AI Adoption refers to the process by which organizations integrate artificial intelligence technologies into their operations, products, services, and decision-making processes.

AI Alignment refers to the process and goal of ensuring that artificial intelligence systems act in accordance with human values, intentions, and objectives.

An AI Assistant is an artificial intelligence system designed to interact with users through natural language to provide information, perform tasks, answer questions, and assist with various activities.

AI Ethics refers to the branch of ethics that focuses on the moral implications of developing, deploying, and using artificial intelligence systems.

AI Governance refers to the frameworks, policies, processes, and practices that organizations implement to ensure the responsible development, deployment, and use of artificial intelligence systems.

AI hallucination refers to the phenomenon where artificial intelligence systems, particularly large language models and generative AI, produce content that appears plausible but is factually incorrect, fabricated, or nonsensical.

AI Inference refers to the process by which a trained artificial intelligence model applies its learned patterns and knowledge to new, unseen data to generate predictions, classifications, or other outputs.

AI Integration refers to the process of incorporating artificial intelligence capabilities into existing business systems, applications, workflows, and processes to enhance their functionality, efficiency, and value.

AI Maturity refers to the level of sophistication, capability, and integration of artificial intelligence within an organization.

AI Model Training is the process by which artificial intelligence systems learn patterns and relationships from data, developing the ability to make predictions, classifications, or generate outputs for new, unseen inputs.

AI ROI (Return on Investment) refers to the measurement of financial and business value generated by artificial intelligence initiatives relative to the costs of implementing and maintaining those initiatives.

AI Strategy is a comprehensive framework that guides an organization's approach to adopting, implementing, and scaling artificial intelligence technologies to achieve business objectives.

AI Transformation refers to the comprehensive process of reimagining and rebuilding an organization's operations, products, services, and business models through the strategic implementation of artificial intelligence technologies.

AI Workflow Automation refers to the application of artificial intelligence technologies to automate, optimize, and enhance business processes and workflows.

Agentic AI describes artificial intelligence systems that function as "agents" capable of operating with a degree of autonomy to achieve specific objectives.

Artificial Intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence.

Augmented Reasoning refers to the collaborative approach where artificial intelligence systems enhance human cognitive capabilities to solve complex problems, make better decisions, and generate novel insights.

An Autoregressive Model is a statistical and machine learning technique that predicts future values in a time series based on previous observations.

Computer Vision is a field of artificial intelligence that enables computers to derive meaningful information from digital images, videos, and other visual inputs, and take actions or make recommendations based on that information.

Conversational AI refers to technologies that enable computers to understand, process, and respond to human language in a natural and meaningful way.

Deep Learning is a specialized subset of machine learning that uses artificial neural networks with multiple layers (hence "deep") to progressively extract higher-level features from raw input data.

Enterprise AI refers to the strategic implementation of artificial intelligence technologies across an organization to transform business operations, enhance decision-making, improve customer experiences, and create competitive advantages.

Fine-tuning is the process of taking a pre-trained artificial intelligence model—typically a large model trained on vast amounts of general data—and further training it on a smaller, specialized dataset to adapt it for a specific task, domain, or use case.

Generative AI refers to artificial intelligence systems that can create new content, including text, images, audio, video, code, and 3D models, that didn't exist before.

Knowledge Retrieval refers to the process by which AI systems access, identify, and extract relevant information from large repositories of data or knowledge bases in response to specific queries or needs.

A Large Language Model (LLM) is an advanced artificial intelligence system trained on vast amounts of text data to understand, generate, and manipulate human language in ways that appear natural and contextually appropriate.

MLOps (Machine Learning Operations) refers to the set of practices, tools, and processes that enable organizations to reliably and efficiently deploy and maintain machine learning models in production environments.

Machine Learning (ML) is a subset of artificial intelligence that enables computer systems to automatically learn and improve from experience without being explicitly programmed.

Multimodal AI refers to artificial intelligence systems that can process, understand, and generate information across multiple types of data or "modalities" simultaneously—such as text, images, audio, video, and numerical data.

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language.

A Neural Network is a computing system inspired by the structure and function of the human brain, designed to recognize patterns and relationships in data.

Predictive Analytics is the practice of using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Prompt Engineering is the practice of designing, refining, and optimizing the inputs given to large language models (LLMs) and other generative AI systems to produce desired outputs.

Responsible AI refers to the development, deployment, and use of artificial intelligence systems in ways that are ethical, fair, transparent, accountable, and aligned with human values and societal well-being.

Retrieval-Augmented Generation (RAG) is an AI architecture that combines information retrieval systems with generative AI models to produce outputs that are both contextually relevant and factually grounded in reliable sources.

Semantic Search is an advanced information retrieval approach that focuses on understanding the contextual meaning of search queries rather than simply matching keywords.