AI & Automation Glossary
32 terms, each defined in one self-contained paragraph followed by the practical implication for a business deploying it. Definitions are linked automatically wherever the term appears across the site.
A
Agentic workflow
An agentic workflow is a business process in which one or more AI agents handle the decision points and system actions, with humans supervising exceptions rather than performing each step.
AI agent
An AI agent is software that receives a goal, plans the steps needed to reach it, executes those steps using external tools or APIs, and reports the outcome. What separates an agent from a chatbot is tool use and multi-step execution, not conversation.
AI governance
AI governance is the set of policies, approvals, records and controls that determine which AI systems a business may deploy, on what data, with what oversight, and who is accountable for outcomes.
B
Build vs buy
Build vs buy is the decision between developing an AI capability in-house and licensing an existing product. The deciding factors are whether the capability is a competitive differentiator, how specific your data is, and whether you can staff maintenance.
C
Chatbot
A chatbot is a conversational interface that answers questions in natural language. Modern business chatbots are usually retrieval-grounded, drawing answers from the company's own documentation rather than generating freely.
Context window
The context window is the maximum amount of text — measured in tokens — a model can consider in a single request, covering the instructions, supplied documents, conversation history and the response.
CRM automation
CRM automation removes manual data entry and follow-up from the sales process: enriching records, logging activity, routing leads, drafting follow-ups and keeping pipeline data accurate without rep effort.
D
Deflection rate
Deflection rate is the share of support contacts fully resolved by automation without a human agent. It is the primary efficiency metric for customer service AI and should always be read alongside CSAT.
E
Embedding
An embedding is a list of numbers representing the meaning of a piece of text, produced by a model so that semantically similar texts sit close together in that numeric space.
Evaluation
Evaluation is the systematic scoring of AI output against a fixed set of test cases with known good answers. It is what turns 'the demo felt good' into a measurable quality baseline you can defend changes against.
F
Fine-tuning
Fine-tuning further trains an existing model on your own examples so it adopts a specific format, tone or classification behaviour. It changes how a model responds, not what facts it knows.
G
Generative engine optimisation
Generative engine optimisation is the practice of structuring content so AI answer engines — ChatGPT, Gemini, Perplexity, Google AI Mode — can extract, trust and cite it. It favours direct answers, clear structure, verifiable sources and machine-readable schema.
Guardrails
Guardrails are the technical constraints that stop an AI system from acting outside its permitted scope — allowed tools, spending limits, output filters, escalation triggers and refusal rules.
H
Hallucination
A hallucination is a confident, fluent model output that is factually wrong or fabricated. It is a structural property of next-token prediction, not a bug that a better prompt fully removes.
Human in the loop
Human in the loop means a person reviews or approves AI output before it takes effect. It is the standard control for automated decisions with financial, legal, safety or reputational consequences.
I
Inference
Inference is the act of running a trained model to produce an output. In business terms it is the recurring per-request cost of an AI feature, as distinct from the one-off cost of building it.
L
Large language model
A large language model is a neural network trained on very large text corpora to predict the next token in a sequence. That single capability, at scale, produces summarisation, translation, classification, code generation and reasoning-like behaviour.
Lead scoring
Lead scoring ranks inbound prospects by likelihood to buy, using firmographic fit and behavioural signals. AI improves it by reading unstructured signals — email replies, call notes, site behaviour — that rule-based scores miss.
O
Orchestration
Orchestration is the coordination layer that decides which model, tool or sub-process handles each step of a task, manages retries and failures, and keeps state across the sequence.
P
Process mapping
Process mapping documents each step, decision, system and handoff in a business process before automating it. It is the step most AI projects skip and the one that most often determines whether they succeed.
Prompt engineering
Prompt engineering is the practice of structuring the instructions, context and examples given to a language model so it produces reliable, correctly formatted output for a specific task.
Prompt injection
Prompt injection is an attack in which malicious instructions hidden in content the model reads — a web page, an email, a document — cause it to ignore its original task or misuse its tools.
Proof of concept
A proof of concept is a time-boxed build that tests whether an AI approach can meet a defined success threshold on real data, before committing to production investment.
R
Retrieval-augmented generation
Retrieval-augmented generation is a pattern where relevant documents are fetched from a knowledge base at query time and inserted into the model's prompt, so answers are grounded in your own content rather than the model's training data.
Return on investment
Return on investment for AI automation is the net benefit — labour hours recovered, revenue gained, error cost avoided — divided by total cost of ownership, including build, licences, maintenance and change management.
S
Structured data
Structured data is machine-readable markup — usually JSON-LD following schema.org — that states explicitly what a page is about: article, product, FAQ, organisation, author. Search engines and AI models use it to interpret and attribute content.
T
Token
A token is the unit of text a language model processes — roughly three-quarters of an English word. Model pricing, context limits and latency are all measured in tokens.
Topic cluster
A topic cluster is a pillar page covering a broad subject plus a set of child pages covering its sub-questions, all linked to each other. It is the standard architecture for building topical authority.
Topical authority
Topical authority is the degree to which a site is treated as a credible source across an entire subject area rather than for individual keywords. It comes from covering a topic comprehensively, coherently interlinked, with consistent expertise signals.
Total cost of ownership
Total cost of ownership is the full lifetime cost of an AI system: implementation, model and infrastructure spend, integration upkeep, monitoring, retraining, and the internal time spent supervising it.
V
Vector database
A vector database stores text (or images) as numeric embeddings and retrieves items by semantic similarity rather than keyword match. It is the retrieval layer behind most production RAG systems.
W
Workflow automation
Workflow automation is the use of software to move work between systems and people without manual re-entry — triggers, conditions and actions connecting the tools a business already runs.