AI Glossary
for Business
Plain-English explanations of AI and automation terms. No jargon, no complexity: just clear definitions to help you understand.
Agentic AI
AI systems that can take autonomous actions to achieve goals, making decisions and executing tasks with minimal human oversight.
In practice:
An AI agent that monitors your calendar, schedules meetings, sends reminders, and even reschedules when conflicts arise: all without you asking.
Algorithm
A set of step-by-step instructions that tells a computer how to solve a problem or complete a task.
In practice:
A recipe is like an algorithm for cooking. In AI, algorithms help computers learn patterns from data to make predictions.
API (Application Programming Interface)
A way for different software applications to communicate and share data with each other.
In practice:
When you book a flight on a travel website, it uses APIs to check availability with airlines and process payments through your bank.
Artificial Intelligence (AI)
Computer systems that can perform tasks that typically require human intelligence, such as understanding language, recognising patterns, and making decisions.
In practice:
AI powers virtual assistants like Siri and Alexa, recommends products on Amazon, and filters spam from your email inbox.
Automation
Using technology to perform tasks with minimal human intervention, saving time and reducing errors.
In practice:
Automatically sending invoice reminders, syncing customer data between systems, or generating monthly reports without manual input.
Autonomous Systems
Technology that can operate independently and make decisions without constant human control.
In practice:
Self-driving vehicles, warehouse robots that navigate and pick items, or drones that inspect infrastructure autonomously.
Bias (AI)
When AI systems produce unfair or skewed results due to biased training data or flawed assumptions.
In practice:
A hiring AI that favours candidates from certain universities because historical hiring data was biased toward those schools.
Big Data
Extremely large datasets that are too complex for traditional data processing, requiring specialised tools to analyse.
In practice:
A retailer analysing millions of customer transactions, website clicks, and social media mentions to understand shopping behaviour.
Chatbot
A software application that simulates human conversation through text or voice, providing instant responses to queries.
In practice:
Customer service chatbots on websites that answer FAQs, book appointments, or help track orders 24/7.
Computer Vision
AI technology that enables computers to interpret and understand visual information from images or video.
In practice:
Facial recognition to unlock your phone, quality control in manufacturing detecting defects, or medical imaging analysing X-rays.
Dashboard
A visual display of key information and metrics, often updated in real-time, to help monitor business performance.
In practice:
A sales dashboard showing daily revenue, top products, and customer enquiries: all updated automatically.
Data Lake
A centralised repository that stores all your raw data, ready to be analysed whenever needed.
In practice:
A company storing customer emails, sales records, website logs, and support tickets in one place for future analysis.
Data Mining
The process of discovering patterns, correlations, and insights from large sets of data.
In practice:
Analysing customer purchase history to identify buying patterns and predict what products they might want next.
Deep Learning
A type of machine learning that uses multiple layers of neural networks to learn complex patterns from large amounts of data.
In practice:
Self-driving cars use deep learning to recognise traffic signs, pedestrians, and other vehicles in real-time.
Digital Transformation
Using digital technology to fundamentally change how a business operates and delivers value to customers.
In practice:
A traditional retailer moving from paper-based inventory to an integrated system that syncs stock levels across online and physical stores.
Edge AI
AI that runs locally on devices rather than in the cloud, enabling faster responses and better privacy.
In practice:
Smart home devices that process voice commands directly on the device, without sending audio to a remote server.
Fine-tuning
Customising a pre-trained AI model to perform better on specific tasks or for particular industries.
In practice:
Taking a general AI and training it on your company's customer emails so it learns your specific terminology and response style.
Generative AI
AI systems that can create new content such as text, images, code, or music based on patterns learned from existing data.
In practice:
ChatGPT writing emails, DALL-E creating images from descriptions, or GitHub Copilot suggesting code.
GPT (Generative Pre-trained Transformer)
A type of AI model trained on vast amounts of text data that can generate human-like text responses.
In practice:
ChatGPT is built on GPT technology, enabling it to answer questions, write content, and assist with various text-based tasks.
Hallucination
When an AI generates information that sounds plausible but is factually incorrect or made up.
In practice:
An AI might confidently state a fake statistic or invent a historical event that never happened. Always verify important facts.
Hyperautomation
Taking automation to the next level by combining multiple AI technologies to automate as many processes as possible.
In practice:
A system that not only processes invoices automatically, but also checks them against contracts, gets approvals, and predicts cash flow impact.
Intelligent Document Processing (IDP)
AI that extracts and processes information from documents like invoices, contracts, and forms automatically.
In practice:
Scanning a pile of supplier invoices and automatically extracting dates, amounts, and line items into your accounting system.
KPI Tracking
Automatically monitoring and reporting on Key Performance Indicators to measure business success.
In practice:
A system that tracks customer response times, conversion rates, and revenue targets, alerting managers when metrics slip.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human-like language.
In practice:
GPT-4, Claude, and Gemini are LLMs that can write essays, answer questions, translate languages, and summarise documents.
Low-code/No-code
Platforms that let you build applications and automations using visual interfaces instead of writing code.
In practice:
A marketing manager building an automated email campaign workflow by dragging and dropping blocks, no programming required.
Machine Learning
A type of AI where computers learn from data and improve their performance over time without being explicitly programmed.
In practice:
Netflix learning your viewing preferences to recommend shows, or email filters learning to spot phishing attempts.
Model
The result of training an AI system: a file that contains learned patterns and can make predictions on new data.
In practice:
A fraud detection model trained on past transactions that can flag suspicious payments in real-time.
Multimodal AI
AI that can understand and work with multiple types of content: text, images, audio, and video: together.
In practice:
An AI that can analyse a product photo, read its description, and answer questions about both the visual and written details.
Natural Language Processing (NLP)
AI technology that helps computers understand, interpret, and respond to human language.
In practice:
Voice assistants understanding spoken commands, translation services like Google Translate, or sentiment analysis of customer reviews.
Neural Network
A computer system inspired by the human brain, designed to recognise patterns and learn from examples.
In practice:
Neural networks power image recognition, language translation, and game-playing AI like AlphaGo.
OCR (Optical Character Recognition)
Technology that converts images of text into editable, searchable digital text.
In practice:
Scanning a printed contract and being able to search, copy, and edit the text in a word processor.
Pattern Recognition
AI's ability to identify regularities and patterns in data that humans might miss.
In practice:
Detecting that customers who buy product A in spring often buy product B in autumn: a pattern useful for marketing.
Predictive Analytics
Using data, statistical algorithms, and AI to predict future outcomes based on historical patterns.
In practice:
Predicting customer churn, forecasting sales, or anticipating equipment failures before they happen.
Prompt
The input or instruction you give to an AI system to get the output you want.
In practice:
Writing "Summarise this meeting transcript in 3 bullet points" is a prompt that tells the AI exactly what to do.
Prompt Engineering
The skill of crafting effective prompts to get the best possible results from AI systems.
In practice:
Learning to phrase requests clearly, provide context, and specify format to get accurate, useful AI responses.
RAG (Retrieval-Augmented Generation)
A technique that combines AI generation with searching through your own documents to provide accurate, relevant answers.
In practice:
An AI assistant that searches your company documents before answering questions, ensuring responses are based on your actual data.
Real-time Analytics
Analysing data as it arrives, providing instant insights rather than waiting for end-of-day reports.
In practice:
An e-commerce site showing live inventory levels and alerting managers the moment stock runs low.
Recommendation Engine
AI that suggests products, content, or actions based on your preferences and behaviour.
In practice:
Netflix suggesting films based on what you've watched, or Amazon recommending products based on your purchase history.
Robotic Process Automation (RPA)
Software that automates repetitive, rule-based tasks by mimicking human interactions with computer systems.
In practice:
Automatically copying data from emails into spreadsheets, processing invoices, or updating CRM records.
SaaS (Software as a Service)
Software delivered over the internet on a subscription basis, rather than installed on your computers.
In practice:
Using Microsoft 365, Salesforce, or Slack: all accessed through a web browser without installing anything.
Sentiment Analysis
AI that analyses text to determine the emotional tone, classifying it as positive, negative, or neutral.
In practice:
Monitoring social media mentions to gauge customer satisfaction, or analysing support tickets to identify frustrated customers.
Supervised Learning
A type of machine learning where the AI learns from labelled examples, with correct answers provided during training.
In practice:
Training an AI to recognise cats by showing it thousands of images labelled "cat" or "not cat".
Token
A piece of text (like a word or part of a word) that AI uses to process language. Pricing is often based on tokens.
In practice:
The sentence "AI helps businesses" might be split into tokens like ["AI", " helps", " businesses"]. GPT-4 costs about 3p per 1,000 tokens.
Training Data
The information used to teach an AI system, helping it learn patterns and make predictions.
In practice:
Customer emails used to train a spam filter, or product reviews used to teach sentiment analysis.
Unsupervised Learning
A type of machine learning where the AI finds patterns in data without being given labelled examples.
In practice:
An AI discovering customer segments based on purchasing behaviour, without being told what those segments should be.
Workflow Automation
Automating a sequence of tasks to flow from one to another without manual intervention.
In practice:
When a customer fills in a form, automatically: save to CRM, send confirmation email, notify sales team, and schedule follow-up.
Related Resources
Still Have Questions?
Book a free AI Strategy Workshop and we'll explain AI in terms that make sense for your business.
Book Free Workshop