Our AI Glossery
This glossary offers a structured overview of key terms that define the landscape of Artificial Intelligence, data culture, automation, and decision-making. Whether you're taking a crash course, working with on-the-job coaching, or outsourcing tasks, these concepts support practical understanding and confident application of AI in your organisation.
Automation
Agentic Browser
AI agents that navigate and act within web browsers to complete tasks end-to-end. Systems combining reasoning with web interaction (search, click, form-fill) for automation. Read more »
Business Process Automation (BPA)
End-to-end workflow automation across systems and roles for measurable outcomes. Orchestration of processes beyond single tasks, integrating rules, data, and services. Read more »
Digital Process Automation (DPA)
Emphasis on digital experiences. Workflow, rules, and low-code to streamline operations and user interactions. Read more »
Hyperautomation
Strategic combination of RPA, AI, process mining, and integrations to automate extensively. Scalable, continuously optimised automation across the enterprise. Read more »
Process Mining
Uses event logs to discover, monitor, and optimise real processes as executed. Data-driven analysis revealing bottlenecks, variants, and compliance gaps. Read more »
Robotic Process Automation (RPA)
Automates structured, rule-based software tasks. Software robots that replicate deterministic human actions in stable processes. Read more »
Core AI Concepts
Algorithm
Step-by-step method for solving problems or computations. A finite, ordered set of rules or instructions executed to achieve a result in software, analytics, or AI. Read more »
Foundation Model
Large, pre-trained models adaptable to many tasks via prompting or fine-tuning. Broadly trained base models that can be specialised for diverse downstream applications. Read more »
Literacy AI
Understand core AI concepts, capabilities, and limits. The knowledge and skills to use and ethically interact with AI systems and their outputs across tasks and contexts. Read more »
Generative AI
Creates novel content based on learned patterns. Accelerates ideation and creative workflows. Models that produce new data samples conditioned on inputs or prompts, across modalities. Read more »
Large Language Model (LLM)
Power chat, content, and code assistants. A neural model optimised for language tasks such as summarisation and generation. Read more »
Artificial Intelligence (AI)
Machines performing tasks requiring human-like intelligence. AI enables machines to mimic human intelligence, automating tasks and enhancing decisions and efficiency. Read more »
Multimodal AI
Processes and generates across text, images, audio, video. Enables richer reasoning and context-grounded outputs. AI systems that handle multiple data types jointly for perception and generation. Read more »
Tasks AI
AI tasks include classification, extraction, generation, prediction, recommendation, retrieval, reasoning, and decision support. A taxonomy framing what models are asked to do, guiding selection, data, and evaluation. Read more »
Data Culture
Business Intelligence (BI)
Technologies and practices to collect, integrate, analyse, and present business information. Reporting, dashboards, and self-service insights supporting decisions. Read more »
Data Governance
Policies, roles, and standards ensuring data quality, security, privacy, and compliance. Management framework defining ownership, stewardship, and controls for trusted data. Read more »
Data Steward
Role responsible for data domain quality, definitions, access, and lifecycle. Bridge between business and technical teams to keep data fit for purpose. Read more »
Data-Driven Culture
Organisational mindset prioritising evidence-based decisions with accessible, high-quality data. Alignment of incentives, processes, and tools so teams routinely use data to act. Read more »
Data-Driven Organisation
Company-wide use of data and analytics to guide strategy, operations, and innovation. Investments in governance, platforms, and skills to scale analytic impact. Read more »
Data-Oriented Work
Day-to-day practices embedding data capture, analysis, and feedback into operations. Defining metrics, instrumenting processes, and iterating based on measured outcomes. Read more »
Not-Invented-Here Syndrome (NIH)
Tendency to reject external ideas or solutions in favour of internal ones, even if inferior. Organisational bias that hinders adoption of proven external data and models. Read more »
Decision-Making
Decision-Making
Shapes strategy and operations. Selecting the best option among choices by weighing pros and cons, risks, and information. Read more »
Decision-Making Process
Stages from defining the problem to learning from outcomes improve consistency. Structure including identify, gather, generate, evaluate, choose, act, review. Read more »
Data Analytics
Examination, transformation, and modelling of data to derive insights for decisions. Practices spanning descriptive, diagnostic, predictive, and prescriptive analysis. Read more »
Data-Driven Decision-Making (DDDM)
Uses analysis and empirical evidence to guide choices rather than intuition alone. Decisions grounded in data quality, context, and well-posed questions. Read more »
Automated Decision-Making
Delegates decisions to rules, models, or AI, often in real time; needs oversight. System-executed choices with governance proportional to risk. Read more »
Collaborative Decision-Making
Groups jointly make choices through shared information, discussion, and alignment. Decision processes leveraging diverse perspectives to improve buy-in. Read more »
Heuristic Decision-Making
Uses rules of thumb to make fast, satisfactory choices under uncertainty. Prone to biases. Shortcut-based judgments that trade accuracy for speed and effort. Read more »
Intuitive Decision-Making
Relies on experience and pattern recognition to reach quick judgments. Non-analytical, experience-based reasoning that benefits from validation. Read more »
Self-Service Analysis
Enables business users to explore data and build insights without heavy IT reliance. Governed analytics access expanding insight generation across teams. Read more »
Responsible AI
Trustworthy AI
Reliable, robust, transparent, fair, and values-aligned systems with governance and oversight. AI designed and operated to meet ethical, legal, and safety standards throughout its lifecycle. Read more »
Fairness AI
Identify and mitigate biased outcomes to ensure equitable treatment and performance across groups. Methods and policies to reduce disparate impact in datasets, models, and decisions. Read more »
Transparency AI
Clarity on model behaviour, data, limitations, and rationale for outputs. Practices that make AI understandable to stakeholders via disclosures and explanations. Read more »
Accountability AI
Assign responsibility and enable oversight, redress, and compliance for AI decisions and impacts. Governance structures and controls ensuring answerability for AI-driven outcomes. Read more »
AI Readiness
Data Literacy
Involves understanding data, deriving insights, storytelling with visuals, and applying findings. The ability to read, interpret, analyse, and communicate data effectively. Read more »
Digital Literacy
Encompasses platform navigation, credibility assessment, privacy, and collaboration. The ability to navigate, assess, and create digital content responsibly across platforms. Read more »
Information Literacy
Supports critical thinking, problem-solving, and lifelong learning across contexts. The ability to identify, find, evaluate, and use information effectively. Read more »
Maker Literacy
Hands-on competency in designing, prototyping, and iterating with tools and materials. Practical capability to conceive, build, and refine artifacts as a way to learn and solve problems. Read more »
Metaliteracy
Integrates multiple literacies with emphasis on critical thinking, ethical participation, and producing content across formats. An overarching framework that encompasses information literacy and related literacies needed in the digital age. Read more »
Other
Matrix
Rectangular array used in linear algebra to represent transformations and relations. Powers ML computations, embeddings, and graph operations. Read more »
Grid
Regular arrangement of rows and columns for layout or indexing. Supports spatial organisation in data and UI. Read more »
Table
Structured data in rows and columns enabling sorting, filtering, and aggregation. Foundation of relational data management and reporting. Read more »
Prompt Engineering
Prompt Engineering
Craft prompts, structures, and workflows to elicit reliable, useful, and safe outputs. The practice of designing inputs and interactions that steer AI systems toward desired results. Read more »
Graph Theory
Adjacent Vertices
Two vertices connected directly by an edge. Fundamental local relationship used in many graph algorithms. Read more »
Entity
A distinct object or concept represented as a node or record. Has attributes and relationships forming knowledge structures. Read more »
Generalisation
A hierarchical relationship where one concept abstracts or inherits properties from another. Supports ontology/knowledge graph structuring and reuse. Read more »
Label
Attribute attached to nodes or edges describing type, identity, or properties. Guides traversal, filtering, and semantics in labelled graphs. Read more »
Model
Abstract representation of a system or phenomenon capturing entities and relations. Encodes structure or learned parameters for explanation or prediction. Read more »
Subgraph
A graph formed from a subset of a larger graph's nodes and edges. Preserves incidence relationships within the selected portion of the original graph. Read more »