AI Glossary

A standardised communication framework that enables multiple AI agents to coordinate, share information, and collaborate autonomously to complete complex business objectives. These protocols establish the rules and data formats that allow AI agents to negotiate tasks, exchange resources, and synchronise their activities without human intervention. Businesses can leverage A2A protocols for sophisticated automation scenarios such as supply chain optimisation, where purchasing agents coordinate with inventory agents and logistics agents to maintain optimal stock levels. This collaborative approach enables organisations to deploy multiple specialised AI systems that work together seamlessly, dramatically increasing operational efficiency whilst reducing the need for manual oversight.

An autonomous software program that can perform tasks, make decisions, and interact with systems or users on behalf of a business. AI agents can help businesses automate complex workflows and provide intelligent assistance.

When AI systems produce unfair or discriminatory results due to biased training data or algorithms. Businesses should be aware of potential bias in AI tools and choose solutions that promote fairness and equality.

When AI systems generate information that appears credible but is actually false or fabricated. Businesses should be aware of this limitation and verify important AI-generated content.

A comprehensive plan for how your business will adopt, implement, and benefit from AI technologies. A clear AI strategy helps businesses make informed decisions about which AI solutions to pursue and when.

A set of rules or instructions that tells an AI system how to solve problems or complete tasks. Think of algorithms as the "recipes" that AI systems follow to process data and make decisions.

A set of rules that allows different software applications to communicate with each other. APIs enable businesses to connect various business tools, automate workflows, and integrate services without custom development.

The simulation of human intelligence in machines that can learn, reason, make decisions, and perform tasks that typically require human cognition. AI can help streamline operations, enhance customer service, and provide data-driven insights for better decision-making.

The use of technology to perform tasks with minimal human intervention. AI-powered automation can help businesses streamline operations, reduce errors, and improve efficiency across various business processes.

Extremely large datasets that require special tools and techniques to store, process, and analyse. While traditionally associated with large enterprises, cloud-based solutions now make big data analytics accessible to businesses of all sizes.

Tools and processes that collect, analyse, and present business data to support informed decision-making. BI helps businesses understand their performance, identify trends, and make data-driven strategic decisions through dashboards and reports.

An AI-powered software application designed to simulate conversation with users. Chatbots can help businesses provide 24/7 customer support, handle routine inquiries, and improve customer engagement.

AI technology that automatically generates functional programming code from natural language descriptions, business requirements, or high-level specifications. The system interprets human descriptions of desired functionality and produces working scripts, applications, or automation tools in various programming languages. Businesses can accelerate software development, create custom automation scripts, and build rapid prototypes without requiring extensive programming expertise from their teams. Examples include generating data analysis scripts from business questions, creating workflow automation tools from process descriptions, and building simple applications from functional requirements.

AI technology that enables machines to interpret and understand visual information from images and videos. Businesses can use computer vision for quality control, inventory management, and security monitoring.

The practice of protecting digital systems, networks, and data from cyber threats. As businesses adopt AI tools, maintaining robust cybersecurity measures becomes increasingly important.

The process of discovering patterns, trends, and insights from large datasets. Data mining can reveal customer behaviour patterns, operational inefficiencies, and business opportunities.

The practices and regulations governing how personal and business data is collected, stored, and used. UK businesses must comply with GDPR and other data protection requirements when implementing AI solutions.

An advanced form of machine learning that uses artificial neural networks (inspired by the human brain) to analyse complex data patterns. Deep learning powers many modern AI applications like image recognition, voice assistants, and language translation.

A mathematical framework that enables organisations to gain insights from datasets whilst providing formal guarantees that individual data cannot be identified or reconstructed. The technique works by adding carefully calibrated "noise" to data analysis results, ensuring that the presence or absence of any single person's information doesn't significantly affect the outcome. Major tech companies like Apple, Google, and Microsoft use differential privacy to improve services whilst protecting user privacy - for example, Apple uses it to enhance predictive text suggestions without accessing your actual messages. For businesses implementing AI systems, differential privacy offers a proven method to extract valuable patterns from sensitive data whilst maintaining regulatory compliance and customer trust, making it particularly valuable for healthcare, finance, and any sector handling personal information.

The integration of digital technologies into all areas of business, fundamentally changing operations and customer value delivery. AI is often a key component of digital transformation strategies for modern businesses.

AI technology that creates new content including text, images, code, or other media based on training data and user prompts. Examples include ChatGPT for text generation and DALL-E for image creation. Businesses can use generative AI for content creation, marketing materials, and customer communications.

Cloud-based computing resources (servers, storage, networking) delivered on-demand. IaaS provides the foundational infrastructure needed to run AI applications cost-effectively.

A type of AI model trained on vast amounts of text data that can understand and generate human-like language. LLMs power chatbots, writing assistants, and customer service tools that can help businesses automate communication and support tasks.

A subset of AI where systems automatically learn and improve from data without being explicitly programmed for each task. ML enables your business tools to get better over time, helping with tasks like personalising customer experiences, automating processes, and predicting trends.

The "brain" of an AI system - a mathematical representation that has been trained on data to make predictions or decisions. Different models are designed for different tasks, such as recognising images or understanding text.

A standardised framework that enables AI systems to securely connect with external tools, databases, and applications. MCP allows businesses to integrate AI capabilities with their existing software systems, enabling workflows like automated data analysis, customer relationship management, and inventory tracking without complex custom development.

Technology that enables computers to understand, interpret, and generate human language. NLP powers chatbots, email analysis, customer feedback processing, and automated document handling.

A communication framework that enables AI systems to interact with web services, APIs, and online tools using natural language commands rather than traditional programming interfaces. This technology allows AI agents to autonomously browse websites, retrieve information, submit forms, and coordinate with web-based business systems through conversational instructions. Businesses can streamline their digital workflows by connecting AI assistants directly to their existing web platforms, enabling automated data collection, customer service integration, and cross-platform coordination. Practical applications include AI systems that automatically update inventory across multiple e-commerce platforms, gather competitive intelligence from websites, and coordinate between different business software systems.

A computing system inspired by biological neural networks that processes information through interconnected nodes. Neural networks form the foundation of most modern AI systems and enable them to recognise patterns and learn from data.

A cloud computing model that provides a complete development and deployment environment. PaaS enables businesses to build and deploy AI applications without managing underlying infrastructure.

Using historical data and AI to forecast future trends, behaviours, or outcomes. Businesses can use predictive analytics for demand forecasting, customer retention, and risk management.

The input or question given to an AI system to generate a response. Well-crafted prompts are essential for getting useful outputs from AI tools like ChatGPT or other generative AI systems.

The practice of designing effective prompts to get the best results from AI systems. This skill is becoming valuable for businesses using AI tools for content creation, customer service, and business analysis.

A small-scale demonstration that tests whether an AI solution is viable for your business needs. Starting with a PoC allows businesses to evaluate AI benefits with minimal risk and investment.

An AI technique that combines information retrieval with content generation to produce more accurate and contextually relevant responses. RAG systems first search through existing documents or knowledge bases to find relevant information, then use that information to generate responses, significantly reducing AI hallucinations. Businesses can use RAG for customer support systems, internal knowledge management, and document analysis where accuracy is critical.

A measure of the efficiency and profitability of an AI investment. Businesses should calculate expected ROI when considering AI implementations to ensure business value.

Software that uses "bots" to automate repetitive, rule-based business tasks like data entry, invoice processing, and system integration. RPA can help businesses reduce manual work, improve accuracy, and free up employees for more strategic activities.

AI-enhanced computational modelling that accelerates scientific research and development by simulating complex real-world phenomena with greater accuracy and speed than traditional methods. These systems use machine learning to improve simulation parameters, predict outcomes, and identify patterns that might be missed in conventional modelling approaches. Businesses in research-intensive industries can leverage scientific simulation for faster product development, risk assessment, and innovation cycles in fields like pharmaceuticals, materials science, and engineering. Applications include drug discovery acceleration, climate impact modelling for business planning, materials testing for manufacturing, and financial risk simulation for investment strategies.

Cloud-based software delivered over the internet on a subscription basis. Most AI tools for businesses are delivered as SaaS, eliminating the need for complex on-premise installations and maintenance.

AI technology that creates organised, machine-readable data outputs such as JSON, XML, databases, or spreadsheets from unstructured information or natural language inputs. This process transforms chaotic information like customer emails, documents, or conversational data into structured formats that business systems can easily process and analyse. Businesses can use structured data generation for automated report creation, database population, API integrations, and converting manual data entry tasks into streamlined digital workflows. Common applications include extracting customer information from emails into CRM systems, generating structured product catalogues from descriptions, and creating organised survey data from free-form responses.

The information used to teach AI systems how to perform specific tasks. High-quality, relevant training data is crucial for AI systems to work effectively in your business context.