Artificial intelligence (AI) is everywhere today, and with its rise comes a whole new set of terms. For beginners exploring AI or using its tools, this specialised vocabulary can feel overwhelming. From machine learning and deep learning to large language models (LLMs) and generative AI, the concepts are many, but understanding them is important to using AI effectively. To help you navigate this landscape, here is a list of AI business terms you should know.
Artificial Intelligence (AI)
Artificial intelligence refers to computer systems designed to mimic human intelligence. These systems can carry out tasks that usually require human input, such as recognising images, understanding speech, making decisions and translating languages.
Generative AI
Generative AI is a branch of AI that focuses on creating new content, including text, images, music and videos, that closely resembles human-created work. It usually uses deep learning models, such as generative adversarial networks (GANs) and autoregressive models, to produce original and realistic outputs.
Large Language Model (LLM)
A large language model is an AI system trained on vast amounts of text data to understand and generate human-like language. Models such as GPT use deep learning and large neural networks to produce coherent, context-aware responses based on user input.
Chatbot
A chatbot is an AI-powered application that simulates conversation with users, usually through text or voice. Chatbots can be simple rule-based systems or advanced AI models capable of understanding natural language, identifying user intent and providing relevant responses or support.
Fine-tuning
Fine-tuning is the process of adapting a pre-trained language model by training it further on a specific dataset or task. This helps improve the model’s performance in specialised areas or for particular business use-cases.
Neural Network
A neural network is a type of machine learning system inspired by how neurons connect and work in the human brain. By processing data in a similar way, neural networks can learn patterns and adapt effectively to change.
Deep Learning
Deep learning is a specialised area of machine learning that uses neural networks with many layers to identify patterns in complex data. It is used in applications such as image recognition, speech processing and advanced data analysis.
Natural Language Processing (NLP)
Natural language processing enables machines to understand, interpret and generate human language. It bridges the gap between human communication and computer understanding.
Computer Vision
Computer vision is an area of AI that allows machines to interpret and understand visual information from images and videos. It supports tasks such as recognising objects, detecting patterns and analysing visual data.
Machine Learning (ML)
Machine learning is a subset of AI in which systems improve their performance on tasks by learning from data without being explicitly programmed for each specific task. It includes a range of algorithms that discover patterns and make predictions, transforming business analytics, forecasting and operational automation.
Algorithm
An algorithm is a clear set of step-by-step instructions used to solve a problem or complete a task. In AI and machine learning, algorithms guide how models learn from data, make predictions and carry out specific functions.
Prompt Engineering
Prompt engineering is the practice of designing clear and effective instructions that guide how an AI model responds, especially in tasks such as text generation and conversational interactions.
Optical Character Recognition (OCR)
OCR is a technology that reads text and numbers from images or scanned documents and converts them into machine-readable text. Most modern OCR systems use deep learning to improve accuracy and speed.
AI Hallucination
AI hallucination occurs when a system produces outputs based on patterns or details that do not actually exist in the data. This can lead to incorrect conclusions, such as identifying a medical condition that isn’t present, highlighting the need for strong human oversight and validation.
AI Bias
AI bias refers to situations where algorithms produce unfair or distorted results, often due to biased training data or flawed decision-making rules. This leads to outcomes that unintentionally favour certain groups over others.
Confidence Score
A confidence score is a probability value that shows how certain an AI model is that it has completed a task or produced an output correctly.
Transformer
A transformer is a type of AI model that understands meaning by analysing relationships within sequential data, such as words in a sentence. It uses attention mechanisms to identify patterns and connections between data points, enabling more accurate and context-aware results.
Foundation Models
Foundational models are trained on large and diverse datasets and can be fine-tuned for specific tasks, making them highly versatile. Their flexibility reduces the need to build separate models for each use-case, while techniques such as retrieval-augmented generation (RAG) help combine broad knowledge with task-specific accuracy.
Reinforcement Learning
Reinforcement learning is a type of machine learning in which an AI system learns by taking actions and receiving rewards or penalties in response. A well-known example is DeepMind’s AlphaGo, which mastered the game of Go by playing millions of games against itself.
Transfer Learning
Transfer learning involves using knowledge gained from one task or domain and applying it to a related one. For example, a model trained on general images can be retrained on a smaller dataset to recognise specific objects more efficiently.
Predictive AI
Predictive artificial intelligence uses statistical analysis and machine learning to identify patterns in data and forecast future outcomes. Organisations apply predictive AI to anticipate behaviour, assess risks and support more informed decision-making.
Embeddings
An embedding represents data such as text, images or audio as numerical vectors in a shared space, where the distance between points reflects their meaning and similarity, helping machine learning models understand context and relationships.
Multimodal AI
Multimodal AI refers to machine learning models that can process and combine different types of data, such as text, images, audio and video, to gain a more complete understanding of information.
Automation
AI automation uses technologies such as machine learning and natural language processing to carry out routine tasks and streamline workflows. It supports various business functions such as customer service, marketing, supply chain management and human resources.
AI Ethics
Ethics refers to moral principles that guide judgements about right and wrong. AI ethics is a multidisciplinary field focused on maximising the positive impact of artificial intelligence while minimising risks, harm and unintended consequences.