unit of data processed by AI models. In natural language processing, tokens typically represent words or subwords. Tokens determine the length and complexity of AI model inputs and outputs, influencing processing speed, cost, and performance.
AI ethics, compliance, and governance
AI agent auditing
AI agent auditing is the process of systematically evaluating AI models to ensure they function as intended, comply with regulations, and do not produce harmful or biased outcomes. Auditing involves assessing data sources, reviewing decision-making transparency, and stress-testing models to improve accountability.
AI auditor
An AI auditor is a tool or process to assess the fairness, compliance, and reliability of AI systems. AI auditors evaluate models for bias, ethical risks, transparency, and adherence to regulatory standards.
AI governance
AI governance encompasses the policies, frameworks, and best practices that ensure artificial intelligence is used responsibly, ethically, and in compliance with regulations. It involves setting guidelines for transparency, bias mitigation, data privacy, and risk management.
Bias
Bias in AI agents occurs when AI systems produce skewed or unfair outcomes due to imbalances in training data, model design, or unintended biases in algorithms. Businesses can reduce AI bias by using diverse datasets, regularly auditing models, and implementing fairness assessments to promote ethical and responsible AI deployment.
Data privacy
Data privacy refers to the safeguarding of user data when AI systems process and store information. Ensuring compliance with data protection regulations (such as GDPR and CCPA) is crucial to maintaining user trust. To minimize risks, businesses should implement encryption, anonymization, and strict access controls.
Explainable AI (XAI)
Explainable AI is a set of techniques and methods that make AI model decisions transparent and understandable to humans. It helps businesses ensure compliance with regulations and identify potential biases.
For example, an explainable AI system can show why an applicant was approved or denied in loan approvals by highlighting key factors such as credit score, income, and payment history.
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Next steps: Turning AI terms into AI projects
AI is complex, but this list of AI terms helps you understand the concepts and core AI technologies that will shape the way B2B business gets done in the future.
If you’re looking for help with creating an effective AI strategy and deploying AI agents, Sendbird can help.
Our team of AI experts—machine learning engineers, data scientists, and more—are here to help B2B companies move from strategy to deployment to scaling AI across operations without sacrificing security, compliance, or performance.
Sendbird’s robust AI agent platform makes it easy to build AI agents on a foundation of enterprise-grade infrastructure that ensures optimal performance with unmatched adaptability, security, compliance, and scalability.
Contact our team of friendly AI experts to get started.
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